Geographic Information System Skills for Foresters and Natural Resource Managers 0323905196, 9780323905190

Table of contents :
Cover
Geographic Information System Skills for Foresters and Natural Resource Managers
Copyright
Dedications
Preface
Reflection 2.1
Diversion 5.4
Inspection 3.3
Translation 7.1
1. Geographic information systems
Introduction
Brief history of the development of GIS
GIS hardware
GIS software
Commercial off-the-shelf software
Open-source software
Conducting spatial analyses
Conclusions
References
2. Geographic data
Introduction
Data models
Vector data models
Tabular data
Topology
Raster data models
Other data models
Resolution
Data format
Data precision and accuracy
Data errors
Missing data
Inconsistent data
Attribute issues
Positional problems
Database design
Data file types
Shapefiles
Geodatabases
Other common file types
Metadata
Conclusions
References
3. Reference systems
Introduction
Ellipsoids, datums, geoids
Projection systems
Cylindrical projections
Conic projections
Azimuthal projections
Coordinate systems
Geographic coordinate systems
Projected coordinate systems
Universal transverse mercator system
US state plane system
Other coordinate systems
Metes and bounds surveys
US public land survey system
Canadian Dominion Land Survey
Conversion between systems
Conclusions
References
4. Making maps
Introduction
Map components
Map title
Orientation
Scale
Symbols
Legends
Neat lines
Labels
Insets
Graticule
Other components
Background image
Data visualization
Map types
Conclusions
References
5. Geographic data collection
Introduction
GIS database development planning
Creating GIS databases
Digitizing a hand-drawn map
Digitizing on a computer using a raster image as a base
Collecting vector features with GPS
Collecting vector features with a drone or an aircraft
Collecting structured and unstructured data
Scanning a map
Capturing an aerial image, then perhaps classifying the image
Capturing a satellite image, then perhaps classifying the image
Where to find GIS databases
Data quality, accuracy, and errors in GIS databases
Conclusions
References
6. Geographic data management
Introduction
Storage and file size
Local disk
External drives
Cloud storage
Internet of Things and online mapping
Conclusions
References
7. Geographic data processing—vector data
Introduction
Physical selection of spatial features
Attribute query
Buffer
Split
Clip
Erase
Intersect
Union
Identity
Merge
Dissolve
Generalize
Simplify
Densify
Smooth
Join
Spatial join
Conclusions
References
8. Geographic data processing—raster data
Introduction
Resolution
Elevation and topography
Reclassification
Map algebra
Interpolation
Classification
Supervised classification
Unsupervised classification
Object-based classification
Spectral indices
Normalized difference vegetation index
Enhanced vegetation index
Normalized difference moisture index
Data conversion
Conclusions
References
9. Remote sensing
Introduction
Aerial imagery systems
Satellite-based imagery systems
Unmanned aerial vehicle imagery systems
Conclusions
References
10. Advanced applications in forestry and natural resource management
Introduction
Case study: riparian areas
Case study: recreation opportunity spectrum
Case study: fertilization options
Case study: forested area by management unit
Conclusions
References
11. Professional practices
Introduction
Professional standards
Potential legal issues involving GIS
Professional responsibility
Conclusion
References
Appendix: questions
Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
Y
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Citation preview

Geographic Information System Skills for Foresters and Natural Resource Managers Krista Merry Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA, United States

Pete Bettinger Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA, United States

Michael Crosby Agricultural Sciences and Forestry, Louisiana Tech University, Ruston, LA, United States

Kevin Boston Oregon Department of Forestry, Salem, OR, United States

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright © 2023 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. ISBN: 978-0-323-90519-0 For information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Candice Janco Acquisitions Editor: Jessica Mack Editorial Project Manager: Clark M. Espinosa Production Project Manager: Bharatwaj Varatharajan Cover Designer: Mark Rogers Typeset by TNQ Technologies

Dedications Krista Merry: For Phil. Thank you for your patience. Pete Bettinger: To Kelly. Michael Crosby: For a grandfather that told me from a very early age to learn computers. To amazing parents that encouraged me to go to school and forgot to tell me to stop. For Memaw and our Saturday conversations. In memory of family and friends who have gone on. To my brothers, sisters, aunts, uncles, cousins, and friends that remind me my support network is legion. To my colleagues that let me work with them on very cool and exciting projects. For Lauren and Aidan that push me to push myself in every aspect of life. To every teacher I’ve ever had, with a special dedication to Mrs. Kathy Smith a fourth grade teacher in Ellisville, MS, that said I’d write a book someday (this is maybe not what you meant but I’ve never forgotten that early faith in me). To the students that have suffered my attempts in honing the craft of teaching and those that might benefit from this work into the future. Thank you all, this is for all of you.

Preface Geographic information systems (GIS) are powerful tools comprised of hardware and software components that allow for the collection, visualization, and analysis of spatial data. GIS is commonly used by forestry and natural resource management professionals to answer questions about landscape changes, wildlife, recreation, water, and anything with a geographic location on the Earth. Using spatial databases, such as vector and raster data models, GIS can answer questions including Where is the best location for a new trail? How close is this silviculture treatment to an animal den? What proportion of this stand falls within a streamside management zone? and many others. Further, GIS allows for the creation of maps, the introduction of remotely sensed data into analysis, and the implementation of spatial analysis using spatial databases. Krista Merry was first introduced to GIS during her undergraduate education in geography. Software that allowed for GIS processing included a basic graphical user interface (GUI) but commonly required working within the command line for certain processing features. Following the frustration of working with early GIS software programs, she swore off making GIS the focus of her educational and career pursuits. However, later exposure to remote sensing and advanced GIS processing, along with improvements in commercial GIS software, illustrated to her how GIS is, at its core, a problem solving tool. GIS brought together her ability to understand and visualize spatial relationships with the use of GIS databases to answer questions. Now, her interests focus on projects using GIS to model land cover change across various landscapes, identify potential forested areas impacted by hurricanes, assess accuracy of smartphone GPS receivers, and others. Pete Bettinger was introduced to GIS in the 1980s, when the old form of GIS was prevalent. In this system, a person drew a map by hand and then associated features on the map with a tabular (printed) report that contained forest stand conditions (volumes, densities, etc.) that were developed with the assistance of a calculator. Over the next decade, he gained first-hand experience in the implementation of GIS in forestry, as forest organizations attempted to develop both centralized and distributed GIS programs. In the late 1990s, he began to teach GIS to forestry students, knowing that it would become a standard skill needed by natural resource management professionals which could increase both the accuracy and efficiency of forest management endeavors. Today, he continues to use GIS for a number of purposes mainly involving instruction to research, and in many cases his students and colleagues are teaching him new ways to use the technology. Kevin Boston has been using GIS since the mid-1980s in a variety of forestry and natural resources projects incorporating GIS into conservation and production management projects from around the world. GIS has been the information core for solving these problems as it maintains spatial and nonspatial data. He finds it useful for displaying both the problems and possible solutions that lead to discussions and improvements in the decision making processes that support improved natural resource management. Michael Crosby serendipitous, yet frustrating, introduction to GIS in the spring of 2005. Sparing the reader humorous and head-scratching anecdotes, he had a patient professor and lab instructor navigating Esri’s ArcView 3 and ArcGIS somewhere in the 8.2 release of the

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software. In the summer of 2005, he found himself working at the Naval Oceanographic Office and was allowed to work on incorporating GIS into ocean modeling and mapping efforts. It was here he began to learn more about remote sensing capabilities and applications and continued his graduate education in forestry beginning in 2007. There he learned to apply spatial technologies to forest inventory design and analysis and transitioned into modeling impacts of extreme weather events on forested ecosystems. He continues his education today teaching and learning from students and incorporating high-resolution datasets into tree and forest ecosystem assessment and change. GIS is a rapidly changing discipline advancing from simply making a map or viewing an image to the incorporation of newer, finer resolution sources of spatial information that allow for the development of three-dimensional models of landscape features or the creation of GIS databases simply using an application (app) on a smartphone. These advances, along with the value of established GIS processes, make GIS a dynamic field that is important for early career or seasoned professionals in forestry and natural resources to have comprehensive knowledge of and basic skill sets to implement analysis in a GIS environment. This book seeks to introduce readers to several aspects of GIS including components of GIS and spatial analysis, GIS data models and types of geographic data, coordinate systems and the role of reference systems in GIS, mapping, creating, collecting, and managing GIS data, processing different GIS data models, remote sensing, and professional ethics and practices associated with the use of GIS. Additionally, we provide several case studies that focus on how GIS can be used to answer questions in a natural resources context. Through discussion of these topics, we hope to encourage readers to think past basic GIS concepts and consider more deeply the functionality and implications associated with GIS and GIS processes. We hope to engage readers and inspire more in-depth consideration of topics presented in this book through exercises distributed across each chapter referred to as “reflections, diversions, inspections, and translations.” Reflections encourage readers to think about ideas or concepts, usually from a personal perspective, and to organize their thoughts into a cohesive, short summary. For example, from Chapter 2 we find this reflection:

Reflection 2.1 Imagine that you have recently been hired as a forester for the U.S. Forest Service, and that you will work on the Ocala National Forest in Florida. What types of GIS databases would the forest have, or could the forest develop, that would require the use of point data? Diversions ask readers to take a break from reading the book and use critical thinking to solve a problem. These may be as simple as a basic spatial analysis question. For example, readers may be encouraged to organize data and determine the appropriate spatial analysis process to find a solution. Often, the purpose of a diversion is to develop a plan for answering a question. As an example of a diversion, the one noted below can be found in Chapter 5.

Diversion 5.4 Use your cellular phone and an application (app) such as Avenza Maps to mark the location of a few trees outside your home or office. Find a way to save these point positions as a GIS database that can be opened in GIS software or Google Earth. In general terms, how accurate (spatially) are the points that represent the trees?

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Inspections encourage readers to analyze a GIS database or a result of a spatial analysis function and assess its quality. With inspections, we direct readers to more specific databases or maps available through the Internet, including on this book’s website (gis-book.uga.edu). Inspections encourage readers to compare concepts presented in the book to real-world applications. An example of an inspection from Chapter 3 involves accessing a common type of map used in the United States.

Inspection 3.3 Access the Placitas (New Mexico) quadrangle map that is available on the book’s website (gis-book.uga.edu). Alternatively, this map can be accessed through topoView, a service hosted by the USGS. The Placitas area is northeast of Albuquerque. In which township and section, would you find Ranchos de Placitas? The purpose of a translation is to think about hypothetical situations readers may encounter after being provided with background on a specific subject in the text. For example, readers might be asked to concisely describe a GIS process to a person in their life who has little to no understanding of the topic. For example, from Chapter 7, we find this translation exercise:

Translation 7.1 Imagine you are explaining GIS to your parents or siblings. In general terms, describe for them the concept of selecting features, and the various ways that this can be accomplished. General GIS and GIS analysis topics are important in developing knowledge and skills for the lifelong use of GIS. Many are incorporated into this book. Our goal is to promote skills and analytical capabilities in the readers of this book. Through the topics presented across the eleven chapters, we hope to improve the reader’s ability to understand GIS data sources, identify GIS data types and quality, perform common spatial analysis processes, create GIS databases, produce a map, and develop the skills necessary to use GIS to analyze real-world questions related to forestry and natural resources.

1 Geographic information systems Introduction A geographic information system (GIS), in its most basic sense, is a computer mapping program that integrates spatial data (points, lines, polygons, grid cells that have a geographic assignment) and tabular data (numbers, text, codes that describe the features) and allows sophisticated geographical analyses to occur. The power of a GIS rests in the fact that it can be used for many more purposes than to simply make a map. More broadly, a GIS is an entity for collecting, managing, analyzing, and displaying geographic information (Fig. 1.1). A GIS is geographic in the sense that the work one conducts with it generally relates to places of interest, whether on Earth, on Mars, underwater, or inside the human brain. If the places of interest can be associated with a coordinate system, those rules for defining the positions of things, then they are geographic in nature. A GIS allows one to make a connection between physically drawn features and their associated attributes, and this facilitates the development of knowledge about the shape, size, location, and character of the physically drawn features. This information can be of great value in understanding the condition of the landscape or water body to which the data refers. A GIS is considered a system because it is collectively a group of items (hardware and software) that form an organized entity. As was noted in the third sentence of this chapter, formal definitions of GIS often suggest that they are capable of gathering, organizing, and storing data, that they provide the opportunity for people to manipulate and manage this data, that they have great capacity for complex analysis, and that they physically consist of the necessary hardware, software, people (human capital), and communication processes to accomplish some of the most sophisticated geographical analyses one could imagine (Bolstad, 2012; Jensen and Jensen, 2013; Chang, 2019). A synthesized combination of these ideas forms a working definition of GIS as a system that allows for the organization, management, analysis, and visualization of spatial data. In searching widely for published works on GIS, one may find instances when geographic information systems and geographic information sciences are used synonymously. However, geographic information sciences focus on theoretical advances in the field made through novel academic research and industrial applications (Yuan, 2017; Goodchild, 2018). Therefore, for the purposes of this book, these concepts will be treated as separate ideas. Here, we concentrate mainly on the computer-based methods and means to store, access, analyze, manipulate, and visualize spatial and nonspatial data (Fig. 1.2), or basic geographic information systems. Geographic Information System Skills for Foresters and Natural Resource Managers. https://doi.org/10.1016/B978-0-323-90519-0.00007-8 Copyright © 2023 Elsevier Inc. All rights reserved.

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2 Geographic Information System Skills for Foresters and Natural Resource Managers

FIGURE 1.1 The general processes associated with creating GIS databases and maps that describe a location on Earth.

When GIS is used for forestry and natural resource management purposes, it inherently involves mapping elements of landscapes and water bodies. These features could include streams, inventory plots, wildlife habitat patches, recreation areas, or timber stands, and they all are referenced to a place on Earth. Perhaps aerial or satellite imagery assist in the development of databases and in the two-dimensional display of the various resources of interest. Perhaps even global positioning systems (GPS) or physically drawn features assist in the development and display of the resources of interest. Regardless of how the data were developed, a GIS can be used in many interesting ways, such as for representing the three-dimensional aspect of above-ground, underground, or underwater resources (Figs. 1.3 and 1.4) or for assisting in construction and maintenance operations (Huang et al., 2021). The Titanic Mapping Project, for example, which concerned the RMS Titanic, a ship lost in 1912, used GIS to map the underwater location of recovered artifacts and to link these locations to detailed profiles of the ship’s features (Vrana et al., 2012). Similarly in forestry and natural resource management, one might use GIS to organize and catalog important features such as wildlife nest locations, property corners, and hiking trails. Of course, there are many other applications of GIS that can help develop knowledge, address management concerns, and investigate issues that have an inherent geographical context (Chen et al., 2015).

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FIGURE 1.2 Year of origin for forested areas of a small portion of the Francis MarioneSumter National Forest, South Carolina, USA. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021b).

However, even today with the widespread availability of computers and GIS programs, some maps are still drawn by hand. There are many reasons why people still do this (convenience, cost, training), but these should not diminish the fact that a welldeveloped map can be of value to the purpose for which it was designed. For perspective, 40 years ago, nearly all forest management maps were hand drawn (Fig. 1.5). GIS was beginning to mature in the 1970 and 1980s, and adoption of the technology by forest management organizations as a standard way of making maps really began to take hold in the 1990s. Today, likely all of the larger forestry and natural resource management organizations utilize some type of computerized mapping program. Fortunately, there are many benefits associated with using computerized systems for map development:
The symbology (symbols, colors, text) of a map can be adjusted easily.
Errors within maps can be identified and corrected quickly.
Maps can be reprinted or saved in digital form.

4 Geographic Information System Skills for Foresters and Natural Resource Managers

FIGURE 1.3 A 3-dimensional radar image of Glacier Bay National Park, Alaska, USA, 2012. Credit: U.S. Department of the Interior, Geological Survey (2012).


Computer-generated maps often have a more professional appearance than handdrawn maps.
Map files can be shared with other people who use the same software.
Maps can be saved in various graphics formats without having to scan them.
Within an organization, maps can be developed using consistent data management protocols and templates. Employers of forestry and natural resource management professionals generally expect new employees and recent graduates to know how to use GIS, and to possess some basic knowledge and skills with respect to computerized mapping technologies (Merry et al., 2007, 2016). If one were to examine job announcements related to entrylevel forestry positions, one might further understand the importance of these technologies from an employer’s perspective. Two recent studies of job advertisements for entry-level forestry positions in the United States suggest a large percentage of entrylevel positions require young professionals to have basic knowledge, experience, or skill in the use of GIS, along with a basic ability to read and interpret maps (Bettinger et al., 2016; Bettinger and Merry, 2018).

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FIGURE 1.4 A sea floor map of the Puerto Rico Trench. Credit: U.S. Department of the Interior, Geological Survey (2006).

Inspection 1.1 Using the Internet, visit the Occupational Outlook Handbook hosted by the U.S. Bureau of Labor Statistics and review the duties that conservation scientists and foresters commonly perform (through the What They Do tab). While your exposure to the type of work that conservation scientists and foresters do may just be beginning, try to make a list of the types of maps that might support these duties. Then, compare your list to the lists of others who have also attempted this task.

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FIGURE 1.5 Hand drawn timber sale map from 1983.

In today’s contemporary resource management environment, it is reasonable to find that a high percentage of forestry and natural resource management professionals use GIS to support their normal work activities. In one survey of southern United States

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foresters, nearly 60% of the people surveyed acknowledged that they used GIS two or more days per week, often to navigate across the landscape and to map the boundaries or edges of properties or recent management activities (Bettinger et al., 2019). Forestry and natural resource management careers are not unique in this regard, as other similar natural resource management careers, such as those involving land use planning, may also require professionals to frequently use GIS (Merry et al., 2008). However, natural resource professionals who engage in computerized mapping efforts need not be computer experts. On the other hand, employers are increasingly in need of problem solvers, people who can be relied upon to access adequate and accurate data and make timely management decisions based on their use and on an analysis of spatial data. Therefore, some familiarity with spatial technology, both the theory and the associated technical skills, is part of the routine education and training of natural resource professionals. Educational institutions attempt to instill this knowledge and develop these skills in their students through GIS courses. Although it is suggested that natural resource professionals need not be computer programmers, knowledge of certain basic technical aspects of GIS might be expected of people who use GIS frequently as part of their job. For example, it would be highly beneficial for professionals to understand the differences among data models, types, and formats associated with GIS, how landscape and water features are referenced using coordinate systems, how to obtain, edit, and manage geographic data, how to employ basic spatial analysis functions and spatial statistics, and how to effectively communicate a message to other people using maps. Likely, few people employed in a forestry and natural resource management organization will have extensive knowledge or skill in all of these areas, but some understanding of these concepts by everyone employed is important. The goal of this book is therefore to provide insights into the building blocks of GIS and help develop the skills commonly used in natural resource management. As one’s career in forestry or natural resource management progresses, additional responsibilities (managing personnel, resources, etc.) are likely to follow. An understanding of the building blocks of GIS and the skills commonly used in natural resource management are of great value even when others are the ones conducting the spatial analyses and making the maps. For example, one may find themselves as part of a team responsible for scheduling management activities on several 100,000 acres of forest lands, and almost certainly in this case, utilizing a collection of spatial databases to address the associated tasks (Crosby and Booth, 2011). Or one may find themselves needing to present information on subjects such as biomass or timber availability during a meeting with colleagues, and almost certainly in this case too, utilizing a collection of spatial databases to address the associated questions (Wulder et al., 2008). Understanding how the final product (the plan, the report, the estimate) is developed is important even if the technical analyses and procedures were conducted by others. In managing computerized maps, foresters and natural resource professionals often edit the shape or location of features (timber stands, roads, etc.), query the associated databases to answer questions (Which stands are of an age that can be thinned?), and

8 Geographic Information System Skills for Foresters and Natural Resource Managers

regularly manipulate and update graphic features to illustrate management activities and concerns. Editing spatial features, editing associated attribute tables, and digitizing landscape features are common tasks for forestry and natural resource management professionals (Merry et al., 2007, 2016). In one survey of these professionals, the most frequently developed maps included basic maps indicating the location of a stand of trees and specific maps indicating areas scheduled for harvest. In association with these products, ownership boundaries, roads, streams, and management units were the most frequently used types of GIS databases (Merry et al., 2016). With time, foresters and natural resource managers will realize that they need reliable access to a myriad of databases in order to make rapid, accurate, actionable decisions and to disseminate the outcomes and implications of these decisions to others. To accomplish this, many people utilize GIS, if not for the types of analyses that are possible, but at least as a clearinghouse within which the data can be accessed and displayed. A GIS can be used to plan and store inventory data (Kӧhl et al., 2006), to integrate data collected with GPS (McConnell et al., 2020), to plan forest operations (Bettinger and Sessions, 2003; Grigolato et al., 2017) and to analyze remotely sensed data for management or wildlife habitat assessments (Wulder et al., 2005; McDermid et al., 2009). A GIS can help address many land management issues that are of interest to society, to research, and to practice. GIS has become so ubiquitous in natural resources fields over the last few decades, that when asked what data might be needed to determine forested areas in need of a thinning treatment, one’s first inclination may be to turn on the computer and access a vegetation-related GIS database. Reflection 1.1 Imagine you have been asked by your employer to determine what forested area(s) should be thinned in the next year or two. Think about what information you would need to address this request. For example, what assumptions would you develop about areas that could be thinned, and what GIS databases might you need? What would you need to do with the data, in terms of analysis, to arrive at a reasonable estimate of forest areas to be thinned for your employer? Develop a short summary of how you would respond to this request, one that points directly to the specific questions noted here in this reflection exercise.

Brief history of the development of GIS Although an incredibly enticing technology for young professionals to investigate, the structures and underpinnings of GIS were developed over 50 years ago. One of the early efforts to enable the analysis of landscape features through the use of computers involved comprehensively classifying land in Canada and detailing the capabilities of lands for both active management and conservation (Goodchild, 2018). In fact, the very history of GIS is tied to the development of computer systems and the

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management of natural resources, as evidenced by the early work of Roger Tomlinson’s team at IBM (Tomlinson, 1968) creating a system that gathered, stored, and analyzed data related to land cover and inventory in Canada. Along with this effort came the creation of the acronym GIS (Yuan, 2017). Early GIS programs were constrained by the processing speed, available storage, and power of the computer systems that were available; therefore, the use of GIS was relegated to specialists and researchers in mathematics and statistics, geography, cartography, and computer science fields (Goodchild, 2018; Coppock and Rhind, 1991). However, GIS by its nature is interdisciplinary, and as advancements in computing capabilities were made, more people with widely diverging interests began to incorporate GIS into science, business, and education. Other interesting developments followed: Chicago’s transportation system was examined through work at Northwestern University, the U.S. Census Bureau began to spatially reference addresses, and the U.S. Geological Survey integrated their classic 7.5-minute topographic maps with computer systems. From these efforts, the promise of GIS became evident for fields spanning the spectrum of planning, geology, demography, cartography, natural resource management, and others (Coppock and Rhind, 1991). In the time since Tomlinson began his work, local and national governments, natural resources organizations in the public and private sectors, and individuals have embraced the opportunities GIS and spatial technologies present. Diversion 1.1 Either alone, or as an informal team of your classmates or colleagues, search the Internet to learn how the following people have been influential in the development of GIS as we know it today: Cynthia Brewer, Jack Dangermond, Howard Fisher, Michael Goodchild, Gerardus Mercator, Ptolemy, Roger Tomlinson, and Dawn Wright. Then, develop a short summary of the contributions of one or more of these people to the development of GIS. In forestry, the push to enable field foresters to use GIS for their daily mapping needs began in the mid-1990s (Bettinger, 1999). Today, digital mapping through smartphone applications and Internet-based mapping programs promotes the use of GIS by nearly everyone (Teixeira, 2018) by making the technology much more accessible. Great advances have been made in computing systems over the last 2 decades, yet GIS may still be limited today by legacy decisions enacted early in its development, such as using a planar (flat) surface as the basis for display, development, and analysis (as it is with paper maps) rather than the curved surface of the Earth (Goodchild, 2018). Even so, a number of technological advances in the 1990s enabled GIS to be accessible through Windows-based operating systems (rather than command-line systems, where one would type a long string of codes to tell a GIS system to perform an operation) and through computers equipped with increasing central processing unit (CPU) speed and random-access memory (RAM). Society is increasingly demanding real-time, locationbased services; thus, recent progress in the evolution of GIS involves exploring the

10 Geographic Information System Skills for Foresters and Natural Resource Managers

ability to accommodate Big Data (defined by its volume, velocity (frequency of availability), or variety) and the various opportunities that might be accommodated by crowd-sourced data (Wilson, 2015). As computer systems, and our knowledge of the world in general advance, we are beginning to acknowledge that forestry and natural resource management GIS data can be collected and analyzed at increasingly finer scales, giving rise to the term precision forestry. Computer processing speed and data access are improving with cloud-based platforms; therefore, GIS and spatial analysis are poised to play an even greater role in the management of natural resources in the future. Translation 1.1 Imagine that you are gathered with a group of friends from high school, and they are interested in what you are learning in college. You mention precision forestry, and they become intrigued. Develop a short, 100-word (or so) summary of the field of precision forestry. Write it in a manner that you would offer it to your high school friends. In general, a GIS provides an efficient means for collecting, managing, and sharing data. GIS allows for the classification of management areas by accessibility and status (Stinson et al., 2019), and by ownership, forest age, dominant species, and so on (Bettinger et al., 2017). In natural resource management, many types of GIS databases are beneficial in addressing immediate and longer-term management issues. Some of these will be described in greater detail later in this book. As brief examples of data availability in the United States, detailed forest data for national forests can be obtained from the U.S. Forest Service (Fig. 1.6), soils data can be obtained from the U.S. Natural Resources Conservation Service, wildlife habitat information can be obtained from various fish and wildlife agencies, and current and historical weather and climate data can be obtained from the National Oceanic and Atmospheric Administration (NOAA). These databases are all freely and readily available to the public, and several of these will be referenced throughout this book. Provided also will be examples of how these data can support forest management and planning. However, some types of data (e.g., forest types) may be unavailable for privately owned lands. In these cases, GIS databases may need to be created. Therefore, expectations of the outcomes of a mapping project should be informed through an assessment of the needs of the project and an assessment of data available (and associated quality). Further, depending upon where one works, foresters or natural resource professionals may use a proprietary GIS system, commercial GIS software, a free, open-source GIS system, or some combination of these to conduct their work. Some unique nuances can be found in the use of these systems, but the core concepts (the theory) should be similar. Therefore, expectations of the outcomes of a mapping project should also be informed through an assessment of the capabilities of the GIS software program being used.

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FIGURE 1.6 The location of aspen (Populus spp.) stands in the Chippewa National Forest, Minnesota, USA. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021a).

Several critical components are necessary to maintain a functioning GIS program. As has been noted, data is paramount to GIS, as are the people who perform the tasks, manage the databases, make the maps, ensure scripts (computer programs) are running, and so on. Two other critical components of GISdhardware and softwaredcan have a multitude of options, at times making them seem dizzyingly complex.

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FIGURE 1.7 A desktop computerdcentral processing unit (CPU), dual monitors, keyboard, and mouse.

GIS hardware Hardware refers to, essentially, the physical pieces of equipment that define a computer (e.g., Fig. 1.7). These components include, but may not be limited to the following:




















Computer box or case Internal hard drives RAM modules External storage devices Motherboard Graphics card Sound card Monitor(s) Wiring USB and other ports CD, DVD, and other drives Speaker(s) Microphone Camera Mouse Keyboard Printer and plotter Scanner External power supply or adapter

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Many of these components of GIS are the same as the components of a typical personal computing system found in an office environment. In the case of a laptop personal computer, many of these components (e.g., monitor, case, keyboard, camera, speaker, etc.) are closely integrated. In the early stages of the development of GIS, physical maps were transferred to a computer using digitizing tables or boards. Scanners were, and still are, necessary for converting hardcopy maps to a digital format. A plotter might be required for printing large maps, and additional storage (external or internal hard drives, or cloud storage) may be necessary to host the data. While we now use cloud-based or Internet services to host and share GIS data, in some instances, GIS data is still shared or stored using removable media such as a CD (compact disc), DVD (digital video disc), USB flash drive, or other types of memory devices. Therefore, the ability to accommodate these through various drives or ports many be important. The hardware needed to use GIS will depend on the project or data management goals, which may evolve as GIS skills grow and the scope of projects becomes more complex. In some instances of employment, such as a land management organization or educational institution that has an information technology (IT) group, many of the hardware decisions may already have been made. However, if this is not the case, and one needs to obtain a desktop computer or laptop to use GIS, one that has plenty of speed and memory will likely be necessary.

GIS software The other main component of GIS, software, includes many options and opens many debates regarding their advantages and disadvantages. An organization’s software needs can be influenced by a number of different issues (Bettinger et al., 2010):










The costs related to the software (purchase cost and annual licenses). The amount and cost of training that is needed. Whether the software is well-documented. Whether the software is user-friendly and intuitive. Whether assistance is available from the developer or from other online help platforms. Whether data requirements are compatible with the data you already developed. The speed of the software on a new computer. Whether the necessary spatial analysis tools are available. Whether a network key is needed, or whether the software can be installed locally.

Many GIS software programs offer a graphical user interface (GUI) that consists of tools, buttons and icons, and menus to navigate the array of processes they contain. Other software programs are command- or code-driven and users are required to write computer programming code or use prewritten programs or modules to execute operations and process data. Regardless of the interface, many software programs contain

14 Geographic Information System Skills for Foresters and Natural Resource Managers

functions that allow a user to write scripts or codes enabling complex, but repeatable tasks. As we noted earlier, understanding the basic concepts for analyzing data in GIS will allow one to more easily adapt work processes across different software program. Within this book and the accompanying website (gis-book.uga.edu), some help and guidance will be available for conducting various analytical tasks using different software programs. However, the interested and motivated professional can find GIS tutorials both online and via paid services. These can help one obtain additional skills or delve more deeply into a specific software program. The following offers an overview of both commercial and open-source GIS programs with accompanying links to each. No part of the following discussion constitutes an endorsement of any particular software program over another.

Commercial off-the-shelf software The first software companies were established in the late 1960s and were soon producing software products for the interested and capable user. Commercial software is commonly referred to as off-the-shelf or programs that require an exchange of funds (money) to obtain a license. There are many GIS software program options available that work within desktop or laptop personal computing, enterprise, and cloud-based environments. Each of these options should be evaluated depending upon the needs and infrastructure of an organization or institution. For example, a small consulting company might only need a single desktop GIS program license to meet the needs of their organization, although this may allow only a single user to use the software at any one time. A large corporation will almost certainly need a multi-user database or enterprise license, and perhaps cloud-based software programs that are dedicated to GIS operations. Commercial GIS software programs widely accepted in forestry and natural resource management, typically have long track records of use and development, and often offer robust training, support, and educational materials. Further, these software programs often offer different licensing levels, add-ons and extensions, and functionality that can affect their cost. While some of these require an additional cost, these additional tools often add significant depth to the capabilities offered by the GIS software program. Examples of commercial off-the-shelf GIS software include ESRI’s ArcGIS and ArcPro, Geomedia, IDRISI, and others (Table 1.1).

Open-source software Open-source GIS software programs are free to the user and contain source code that is also modifiable. However, people who make modifications to these software programs often do not have the ability to license the modifications. The introduction of freely available GIS software programs began in the 1980s with the introduction of the Geographical Resources Analysis Support System (GRASS) (Neteler et al., 2012). The proliferation of the Internet expanded the availability and capability of open-source GIS

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Table 1.1 Commonly used commercial and open-source software options for GIS/ spatial analysis. Software Commercial

Link

ArcGIS desktop ArcGIS Pro

https://www.esri.com/en-us/arcgis/ about-arcgis/overview https://www.esri.com/en-us/arcgis/ products/arcgis-pro/overview Maptitude https://www.caliper.com/maptitude/ solutions/forestry-gis-mappingsoftware.htm IDRISI/TerrSet https://clarklabs.org/terrset/idrisi-gis/ GeoMedia https://www.hexagongeospatial.com/ products/power-portfolio/geomedia Open source QGIS https://www.qgis.org/en/site/ R

https://www.r-project.org/

Google Earth https://earthengine.google.com/ Engine GRASS https://grass.osgeo.org/ SAGA

http://www.saga-gis.org/en/ index.html

Operating system Windows

Specifications

Windows

4 GB RAM (8 GB for ArcGlobe), 4 GB storage 8 GB RAM, 32 GB storage

Windows

4 GB RAM, 10 GB storage

Windows Windows

8 GB RAM, 9 GB storage 4 GB Ram, 10 GB storage

Windows, Linux, macOS Windows, Linux, Unix, macOS Online

2 GB RAM, w1.5 GB storage As little as 1.5 GB RAM,

Windows, Linux, macOS Windows, Linux

800 MB disk space None listed

software packages. University of Minnesota researchers (a collaboration between the Minnesota Department of Natural Resources and National Aeronautics and Space Administration (NASA)) created MapServer in the 1990s. MapServer provides online mapping applications along with a data publication platform even though MapServer was not developed to be a complete GIS program (i.e., it did not provide standard GIS processing and analysis functions). One might think of MapServer as a precursor to Google Earth, serving maps that a user can zoom in and out of, and pan across space (Lime, 2008). As of 2022, both platforms are still functional and have evolved to allow users to take advantage of the open-source nature of their source codes (stored in public repositories) to develop new tools, scripts, plugins, and maintaining the basic program source code (Neteler et al., 2012). Other open-source GIS software programs available today include QGIS, R, Google Earth Engine, and SAGA (Table 1.1). Nonproprietary open-source GIS software programs allow a user to develop and share their own applications and enhance a software’s capabilities and functionality to meet specific needs. Often open-source GIS software programs are developed using common computing languages like C, Cþþ, Java, and Python further increasing their adaptability and flexibility (McInerny and Kempeneers, 2015). Such developments require a person

16 Geographic Information System Skills for Foresters and Natural Resource Managers

to have software programming knowledge, which is beyond the scope of this book, but is important when weighing the advantages and disadvantages of using an open-source GIS software program versus a commercial off-the-shelf program. It should be noted that extending GIS software program capabilities through customized, coded applications is not unique to open-source GIS software programs. Some commercial GIS software can also be customized using scripts and tools that have been developed specifically to work within the parameters of the commercial GIS software program. Open-source GIS software packages may also have GUIs that are similar to those found in commercial GIS program interfaces, where users can access various routines and functions through menus or buttons, or by using keyboard-enabled commands or selections facilitated by a computer mouse. Within open-source GIS software programs, the results of various spatial analysis processes, menu item actions, or mouse clicks might also be visualized within a typical software window. However, some open-source GIS software program functions may only be accessible through command line-driven functions or APIs (application programming interfaces) (McInerny and Kempeneers, 2015). In this regard, using open-source GIS software may require a bit more technical prowess than similar uses within a commercial GIS software program, but if a user understands the fundamentals of GIS, they should be able to learn how to effectively interact with open-source GIS program interfaces. Today’s open-source GIS software programs often have advanced functions that are comparable to commercial GIS software programs, including functions that facilitate network analysis, UAV (unmanned aerial vehicles) and satellite imagery processing, and LiDAR (light detection and ranging) data processing. Diversion 1.2 Imagine that you have been hired by a local forestry consultant as the GIS manager and analyst. Further, assume that the consultant recently created this position based on a perceived need for these services by their clients. In other words, you are the first GIS manager and analyst to work in this company. Listing individual items explicitly, develop a budget to describe what it would cost to acquire the technology to perform GIS analysis. Provide a price for all of the hardware and software you might need, develop a total cost, and in a short memorandum, communicate your budgetary needs to the owner of the company.

Conducting spatial analyses In our dynamic world, events (planned or random) occur somewhere, at some point in time, and at some distance from resources that may be of great interest or impacted by the event. Of importance to foresters and natural resource managers are the who, what, when, where, how, and why of each problem or issue. Conceptually, this is the framework for defining spatial analysis, one of the most powerful functions of GIS. Simply put, spatial analysis conducted within GIS provides people with rationale and logic to help

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them understand the spatial distribution of events and associated outcomes (Lu¨ et al., 2019). One fundamental concept behind spatial analysis is partially explained through Tobler’s First Law of Geography, which suggests that "everything is related to everything else, but near things are more related than distant things” (Tobler, 1970). The interconnectedness of natural or managed phenomena and their location, and the factors that influence the characteristics of the phenomena can be better understood through spatial analysis. Three general sets of questions help put these into perspective. 1. Problem or issue a. Who proposed the problem or issue? b. Why is a map or answer needed? c. How could GIS assist in addressing the problem or issue? 2. Place a. Where is the location of the area of interest? b. What attributes are needed to describe the area of interest? c. How much other land or water around the area of interest should be included? d. What geographic system and orientation are needed? 3. Desired outcome a. Who is the customer of the outcome? b. What is the desired outcomeda map, a database, or simply an answer? c. What other specific outcomes can be developed to assist in meeting the needs of the customer? d. What format (scale, image type, map size, table, etc.) is required of the outcome? e. When do they need the outcome? The problem or issue needs to exist in geographic space. This might involve a city, a county, a forest, a water body, a forest stand, an animal den, or many other resources of interest. At the onset of a spatial analysis or mapping effort, perhaps a supervisor or colleague requires information on natural resources that are under their control, prompting work that involves GIS. Perhaps this work will assist in the development of a contract, plan, report, or publication. If the information desired could be developed in an alternative fashion without the assistance of GIS, then trade-offs might be considered (time required, quality of end product). Along these lines, one might also ask whether the problem or issue is really important, and whether the time and energy required to conduct the GIS analysis is worth the effort. If someone is simply curious about the extent or distribution of a resource, yet several hours of work are required to satisfy their curiosity, then perhaps there is no need for action involving GIS. The geographic location or place in space where geographic phenomena exist are commonly defined by a set of X,Y pairs also called coordinates. X,Y pairs are best understood by imagining a grid that spans the surface of the Earth (Fig. 1.8). Each point along the grid has a value associated with its position along an X and Y axis. Many people commonly know that lines of latitude (the Y axis) run east to west across the globe and

18 Geographic Information System Skills for Foresters and Natural Resource Managers

FIGURE 1.8 Graphical representation of X (longitude) and Y (latitude) values for locations on Earth.

provide the ability to reference a location north or south of the Equator. Lines of longitude (the X axis) run north to south and provide the ability to identify a location east or west of a Prime (standard) Meridian. Where these lines intersect defines the values associated with X,Y coordinates. Other systems that have baselines (east-west lines) and meridians (north-south lines), such as the Public Land Survey System or the State Plane systems in the United States, or the Dominion Land Survey in Canada, can also provide a set of X,Y pairs or coordinates identifying where geographic phenomena exists. Diversion 1.3 To better understand location and X,Y coordinates, open your preferred Internet browser and navigate to a web-based mapping application, Google Earth, for example. Navigate to the following longitude (X) and latitude (Y) coordinates. What landmarks have you navigated to? a. 27 100 30.1000 N, 78 020 31.6700 E b. 51 300 05.2700 N, 0 080 30.9100 W c. 33 510 24.2800 S, 151 120 55.0700 E In addition to physical space, geographic phenomena occur in (and often across) temporal space. For example, a GIS database containing information concerning forest stands is a literal snapshot in time of the forest. In GIS and spatial analysis, the issue of when is nearly as important as where. While the location of a place in space may be known and its characteristics describable, without temporal information it can be difficult to determine if the character of the place represents current or historical conditions. Some characteristics about a place that help decipher the issue of when, for

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example, include background knowledge of the place or imagery captured of the place. But consider using a GIS database that describes forest stands, and includes the age of the stand, the time since it was last burned, and so on as attributes of each stand, but no date to identify when the data were collected or updated. While this database could likely garner some information about each stand, it may be difficult to determine when that information described the stand. The age of the stand when? The time since the last fire in relation to when? Now consider a series of GIS databases of that same forest stand that span across 50 years, where the date when each GIS database was created or updated is known. With this data series, characterizations of the condition of each stand through time can be made by identifying changes that have occurred, and this information can help inform management decisions. How and why questions focus on the reasons and drivers associated with a spatial phenomenon occurring at a location. For example, assume there is interest in identifying the ideal location for establishing a multiuse forest recreation area. One would look for an area with enough species diversity to support wildlife habitats, an area that might support hiking trials or other forms of outdoor recreation, and (perhaps) an area that might be managed for timber production. Factors like annual precipitation, soil moisture, and ground slope may be of value in determining how and why different plant and animal species are present or absent in this landscape. Changes in any one factor may lead to changes in where a species is located, how or why it is located there, and where else it might be located. Spatial analysis and the GIS processes associated with spatial analysis thus provide a foundation for addressing the how and why questions related to the management of natural resources. To further illustrate the value of spatial analysis in natural resources, spatial analysis is often conducted in an evaluation of the tradeoffs between economic and environmental development opportunities. In the case of natural resource management, this may involve the determination of where to build a forest products sawmill (Jones et al., 2010). Potential investors would need to know what forest resources were available, where these resources are located (within some distance of the mill location), and when these resources would be available (e.g., based on stand ages and other factors). Other necessary information that would inform this analysis might include a GIS database of the road network, other GIS databases illustrating the location of municipalities, water resources, and potential wildlife habitat areas, and information regarding local severance taxes. This information can be analyzed within GIS to determine the best location within a broad landscape for a sawmill. Common processes for conducting spatial analysis within a GIS program include buffering, querying, and intersecting GIS databases. These and many other analysis techniques will be covered in more detail throughout the book.

20 Geographic Information System Skills for Foresters and Natural Resource Managers

Conclusions GIS provides an efficient and effective way to organize spatial databases and make them accessible to everyone who needs them to address forestry and natural resource management issues. GIS allows multiple data types from multiple sources to be analyzed simultaneously, it facilitates the development of complex models, and provides a platform for rapid dissemination of information via maps (see Bettinger et al., 2020). As GIS and computer technology evolves, more data will be accessible and more capabilities for spatial analysis will be enabled. Over time, the sophistication and ease of use of GIS will likely increase. For those who work in forestry and natural resources management, it is important to have a basic understanding of the capabilities of GIS programsd particularly the data types that can be created and used in an analytical capacity. On a grand scale, GIS is a wonderful system that provides the ability to accomplish the same (or better) result as can be accomplished in mapping by hand, but GIS can accomplish some of the goals and objectives of a mapping effort much more efficiently and effectively. Therefore, GIS provides a means to manage spatial databases and produce meaningful results that can be passed on to landowners, decision makers, colleagues, or other stakeholders. Inspection 1.2 Using the Internet, navigate to the FS National Forests Dataset (U.S. Forest Service Proclaimed Forests) hosted by http://arcgis.com/. This dataset shows the proclamation boundaries for national forests in the United States. Proclamation boundaries represent the boundary of a national forest proclaimed by federal law, however not all lands within the proclamation boundary may be owned by the federal government. With this in mind, maneuver around in the map or use the search function to find your favorite national forest. By clicking on the forest boundary, a small window will appear with information associated with that national forest that is stored in the attribute table of the underlying GIS database. GIS_ACRES is the field that provides the area in acres inside the proclamation boundary. Through visual inspection of the entire United States, where is the greatest area of proclamation boundaries for national forests located (east vs. west, state, etc.)? Where are there few or none? The remainder of this book provides background on data models and types, location and reference systems, mapping, and data collection (Chapters 2e5). The overview of these foundational skills will be followed by investigations into data management options (Chapter 6), basic processing operations for vector and raster data (Chapters 7 and 8), and the incorporation of remotely sensed data into forestry and natural resource management (Chapter 9). A chapter including a series of applications will tie together the concepts covered throughout the book (Chapter 10). The book concludes with a chapter on professional ethics and practices (Chapter 11), which can be particularly important when handling potential sensitive data.

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Readers should keep in mind as they progress through the book that it is not necessary to ultimately become an expert in all aspects of GIS. One goal of the book is to provide reference material which can serve as a firm foundation of the concepts and capabilities of GIS. This knowledge is important for professionals who work in interdisciplinary groups. When GIS analyses are proposed to address forestry and natural resource management issues, the language used by everyone involved should be comprehendible, regardless of one’s position in an organization. Understanding the concepts introduced in the chapters of this book will also provide knowledge that is portable across different GIS software program platforms. Exercises exploring the concepts covered in this book using ArcGIS or QGIS are available on the book website: gis-book.uga.edu.

References Bettinger, P., 1999. Distributing GIS capabilities to forestry field offices: benefits and challenges. Journal of Forestry 97 (6), 22e26. Bettinger, P., Boston, K., Siry, J.P., Grebner, D.L., 2017. Forest Management and Planning, second ed. Academic Press, London. Bettinger, P., Lowe, T., Siry, J., Merry, K., Nibbelink, N., 2010. Analytical decisionmaking considerations for upgrading or changing geographic information system software. Journal of Forestry 108 (5), 238e244. Bettinger, P., Merry, K., 2018. Follow-up study of the importance of mapping technology knowledge and skills for entry-level forestry job positions, as deduced from recent job advertisements. Mathematical and Computational Forestry & Natural-Resource Sciences 10 (1), 15e23. Bettinger, P., Merry, K., Bayat, M., Tomastı´k, J., 2019. GNSS use in forestry e a multi-national survey from Iran, Slovakia and southern USA. Computers and Electronics in Agriculture 158, 369e383. Bettinger, P., Merry, K., Boston, K., 2020. Mapping Human and Natural Systems. Academic Press, London. Bettinger, P., Merry, K., Cieszewski, C., 2016. The importance of mapping technology knowledge and skills for students seeking entry-level forestry positions: evidence from job advertisements. Mathematical and Computational Forestry & Natural-Resource Sciences 8 (1), 14e24. Bettinger, P., Sessions, J., 2003. Spatial forest planning: to adopt or not to adopt? Journal of Forestry 101 (2), 24e29. Bolstad, P., 2012. GIS Fundamentals: A First Text on Geographic Information Systems, fourth ed. Eider Press, White Bear Lake, MN. Chang, K.-T., 2019. Introduction to Geographic Information Systems, ninth ed. McGraw-Hill Education, New York. Chen, C., Sun, F., Kolditz, O., 2015. Design and integration of a GIS-based data model for the regional hydrologic simulation in Meijiang watershed, China. Environmental Earth Sciences 74 (10), 7147e7158. Coppock, J.T., Rhind, D.W., 1991. The history of GIS. In: Maquire, D.J., Goodchild, M.F., Rhind, D.W. (Eds.), Geographical Information Systems: Principles and Applications, vol 2. Longman Scientific and Technical, London.

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Crosby, M.K., Booth, M.T., 2011. Development of a geodatabase for use in forestland management. In: Ruas, A. (Ed.), Proceedings of the 25th International Cartographic Conference, ICACI, 3e8 July 2011, Paris, France. French Committee of Cartography, Paris, France, CO-388. Goodchild, M.F., 2018. Reimagining the history of GIS. Annals of GIS 24 (1), 1e8. Grigolato, S., Mologni, O., Cavalli, R., 2017. GIS applications in forest operations and road network planning: an overview over the last two decades. Croatian Journal of Forest Engineering 38 (2), 175e186. Huang, M.Q., Nini c, J., Zhang, Q.B., 2021. BIM, machine learning and computer vision techniques in underground construction: current status and future perspectives. Tunnelling and Underground Space Technology 108, Article 103677. Jensen, J.R., Jensen, R.R., 2013. Introductory Geographic Information Systems. Pearson, Toronto. Jones, T.L., Schultz, E.B., Matney, T.G., Grebner, D.L., Evans, D.L., Collins, C.A., Glass, P., 2010. A forest product/bioenergy mill location and decision support system based on county level forest inventory and geo-spatial information. In: Gan, J., Grado, S.C., Munn, I.A. (Eds.), Global Change and Forestry: Economic and Policy Impacts and Responses. Nova Science Publishers Inc., Hauppauge, New York, pp. 131e138. Kӧhl, M., Magnussen, S.S., Marchetti, M., 2006. Sampling Methods, Remote Sensing and GIS Multiresource Forest Inventory. Springer, Berlin. Lime, S., 2008. MapServer. In: Hall, G.B., Leahy, M.G. (Eds.), Open Source Approaches in Spatial Data Handling, Advances in Geographic Information Science, vol 2. Springer, Berlin, pp. 65e85. Lu¨, G., Batty, M., Strobl, J., Lin, H., Zhu, A.-X., Chen, M., 2019. Reflections and speculations on the progress in Geographic Information Systems (GIS): a geographic perspective. International Journal of Geographical Information Science 33 (2), 346e367. McConnell, T.E., Crosby, M.K., Holderieath, J.J., VanderSchaaf, C.L., 2020. Financial assessment of future stand conditions required to recover the opportunity costs of a north Louisiana streamside management zone. Forest Products Journal 70 (1), 39e49. McDermid, G.J., Hall, R.J., Sanchez-Azofeifa, G.A., Franklin, S.E., Stenhouse, G.B., Kobliuk, T., LeDrew, E. F., 2009. Remote sensing of forest inventory for wildlife habitat assessment. Forest Ecology and Management 257 (11), 2262e2269. McInerney, D., Kempeneers, P., 2015. Open Source Geospatial Tools: Applications in Earth Observation, Earth Systems Data and Models, vol 3. Springer, Cham, Switzerland. Merry, K.L., Bettinger, P., Clutter, M., Hepinstall, J., Nibbelink, N.P., 2007. An assessment of geographic information system skills used by field-level natural resource managers. Journal of Forestry 105 (7), 364e370. Merry, K., Bettinger, P., Grebner, D.L., Boston, K., Siry, J., 2016. Assessment of geographic information system (GIS) skills employed by graduates from three forestry programs in the United States. Forests 7 (12), Article 304. Merry, K.L., Bettinger, P., Hubbard, W.G., 2008. Back to the future part 1: surveying geospatial technology needs of Georgia land use planners. Journal of Extension 46 (3), Article 3RIB6. Neteler, M., Bowman, M.H., Landa, M., Metz, M., 2012. Grass GIS: a multi-purpose open source GIS. Environmental Modelling & Software 31, 124e130. Stinson, G., Thandi, G., Aitkin, D., Bailey, C., Boyd, J., Colley, M., Fraser, C., Gelhorn, L., Groenewegen, K., Hogg, A., Kapron, J., Leboeuf, A., Makar, M., Montigny, M., Pittman, B., Price, K., Salkeld, T., Smith, L., Viveiros, A., Wilson, D., 2019. A new approach for mapping forest management areas in Canada. The Forestry Chronicle 95 (2), 101e112. Teixeira, S., 2018. Qualitative Geographic Information Systems (GIS): an untapped research approach for social work. Qualitative Social Work 17 (1), 9e23.

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Tobler, W.R., 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography 46, 234e240. Tomlinson, R.F., 1968. A geographic information system for regional planning. In: Stewart, G.A. (Ed.), Symposium on Land Evaluation. Macmillan, Australia, pp. 200e210. U.S. Department of Agriculture, Forest Service, 2021a. Chippewa National Forest Geospatial Data. www. fs.usda.gov/main/chippewa/landmanagement/gis (accessed 18.12.21). U.S. Department of Agriculture, Forest Service, 2021b. Francis Marion and Sumter National Forests Geospatial Data. www.fs.usda.gov/main/scnfs/landmanagement/gis (accessed 14.12.21). U.S. Department of the Interior, Geological Survey, 2006. Seafloor Map of Puerto Rico Trench. www.usgs. gov/media/images/seafloor-map-puerto-rico-trench (accessed 06.07.21). U.S. Department of the Interior, Geological Survey, 2012. USGS EROS Archive - Radar - IFSAR Orthorectified Radar Image (ORI) Alaska. www.usgs.gov/centers/eros/science/usgs-eros-archiveradar-ifsar-orthorectified-radar-image-ori-alaska?qt-science_center_objects¼0#qt-science_center_ objects (accessed 06.07.21). Vrana, K.J., Nargeolet, P.-H., Sauder, W., Dlingelhofer, A., King, R., Pasch, L., Acmoody, S.J., Goodwin, R. F., Lusch, D.P., Abbott, B., Sherrell, A., 2012. Mapping RMS titanic with GIS: implications for forensic investigations. Marine Technology Society Journal 46 (6), 111e128. Wilson, M.W., 2015. New lines? Enacting a new history of GIS. Canadian Geographer 59 (1), 29e34. Wulder, M.A., Hall, R.J., Franklin, S.E., 2005. Remote sensing and GIS in forestry. In: Aronoff, S. (Ed.), Remote Sensing for GIS Managers. ESRI Press, Redlands, CA, pp. 351e356. Wulder, M.A., White, J.C., Fournier, R.A., Luther, J.E., Magnussen, S., 2008. Spatially explicit large area biomass estimation: three approaches using forest inventory and remotely sensed imagery in a GIS. Sensors 8 (1), 529e560. Yuan, M., 2017. 30 years of IJGIS: the changing landscape of geographical information science and the road ahead. International Journal of Geographical Information Science 31 (3), 425e434.

2 Geographic data Introduction Datadfacts, estimates, synthesized knowledge, statistics, and real-world observationsdare the foundation of information that we use to describe a situation or system and to make decisions. Any piece of work that contains geographically referenced observations is considered geographic data (International Organization for Standardization, 2014). As foresters or natural resource managers, we often make decisions about where to go and what to do across the landscape that we manage. We also often need to provide direction and guidance for others (colleagues, contractors, general public) to understand where activities are planned (or happened), and where we have made decisions regarding the status of resources we manage. Maps and information derived from the manipulation of geographic data are common outcomes of our efforts. As resource managers, decisions regarding the management of forest resources need to be made. Even if a decision is to do nothing at all, these decisions should be based on reliable knowledge and information (data). Further, messages regarding past and current events, as well as planned future activities, need to be conveyed to stakeholders requiring reliable knowledge and information. Making a decision or attempting to describe a place on Earth with very little information would not be rational. Particularly today, with the computing resources (hardware and software) we have that allow for very efficient, and quite deep development of knowledge about our world. However, to address these issues, we need data, and not just any data, we need good data. Therefore, as background material for understanding the value of geographic information systems (GIS) to the management of forests and other natural resources, this chapter presents a number of topics related to data. Included are discussions of common data models, formats, types, resolutions, accuracy, and precision.

Data models Geographic data, those features that are developed, managed, used, and viewed in conjunction with a GIS, are also referred to as geospatial data, geoinformation, georeferenced information, or more simply, geodata. Geographic data are represented using a data model to describe a location in space and time, and their attributes. Data models are a set of rules that define how the model is structured, how it can be used and manipulated, how it interacts with other data models, and the types of data represented (Longley et al., 2005). The simplest form of a data model in GIS is a table. Tabular data Geographic Information System Skills for Foresters and Natural Resource Managers. https://doi.org/10.1016/B978-0-323-90519-0.00003-0 Copyright © 2023 Elsevier Inc. All rights reserved.

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models are structured to have rows and columns filled with numbers or text that describe spatial features. While this type of data model may seem basic, it is often the basis for analyses that are completed using GIS. The function of tabular data models is explained in more detail later in this chapter. When tables are associated with spatial features (e.g., points, lines, and polygons), the resulting combination can facilitate informative analyses. To begin this chapter, we introduce the foundation of how geographic data are commonly represented as two basic spatial data models: vector and raster.

Vector data models With respect to GIS, vector data are typically one- to three-dimensional features that represent some sort of object on the landscape. Vector data models often consist of points, lines, and polygons (Fig. 2.1), but can be extended to triangular irregular networks (TINs) and other three-dimensional objects. Vector data are often said to represent real-life landscape features in a much more realistic manner than raster data (grid cells). A vector GIS database, in contrast to a raster GIS database, can be as simple as a single point that represents the nesting location of a northern spotted owl (Strix occidentalis caurina) or as complex as the 8900 polygons that represent the management units (timber stands) of the Talladega National Forest in Alabama. A point is created using X,Y coordinates, and in some cases a Z coordinate (Fig. 2.2). As mentioned in the previous chapter, the X coordinate references some distance in the east-west direction (often referred to as an easting). The Y coordinate references some distance in the north-south direction (often referred to as a northing). The Z coordinate, when present, is an expression of elevation or height. As geographic data, a point is often

FIGURE 2.1 Points, lines, and polygons are components of vector data models.

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FIGURE 2.2 Points are comprised of an X,Y coordinate and sometimes a Z (or elevation) coordinate.

used to represent a specific place on Earth. For example, a point may reference the center of a city, the location of the center of an inventory plot, the site of an archeological reserve, or as we suggested earlier, the location of a nest used by a specific species of wildlife. For example, land managers might have collected global positioning system (GPS) point locations of eastern wild turkeys (Meleagris gallopavo silvestris) within forests of Louisiana. Telemetry collars on animals might have recorded every few minutes the X,Y coordinate locations of these animals as they traveled, illustrating paths of movement. In addition to the X,Y coordinate pairs that represent the location of an individual turkeys throughout the day (Fig. 2.3), information about these points can be stored in a table to explain characteristics of the spatial phenomenon. Land managers might associate the time of day and the habitat conditions around each place that a turkey was recorded to have been with the appropriate vector feature (each point). As is suggested, a GIS database containing point data can contain more than one point, and certainly each point can be described by many different attributes, or information associated with the point. Points have no dimension, meaning that no length or area calculations can be made from a point. However, most GIS programs have measuring tools to calculate a distance or area by connecting one point or more points.

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FIGURE 2.3 The locations visited by a single eastern wild turkey in West Feliciana Parish, Louisiana, USA, on March 14, 2012. Credit: Base map from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021).

Reflection 2.1 Imagine that you have recently been hired as a forester for the U.S. Forest Service, and that you will work on the Ocala National Forest in Florida. What types of GIS databases would the forest have, or could the forest develop, that would require the use of point data? A series of points (hereafter called vertices or endpoints) might be connected to create a line. A GIS line database can contain a single line, or many different lines. When lines are connected through the sharing of a vertex or end point, a linear network is created. Lengths of each line segment can be calculated and summarized to account for the total length of the network. In addition, each line segment can be associated with many different attributes. For example, a roads GIS database that consists of many different line segments might contain attributes such as road length, road width, road class, surface type, and date of the last maintenance activity. Diversion 2.1 Access the Chippewa National Forest data from this book’s website (gis-book.uga. edu). In your preferred GIS software program, open the roads GIS database. How many road segments are contained in this database? What types of attributes are contained in the table associated with each road segment? A polygon uses multiple (at least three) X,Y coordinates connected to form a closed feature or area that has three or more straight sides (in most cases, many short pieces).

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In some GIS software programs, a polygon is created through the closed area formed by several individual, irregularly shaped lines. As with point and line databases, a GIS polygon database can consist of a single polygon (e.g., a property boundary) or many different polygons (e.g., the soil management units for a large property). As with points and lines, each polygon can be associated with many different attributes. With a GIS polygon database, both area and perimeter length can be calculated if the coordinate system of the database has been defined. For example, the perimeter and area of each polygon representing recreation opportunity spectrum (ROS) classes of the Cibola National Forest in New Mexico (Fig. 2.4) can be computed by a GIS software program once the polygons that represent closed areas have been developed. Inspection 2.1 Examine the Yellowstone National Park overview map (Fig. 2.5) that was available through the U.S. National Park Service website on February 22, 2021. Certainly, the one polygon that represents the park boundary has a shape that is more complex than a simple rectangle. Describe as well as you can what may have influenced the shape of the polygon that represents the boundary of the park, and how the irregular shape of the boundary may have been created in GIS. Vector data models have several advantages: they may more accurately represent spatial data, they are more efficient in terms of file size and data storage, and they are easily generalized. However, there are disadvantages to the use of vector data models, which include the fact that geographic analysis may be more computationally intensive than when using raster data. Further, developing vector data models (collecting field data, generating vector data models from satellite imagery, digitizing and editing processes, etc.) can be time-consuming and cost-intensive.

Tabular data Often associated with vector data are attribute tables that act to describe characteristics of spatial phenomena. Data stored in an attribute table are arranged in rows and columns, with each column having a heading similar to the standard format of a spreadsheet. Each entry in a cell of the table is commonly called an attribute. With tables, each column is referred to as a field while each row is called a record (Chang, 2010). Rows are directly related to vector data features. For example, in a roads GIS database, each independent section of road will have one associated row in an attribute table. Each column within an attribute table contains one type of data, such as numbers, text, or dates (Fig. 2.6). And each column can contain a different type of data than other columns. While these tables may seem simple in their construction, they are an integral and important tool in GIS. Attribute information in a table can be used to answer questions through queries (How many pine stands larger than 10 acres are there? How many owners in the county have the last name “Jones”?), may be used to analyze spatial relationships between different databases (How many miles of stream fall within the boundary of a

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FIGURE 2.4 ROS areas on the Cibola National Forest, New Mexico, USA. Credit: U.S. Department of Agriculture, Forest Service (2016).

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FIGURE 2.5 Overview map of Yellowstone National Park. Credit: U.S. Department of the Interior, National Park Service (2021).

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FIGURE 2.6 A portion of the attribute table for the Chippewa National Forest stands GIS database. Credit: U.S. Department of Agriculture, Forest Service (2021).

state park? What volume of timber is within a streamside management zone (SMZ)?), and could be used to visualize information on maps (What do all of the green polygons represent? Where is a city in relation to the town I live in?).

Topology Topology defines rules that regulate the interactions of the geometries associated with vector data models (points, lines, or polygons). A topological model for line and polygon features has three foundational components: nodes, vertices, and links (Fig. 2.7). A node is a point at the beginning of a line or at the terminus of a line. A vertex is technically a node, but it does not only have to be located at the beginning or end of a line. Vertices FIGURE 2.7 Nodes, vertices, and links are the topological foundations of polygon and line features.

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may be connected, defining a line and helping to define the shape of a polygon (which technically consists of one or more lines that form a closed area). Links are lines that connect two nodes. Links have a starting point and an ending point, often defined by the manner in which the line was created. Topology rules dictate that all lines must be connected, and that all polygons must be closed. By adhering to topological rules in the development of vector databases, a user of a GIS database can expect a high level of data quality and integrity. Additionally, topology allows for vector data to be edited, queried, and used in analytical or data management processes (Longley et al., 2005). For example, the use of topological relationships when applied to GIS data helps us understand issues such as adjacency, connectivity, and containment (Wing and Bettinger, 2008) allowing for more complex spatial analysis. The idea of adjacency helps one understand either direct neighboring features (this polygon touches that polygon) or features that are within some proximity to others. Further, topological relationships require that polygons sharing a border cannot overlap and that gaps should not exist between their shared boundary (Fig. 2.8). Think of two land parcels that share a border. These two parcels share an edge and by sharing that edge, the borders are restricted from overlapping or having gaps. Translation 2.1 You are visiting your family during a holiday, and some relatives have really taken an interest in what you do. As you are talking about computerized mapping (GIS) and the various ways in which you use geography to attend to the responsibilities of your job, one of your relatives mentions that they heard the word topology somewhere, but they don’t know what it means. How would you describe the concept to them? Connectivity requires that nodes and links be connected resulting in complete linear networks or closed polygons. For example, within a roads GIS database, the lines are

FIGURE 2.8 Topological relationships require that polygons do not overlap, and gaps do not exist between shared borders.

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composed of a series of connected nodes and links. Where roads intersect, there will likely be a node, even if one of the two roads does not end at the intersection point. In other words, the node of one road may intersect a node or vertex of another road. Intersecting roads on each side of the node must connect to the node with no gaps between the link and the node. Common topology errors associated with connectivity include overshoots, undershoots, and dangles (Fig. 2.9) (McDonnel and Kemp, 1995). An overshoot occurs when a line crosses past the intersection point of another line segment. An undershoot occurs when a line falls short of intersecting with another line segment. Dangles are the result of a gap between line segments or disconnected nodes (Bettinger et al., 2020). Not all dangles are bad. Some represent dead-end roads, while others may represent the upper end of a stream system. When considering correcting dangles, attention should be paid to the length of the dangle. If quite short, this may be an error rather than a correct description of real-world phenomenon. Reflection 2.2 One day, not so far off in the future, you may be assisting in the analysis of riparian area (SMZ) rules on the ability of your company to conduct forest management activities in this sensitive area. This would involve buffering the streams (a topic in Chapter 7) to understand the necessary land area needed to meet SMZ guidelines. The streams GIS database that you are using, consisting of thousands of line features, will be the basis for these analyses. Certainly, you expect that the database will be perfectly digitized, but often it is not. So, how can overshoots, undershoots, or dangles associated with the line features affect the outcomes of your analysis?

Raster data models Raster data models are comprised of a series of rows and columns dividing the surface of the Earth into a grid of cells (or pixels). For reference to the Earth’s surface, one corner of the grid will have a known X,Y coordinate (Dawsen, 2011), and the position of the

FIGURE 2.9 Connectivity dictates that nodes and links connect avoiding unnecessary overshoots, undershoots, and dangles.

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remaining grid cells, which have a regular size (often a square), is mathematically derived. In thinking about raster data models, it is important to note that they represent a continuous surface. This is in stark contrast to vector GIS databases which only cover certain parts of the landscape with points, lines, or polygons. Common raster GIS databases include models of the Earth’s elevation, scanned hardcopy maps, aerial images, and satellite images. A digital elevation model (DEM) is another example of a raster GIS database with each pixel’s value representing the average elevation within the area. Further, a digital orthophotograph is an aerial image represented by grid cells (pixels) that have been assigned ground coordinate positions, or which have been registered. In raster GIS databases, the grid cells are regularly shaped (we mentioned squares a moment ago), but any regular shape and size can represent as a raster database. These cells can vary in size from one raster GIS database to another, but the grid cells are consistently sized within a single raster GIS database. For example, a raster GIS database with a 30 m spatial resolution is comprised of cells that are 30 m wide and 30 m tall (Fig. 2.10). Each cell in a raster GIS database contains a value. In some raster GIS databases created from satellite or aerial imagery, these values represent the reflectance of light energy collected by sensors that have been developed to specifically detect the visible, infrared, and ultraviolet portions of the electromagnetic spectrum. Reflectance values can be correlated to land use or vegetation conditions. However, these values could represent any phenomena, such as land use class, wood volume per acre, or habitat quality. The key point is that each grid cell contains one value. This is in contrast to

FIGURE 2.10 A raster data model comprised of rows and columns with a grid cell size of 30 m  30 m.

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vector data features which could have many associated attributes. Fig. 2.11 illustrates how a land cover class raster GIS database might be created. Imagine a grid with uniform shaped squares is overlaid onto a landscape. Where landscape features intersect with a cell in the grid, a single land cover classification is assigned to the cell, no matter how large or small the grid cell. As we suggested, grid cell size dictates the spatial resolution or precision of raster data. Raster data are said to have high resolution when smaller cell sizes are used. For example, the U.S. Department of Agriculture’s National Agriculture Imagery Program (NAIP) develops raster GIS databases from aerial imagery and provides these with a 1 m grid cell resolution. Conversely, low-resolution raster GIS databases have larger cell sizes. As an example, the United States’ Advanced Very High Resolution Radiometer (AVHRR) scanner produced raster GIS databases with about a 1 km spatial resolution. Depending on the resolution of the raster data being used and the scale at which the raster data is viewed, the spatial resolution may not be obvious. For example, Fig. 2.12 shows a point located in St. Mary’s, Pennsylvania overlaid on a 1 m (1  1 m) resolution aerial image. At the first extent (A), the spatial resolution of the aerial image is not apparent. After zooming into the point (B), and essentially increasing the scale of the view, the image may not seem as crisp and the individual raster cells may start to be more prevalent. At some point, when scale is increased considerably (C), the raster cells become evident.

FIGURE 2.11 An illustration of how a land cover raster GIS database might be created.

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FIGURE 2.12 St. Mary’s, Pennsylvania illustrating raster resolution at varying scales. Credit: Base map from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021).

In addition to the creation of raster GIS databases from imagery captured by satellites and aerial vehicles (planes, unmanned aerial vehicles (UAV)), raster GIS databases can be created by converting a vector GIS database to a raster database. This can be accomplished within GIS or through the process of scanning. Issues related to generalization of landscape features and accuracy arise with these types of conversion. Reflection 2.3 You are working on a mapping project and have chosen to use a raster data model with cells containing values representing different land cover types (forest, crops, and developed areas). How would you determine the area of each land cover type represented? Raster data models have several advantages over vector data models; one is their simplicity in representing the real world, making them easy to understand. Additionally, raster data models are inherently good for representing continuous surfaces like elevations, land cover, or climatic data (precipitation amounts, air temperatures, etc.). Computationally, raster data models are relatively easy for software to read and write, and their simplicity makes analytical operations more efficient. However, due to the simplistic structure and potential issues related to the spatial resolution, raster data models are inefficient at representing points, lines, and polygons (i.e., areas) (Fig. 2.13). For example, large raster cell sizes result in a loss of fine-scale detail when representing the real world. Finally, as we suggested earlier, in a raster data model, each cell can only be representative of a single landscape characteristic. Because of this, there is a risk for what is called the mixed-pixel problem. The mixed-pixel problem occurs because in the real world, often more than one landscape feature will fall within the boundaries of a single raster grid cell. Typically, the real-world feature that occupies the largest

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FIGURE 2.13 When converting vector data models to raster data models, a loss of detail may occur.

proportion of the cell is the value assigned to that cell (Chang, 2010) leading to a possible lack of accuracy in representing the landscape. When grid cells contain spectral reflectance values, the variation in spectral reflectance of real-world features in the area that a grid cell represents may present to the user an average reflectance, which may not represent well the real-world phenomena where the grid cell is positioned. Diversion 2.2 For the resources and land management features noted below, make a decision as to whether you would prefer that the GIS database representing it would consist of a vector or raster data model. Now, focus on your choice (vector or raster) and provide a brief explanation to support why you chose it. Finally, provide at least one supporting statement why you might have chosen the other data model. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.

Streams Ponds Air temperature Soils Owl nest locations Hiking trails Timber stands Roads Burned areas Fire ignition locations Homes Land uses Research study areas Underground pipeline

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15. Elevation 16. Owl habitat

Other data models A TIN is an additional data model that is a three-dimensional representation of a continuous surface commonly used for representing elevation comprised of nonuniformly shaped triangles (Fig. 2.14). TINs are unique in that they are created using vector features (points, lines, and polygons) to form nonoverlapping Delaunay triangles derived from connecting a series of vertices and edges to build faces (or the area within the triangle). In deriving a TIN, it is assumed that the face of each triangle represents a constant surface (Chang, 2010). Smaller triangles are an indication of large amounts of variability across vertices. The source data for a TIN is a terrain model. This is often a DEM, a raster data model, but it can also be created from points of elevation or elevation contours (lines). When TINs are created from a raster GIS database, the center of each cell in the raster database is used to develop vertices. An advantage of a TIN is that it often more accurately represents changes in elevation that can be missed using the standard format, the regularly shaped square pixels in a raster database. Specifically, the irregular triangles in a TIN can represent smaller areas of elevation that would be lost with large pixels (Wing and Bettinger, 2008). Additionally, when TINs are created,

FIGURE 2.14 TINs are three-dimensional data models often representing elevation characteristics.

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elevation characteristics such as slope and aspect may also be stored as attributes of the triangles that are created (Bernhardsen, 2002). There will be times in working with GIS where one might encounter vector databases with overlapping polygons. These databases are sometimes referred to as regions. Regions may seem to subvert topological rules discussed earlier in this chapter in that they can represent spatial features that overlap, are disconnected from adjacent polygons representing the same feature, or may not cover the whole of an area. The polygons within a region GIS database are represented by a single table attribute even if they are disconnected or if they overlap. For example, a region GIS database might consist of forest fire boundaries organized by the date of the fire. Each date might contain several different polygons illustrating different fire boundaries that share the same date and identification code but are separated in space. Or fire polygons may span various dates, but their boundaries overlap (Fig. 2.15) and may be attributed in a data table with a different name or identification number. These region GIS databases may also have a hierarchical structure. Examples from the United States may include census GIS databases and others that represent hydrological features (Chang, 2010). In GIS software, region GIS databases are often referred to as multipart shapefiles. Finally, dynamic segmentation can be used to assign point features and attributes to previously developed linear features, creating new GIS databases. The insertion of point features and attributes on linear features that may not already have assigned X,Y coordinates is determined based on a distance to other known locations (i.e., distance from another feature). These new point features and attributes might represent events that are occurring or have occurred along a route. Picture opening a map application (app) on a phone and noticing an indicator that a traffic accident has occurred along an interstate, or construction work is currently under progress. Dynamic segmentation may have been

FIGURE 2.15 Regions, or multipart shapefiles, allow for both overlapping polygons and disconnected polygons to represent spatial phenomena.

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used to break linear features (roads) and assign new attributes based on events that are unfolding. Dynamic segmentation is commonly used in fields involving transportation features but can be implemented on any network. For instance, natural resource managers may use dynamic segmentation to bolster trail network information by assigning trail quality rankings to various places along the network.

Resolution In our earlier discussion of raster data models, we mentioned the spatial resolution of raster GIS databases, and suggested that it is related to the size of the grid cells in the database. For vector GIS databases, the spatial resolution may refer to the scale at which the data were developed. Large-scale maps (e.g., 1:1000) used to develop vector GIS databases may result in better spatial resolution than when small scale maps (e.g., 1:500,000) are used. While viewing data in a computer program, it is tempting to believe that spatial resolution improves if one were to move closer (zoom in, change the viewing scale); however, spatial resolution of data does not change under these circumstances (Bettinger and Wing, 2004). A second type of resolution that applies only to certain raster GIS databases is the spectral resolution. When a raster GIS database contains measures of energy (reflectance values from the surface of the Earth), the range of energy that is collected by the device (film or digital sensor) and stored as a value in each grid cell is the spectral resolution of the data. These raster GIS databases are often informally called bands, as they represent a distinct range of electromagnetic energy that was sensed. For example, a grid cell of a blue band of imagery may contain an amount of reflected blue energy (0.4e0.7 micrometers (mm)). Finally, GIS databases may also have an associated temporal resolution. Here, we are referring to time. For example, in successive satellite images of the Bangor, Maine area, the date each image was developed would reflect the temporal resolution of each GIS database. Diversion 2.3 Access the Internet and locate the specifications of the various bands of imagery available from the Landsat 8 mission. For each of the bands, describe the spectral and spatial resolution. In a short summary, describe how you think the different bands could assist in the management of forests.

Data format With respect to spatial features, discrete data can be described as those features related to a specific object such as a point, line, or polygon. More specifically, discrete physical units are those features that have a definitive boundary or place (Longley et al., 2005). Forest stands, soil types, land parcels, roads, and inventory plot locations are all examples of discrete physical units. The attributes that these features contain can also often be described as containing discrete data values that are integers, codes, or classes.

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Continuous data are represented by attribute values that are not integers, and also by values that change across a surface (Longley et al., 2005). Temperature, elevation, and slope are examples of phenomena described using continuous surface data, while the area of forest stands measured to 0.01 ha would be an example of continuous attribute data. To further illustrate, if one were to examine a management unit (polygon), one may find that the area of the management unit would be represented by a continuous attribute (hectares or acres to 0.01 precision) and the land cover class would be represented by a discrete land cover class (pine, hardwood). Comparatively, land cover represented by a raster GIS database may provide a different land cover value for each grid cell in the area occupied by the management unit across a continuous surface (like a sheet laid across a landscape). Data that are contained in the attribute tables of vector GIS databases or associated with grid cells in raster GIS databases, at the highest level, could be quantitative (numeric values) or qualitative (text, class codes, etc.). Further, data can be numerical or categorical (Chang, 2010). To further complicate matters, nominal data are categorical and may be both textual and numerical. Therefore, one needs to be careful when working with qualitative data, in that it could be composed with numbers yet not technically be numeric. Stream class (1, 2, 3, etc.) and tree species codes (110 (Pinus echinata), 111 (Pinus elliottii), 129 (Pinus strobus), etc.) are examples. When numbers seem to be used to represent qualitative or categorical data, they are representative of a class, category, or object and are not useful for mathematical computations (addition, subtraction, etc.). Ordinal data are also categorical data that can be ranked. For example, an attribute field in a table may contain records that define the likelihood for a land unit to be susceptible to fire, noted as “low,” “medium,” or “high” probability. These units can then be ranked by their fire risk and color coded in a map accordingly. Two common types of numerical data involve intervals and ratios. Interval data are those that have a known interval between values. For instance, with interval data, 8 is always twice as much as 4. Or using temperature as an example, 45 degrees is always 5 degrees less than 50. Ratio data are like interval data yet are defined as having a meaningful zero value. Examples of ratio data include weight, distance, or area estimates. When creating tabular data associated with a spatial feature, decisions need to be made regarding the definition of the data type for each attribute field. These decisions are important and define how attributes can be collected, stored, analyzed, and manipulated. These decisions are also important for maintaining data integrity and for specifying the precision of the data. Common data types include integer, float, double, text, and date. Integers are numeric values that lack decimal places. They can be signed (negative or positive) or unsigned (all positive). Short integer values can range between 32,767 and 32,767 when signed. Short integers are useful for describing discrete values of features that will not exceed the maximum (or minimum allowed). For example, it would be unwise to represent an attribute (state or province population) with a short integer knowing that the actual values could be in the millions. Further, it would be unwise to

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represent an attribute (average temperature, for example) with a short integer knowing that the precision desired would be less than a whole number. Each short integer value requires 2 bytes (16 bits, or binary digits) of computer memory. Each bit contains a 0 or 1 value; therefore, a signed, short integer can range from (2151) to 2151 (32,767 and 32,767). The unsigned range of a short integer is 0e65,535 (2161). A long integer would be used for values that are quite large and are required to be integers. Each long integer value requires 4 bytes (32 bits, or binary digits) of computer memory and can range from (2311) to 2311 (2,147,483,647 and 2,147,483,647) (Bernhardsen, 2002). So as long as the potential value of an attribute of a geographic feature does not exceed about 2 billion 147 million, a long integer may be used for storing the data. The unsigned range of a long integer is 0e4,294,967,295 (2321). Variations on these ranges of values exist for different computer software programs. These differences in memory required may seem minor but they may affect computational speed when processing large databases (Chang, 2010), and the choice made may affect the accuracy of the data. Short integers are therefore limited by the maximum value allowed, while long integers may require twice as much computer storage memory, but as the cost of memory has fallen, the need to economize data types has been reduced. Floating point values are numeric values that include decimal places. In instances where attribute data need precision beyond that of an integer, a float data type seems necessary. Single precision floating point values can range from about 1.175 E38 (1.175  1038) to 3.402 E38 (3.402  1038), while a double precision floating point value can range from about 2.225 E308 to 1.797 E308. Single precision floating point values require 4 bytes (32 bits) of computer memory and can represent 7 or 8 significant digits, while double precision floating point values require double the memory (8 bytes, or 62 bits) yet can represent 15 or 16 significant digits. Each number stored in memory contains 1 bit to represent the sign (positive or negative), 8 (single precision) or 11 bits (double precision) to represent the exponent, and the remaining bits to represent the numeric values. Double precision floating point values are used in instances that require the highest degree of precision. In making a decision on which data type value to select for each attribute that requires a noninteger value, the potential range of the values stored in a GIS database might be balanced against the memory required. For example, if one were associating average stream temperatures with stream lines in a GIS database, a double precision data type would seem unnecessary. Text data types (character data, string data) are collections of alphanumeric characters that are recognized by a computer not as numerical values, but as strings of characters. Therefore, if an attribute field in a GIS database was defined as containing text data, values could include “1234,” “Pinus taeda,” “2C,” “WD40,” “Yellow-poplar” and any other appropriate combinations of numbers, letters, and symbols. Values such as 1234 or 1542.687 stored in text fields are not available for common mathematical operations because they are not viewed as numeric data. Often the minimum amount of memory required is 8 bits, and the maximum amount depends on the number of potential characters desired by the user to represent the attribute. For example, in computer

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programming languages and in GIS, each text or string variable would be declared as having some maximum size (number of characters), and this would determine the amount of memory required to store the text in the memory of the computer. In representing forest types as an attribute field for vegetation polygons, one might examine all of the potential forest type names, locate the longest (in terms of characters), and define the attribute field as having at least this many characters even when they are not in some instances necessary. Boolean data types, when available, represent true and false conditions. These are the only two values that can be represented. Binary data is very similar, where only two values are possible, 0 (no) and 1 (yes). Data fields can be designed with the short integer data type to represent both of these types of data if they are not explicitly available. One can then enter either of the two values (0 or 1) to represent the binary or Boolean cases where 0 is a proxy for False and 1 is a proxy for True. Finally, one interesting data type, a BLOB (Binary Large OBject) or set of segmented strings, often consists of long chunks of binary data or data with the same value. In a raster GIS database, these would consist of grid cells that have the same value yet are connected (adjacent) and irregularly shaped, and perhaps used to detect patterns of similar statistical or geometrical conditions (Minor and Sklansky, 1981; Liu et al., 2015).

Data precision and accuracy An understanding of the quality of spatial data is commonly achieved through consideration of its precision and accuracy. To this point, we have mentioned precision while only alluding to its meaning. Precision of data reflects the degree of exactness of measurements associated with spatial phenomena. For example, records in an attribute table that have values expressed with several decimal places (e.g., acres expressed to the 0.00001 acre, or 8 inches square) are considered to be very precise (Wing and Bettinger, 2008). In considering precision, it should be noted that when dealing with numerical precision (i.e., the number of decimal places in a recorded measurement), more is not always better. For example, when recording the size of a forest stand, an acreage of 250.1111111 is very precise but of little use. It is important to know how accurate the instrument used for measurement is known to be, and measurements from that instrument should be interpreted accordingly. When an instrument is accurate to a 10th of a decimal place, measurements recorded to three or four decimal places are meaningless (or inaccurate). Further, the precision of output data resulting from GIS processing is linked to the precision of input data used in the process (Goodchild and Gopal, 1989). However, precision may not solely be assessed on numeric data but also on spatial features as a result of data collection practices. For instance, if a mistake is made when a property boundary is surveyed, the estimated acreage from the resulting polygon may be represented by several decimal points (suggesting a precise piece of data), but the shape may be incorrect and therefore the polygon would not be a very precise description of the boundary. Accuracy reflects how well data in a GIS database represents the true value, such as the location or attributes of a spatial phenomenon in the real world. Imagine you are

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collecting GPS data where a known location is marked with a surveyed benchmark. By comparing the GPS data collected in the field to the true location of the surveyed benchmark, one can estimate the accuracy of GPS data. Measuring accuracy can be accomplished through statistical methods like root mean square error (RMSE). Certainly, the GPS position can be presented in a very precise manner, perhaps to the 0.001 m of the Universal Transverse Mercator (UTM) coordinate system, but the GPS position may not be very accurate. Data can be (a) precise and accurate, (b) accurate but not precise, (c) precise but not accurate, or (d) neither precise nor accurate (Fig. 2.16). There are many reasons why errors may find their way into spatial data, making it imprecise or inaccurate. Errors in accuracy and precision can be the result of errors incurred while measuring spatial features, mistakes made during data entry, through inappropriate analyses, through issues associated with topology (overshoots, overlapping polygons, etc.), and many other reasons. While absolute accuracy expresses how closely a measured location aligns with a known location on the Earth, relative accuracy is an expression of how spatial features relate to other spatial features (Stanislawski et al., 1996). When thinking about absolute verse relative accuracy, think about creating a map by digitizing features from a digital aerial image. Unfortunately, the coordinate system used to create the new GIS data may be different than the one used by the digital aerial image, leading to a slight offset of the digitized features from their true location. The resulting data may then be relatively FIGURE 2.16 Example of precision and accuracy (A shows data that are precise and accurate; B shows data that are precise but inaccurate; C shows data that are imprecise but accurate; D shows data that are both imprecise and inaccurate).

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accurate in that the digitized features are spatially arranged correctly (i.e., features are the correct distance from each other and properly oriented), but they may be slightly off in terms of absolute accuracy (location on the globe). Precision and accuracy may be part of a larger underlying aspect of spatial data, uncertainty. Uncertainty is the result of spatial data not fully and completely representing the condition and character of a phenomenon. This may be the result of a lack of accuracy and precision, lack of knowledge about the phenomenon, overgeneralization of the phenomenon, or errors in the data collection process.

Data errors Data errors include both the inaccuracy and imprecision that might be found in GIS databases. Sources of error generally fall into three categories: systematic, random, and gross errors. Systematic errors are commonly associated with the instruments used to obtain or create spatial data. This might be errors generated by GPS receivers, satellite system sensors, or other instruments used for measurements (Rae et al., 2007) and may be constant across the data that is collected or created. These errors might also be the result of changes in the spatial phenomena being measured. Gross errors are errors resulting from mistakes made when creating data, maps, or during data collection. These types of errors can be the result of a poor digitizing (data creation) effort, conversion between data models (e.g., converting between raster and vector data models and the mixed-pixel problem), inaccurate editing of topology or data, the choice of an inappropriate data precision, the use of old or temporally incorrect data, the use of poor methodologies during the data collection effort, and through conducting incorrect analyses (Rae et al., 2007). Random errors are just that, they occur randomly. These errors are not intentionally caused by the person creating or editing GIS data. They are typically outside the control of the data creator and are often referred to as noise in the data. However, random error does occur, and an effort should be made to limit and correct this type of error when possible, perhaps by auditing (checking) a subset of the data that is created or collected. Errors in data can compound when spatial analyses are conducted (Heuvelink, 1999). Additionally, errors in data can result in errors in maps, leading to errors in interpretation or conclusions made, and potentially resulting in errors in the decisions that are made. In the United States, many GIS databases are created and provided by federal, state, and local agencies. These databases are frequently developed using spatial data quality standards like those suggested by the United States Federal Geographic Data Committee (FGDC). The FGDC is a group of experts and professionals across the federal government, who are advised by other experts inside and outside the federal government, that provide guidance on data that are created and distributed by the federal government (Federal Geographic Data Committee, 2011). While these standards are mandatory for spatial data created by federal government entities, these standards may not be universally implemented across the public and private sectors. The FGDC also created

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standards for the development of metadata (or data about the data) that will be discussed later in this chapter. Inspection 2.2 Using the Internet, search for the FGDC Geospatial Standards webpage. On the website of the FGDC you should see a list of standards designed for different types of natural phenomena (i.e., wetlands, soils, surface water, etc.). Write a short memorandum that provides a general description of one of these geospatial data standards and its purpose. In this memorandum, describe how this information might be important when using or creating GIS data.

Missing data It can be frustrating to discover that a GIS database is incomplete. Missing data can occur for a variety of reasons: a sensor is broken or is no longer collecting information, a site is inaccessible, a respondent provides no answer, or perhaps there was no data to collect and the person creating a GIS database left a record blank. It is also possible that data become corrupt or were not copied completely from another source, resulting in lost data. Upon opening the attribute table of a GIS database, missing attributes in the table might be noted with a null () value depending on the assigned attribute data type. Some data types do not allow null values and indicate missing values as zeros (0) and missing text as spaces that may look like an empty record. To manage missing data, it may be possible to simply remove the affected record. Alternatively, one might use an interpolation process or maximum likelihood analysis to estimate (fill in) the missing values. These concepts will be introduced in Chapter 8.

Inconsistent data Data inconsistencies are those where the desired or intended spatial or tabular information in a GIS database are occupied by undesired or unintended information. Data inconsistencies can also occur if GIS databases are projected to the wrong coordinate and reference system. For example, a person may access a digital aerial image and use it to create a database of stand boundaries through a digitizing effort. When completed, they may realize that the digital aerial image and the GIS database they created were assigned different UTM zones. Or imagine that a project involves several data collectors (people) working in the forest, and each interpret the conditions using different measurement units or protocols. The resulting records associated with a GIS database might include fields that are populated with different interpretations of forest conditions. An example may be tree species type where some entries were populated with the common name while others were populated with the scientific name or some other tree species code. Spatially, data creation and editing may lead to logical inconsistencies that violate topological rules, leading to incomplete polygons, overlapping boundary lines, features that do not intersect as they should, and many other potential data consistency issues

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(Gong and Mu, 2009). While most data inconsistency issues are not insurmountable, the possibility of their occurrence highlights the importance of checking the quality and accuracy of data during the data creation process or before the GIS databases are used for other analyses or map creation.

Attribute issues In a world now heavily influenced by the collection and use of data to make decisions, we often find ourselves wanting or wishing we had more. Attribute issues may arise through missing information in a GIS database. For a single tree record in a forest inventory, a data collector might have only provided the tree species and nothing else. This information has likely made the observation of the tree of little use. Ideally, a forest inventory GIS database would also include tree height, diameter at breast height (DBH), and other attributes such as tree health condition, tree form, and crown ratio. Temporal information concerning when data were collected or created may also be included to help forest managers understand net and gross growth of the forest. The utility of a GIS database will depend on the analysis being performed or the end product required, much of which is dictated by the presence (or lack thereof) of attributes in the database.

Positional problems When viewing a map online and focusing on (zooming into) a landscape feature such as a road, one may find that it may not be positioned perfectly with respect to other GIS data, such as an aerial image or digital map (Fig. 2.17). This sort of positional error can arise as a result of the data creation process employed or as a result of using different FIGURE 2.17 Slight positional error of a road near Pine Lake, Minnesota. Credit: Base map from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021) for a portion of the Chippewa National Forest.

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coordinate and projections systems. For example, the scale at which a roads GIS database was created might have not been appropriate for digitizing the fine detail associated with forest roads. If the data creator used a 1 m spatial resolution aerial image to assist in digitizing the roads yet viewed the image from a broad perspective when digitizing the road features, the result may consist of road locations and intersections that are misaligned or positionally shifted from their true (or more correct) position by several meters. Point, line, or polygon features acquired with a GPS receiver may also be inconsistent or erroneous, depending upon the capabilities of the GPS receiver used to collect the data. For example, if one were to use a cell phone to collect point features, the positions of those features may be 7e13 m off from their true location, in any direction (Merry and Bettinger, 2019). If positional problems are of concern, it is wise to check the metadata to determine whether a GIS database may have potential issues that would affect the positional accuracy of landscape features.

Database design Schema refers to the “rules” a database follows for both the physical storage of files and how the data (tables, etc.) relate to and interact with each other. These may include a standardization of the structure of database tables and the fields that define the condition or character of landscape features, which would allow the databases to be functionally related. Domains provide a definition or rule for what is allowed within certain attribute fields. For example, if a person was collecting GIS data concerning urban street trees, it would be convenient to develop a domain for speciesdparticularly if there are multiple people collecting data. In this case, the rule for the species attribute (column) may consist of a (drop-down) list of predefined species within the data recorders or apps that are used to record information. This list may be exhaustive or allow the use of an “other” value to be entered. The use of a predefined domain prevents inconsistency in data entry and helps maintain data integrity (e.g., misspellings, etc.) that might impact subsequent analysis.

Data file types Shapefiles A shapefile is a collection of files that represent features described in vector GIS database format. Shapefiles were originally developed by the Environmental Systems Research Institute (now Esri), the company that created ArcGIS, yet the data format can be used across various software packages including QGIS, Erdas Imagine, GRASS, and others. Shapefiles are simply containers for spatial data. They are derived from data that have geographic locations (X,Y coordinates and sometimes the Z coordinate) and often have attribute data stored in a table associated with the spatial phenomena they represent. Shapefiles contain one type of data (point, line, polygon) and ideally would contain one

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data theme (roads, timber stands, wildlife nest locations, urban trees, etc.). These files have the following filename extensions (Esri, 2020): .shpdA file that contains the feature geometry .shxdA file that contains an index to the feature geometry .sbxdA file that contains a spatial index of the features .sbndA file that contains a spatial index of the features .prjdA file that contains the projection and coordinate system information .dbfdA file that represents the attribute table, in dBASE format .xmldA metadata file At a minimum, a shapefile needs the main file (.shp), an index file (.shx), and a database file (.dbf) (Environmental Systems Research Institute, Inc., 1998). Other files may be associated with a single shapefile. While the file with the .shp extension may be referred to as the main file, since it contains the geometry related to the spatial phenomena, the entire collection of associated files is needed for the shapefile to correctly function in GIS software programs. Therefore, when sharing a shapefile with other people, all of the associated files need to be shared and stored in the same file folder. Inspection 2.3 Navigate to the book’s website (gis-book.uga.edu). Download and unzip the GIS data for Allegheny National Forest. Navigate to the folder where these data are stored. Focusing on the Allegheny National Forest boundary file, all files included in the folder are associated with the boundary shapefile. How many files are listed? What are the functions of these files? For over 2 decades, the shapefile format has facilitated data collection and data display within GIS and mapping-grade GPS receivers (Sutton and Gonzales, 1999); thus, the format has remained stable for quite some time. Aside from general forest land ownership and management purposes, early use of the shapefile data file format included describing the perimeters of fires (Finney and Andrews, 1999), habitat areas, mineral surveys, and cultural resources (Commerce Business Daily, 1999).

Geodatabases At their most basic level, geodatabases are a method for storing spatial data. More specifically, geodatabases are storage systems designed as an extension of the traditional database for managing, accessing, and manipulating spatial data and associated tabular data representative of a specific entity in one location. One might think of a geodatabase as a container that can hold various types of GIS data including raster, vector, and tabular databases. The geodatabase provides a means of managing multiple GIS databases, rules, and relationships (e.g., topology), which can be conducted on individual computers, using a file structure for storage and management, or within a larger, multiuser computer system. For example, consider compiling all of the available GIS

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data related to a state park. This information might include streams, hiking trails, forest stands, park infrastructure, aerial images from various points in time, GPS data indicating the locations of benches, and a stand-alone table with additional information important to managing resources within the park. One could manage all of these GIS databases individually or store them in a geodatabase using a common schema, a set of domains, and coordinate system. As a multiuser system, or database management system (DBMS), a geodatabase might be a convenient method for sharing GIS databases and offering multiple users the ability to access and edit data while maintaining versioning and rules of access. A common DBMS can use a hierarchical database structure with tables related to each through shared common keys, and indexed files that use a unique identifier field in a “parent” table that are shared to other “child” tables through a one-to-one or one-tomany relationship (Burrough and McDonnell, 1998). However, some tables in a DBMS may not share common keys. Typically, each table is unique and is composed of information that can be related to distinctly different aspects, say of a forest (one table for soils, another table for vegetation, etc.) (Burrough and McDonnell, 1998). Multiuser systems may use database platforms such as Oracle or SQL, or PostgreSQL (an opensource system). In a large organization operating in multiple locations, a DBMS provides a means of rapidly accessing, editing, securing, and communicating data sources. For smaller, more localized projects, a simpler file system may suffice. A geodatabase need not only be utilized for data related to stand management, active wildlife habitats, and volume projections but can also be used to index and store historical events related to private or public properties (Crosby and Booth, 2011).

Other common file types There are a number of text-based file formats that may be familiar, such as those that use Unicode and ASCII (American Standard Code for Information Interchange) formatting conventions. Some of these can be used to facilitate the development of GIS databases (Fig. 2.18), yet inherently, they do not actually represent spatial data. An ASCII

FIGURE 2.18 A simple text file indicating the northing (Y) and easting (X) UTM coordinates of some important places around Washington, D.C.

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file format uses a coded set of 128 characters for information exchange among computers. The ASCII format was adopted in 1968 as a U.S. federal standard, and thus perhaps represents one of the first attempts to make use of computer systems more efficient by resolving computer file compatibility issues (National Bureau of Standards, 1972). The ASCII format is perhaps the most generic of all file formats and is convenient since this format is composed of readable characters stored in text files. Unicode file format accommodates over 143,000 characters that reference a great number of national writing system conventions, and includes emoji characters (Unicode, Inc., 2020). As compared to the ASCII file format, a file stored in Unicode file format may not be as easily readable. Diversion 2.4 Using the text file illustrated in Fig. 2.18, which can be acquired from the book’s website (gis-book.uga.edu), create a GIS database containing the points described by the northing and easting values. The locations of the places are in UTM Zone 18. Inspection 2.4 After creating the GIS database of some of the important places around Washington D.C. from the text file illustrated in Fig. 2.18, open the attribute table and describe what you see (types of attributes and their data types). How do these compare to the original text file? Another type of text file commonly used to store raster GIS data has an interesting format that presents at the top of the file (in the header information) the spatial resolution of the grid cells and the number of rows and columns in the grid (Fig. 2.19). Following this information are the values associated with each grid cell. The lower left X

FIGURE 2.19 The beginning of a rather large raster text file that has 9279 rows and 8218 columns of 5 m pixels.

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and Y coordinates of the grid are noted in the header information of the example, and the pixel values for a portion of the first row are provided below the header information. As is evident, at the top of this raster GIS database, the pixels contain no valid data values (no data ¼ 9999). At times, these types of files are stored with the DAT or TXT file extension, but no matter the extension, they are simply text files. In certain instances, these files may more easily facilitate data sharing than other file formats. A TIFF (Tagged Image File Format) file is a format commonly used for raster data storage. The first version of the TIFF process for describing and storing raster image data appeared in 1986 (Adobe Developers Association, 1992). Scanners, photo modification software, and screen capture processes can save images as TIFF files. A TIFF file contains a header, an image file directory (noting among other things the number of rows and columns in the image), and the data for each pixel in the image (Adobe Developers Association, 1992). TIFF is a versatile data format that can be incorporated with projection files (tif world (.tfw), GeoTIFF), which allows GIS, Internet browsers, and other image processing programs to display the data in the appropriate geographic location (U.S. Department of the Interior, Geological Survey, 2000). TIFF files are displayed and analyzed like other raster-based file formats. This is a widely used format for many spatial applications as multiple raster bands and data types (e.g., multibit sign or unsigned data) can be stored. The JPEG (Joint Photographic Experts Group) or JPG file format is a raster format that was developed for displaying images on the Internet, yet they may have difficulty reproducing sharp lines and color contrasts in some cases (Bryan, 1998). JPG files provide a means of storing raster data in a format that is compressible, though with the possibility of a loss in resolution. So, while this format can store and compress original image data, it is considered a lossy format in that the decompressed images that one can view after saving an image as a JPG are not exactly the same as the original images. Some color blending occurs at the edges of features with two distinct colors and therefore some pixels do not contain the original colors. Depending on the degree of compression and the scale at which the images are viewed, these losses in color fidelity may not be evident to the human eye (O’Neal et al., 1996). Other common graphics formats include Encapsulated Postscript (EPS), which is a vector-based file format that saves graphics as high resolution features that are associated with information and allows for color separation (Bryan, 1998). An EPS file can also be associated with raster data through a metafile format. High-quality printing of graphics in publications and reports may require this type of file format. Graphic Interchange Formate (GIF) is a graphics file used to display images, yet they may lack the resolution necessary for editing (Bryan, 1998). The legal history regarding the patented compression process available in a GIF file (Welch, 1984) is colorful and likely led to its decline in use. The Portable Network Graphics (PNG) file format for raster data is considered a lossless type of file format when compression is considered. Often, PNG files are recommended for use in Internet browsing or printing, and at one time it was considered as a replacement for files saved in the GIF format (U.S. Library of Congress, 2020). A Computer

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Graphics Metafile (CGM) can accommodate vector, raster, and text information, yet rarely are all of these types of data included in a single file (Murray and vanRyper, 1996). Other spatial data formats include Keyhole Markup Language (KML) or KMZ (zipped version) files that are often used to view geographic data in Google Earth. These formats were developed originally by Keyhole, Inc., a company which was later purchased by Google. A KML file commonly contains vector data (points, lines, or polygons) that are referenced using latitude and longitude coordinates (Open Geospatial Consortium, 2015). Geographic data created in Google Earth can be saved as KML (or other) format and imported into other commonly used GIS software programs. Similarly, other GIS software programs can be employed to create GIS databases, and these can be saved in KML format for use in Google Earth or other mapping programs. By now, most of the world should be familiar with PDF (Portable Document Format) files. A GeoPDF is essentially a map or image that has been saved in PDF format, yet this file format also includes the georeferencing necessary to understand where the land (or water body) is located around the Earth. Each map stored as a GeoPDF therefore is assigned a reference system (coordinate and projection) that allows the location of places on the map and the calculation of distances between points within the map. Some GIS software programs are able to create or use GeoPDF documents. Like other file formats, the Erdas Apollo image files (IMG) allow for multiband, multibit storage of raster databases, which are compressible. Within an IMG file, one can find discrete (e.g., land classes) and continuous (e.g., temperature) types of data that are useful in natural resource management. An IMG file has a tiled, image pyramid format that allows fast representation on computer device screens at different viewing scales. Each level of the tiled hierarchy has half the spatial resolution as the previous, and unlike JPG files, the compression that may be necessary retains more effectively the fidelity of the original image without as much visible degradation (Hexagon Geospatial, 2021). MrSID multiresolution seamless image data (SID) files are similar in some respects to IMG files in that raster databases are divided into rectangular bit planes that are available as the scale of the viewed landscape changes. In contrast to other raster GIS database file formats, a MrSID compressed file is considered lossless with respect to the fidelity of the original image (Extensis, 2019). The compressed county mosaics of the U.S. Department of Agriculture’s National Agricultural Imagery Program (NAIP) are currently provided in SID format (Fig. 2.20). As one can see, there are many types of formats for saving and subsequently viewing, manipulating, and analyzing geospatial data in GIS software programs. These formats are continuously being developed and modified by organizations that develop data standards, by governmental agencies, and by technology companies (U.S. Library of Congress, 2017); therefore, our understanding of the availability, use, and capabilities of each requires reexamining periodically. For example, many of the most recent 7.5-minute topological maps of the United States can be acquired in JPEG, KMZ, GeoTiff, or GeoPDF formats. However, these offerings are relatively recent only becoming available in the last decade or so.

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FIGURE 2.20 NAIP compressed county mosaic SID image of Kittitas County, Washington (USA). Credit: NAIP county mosaic from U.S. Department of Agriculture, Natural Resources Conservation Service (2021).

Metadata Metadata are data concerning the content, quality, and condition of data (Federal Geographic Data Committee, 2002). Metadata therefore might be viewed as a resource one can access to understand the assumptions and limitations of a set of data (International Organization for Standardization, 2014). Our focus in this chapter is on how this concept applies to GIS databases. Metadata in this sense are information that can be used to describe the development, and the components of GIS databases and allow users of spatial data to determine whether the data suit their needs. There are four main roles that metadata play: (1) to describe data availability, (2) to describe the fitness of the data for certain needs, (3) to describe access opportunities and restrictions, and (4) to describe processes for transfer and use of the data (Federal Geographic Data Committee, 1998). Specifically, metadata includes information on where and when the data were created or updated, at what scale these data were developed, which coordinate and projection system was used, and many other values pieces of information concerning a GIS database, including







Who created the GIS database? Who modified the GIS database? What was the original purpose for the GIS database? What sources of data were used to create the GIS database (what is the lineage)? What is the spatial domain (extent) of the data? What is the status of the data (e.g., complete, in progress, planned)?

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What types of features are contained in the GIS database? What is the quality of the data (logical consistency, completeness)? What is the accuracy of the data (positional, attribute, etc.)? What is the spatial and spectral resolution of the data (for raster GIS databases particularly)? What keywords best describe the GIS database? What are the attributes and their data types? What handling restrictions apply to the GIS database (e.g., top secret, restricted use, unclassified)? When was the GIS database created or edited? Where was the GIS database created? Where can the GIS database be accessed? Where do the people work who created the GIS database? How was the GIS database originally created? How was the GIS database edited or modified? Why was the GIS database created? Why was the GIS database edited, modified, or updated?

Metadata can contain explanations of the fields that are included in an attribute table, and any constraints associated with how the data should or should not be used. The metadata may therefore provide direction concerning the allowable values for information contained in the attribute table and the overall database design that was used to develop a GIS database. Metadata are very important for determining the validity of the GIS database that one might acquire from a government agency or other organization. If one were to share the GIS databases that they created, the development of metadata can be invaluable to the users of these GIS databases. Further, GIS databases that can be downloaded from the Internet are more discoverable when metadata are associated with them. Inspection 2.5 Using the Internet, navigate to this book’s website (gis-book.uga.edu). Review the metadata associated with the forest compartments in the Allegheny National Forest. When was the compartment GIS database published? What data model was used to represent the compartments? What other information is available in the metadata? If one were to access the references mentioned in this section of the chapter, you will see that metadata standards have been developed by national-level committees and working groups concerned with the issues of data access, transfer, fitness, and availability. These standards are often used by governmental agencies in the United States. However, while metadata are extremely helpful, nongovernmental entities and individuals are not required to develop metadata for the GIS databases that they create.

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Conclusions Foresters and natural resource management professionals make decisions regarding the condition and character of a landscape. These decisions, ideally, are based on good data. We are fortunate today to have computer systems and software to allow tabular data (e.g., field measurements) to be integrated and associated with spatial data (e.g., digital maps), which provides an assurance of efficiency and repeatability that would otherwise be unavailable if we still were hand-drawing maps and computing resource values with a calculator. Raster and vector data models are the two most common types of spatial data used by foresters and natural resource management professionals. Issues of resolution, format, precision, accuracy, and error are of great interest, as these may provide a level of confidence in the information developed to guide decisions, or they may suggest improvements in data are necessary before a decision should be made. The data file types that are used by natural resource professionals are also important, since data sharing, and distribution are becoming more common. Integration of a GIS database acquired or created, with other standard GIS databases within an organization is critical to maintain consistency and to assure users of the data that positional (e.g., georeferencing), and other issues related to accuracy, precision, and errors are minimized. Exercises exploring the concepts covered in this book using ArcGIS or QGIS are available on the book website: gis-book.uga.edu.

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Rae, C., Rothley, K., Dragicevic, S., 2007. Implications of error and uncertainty for an environmental planning scenario: a sensitivity analysis of GIS-based variables in a reserve design exercise. Landscape and Urban Planning 79, 210e217. Stanislawski, L.V., Dewitt, B.A., Shrestha, R.L., 1996. Estimating positional accuracy of data layers with a GIS through error propagation. Photogrammetric Engineering & Remote Sensing 62 (4), 429e433. Sutton, F., Gonzales, R., 1999. Field Data Collection Equipment Guide. U.S. Department of Agriculture, Forest Service, Technology & Development Program, San Dimas Technology and Development Center, San Dimas, CA, 9977 1203-SDTDC. Unicode, Inc., 2020. Unicode 13.0.0. http://www.unicode.org/versions/Unicode13.0.0/ (accessed 18.12. 21). U.S. Department of Agriculture, Forest Service, 2016. Recreation Opportunity Spectrum. Cibola National Forest. www.fs.usda.gov/Internet/FSE_DOCUMENTS/fseprd510479.pdf (accessed 18.12.21). U.S. Department of Agriculture, Forest Service, 2021. Chippewa National Forest Geospatial Data. www.fs. usda.gov/main/chippewa/landmanagement/gis (accessed 18.12.21). U.S. Department of Agriculture, Natural Resources Conservation Service, 2021. Geospatial Data Gateway: Direct Data/NAIP Download. https://datagateway.nrcs.usda.gov/GDGHome_DirectDownLoad.aspx (accessed 18.12.21). U.S. Department of the Interior, Geologic Survey, 2000. USGS GeoData. U.S. Geologic Survey, Reston, VA. U.S. Department of Interior, National Park Service, 2021. Yellowstone National Park Map. www.nps.gov/ yell/planyourvisit/maps.htm (accessed 18.12.21). U.S. Library of Congress, 2017. Sustainability of Digital Formats: Planning for Library of Congress Collections, Introduction to Geospatial Resources and Formats. www.loc.gov/preservation/digital/ formats/content/gis_intro.shtml (accessed 18.12.21). U.S. Library of Congress, 2020. Sustainability of Digital Formats: Planning for Library of Congress Collections, PNG, Portable Network Graphics. www.loc.gov/preservation/digital/formats/fdd/ fdd000153.shtml (accessed 18.12.21). Welch, T.A., 1984. A technique for high-performance data compression. Computer 17 (6), 8e19. Wing, M.G., Bettinger, P., 2008. Geographic Information Systems: Applications in Natural Resource Management. Oxford University Press, Don Mills, Ontario.

3 Reference systems Introduction The act of describing a position on Earth, relative to other positions, is possible through map reference systems. Map reference systems, formed by the selection of a coordinate system, a projection system, and a datum assist us in referencing where we are on Earth. Over the last several centuries, many reference systems for maps have been developed to best represent features of an ellipsoid (the Earth) on a flat surface such as a computer screen or piece of paper (Arnaud, 2013). The goal of these efforts is to minimize error between the actual surface of the Earth and the estimated surface from the reference system used. The goals and objectives of creating and using geographic information system (GIS) databases play an important role in choosing the appropriate coordinate and projection systems that are needed to present geographic features (Yildirim and Kaya, 2008). The physical representation of a GIS database depends on these and other economic, strategic, and organizational constraints (Nagaraj and Stern, 2020). Much to the consternation of cartographers, no special certification is needed to become the creator of geographic works (maps); however, the professionalism of the creator is reflected in the quality of the product they create. This is also especially important today as many maps are being created and delivered electronically through Internet services where speed and usability are viewed as a priority often at the expense of positional accuracy (Favretto, 2014).

Ellipsoids, datums, geoids Just how does one describe in a GIS database the irregular surface of the Earth? From a horizontal perspective (feet on the ground), it’s known that the surface of the Earth is generally not smooth. Maps represent the Earth’s irregularities on a flat surface. Many kinds of maps can be developed, but meaningful maps are related to the terrain in which the land described is found (Nagaraj and Stern, 2020). To precisely locate places on Earth, we need to adopt a modeldan ellipsoid, datum, or geoiddthat describes the Earth’s surface. As was suggested, the Earth is an ellipsoid which is essentially a sphere, but not a perfectly round sphere, as it is pressed down or flattened by about 20 km (Bettinger and Wing, 2004) at the poles. The North and South Poles of Earth in this model are slightly compressed; thus, there is a slight bulge of the model around the Equator. Fortunately, our best model of Earth suggests that it is a closed three-dimensional surface that is relatively symmetrical in nature. If one were to slice an ellipsoid Geographic Information System Skills for Foresters and Natural Resource Managers. https://doi.org/10.1016/B978-0-323-90519-0.00002-9 Copyright © 2023 Elsevier Inc. All rights reserved.

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perfectly straight through, the edge created would either be a circle or an oval. A reference ellipsoidal shape of the Earth attempts to closely emulate the dimensions of a geoid, which is the shape formed with respect to some theoretical undisturbed mean sea level surface of the oceans (U.S. Army Corps of Engineers, 1996) (Fig. 3.1). A geoid represents the shape of the Earth at mean sea level where water bodies exist, and the theoretical extension of this under areas of land. A geoid is a refinement on an ellipsoid, it is irregularly shaped since it attempts to approximate mean sea level in a manner perpendicular to forces of gravity caused by variations in the density of the Earth (Bettinger and Wing, 2004). While in some cases, the ellipsoid or geoid models may adequately describe the shape of the Earth (Arnaud, 2013), a datum can be used as a basis for the construction of these theoretical constructs or as a reference point or reliable surface upon which a coordinate system emanates. A datum is developed by creating a mathematically smooth model of the Earth’s surface from a large volume of Earth surface measurements (Bettinger and Wing, 2004). In addition to several global datums, there are hundreds of local datums that represent national systems of reference points (Baumann, 2019). Through the discussion so far, we have tacitly suggested these are developed to represent horizontal positions on Earth, yet vertical datums also exist to provide a basis for elevations (Loomis, 1961). The World Geodetic System of 1984 (WGS 84) is a global datum that acts as both an ellipsoid model and a datum; its datum point of origin is the center of mass of the Earth. WGS 84 is one of the best geodetic models for representing places on Earth (National Geospatial-Intelligence Agency, 2014) and is employed by the global positioning system (GPS) program of the United States. Other broad area, but local, datums include the North American Datum of 1927 (NAD 27), the North American Datum of 1983 (NAD 83), the South American Datum of 1969 (SAD 69), the European Datum of 1950, and the European Terrestrial Reference System of 1989 (ETRS 89). Each of these local datums uses a prime meridian (north-south reference line) that runs through Greenwich, England, but they may be used in conjunction with different ellipsoid models (Bettinger et al., 2020), and their datum points of origin can include the center of mass of the Earth or some other place. In the future a new global datum, the North American-Pacific Geopotential Datum of 2022 (NAPGD 2022), may provide a better representation of Earth’s gravity field (Baumann, 2019). This datum is being developed by the U.S. National Geodetic Survey and the Canadian Geodetic Survey to replace the NAD 83 datum (U.S. Department of Commerce, National Oceanic and Atmospheric Administration, 2021). FIGURE 3.1 A graphical representation of the relationship between an ellipsoid, geoid, and the Earth’s surface.

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Inspection 3.1 On the website associated with the book (gis-book.uga.edu), find the Mount Shasta, California 1:24,000 scale, 7.5-minute U.S. Geologic Survey (USGS) map that was developed in 2018. Alternatively, the topoView website application developed by the USGS should be of great assistance. What horizontal datum was used to describe the shape of the Earth when developing this map? Compare this to the 1986 version of the topographic map for this area. Which horizontal datum was used in this case?

Projection systems With respect to mapping, a projection is a systematic transformation of positions from an irregularly shaped surface, the Earth’s surface, to a mathematically derived surface. More technically, map projections are mathematical systems that convert threedimensional positions on Earth to two-dimensional positions on a flat surface. In essence, a projection involves taking places on Earth and extending them outward (projected) onto a plane (flat surface) that then allows a map to be printed or displayed digitally on the screen of a computing device. This process of relating points from a curved surface to the coordinates of points on a flat surface is called the map projection. In this section of the chapter, several types of projection systems are described. In the next section of this chapter, the coordinate systems that these projections relate to are further explained. The selection of a projection system may be made according to organizational convention, yet the selection should be based on the goals of the project, sources of the GIS data, and the potential area, distance, and shape distortions that may result. From a broad scale perspective, some global projection systems involve meridians (north-south base lines) and parallels (east-west base lines) that can be straight or curved. When distortions in shape, area, or distance are too severe to overcome, continental or country-specific projection systems have been developed. Each projection system has its advantages and disadvantages with respect to errors in shapes, distances, areas, or directions; therefore, our main concern should be what a map projection will do to minimize or exacerbate these errors (Kuniansky, 2017). As has been recognized for quite some time, one of the main challenges of using GIS involves creating a map without significantly distorting the landscape being mapped (Deetz, 1918). Map projection systems can be classified as equal area, or equivalent, where areas (sizes of places) on Earth are relatively consistent with areas computed from the data on a flat map, although the land masses or water bodies may not be represented well. As the name suggests, equal area projections reduce the amount of distortion in areas. While there are different kinds of equal area projections, generally equal area projections try to maintain the relative sizes of areas even though they cannot completely remove distortions of mapped features (Ghaderpour, 2016; Snyder and Voxland, 1989). In mapping

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small areas, the precision of these projection systems in portraying land areas correctly can be very high, which is important since calculations of area often use projected coordinates in GIS (Yildirim and Kaya, 2008). Equivalent projections / represent areas (sizes) fairly well

Projection systems can also be classified as conformal, where the relative angles of places (representing the shapes of places) on Earth are similar to those computed from the flat map, although the sizes of these areas might not be represented well. Technically, conformal projection systems preserve the relative angles that describe the relationship between real, mapped features and a model of the Earth (Ghaderpour, 2016). If one were interested in a projection system that portrayed features as closely as possible to their original form (shape), a conformal projection might be selected. These projection systems are important alternatives to consider particularly if a rectangular coordinate system, such as the Public Land Survey System (PLSS) of the United States (described shortly), is employed, as they tend to represent the true shapes of landscape features better than other systems (Robinson, 1974). Conformal projections / represent local angles (shapes) fairly well

Finally, an equidistant projection could be used when the relative distances along major lines of longitude and latitude are important, such as maps used for navigational purposes requiring the scale to be preserved. As opposed to the equal area and conformal projections, this characteristic of projection systems may only be successfully applied to geographic data that represents limited land areas (Snyder and Voxland, 1989). Equidistant projections / represent scale (lengths) fairly well

With these general characteristics of projections in mind, we will discuss cylindrical projections that extend places from the surface of the Earth onto a cylinder (think of a piece of paper wrapped around a soccer ball without deforming the paper), conic projections that extend places from the surface of the Earth onto a cone (imagine a cone placed over a soccer ball), and the more obtuse azimuthal projections that project the surface of the Earth onto a plane. From our examination of projection modification software, we found that there are at least 200 map projections one can assign (Schmunk, 2021; Jung, 2021). Therefore, in the following sections of this chapter, we only describe a few of the more common choices.

Cylindrical projections Cylindrical projections take positions on Earth and project them to positions on a cylinder (Fig. 3.2). When the cylinder is cut and laid flat, Earth is represented in a rectangular model. Here, the North and South poles are not represented as a single location, but as a line across the top and bottom of a map. A line of tangency exists between the sphere representing Earth and the cylinder model, and it is found along the Equator. Everywhere else on Earth does not touch the cylinder model and is therefore projected onto the cylinder.

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FIGURE 3.2 Conceptual model of tangent (left) and a secant (right) cylindrical map projections. Credit: Bettinger et al. (2020). Earth image from the National Aeronautics and Space Administration (2000).

With this projection, the meridians (north-south reference lines) are projected onto a cylinder as straight and equally spaced lines, while the parallels (east-west reference lines) are also straight yet unequally spaced. The meridians and parallels represent a constant amount of change in degrees (e.g., every 30 change in latitude and longitude). The meridians seem equally spaced apart; the parallels are not. The Mercator map projection (Fig. 3.3) is a conformal cylindrical system and perhaps the most well-known example of

FIGURE 3.3 A Mercator projection of the conterminous United States. Credit: Boundary data from the U.S. Department of Agriculture, Forest Service (2021).

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these (Snyder, 1987; National Geospatial-Intelligence Agency, 2014). As you may notice, while shapes of land areas seem to be represented well (since this is a conformal system), the size of the land areas becomes distorted and the areas are not consistently measured, the closer one moves toward the poles. Nonetheless, the Mercator system is often used in the development of bathymetric charts and for ocean navigation purposes (Defense Mapping Agency, 1990). The Transverse Mercator map projection is a conformal system similar to the Mercator system, yet it uses a meridian as a line of tangency with the cylinder model. In other words, the Equator (an east-west line) is not the line of tangency for this model, but rather some chosen meridian (a north-south line) is selected. More technically, the cylinder on which the Earth is projected has been rotated 90 (Defense Mapping Agency, 1990). This map projection system is used in association with the Universal Transverse Mercator (UTM) coordinate system (described later in this chapter). The Web Mercator map projection is used commonly with Internet-based mapping applications. This system is a simplified spherical variant of the Mercator system, and thus is nonconformal (National Geospatial-Intelligence Agency, 2014). The Web Mercator projection includes errors in positioning, area, and distance that increases the closer one gets to the polar areas of Earth, as a result of trade-offs needed for delivery speed (Favretto, 2014). Thus, positional error increases as one moves farther away from the Equator (Battersby et al., 2014). As might be noted in many maps presented on television or the Internet (Fig. 3.4), when using some form of the Mercator projection, areas located farther away from the Equator are represented larger than areas located closer to the Equator, which can inadvertently suggest greater size to those areas located at greater latitudes (Nagaraj and

FIGURE 3.4 Locations of active fire incidents, January 26, 2021, as displayed for the United States and parts of Mexico and Canada on a map using the Web Mercator projection. Credit: National Interagency Fire Center (2021).

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Stern, 2020). For example, it is common for Greenland to look larger than the continent of Africa when using a Mercator projection when the opposite is true. There are a few pseudocylindrical projections that may be of interest where the meridians and parallels are represented as elliptical or circular arcs. Two equal area pseudocylindrical projections are the Eckert IV and the Eckert VI, which were introduced over a century ago. These have been used to represent global issues on a map such as climate (Bettinger et al., 2020). Another is the Mollweide map projection, where the North and South poles are presented as points, rather than extensions of meridians that extend off the top and bottom of a map.

Conic projections Conic projections take positions on Earth and project them to positions on the surface of a cone (Fig. 3.5). When the cone is cut along a meridian and laid flat, the meridians (northsouth reference lines) are usually represented as straight lines, yet they radiate outward from the relative position of one of the poles. The parallels are arcs or curved lines when using this projection system, and they may be evenly spaced apart in some cases (Snyder, 1987). A tangent conic model involves simply resting the cone on the surface of the Earth, resulting in a single line of tangent around the globe (a standard parallel). To help illustrate, picture an ice cream cone where the scoop touches the cone. The cone’s edge delineates the standard parallel. A secant conic model involves the cone intersecting the surface of the Earth along two standard parallels. Areas on Earth that are represented with the least distortion when using these models are those located near the standard parallels. For representing temperate landscapes, conic projections may be the easiest to construct (Snyder, 1978). One secant model is the Albers Equal Area Conic map

FIGURE 3.5 Conceptual model of tangent (left) and a secant (right) conic map projections. Credit: Bettinger et al. (2020). Earth image from the National Aeronautics and Space Administration (2000).

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projection (Fig. 3.6), which is common among maps developed today. The Equidistant Conic map projection can be presented as either a tangential or secant model, so when two standard parallels are employed, it is the latter of these. Other models include the Lambert Conformal Conic map projection system and the Lambert Equal Area Conic map projection. Pseudoconic projections have curved meridians that converge at the North or South poles. The Bonne map projection is an example often used for atlas maps.

Azimuthal projections Azimuthal projections take positions on Earth and project them to positions on a plane that is tangent to one of the poles of Earth (Fig. 3.7). Here, the goal is to preserve relative directions and angles as they are viewed from some central point on a map (Bettinger et al., 2020). In using this type of projection system, the meridians are usually straight lines, while the parallels are arcs or complete circles that are centered on one of the poles (Snyder, 1987). As with a conic projection, the meridians radiate outward from one of the poles. Often, the parallels are equally spaced when using this map projection system. The Gnomonic map projection is an example of an azimuthal projection where the Earth touches a plane at a single point nowhere in particular. When they are illustrated, the great circle lines of Earth (any circle that divides Earth into two equal halves) are straight when using this projection (Williams and Ridd, 1960). Other azimuthal projections include the Azimuthal Equidistant map projection (used by ancient Egyptians for star charts), the Orthographic map projection (which may look like the globe), the Stereographic map projection (considered conformal) (Fig. 3.8), and the Lambert Azimuthal Equal Area map projection (considered equal area).

FIGURE 3.6 An Albers equal area conic projection of the conterminous United States. Credit: Boundary data from the U.S. Department of Agriculture, Forest Service (2021).

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FIGURE 3.7 Conceptual model of the Gnomonic Azimuthal map projection using Earth as viewed from the Apollo 17 mission. Credit: Bettinger et al. (2020). Earth image from the National Aeronautics and Space Administration (2000).

FIGURE 3.8 A Stereographic projection of the conterminous United States using a 42 latitude of origin. Credit: Boundary data from the U.S. Department of Agriculture, Forest Service (2021).

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Inspection 3.2 Access the Public Library of Science (plosone.org) and locate the paper “Changes in the Potential Multiple Cropping System in Response to Climate Change in China from 1960 to 2010” by Luo Liu and colleagues which was published in 2013. Which projection system was the GIS data transformed into? Why do you think that they selected this projection system?

Coordinate systems One definition of the word coordinate (with an emphasis on “ate”), when used as a verb, represents the act of bringing together various things (or people, etc.) so that some relationship among them is either made more efficient or harmonious. This is not exactly the subject of this section of the book, but in essence a coordinate system does bring things (positions on Earth) into harmony. The more precise definition of coordinate for the purpose of this book is that the word coordinate (with an emphasis on “at”), when used as a noun, is a set of numbers that represent a position on Earth. As mentioned in Chapter 2, the description of a position involves either a pair of numbers (X,Y) or a triplet (X,Y,Z), where X usually represents a direction east or west, Y usually represents a direction north or south, and Z usually represents a direction up or down in elevation from some base. Ideally, a set of coordinates is unique and allows a person to find, describe a place, or navigate between places. In the case of latitudes and longitudes, the values for northings (latitude) and eastings (longitude) are unique. In the case of the UTM system, the northings and eastings need an additional piece of information, a zone, to make them unique. The measurement units of a coordinate system can be represented as feet, meters, degrees, or any other quantitative value that represents a landscape position. Within GIS, the coordinate system allows distances, directions, and areas to be calculated, but the map projection system chosen may influence the errors that exist between the map and the Earth’s surface, and thus the selection of both coordinate and map projection systems can be important (Bettinger et al., 2020).

Geographic coordinate systems A coordinate system that uses degrees of latitude and longitude to represent places on Earth is often referred to as a geographic coordinate system, or alternatively a geodetic coordinate system. The use of latitudes and longitudes as a coordinate system on projected maps was a direct outcome of navigation using astronomy and early mathematical techniques (Nagaraj and Stern, 2020). This type of coordinate system is quasispherical (since the Earth is not a perfect sphere), where the coordinates are represented as angles projected from the center of the Earth. Latitudes represent positions north or south of the Equator (Fig. 3.9). At the Equator, one would have 0 latitude; at the poles, one would have 90 latitude north or south. These are angles projected from the center of the Earth to the surface of the Earth, using the Equator as the base edge. Lines that can be drawn around the Earth at the same

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FIGURE 3.9 Lines of 0 latitude (the Equator) and 0 longitude (the Prime Meridian). Credit: World countries from E Pluribus Anthony through Wikipedia Commons (https://commons.wikimedia.org/File:BlankMap-World-noborders.png).

latitude (e.g., the Tropic of Cancer or the Tropic of Capricorn) and which are parallel to the Equator are called parallels (east-west lines of constant latitude). Longitudes represent positions east or west of some reference point, which is commonly the prime meridian, a straight north-south line that passes through Greenwich, England. Longitudes range from 0 to 180 , or halfway around the Earth. Depending on a place’s position relative to the Prime Meridian, they are either said to be east (E) or west (W) of the Prime Meridian. Lines that can be drawn half-way around the Earth at the same longitude are called meridians. On a map that uses this coordinate system, the graticule is a grid formed usually by the equal spacing between lines of latitude and longitude. Diversion 3.1 Which major United States city is located 90 west, in longitude, from the prime meridian in Greenwich, England? Diversion 3.2 Imagine that you are using Google Earth or some global mapping program like it and you want to view the Ohio State University football stadium and parking opportunities in the area around it. Using the latitude/longitude system, is the stadium located at 40.0017 North/83.0197 East or 40.0017 North 83.0197 West? If you used the wrong “east” or “west” designation for the longitude, where do you end up on Earth? In describing the location of a specific place, a latitude and longitude coordinate pair that is very precise may be necessary. Latitudes are normally presented before longitudes in these systems. For example, the location of the Skaneateles Country Club in the Finger Lakes region of New York State is 42.9311 north, and 76.4268 west (sometimes noted as latitude 42.9311, longitude 76.4268). These coordinates are presented in decimal

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degrees. Alternatively, latitudes and longitudes can be presented in degrees, minutes, and seconds. Like a clock, there are 60 min in 1 degree, and 60 s in 1 minute. So, 42.9311 is the same as 42 and 55.866, (0.9311  60 min ¼ 55.866 min). Further, 55.866 min (written as 55.866’) is the same as 550 and 51.96 s (0.866  60 s ¼ 51.96 s), or 51.9600 . So, in essence, 42.9311 is the same as 42 550 5200 . Diversion 3.3 What latitude and longitude would you use to describe the position of the top of Mt. Rainer in the state of Washington? Translation 3.1 If a certain place had geographic coordinates of 38.8895 north and 77.0352 west, how would these be presented in degrees, minutes, and seconds?

Projected coordinate systems Understanding the exact position of landscape features is important in both practice (property lines, mineral deposit boundaries) and research. The Earth is a rather large ellipsoid, and so for small areas, we can assume that it can adequately be projected onto a flat plane. North-south meridians and east-west base lines are often used in plane coordinate systems as Cartesian axes around which Cartesian coordinates are used to reference positions on Earth. In these systems, there is an origin where the coordinates begin, and all coordinates are positive. However, in general, coordinates are represented by X,Y pairs of numbers that can have a sign (positive or negative) to indicate their relative position from the system’s origin (where X ¼ 0 and Y ¼ 0). Northings (distances north of south of the origin) describe the Y-value, and eastings (distances east or west of the origin) describe the X-value. As has been mentioned earlier, a third value (Z) may be used to represent elevations. Since some of these values might be negative, which can be confusing, the use of a false origin is common. A false origin ensures that all coordinates within the system are positive, and it does this by moving the origin point with the assistance of a false easting and a false northing. A false easting adds a certain amount of distance to each easting (shifting the origin to the west) to prevent any X-value from becoming negative. A false northing adds a certain amount of distance to each northing to prevent any Y-value from becoming negative. It is possible that the origin may be far enough to the west (or located on the western edge) and south (or located on the southern edge) to avoid having to use one or the other.

Universal transverse mercator system The UTM system is a plane coordinate system based on the Mercator projection system, and among the most commonly used coordinate systems. The UTM system is a world-wide, metric system that essentially describes places on Earth in 60 different zones that are 6 wide at the Equator. All 60 zones taper beginning wide at the Equator

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and becoming narrower as they approach the poles where they nearly converge (the limits are 84 north latitude and 80 south latitude). An example of the UTM zones covering the United States can be found in Fig. 3.10. In the UTM system, the Y-value for the northings depends on whether one is interested in the Northern or Southern Hemisphere. In the Northern Hemisphere, the northings begin at the Equator, and all Y-values to the north represent meters north of the Equator. For the Southern Hemisphere, the Y-values begin at 10,000,000 m at the Equator and decrease in value toward the South Pole. This value (10,000,000 m) can be viewed as a false northing for the Southern Hemisphere. The eastings (X-values) in the UTM system are centered at a value of 500,000 m in the middle of each zone, or the central meridian of each zone. Interestingly, a zone is roughly 668,000 m wide at the Equator, and roughly 498,000 m wide at Chicago. Therefore, all eastings within a zone have positive values, since the central meridian of each zone has an easting of 500,000 m. In other words, given the easting applied to the central meridian and given the width of a UTM zone, the left side of a typical UTM zone does not begin with an easting that is less than 0 m. Thus,

FIGURE 3.10 UTM zones covering the conterminous United States. Credit: U.S. Geological Survey through Wikimedia Commons (https://commons.wikimedia.org/wifi/File:UTM_Zones_AK.jpg), land cover data from the U.S. Department of the Interior, Geological Survey (2018).

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when a certain zone is used in developing a GIS database, it is possible to include locations of landscape features that fall nearby in adjacent zones (Bettinger et al., 2020). Reflection 3.1 Consider the UTM system, and the slicing of the Earth into 60, 6 zones. If you were charged with changing the system, what would you change, and why would you suggest changing it? There are many examples of the use of the UTM system for the development of GIS databases denoting the location of landscape features. For example, GIS databases representing the ownership boundaries, recreation areas, roads, and wilderness areas of the Monongahela National Forest in West Virginia have been developed using the UTM system (West Virginia GIS Technical Center, 2021), as have the databases for most, if not all, of the national forests in the United States. For another more specific example, in an area of about 30 acres (about 12.5 ha) near Baltimore, Maryland, the locations of all of the live deciduous trees were denoted with a point location (a single set of X,Y coordinates) by Dandois et al. (2015) using a UTM projected coordinate system to describe these positions. Knowledge of the system employed and the coordinates that describe the location of each tree may allow others to easily find the trees in this forest. Translation 3.2 A friend of yours has a summer job at the Calamus Reservoir and State Recreation Area in Nebraska. Just having completed their first year of college, they are not as savvy as you are with mapping, particularly projections and coordinate systems. Through a text message, they ask “I need some help. I was out on the reservoir yesterday, and my GPS receiver said my location was 481,800 m E and 4,631,700 m N. What does this mean?” Briefly explain to them the UTM system. They also noted that their GPS position shows up about 3.5 miles west of Baxter, Iowa when viewed in GIS, which is wrong. Briefly explain to them what you think the issue may be. Diversion 3.4 In the UTM system, the northings represent the distance (in meters) north of the Equator in the Northern Hemisphere. The eastings represent an east-west orientation within a specific UTM Zone. Imagine you collected the positions of some urban trees around 330,000 m E and 4,692,000 m N in UTM Zone 17. Unfortunately, you assigned UTM Zone 19 to the data. If someone were to use your urban tree database, they might be confused by the fact that your trees, which should be located in _____ seem like they (erroneously) belong in ______.

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US state plane system The state plane system of the United States is essentially a planar (flat surface) coordinate system designed for each state or part of each state. For example, Oregon has both an Oregon North and an Oregon South area using this system (Fig. 3.11). In these systems, the Earth, represented as an ellipsoid, is projected onto a plane (flat surface) using either the Lambert Conformal (for states or parts of states that are longer in a northsouth direction) or Transverse Mercator (for states or parts of a state that are longer in an east-west direction) projections which preserve angles and shapes (Sincovec, 2008). In the state plane systems, a baseline (east-west line) and a meridian (north-south line) are established, and coordinates are represented as distances from these. In using a state plane coordinate system in the United States, one certainly needs to know in which of the 125 different plane coordinate systems the coordinates should reside.

FIGURE 3.11 Map of U.S. State Plane systems. Credit: U.S. Department of Commerce, National Oceanic and Atmospheric Administration (2018).

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Other coordinate systems Metes and bounds surveys In general, any survey of a property boundary that consists of distances and directions is a form of metes and bounds survey. In this type of survey, measures of distance and direction are called metes and localized descriptions of property boundaries are called bounds, hence metes and bounds. When we refer to these systems in this book, we are referring to the irregular parcel shapes and sizes that arise from headright and other ad hoc systems that were employed before the development of large-scale organized systems such as the United States Public Land Survey System (PLSS). The use of a metes and bounds approach to the development of coordinates is therefore often a localized, decentralized process (Bettinger et al., 2020). While they have been used throughout the world, metes and bounds methods for describing property boundaries were prevalent among areas of North America settled during European Colonization. French, Spanish, British, and other governments employed various survey methods to facilitate landownership during the colonization of the New World. If one looks closely at some of the land use patterns along waterways, one might see evidence, for example, of a long lot approach that was employed (Fig. 3.12) to ensure each landowner had access to water systems for commerce and travel. And, if one looks closely at some of the land use patterns in the states along the eastern coast of the United States, one might see evidence of headright systems employed, as expressed through the irregular shape of land parcels (Fig. 3.13).

FIGURE 3.12 Evidence of long lots around an old oxbow of the Mississippi River, near Baton Rouge, Louisiana. Credit: U.S. Department of Agriculture, Natural Resources Conservation Service (2021b).

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FIGURE 3.13 Evidence of metes and bounds survey, near Cambridge, Maryland. Credit: U.S. Department of Agriculture, Natural Resources Conservation Service (2021b).

US public land survey system In the early years of national independence in the United States, a land survey system championed by Thomas Jefferson was applied to survey and otherwise describe areas of land outside of the original states along the eastern coast and including Tennessee and Kentucky. For most states west of, and including, Ohio, Alabama, and Missouri, the PLSS was used as a coordinate system for describing lands. Texas is an exception, where parts of this large state employed a system similar to the PLSS. Maine is another exception, where a system similar to the PLSS was employed in the northern part of the state. Much of Florida was also described using the PLSS. Further, all lands that were previously surveyed under other systems (e.g., the French seigneurial system which resulted in long lot areas along waterways) generally do not have the PLSS superimposed upon them. Therefore, in many U.S. states, one might find a mixture of land survey systems. There are 37 principal meridians in the PLSS, and each has a baseline. Some states have a single meridian and baseline around which the lands are surveyed. Other states share a baseline and meridian (e.g., Oregon and Washington), while others have more than one set of baselines and meridians (e.g., California). Initially, tracts of land that are 24 miles square are surveyed around the baselines and meridians. This was an effort to reduce the effects caused by the curvature of the Earth. Within each tract, townships were then surveyed, each 6 miles square. A township is denoted by its distance and direction from the baseline and meridian. For example, a township might be located on the 24th row of townships above a baseline and in the seventh column of townships to the east of a meridian. The designation for this township would be Township 24 north

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Range 7 east (or T24N R7E). In describing townships in this manner, no two townships would have the same descriptor within a single baseline/meridian system. Within a township, 36 sections that are theoretically one square mile are surveyed. Ideally, the sections contain 640 acres (1 mile  1 mile ¼ 80 chains  80 chains ¼ 6400 chains2 ¼ 640 acres). The sections are numbered beginning with Section 1 in the upper right corner of a township and ending with Section 36 in the lower right corner (Fig. 3.14). The sections in between are numbered in a serpentine manner, and with the exception of some of the original tests of the system in Ohio, this is the manner in which all townships are designed. Section 27 of Township 24 north Range 7 east would have the designation Section 27 T24N R7E. Inspection 3.3 Access the Placitas (New Mexico) quadrangle map that is available on the book’s website (gis-book.uga.edu). Alternatively, this map can be accessed through topoView, a service hosted by the USGS. The Placitas area is northeast of Albuquerque. In which township and section would you find Ranchos de Placitas?

FIGURE 3.14 Township 24 north, Range 7 East in Michigan, USA. Credit: PLSS boundary data from the U.S. Department of Agriculture, Forest Service (2021).

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Sections can be further subdivided into quarter sections, which ideally would contain 160 acres. The southeast quarter of Section 27 of Township 24 north Range 7 east would have the designation SE 1/4 Section 27 T24N R7E (Fig. 3.15). In these designations, the smallest subdivision is located on the left side. In other words, SE 1/4 is the smallest subdivision, and Section 27 is the next smallest subdivision of the township defined by T24N and R7E. Sections can be further subdivided (Fig. 3.16), and these may result in halves or quarters of the previous piece. For example, SW 1/4 SW 1/4 Section 27 T24N R7E ¼ southwest quarter of the southwest quarter of the section, or 40 acres. N 1/2 NW 1/4 Section 27 T24N R7E ¼ northern half of the northwest quarter of the section, or 80 acres. E 1/2 Section 27 T24N R7E ¼ eastern half of the section, or 320 acres. Canadian Dominion Land Survey Within the provinces of eastern Canada, the arrangement (shape and size) of land parcels is much like that of the northeastern United States. Metes and bounds and other systems that originated with the British and French governments prompted this design (Parson, 1977). For provinces of western Canada, with the exception of Yukon, Northwest FIGURE 3.15 SE 1/4 of Section 27 T24N R7E, Iosco county, Michigan, USA. Credit: PLSS boundary data from the U.S. Department of Agriculture, Forest Service (2021).

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FIGURE 3.16 Parts of SE 1/4 of Section 27 T24N R7E, Iosco county, Michigan, USA. Credit: PLSS boundary data from the U.S. Department of Agriculture, Forest Service (2021), base map from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021a).

Territories, and Nunavut, a system similar to the PLSS of the United States was employed. In contrast to the PLSS, this system had allowances of land for the development of roads. This Dominion Land Survey (DLS) was designed around seven northsouth meridians. Along the first baseline, township numbering for a row of townships begins at the border with the United States (49th parallel of latitude) and proceeds northward. Subsequent baselines are developed every 24 miles further north in latitude to adjust for the curvature of the Earth. Range numbers begin at one of the seven meridians and proceed to the west. In terms of describing them, Township 37 (north) Range 16 (west) of the third prime meridian in Saskatchewan and would be labeled 37-16-W3. Each township in the DLS is divided into 36 sections, and like the PLSS they are each about one mile square. However, in the DLS, section 1 is located in the southeastern corner and section 36 is located in the northeastern corner (Fig. 3.17). Section 15 of Township 37 Range 16 of the third prime meridian in Saskatchewan would be labeled 15-37-16-W3. Quarter sections (about 0.5  0.5 miles, or 160 acres) can then be developed within a section. The southeastern quarter of Section 15 of Township 37 Range 16 of the third prime

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meridian in Saskatchewan would be labeled SE-15-37-16-W3. As with the PLSS, DLS sections can be further subdivided. For example, quarter-quarter sections (about 0.25  0.25 miles, or 40 acres) can further be developed within a section. These are called legal subdivisions and they have a unique numbering system within a section (Fig. 3.18). Legal subdivision eight of the southeastern quarter of Section 15 of Township 37 Range 16 of the third prime meridian in Saskatchewan would be labeled LSD8-15-37-16-W3.

Conversion between systems A conversion between projection systems, coordinate systems, and datums can be an issue when an organization that develops, maintains, and distributes GIS data requires a certain standard to be followed. For example, about a decade ago, the government of the Republic of Korea mandated the use of the WGS datum for digital maps, forcing users to convert from a previous standard (Oh, 2015). Coordinate conversion can involve moving from Cartesian (X,Y position values) to geographic units (degrees of latitude and longitude), or a transformation from one ellipsoid model to another (U.S. Army Corps of Engineers, 1996). In many GIS software programs, a map projection file is associated

FIGURE 3.17 Section numbering convention of a township within the Dominion Land Survey of Canada.

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FIGURE 3.18 The legal subdivisions of a section within the Dominion Land Survey of Canada.

with a GIS database. This file defines characteristics of the GIS database such as the map projection system, data spheroid model, and horizontal datum used, the type of horizontal units that the coordinates represent, and the origin and any assumed false eastings or northings (Greco, 2018). Since many projection and coordinate systems are described by broadly applied mathematical models, it may be possible to convert from one to another. However, conversion processes have been described as troublesome and tedious for those who must change the coordinate system, projection system, datum, or combinations of these to conform to organizational or governmental policies (Oh, 2015). Additionally, a conversion process may take the inherent errors found in one GIS database and compound them in the creation of a second GIS database. Still, converting between coordinate systems is often necessary. Translation 3.3 Access the Public Library of Science (plosone.org) and locate the paper “Social sensing of urban land use based on analysis of Twitter users’ mobility patterns” by Soliman et al. that was published in 2017. In the Datasets section of the Methods and Materials, find the sentence where they reprojected the land use map. What were the coordinates of the land use map prior to the projection, and after?

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Conclusions We have described some relatively new (e.g., Web Mercator) and some widely used projection and coordinate systems in this chapter. Clearly, people have been thinking about the problem of portraying Earth’s features on flat media for quite some time. One must remember that each projection system contains some level of error with regard to distances, angles, shapes, and areas (Borisov et al., 2015). Therefore, the selection of a system needs to be assessed with the purpose of a map or GIS database in mind. For GIS databases that utilize a planar system of projected coordinates, Greco (2018) notes three possible states of a GIS database: 1. The projected coordinate system is correctly defined. 2. The projected coordinate system is incorrectly defined. 3. The projected coordinate system is undefined (the GIS database lacks a projection file). Greco (2018) also notes that one may find GIS databases in one of three similar states when geographic coordinate systems are used: 1. The geographic coordinate system is correctly defined. 2. The geographic coordinate system is incorrectly defined. 3. The geographic coordinate system is undefined (the GIS database lacks information on units). In cases where projected or geographic coordinate systems are incorrect or undefined, some (e.g., Egger, 2016) have proposed automated processes to detect the correct coordinate system. Ideally, we will one day have readily available systems that can troubleshoot these issues. In the meantime, careful consideration of the datum, and the projection and coordinate systems for a GIS database is recommended. Exercises exploring the concepts covered in this book using ArcGIS or QGIS are available on the book website: gis-book.uga.edu.

References Arnaud, J.-L., 2013. Production of georeferenced data - use, cost and accuracy. e-Perimetron 8 (2), 101e105. Battersby, S.E., Finn, M.P., Usery, E.L., Yamamoto, K.H., 2014. Implications of Web Mercator and its use in online mapping. Cartographica 49 (2), 85e101. Baumann, J., 2019. Moving from static spatial references systems in 2022. ArcUser 22 (1), 34e37. Bettinger, P., Merry, K., Boston, K., 2020. Mapping Human and Natural Systems. Academic Press, London. Bettinger, P., Wing, M.G., 2004. Geographic Information Systems: Applications in Forestry and Natural Resources Management. McGraw-Hill, Inc., New York. , M., 2015. Optimal map conic projection - a case study for the Borisov, M., Petrovi c, V.M., Vulic geographic territory of Serbia. Tehni cki Vjesnik 22 (2), 391e399.

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Dandois, J.P., Nadwodny, D., Anderson, E., Bofto, A., Baker, M., Ellis, E.C., 2015. Forest census and map data for two temperate deciduous forest edge woodlot patches in Baltimore, Maryland, USA. Ecology 96 (6), 1734. Deetz, C.H., 1918. The Lambert Conformal Conic Projection with Two Standard Parallels Including a Comparison of the Lambert Projection with the Bonne and Polyconic Projections. U.S. Department of Commerce, Coast and Geodetic Survey, 47. Special Publication No, Washington, D.C,. Defense Mapping Agency, 1990. Datums, Ellipsoids, Grids, and Grid Reference Systems. Defense Mapping Agency DMA Technical Manual 8358.1, Fairfax, VA,. Egger, M., 2016. Shapefile projection finder. Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings 16, Article 11. Favretto, A., 2014. Coordinate questions in the Web environment. Cartographica 49 (3), 164e174. Ghaderpour, E., 2016. Some equal-area, conformal and conventional map projections: a tutorial review. Journal of Applied Geodesy 10 (3), 197e209. Greco, S.E., 2018. Seven possible states of geospatial data with respect to map projection and definition: a novel pedagogical device for GIS education. Geo-spatial Information Science 21 (4), 288e293. Jung, T., 2021. Compare Map Projections. https://map-projections.net/index.php (accessed 16.12.21). Kuniansky, E.L., 2017. Custom map projections for regional groundwater models. Groundwater 55 (2), 255e260. Loomis, F.B., 1961. Subsurface geology. In: Moody, G.B. (Ed.), Petroleum Exploration Handbook. McGraw-Hill Book Company, Inc., New York, pp. 13-1e13-74. Nagaraj, A., Stern, S., 2020. The economics of maps. Journal of Economic Perspectives 34 (1), 196e221. National Aeronautics and Space Administration, 2000. Earth - The Blue Marble. https:// earthobservatory.nasa.gov/images/565/earth-the-blue-marble (accessed 16.12.21). National Geospatial-Intelligence Agency, 2014. Implementation Practice Web Mercator Map Projection, 2014-02-18, Version 1.0.0. National Geospatial-Intelligence Agency, Office of Geomatics, Springfield, VA. NGA.SIG.0011_1.0.0_WEBMERC. National Interagency Fire Center, 2021. Intterra. https://maps.nwcg.gov/ (accessed 16.12.21). Oh, H.-J., 2015. The coordinate transformation of digital geologic map in accordance with the World Geodetic System (A case study of Chungju and Hwanggang-ri sheets using ArcToolbox). Economic and Environmental Geology 48 (6), 537e543. Parson, H.E., 1977. Settlement policy and land evaluation at the turn of the twentieth century in Quebec. Area 9 (4), 290e292. Robinson, A.H., 1974. A new map projection: its development and characteristics. In: Kirschbaum, G.A., Meine, K.-H. (Eds.), International Yearbook of Cartography. Kirschbaum Verlag, Bonn-Bad Godeberg, Germany, pp. 145e155. Schmunk, R.B., 2021. G.Projector, Version 2.6.0. www.giss.nasa.gov/tools/gprojector/ (accessed 16.12. 21). Sincovec, R.J., 2008. Working with Grid Coordinates. Edward-James Surveying, Inc., Colorado Springs, CO. Snyder, J.P., 1978. Equidistant conic map projections. Annals of the Association of American Geographers 68 (3), 373e378. Snyder, J.P., 1987. Map Projections - A Working Manual. U.S. Government Printing Office, Washington, DC, U.S. Geological Survey Professional Paper 1395. Snyder, J.P., Voxland, P.M., 1989. An Album of Map Projections. U.S. Government Printing Office, Washington, DC, U.S. Geological Survey Professional Paper 1453. U.S. Army Corps of Engineers, 1996. Handbook for Transformation of Datums, Projections, Grids and Common Coordinate Systems. U.S. Army Corps of Engineers, Alexandria, VA. TEC-SR-7.

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U.S. Department of Agriculture, Forest Service, 2021. Download National Datasets. https://data.fs.usda. gov/geodata/edw/datasets.php? (accessed 16.21.21). U.S. Department of Agriculture, Natural Resources Conservation Service, 2021a. Geospatial Data Gateway: Direct Data/NAIP Download. https://datagateway.nrcs.usda.gov/GDGHome_ DirectDownLoad.aspx (accessed 18.12.21). U.S. Department of Agriculture, Natural Resources Conservation Service, 2021b. Web Soil Survey. https://websoilsurvey.sc.egov.usda.gov (accessed 16.12.21). U.S. Department of Commerce, National Oceanic and Atmospheric Administration, 2018. Final SPCS 83 (as of 2001). www.ngs.noaa.gov/SPCS/maps.shtml (accessed 16.12.21). U.S. Department of Commerce, National Oceanic and Atmospheric Administration, 2021. Naming convention. https://geodesy.noaa.gov/datums/newdatums/naming-convention.shtml (accessed 16. 12.21). U.S. Department of the Interior, Geological Survey, 2018. National Land Cover Database. https://www. usgs.gov/centers/eros/science/national-land-cover-database (accessed 19.08.22). West Virginia GIS Technical Center, 2021. Monongahela National Forest Datasets. www.wvgis.wvu.edu/ data/dataset.php?ID¼72 (accessed 16.12.21). Williams, C.E., Ridd, M.K., 1960. Great circles and the gnomonic projection. The Professional Geographer 12 (5), 14e16. Yildirim, F., Kaya, A., 2008. Selecting map projections in minimizing area distortions in GIS applications. Sensors 8 (12), 7809e7817.

4 Making maps Introduction For thousands of years, humans have employed maps as storytelling devices. A map often represents features, real or imagined, on the surface of the Earth, as they would appear to a person elevated above it at a great distance (Sullivan, 1859; Bettinger et al., 2020). Maps are representations people use to describe places and experiences within water bodies, in the sky above, or on another planet. Whether people know it or not when maps are being made the process involves cartography. Cartography blends science and art in a manner that allows one to create a map to effectively communicate a message to an intended audience (the world, the colleagues within your profession, your supervisor, and others). Cartography provides a theoretical basis for creating an effective and impactful map using a scale, coordinate systems, and other tools. It also provides a practical basis for the development of products that are aesthetically pleasing, fundamentally attractive, and engaging. Concurrent with advances in computer technology over the last 40 years, geographic information systems (GIS) now extend the ability of society to become map creators, effectively making the map creation process accessible and inclusive for all. While trained mapping professionals are still employed by various public agencies and private companies as subject matter experts, map creators no longer need to obtain specialized training in cartographic sciences to develop a professional map. We do not intend to minimize the importance of specialized training in cartography. While the science and art of cartography continue to evolve, the field will always represent the foundation for map-making. Certainly, for nonspecialists, some exposure and experience with cartographic principles are beneficial. In this chapter, we provide general information on how to engage in, and enjoy the mapping process. As storytelling devices, maps should present basic information that is both useful and informative. Knowledge of the types of maps employed, and the content they might contain can be of great value to the skill set of forestry and natural resource professionals. Diversion 4.1 A mental map is a map created through a person’s perception of a place. You have likely created a mental map in the past when providing directions to your home or workplace. From memory, draw a map of a place you are familiar with. The size of the area is one of the first decisions you must make - it can include a neighborhood, town center, county, or state. On your map, including both natural and man-made features that are important for describing the location. Include labels, street names, or anything else you might need to identify features of the area. Once Geographic Information System Skills for Foresters and Natural Resource Managers. https://doi.org/10.1016/B978-0-323-90519-0.00011-X Copyright © 2023 Elsevier Inc. All rights reserved.

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you have completed your mental map, compare it to an actual map (paper or digital) of the place. What similarities and differences are obvious? Modern technologies, which include cell phones, tablets, smart watches, and other wearables, are very innovative, recent developments in human engineering that have made maps accessible on demand. They are so ubiquitous that many of us take this for granted as we interact with maps online or through a map application. Perhaps we even use maps accessible in these technologies, in conjunction with global positioning systems (GPS), to navigate while driving our cars or while walking around an unfamiliar city. Printed or digital maps may be encountered in books, video games, television programs, movies, advertisements, and other media with which we communicate. More to the point of this book, maps are essential for describing a plot of land, research study area, timber stand, wildlife area, and any other phenomenon that is spatial in nature and important for managing forests and natural resources. Maps can be used to describe past, present, and future landscape conditions, and when offered through the Internet, can include real-time information such as weather, traffic, earthquakes, and many other dynamic events. For communicating and informing, maps can serve many purposes, such as illustrating distances between two locations, identifying orientation, providing management guidance, highlighting important resources that are near or far, and comparing the size or location of one place in relation to another. One of the most common uses of maps involves navigation across the landscape (through the woods, the wilderness, the unknown, etc.). One popular type of map is the hiking trail map (Fig. 4.1), which provides information about trail routes and might also identify areas of interest along trails, such as camping locations and scenic overlooks. Other maps, like the one illustrated in Fig. 4.2, may cover large expanses of a landscape and highlight roads, water bodies, picnic areas, and other landscape features of interest, as well as provide information to guide visitors on proper ways to interact with wildlife, cyclists, and pedestrians. How maps are constructed and what information they contain can help define their usefulness in different situations of our lives. The act of creating a map can be challenging; not only does it require time and creativity, but also the creator of a map must plan for the desired end-product. Before creating a map, it is important to identify and understand its intended audience. If the intended audience of a map includes experts with great knowledge of an area or place, the map creator will likely make different decisions about what the map contains than when the intended audience of a map includes those with little to no prior knowledge about the mapped place. For example, maps like the one shown in Fig. 4.3 of Yellowstone National Park, are designed for visitors who will travel through the park using motorized vehicles. More than likely, they have little prior knowledge of the resources located within the park; therefore, the map includes detailed information on roads, gas stations, and distances between places. The map even includes a quick response (QR) code that can be scanned for visitors to access real-time traffic information. QR codes are optical labels that can be read by an imaging device, such as a

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FIGURE 4.1 A portion of the Isle Royale National Park map. Credit: U.S. Department of the Interior, National Park Service (2016).

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FIGURE 4.2 A map of Grand Teton National Park with additional information on interacting with wildlife, cyclists, and pedestrians. Credit: U.S. Department of the Interior, National Park Service (2019a).

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FIGURE 4.3 A map of Yellowstone National Park was designed with a focus on visitors traveling through the park in motorized vehicles. Credit: U.S. Department of the Interior, National Park Service (2019b).

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smartphone, and they can also be employed to allow access to Internet resources, such as those related to this book (Fig. 4.4). Beginning with the end-product in mind may help improve the chance of the map being used by the intended audience. Reflection 4.1 Think about the last map that you made. What was the theme of the map? On which landscape or water body did it focus? How long did it take you to create the map? As communication devices, maps can often be simple, as long as they convey the appropriate message to the intended audience. In fact, in some situations it is preferable that maps are as simple as possible so that their message is not lost among other annotations, and so that the primary audience does not misinterpret their message. Imagine that there is a need to conduct a sample of vegetation found in a forest and that this sample involves navigating to the locations where sample plots will be centered (Fig. 4.5). Likely, the most important features to include on a map are the sample plot locations, the boundary of the forest to be sampled, and any nearby features (roads, streams) that will help assist the people conducting the sample to understand how to access and successfully navigate through the place. Other features, such as contour lines that indicate the relative steepness of the topography might also be useful. However, too much additional information can act to clutter the map and diminish its usefulness to the primary audience (the people who must conduct the survey). Therefore, when it seems that a map needs to be simple, perhaps the design should follow this course. Inspection 4.1 Access the KeeneEye nature trail map that can be found on this book’s website (gis-book.uga.edu). What messages are being conveyed through this map?

FIGURE 4.4 A QR code that directs your phone to this book’s website.

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FIGURE 4.5 A simple map of a proposed vegetation sample.

As communication devices, there are also instances when maps can be quite complex. Complexity might increase through the incorporation of an immense library of knowledge about a landscape or water body into a map. Consider a forest map that includes features representing elevation, soil, recreation, water, and aesthetic resources. These features may have been collected and archived by foresters and natural resource professionals, but many of these may not be relevant to the main point of a map. The temptation to include these (since they are available) could allow one to create a complex map that may have high aesthetic appeal yet limited usefulness for the intended audience as the additional features may act to detract from the main message of the map.

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While most people interact with maps that are two-dimensional representations of a landscape, maps can also accommodate and provide three-dimensional representations of land and water features. For example, three-dimensional maps derived from LiDAR (light detection and ranging) point clouds can be incorporated into static maps and dynamic computer graphics such as virtual reality or gaming environments (Xiao et al., 2020). LiDAR is a remotely sensed source of data that results in a cloud of points each having X (easting), Y (northing), and Z (vertical) coordinates. Using this additional vertical dimension, complex maps can potentially illustrate the height, volume, density, and structure of landscape features and provide a completely different perspective of forest and landscape resources to the user of the map. Another method for creating a 3dimensional view within a map involves either a digital elevation model (DEM) or a hill shade as a base layer (Fig. 4.6). A hill shade, often derived from a DEM, is a graphic that is created from elevations, azimuths, and an assumed sun angle to illustrate the Earth’s surface character. Other GIS datasets, including vector GIS databases, satellite imagery, and aerial photos, can then be draped over various versions of elevation models to further enhance the view of an area of interest (Fig. 4.7). Perhaps one might find these types of database combinations to be acceptable alternatives to the use of contour lines and elevation labels, and effective in reducing the complexity of a map. What is evident here is that when it seems a map needs to contain more complex representations of landscape and water body features, the design of the map can become challenging.

FIGURE 4.6 A hill shade model was created from a DEM covering a portion of Allegheny National Forest. Credit: Elevation data from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021a).

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FIGURE 4.7 An example of ASTER satellite imagery captured in 2001 draped over the ASTER Global Digital Elevation Model creating a three-dimensional mapping surface in the Himalayan Glacier in Bhutan. Credit: National Aeronautics and Space Administration (2009).

Diversion 4.2 Before we get too far in this discussion of making maps, take a few minutes and write down all of the landscape features you think should be included in a map that illustrates the northern spotted owl (Strix occidentalis caurina) habitat on state-managed lands in western Oregon. Consider issues of simplicity and complexity, and what a land manager might want to see in this type of map. As we have highlighted, creating a good map requires an understanding of the story the map is to tell and then applying creativity and imagination. Take, for example, an interpretation of the summit of Mount Washington in New Hampshire (Fig. 4.8). Here, the map maker has used color and perspective to reimagine a birds-eye view, 360 degree panorama of the mountain. While this map strays from more traditional interpretations of a map and lacks a scale (since scale changes continuously across the panorama), the map does include annotation of physical characteristics in the area, generous use of color, notable structures that might be found at the top of the mountain, and major cardinal directions along the map’s edge. While creativity and imagination are important, not all map makers are expected to be artists, and not all maps need an artistic flair. With increased familiarity with the map-making process, confidence will be gained, releasing and inspiring creativity. Additionally, a review of old maps, to understand how they tell a story, may provide guidance or inspiration for future map production. As illustrated by the birds-eye view of Mount Washington, the process of map creation can include considerations of perspective, color, pattern, font, and layout. Through these considerations, one can arrive at a unique way to tell a story (deliver a message) and

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FIGURE 4.8 An artistic interpretation of the summit of Mount Washington in the White Mountains of New Hampshire from a birds-eye perspective. Credit: Geo H. Walker & Co (1908).

provide an audience with a useable, informative map. However, decisions related to these considerations should always be appropriate for the intended use of the map. Two different maps, one developed to help people find attractions in Rome (Trevi Fountain, Colosseum, Pantheon, etc.) and another developed to help people find vegetation

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sampling plots in a pine stand in Choctaw County, Alabama, would likely contain different perspectives, colors, patterns, fonts, and layout. In making decisions regarding the character of a map, one can take advantage of example maps, books, and online tools to explore options. Several online tools, for example, have been developed to help one explore variations in color palates, and venture outside the confines of default colors offered in GIS software packages. Examples of such tools include Adobe Color (https:// color.adobe.com/), Color Designer (https://colordesigner.io), and ColorBrewer (https:// colorbrewer2.org). Creativity and imagination come easily for some people, yet these skills may require seasoning and development in others. For these reasons, perhaps patience is also needed when one needs to develop a good map. Inspection 4.2 Access the Geo H. Walker and Co. Mount Washington map from the book’s website (gis-book.uga.edu). List three or four features on this map you find most interesting or intriguing. Then, discuss your observations with a few of your colleagues or classmates.

Map components A map contains many components, such as a title, legend, north arrow, scale, and of course a representation of the landscape or water body of interest. Additional components one might insert into a map may include a neat line, labels and annotation, reference information, insets, graticules, disclaimers, author credit, and additional graphics (Fig. 4.9). Effort should be expended to distribute map components across the medium (page of paper, image dimension) without leaving large swaths of empty space. This act of balance helps one avoid clustering map components in one area of the medium. If map components are clustered awkwardly in certain areas of the media, map readers can be distracted from the focus and message, which centers on the landscape and water body of interest. When placing and sizing map components on the medium, it is beneficial to think of the importance of each component and design them accordingly. For example, the size of the text that represents the map title should be larger than other text to reflect its importance. Comparatively, the size of other text, such as map author credits or the date the map was created, is typically smaller.

Map title A map’s title is often placed outside the landscape or water body area of interest and is arguably the second most important map component (after representation of the actual landscape or water body). A map title should be short and pointedly describe the theme of the map. Titles commonly include the name of the area of interest being mapped. As suggested earlier, the size of the text should set the title apart from other map components. The importance of a map title in relation to other map elements can be enhanced by changing the font color, and font size, or by using bold or italicized text.

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FIGURE 4.9 A hypothetical map with several essential map components including a title, main map, north arrow, scale bar, and legend along with several optional map components.

There are no definitive rules for the type of information that should be included in a map title. Information such as place names, dates, and subject matter may be beneficial to the map reader but may not always be necessary, as these may also be presented in other places on a map. Some debate exists even among the writers of this book whether text identifying the place being mapped is always appropriate for the title of a map. If a map itself identifies a well-known place clearly, some may conclude that including its name in the title could be repetitive (Peterson, 2015). However, others may conclude just the opposite. Imagine a map that illustrates the more popular tourist attractions in Rome (Colosseum, Pantheon). The name of the place (Rome) may or may not be of value to the title of the map (“Popular tourist attractions” or “Popular tourist attractions of Rome”). If the place described by the map is not obvious (e.g., “Lincoln Tract”) then perhaps it may be wise to include its name in the title of the map. Consideration needs to be given to the type of information required by a map title that will inform the user guidance of the subject, yet not overwhelm, distract from, or

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make redundant the content of the map itself. Consider the creation of a map of several timber stands located in a national forest. The title of the map might include the compartment number where the stands are located, the feature of interest in the stands (i.e., “pine basal area” “tree height” or “stand age”) being mapped, and possibly the year that the stand data were created. By providing the year of data creation, the map reader can understand the vintage of the map, or the state of the landscape resources at a certain point in time. As these landscape features change over time, map users then may have a reference point for comparison. Translation 4.1 Access the map associated with this exercise, which can be found on the book’s website (gis-book.uga.edu). Does the title of this map reflect the content of the map? If you were to describe the content of the map to a colleague, what would you say? The final resting place of the map should also be considered when developing the map title. While a map title may always be welcome, there may be times when it is not necessary. For example, when a map is to be inserted as a figure into a report, it may not need a title, as the figure caption (e.g., “Fig. 1. Map of . ”) would serve to describe the content of the map and therefore act as a proxy for the map title. However, if a map were to be used in a class presentation or as part of a work-related endeavor, a map title would seem to be important since there would be no other way (other than orally) to convey to the audience the theme of the map. Certainly, when developing a map for use in an online environment, the importance of the map title might be high, as the audience could consist of people located anywhere around the world. In addition, in an online environment, the content of the map title could influence whether the map is accessible through Internet searches and database queries.

Orientation On a map, a sense of orientation is indicated via the north arrow (Fig. 4.10). While one can assume that the north is always oriented directly toward the top of the medium (paper, graphic) that contains a map, this assumption is tenuous, as some maps require the area of interest to be rotated to accommodate every resource or every place (Fig. 4.11). In viewing a north arrow, or a compass rose, a sense of other directions should also be evident. If north is represented at the top of the map, then . . east is toward the right side of the map. . south is toward the bottom of the map. . west is toward the left side of the map. North, south, east, and west are the cardinal directions. Finer subdivisions of these may also be of value. For example, the southeast direction is somewhere between east and south (an azimuth between 90 degrees and 180 degrees).

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FIGURE 4.10 Two different representations of orientation, through a standard north arrow (left) and a compass rose (right).

Reflection 4.2 Are there cases when a map does not need an indication of orientation? If not, why? If so, what are they? On some maps, one may find an indication of the true north, magnetic north, and the grid north (Fig. 4.12). True north represents a place on Earth where all longitude lines meet (the North Pole). Magnetic north is where compasses point, which may not be the same as true north, since, for example, the location of magnetic north in North America is not the North Pole but rather toward northern Canada. Depending on where one is standing in North America, a compass pointing to magnetic north could be 15 degrees or more off to the east or west of true north. This difference is called the declination. Grid north is similar to true north, as it acts as a geographic presentation of orientation that is employed in plane grid systems, such as the U.S. State Plane system. Diversion 4.3 Access the north arrow (orientation) map objects of your preferred GIS software program. Assuming there are a number of alternative north arrow symbols that can be selected, which one do you prefer? In a few sentences, describe why you prefer the north arrow style that you chose. Then, discuss your preference with a few of your colleagues or classmates. A graticule uses evenly spaced longitude and latitude lines on a map to provide a sense of orientation. One could assume that lines running vertically on a map would represent the north-south directions, and lines of the graticule running horizontally on a

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FIGURE 4.11 Mount Rushmore facilities map that indicates a northward direction to the upper right corner of the map. Credit: U.S. Department of the Interior, National Park Service (2021).

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FIGURE 4.12 True north (+), grid north (GN), and magnetic north (MN) on the U.S. Geological Survey Mount Shasta, California Quadrangle map. Credit: Direction indication from the U.S. Department of the Interior, Geological Survey (2018).

map would represent the east-west directions. However, these lines may not be straight, as some map projection systems involve the use of curved graticules when projecting the rounded shape of the Earth on the model assumed (cone, cylinder). In smaller-scale maps, the graticule is often developed using 30 degrees or greater separations between the longitude and latitude lines. For larger-scale maps, the graticule could be described by smaller divisions of degrees, minutes, and seconds, such as 1 degree or less.

Scale A map is a scale model of a real landscape or water body. A map’s scale, therefore, represents the relationship (as a ratio or proportion) of distances and areas on a map in relation to the actual distances and areas in real life. There are three general types of the map scale: 1) representative fraction, 2) equivalence, and 3) graphical (Fig. 4.13). Representative fraction scales are unitless and define a ratio between a mapped distance and a ground distance. For example, 1:24,000 is a representative scale that suggests a mapped feature is 1/24,000 the size of the real-life feature. Here, one unit on the map represents 24,000 of those same units on the ground or across the water. In this case, one inch on a map represents 24,000 inches (2,000 feet) in real life. A 1:1 representative fraction scale map

FIGURE 4.13 A graphical scale associated with the Swanson River (Alaska) canoe route map. Credit: U.S. Department of the Interior, Fish and Wildlife Service (2004).

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presents features at the same size as they are in real life. A 1:1,000,000 representative fraction scale map presents features in a way that they are 1/1,000,000 of their real-life size. When discussing map scale, maps are often categorized as large or small scale. A small-scale map shows a larger area with less detail, and it typically has a scale of 1:250,000 and smaller (approaching 1:1,000,000,000 or more). Conversely, large-scale maps present a small area in a great amount of detail. This may seem counterintuitive unless one thinks of the scale as a fraction. With an increase in the denominator of the fraction, the scale becomes smaller and smaller. With a decrease in the denominator, the scale becomes larger. Typically, large-scale maps are 1:50,000 and greater (approaching 1:1). Therefore, two ideas are important to remember:
As the right-hand side of the scale relationship increases (approaches some large value) ➡ The scale model of land or water becomes smaller ➡ More areas can be viewed on a single map
As the right-hand side of the scale relationship decreases (approaches 1) ➡ The scale model of land or water becomes larger ➡ Less area can be viewed on a single map Translation 4.2 Imagine that you are visiting your family during a holiday and that people are gathered around a certain small-scale map of the State of North Carolina, chatting about the location of their friends’ homes. The map indicates that the physical scale is 1:500,000. In just a few sentences, describe for your family the concept of a small-scale map, and what a 1:500,000 scale implies in real-life terms. An equivalence scale is one that can include either English or metric units on either side of the expressed relationship and may be better expressed orally than the representative fraction scale. For example, a scale of 1 inch ¼ 2 miles suggests that 1 inch on a map is the equivalent of two miles in real life. When spoken, the speaker might say “1 inch is equivalent to two miles” or “1 inch equals two miles.” A common equivalence scale in the United States for forest management maps is 1 inch ¼ 10 chains. A chain (66 feet) is an old English distance measure, which is still present in older property descriptions in the United States. A chain has an intimate relationship to the mile (80 chains) and the acre (10 chains2). Equivalence and representative fraction scales are interchangeable. Once the units on both sides of an equivalence scale are the same, it can be reduced to a representative fraction scale: 1 in. ¼ 10 ch. 1 in. ¼ 660 ft. 1 in. ¼ 7920 in. 1:7,920

Chains (equivalence scale) (Equivalence scale) (Equivalence scale) (Representative fraction scale)

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One could also take a representative fraction scale and add units to both sides of the relationship to create associated equivalence scales: 1:24,000 1 cm ¼ 24,000 cm. 1 cm ¼ 240 m.

(Representative fraction scale) (Equivalence scale) (Equivalence scale)

Inspection 4.3 What is the equivalence scale of the Swanson River (Alaska) canoe route map that can be found on this book’s website (gis-book.uga.edu)? What is the representative fraction scale that relates to this equivalence scale? A graphic scale is commonly referred to as a scale bar. As with the equivalence scale, this scale includes units representative of ground distances in relation to map distances. Scale bars can take many forms but in their most basic form, they include a line or bar with subdivisions incrementally illustrating distances. It is important to note that if a map is expanded or shrunk, through copying, scanning, or the effects of humidity, a scale bar will remain as an example of an accurate scale, which cannot be said of the representative fraction or equivalence scales.

Symbols Map symbols are used to help identify, categorize, and illustrate features of a landscape or water body and highlight one or more of their attributes. Symbols can consist of points, lines, polygons, and other graphics that are illustrative of the objects or places they represent, like a tent graphic identifying a campground location or a graphic illustrating the outline of a hiker denoting the beginning of a trail. The size, font, color, and other qualities of a symbol can often be modified to meet the needs of the map maker and user. In general, point symbols are used to note specific locations. These might include the location of a city center, timber sample points, wildlife nests, and even significant individual plants or trees. A point symbol can also be placed in a relatively nonspecific map location, yet juxtaposed to a feature of interest, to convey information such as road type and number (e.g., the symbol for a U.S. interstate highway). Line symbols are typically used to convey information about linear features such as roads, streams, utility rights-of-way, and railroads. Polygon symbols are most often used to convey information about features that enclose an area, such as property boundaries, water bodies, or forest stands. As with other map components, symbols can also be used to suggest hierarchies in landscape features using color and size gradients, such as the thickness of lines representing roads, to convey order (Peterson, 2015). With raster data, symbolization is restricted to colors illustrative of a grid cell’s value.

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Inspection 4.4 Access the Cottonwood Creek Trail map that can be found on this book’s website (gis-book.uga.edu). What do the various line features that act as symbols on this map add to the message that the map conveys? Organizations, agencies, and institutions may have developed standardized symbology to employ when one of their representatives creates a map. For example, the U.S. National Park Service developed specific symbology for use in national park maps (Fig. 4.14). Similarly, the U.S. Geological Survey developed standard symbols that are used on topographic maps. These include national and state boundaries, topographic

FIGURE 4.14 A subset of standardized symbology used by the National Park Service. Credit: U.S. Department of the Interior, National Park Service (2018a).

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indices and depressions, rivers and lakes, and various other landscape features (Fig. 4.15). Standards may also include direction on the colors, textures, and patterns to use in representing the fill of areas (polygons) on maps, perhaps when identifying swamps, forests, orchards, and other land classes. When one begins working with GIS

FIGURE 4.15 A subset of standardized symbology used by the U.S. Geological Survey. Credit: U.S. Department of the Interior, Geological Survey (2005).

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software, a standard symbology set will likely be encountered for that software package. However, some GIS software programs might allow map makers to use unique symbols that they create themselves (Wing and Bettinger, 2008) or to import symbol catalogs that might be found on the Internet.

Legends To help the users of a map better understand what is being presented, a map needs a key or an index to describe the meaning of each symbol, feature, color, or pattern used. A map legend is that key. An effective map legend explains what each unique symbol, feature, color, or pattern represents, by placing a short keyword or two next to an example of each symbol, feature, color, or pattern that is found on the map. For simple maps, a legend might contain only a few elements. However, when a map contains several types of GIS databases, which illustrate several different data classes using multiple colors and other unique symbols, a map legend can quickly become large and difficult to effectively incorporate into the medium (paper, image) (Fig. 4.16). Inevitably, some serious thought concerning a legend will be necessary during the map design stage. At the very least, the size and shape of a legend will be dictated by the size or format of a map. A legend needs to be large enough to effectively provide guidance, but not so large that it distracts people’s attention from the main body of the map. In creating a legend, the map creator may also decide that only a subset of the unique features described in a map should be included. Suppose, for example, a map includes a lake that is colored light blue and contains a label (“Lake Erie”). In cases like this, it may not be necessary to include a “lake” or “water” entry in the legend, as most people will immediately know what it is when they see it on the map. Paring down the legend to include only those items that may not be immediately obvious to the map user involves decisions such as these. Inspection 4.5 Access the Keen-Eye nature trail map that can be found on this book’s website (gis-book.uga.edu). What does the legend help users of this map to understand?

Neat lines A neat line is simply a border, or box, that encompasses one or more map elements. For example, one might use a border to surround and contain all of the elements on a map. This involves the use of a neat line. With respect to making a map with GIS software, the terms neat line, border, or frame are all commonly used interchangeably. When a neat line surrounds all of the elements of a map, it may also be referred to as a page border (Peterson, 2015). A map may also require borders around the legend, any insets, and other map elements, as the primary functions of neat lines are to create a boundary between map elements, to organize map elements, and to appropriately frame all

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FIGURE 4.16 Multiple legends within the Bureau of Land Management recreation guide for Wyoming. Credit: U.S. Department of the Interior, Bureau of Land Management (2018).

components of the map. Commonly, neat lines are simple, straight black lines that form a rectangle. However, with some creativity, neat lines can be modified through alterations to the weight (thickness) of the line, the number of lines used (single, double, etc.), and the color employed, when appropriate (Fig. 4.17). The weight and condition of a neat line may also indicate its importance. Therefore, a map maker should be mindful of the hierarchy of map elements and not draw people’s attention to portions of a map that are less important than others (Bettinger et al., 2020). Inspection 4.6 How many instances of neat lines are found on the Swanson River (Alaska) canoe route map that can be found on this book’s website (gis-book.uga.edu). What are their purposes? Are they all necessary? Are more neat lines necessary?

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FIGURE 4.17 Variations on neat line components.

Labels When used appropriately, labels are effective means for not only identifying the location of a place or feature but also for informing the map user of some important characteristics of those features (Wood, 2000). Labels (annotation) serve the important function of providing textual, alphanumeric information concerning features displayed on a map, such as the name of a city, water body, or road. Labels can be words, acronyms, contractions, codes, numbers, and any other form of information that would be informative to the end-user of a map. For timber management purposes, labels might include stand identification numbers, ownership codes, forest size class, regeneration status, soil type, productivity class, and many other characteristics necessary for the effective management of timber stands, all of which can be stored within the attribute table of a GIS database and presented on maps. As an example, the stand identification label used by the U.S. Forest Service for timber stand polygons consists of a concatenated eight-digit string of text created by combining district, compartment, subcompartment, and stand numbers (U.S. Department of Agriculture, Forest Service, 2002). As a map is being developed, some important considerations during the cartographic process include:





What labels are needed? Where are the labels to be placed or positioned? What are the label font types? What are the label font sizes?

GIS software programs allow the user great flexibility when it comes to label placement. Labels describing point features can be placed above or below each point, to the side of each point, or oriented in other ways that suit the needs and desires of the map maker. Labels describing linear features can be placed above or below each feature or oriented to follow an irregular path. For example, labels describing the name of a river can be positioned at different orientations (angles) and curved to emulate the river’s path. Similarly, road numbers can be placed in such a way that they follow the meander of a road. This type of text is called spline text. Labels describing polygon features are usually placed inside each polygon if they can fit given the font type and

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font size employed. Labels for features used on digital maps can also be designed to appear only when a map is viewed at a specific scale. This process, dynamic labeling, prevents all labels from appearing at once and cluttering the map with text. While this is not possible with static maps, map creators often use dynamic labeling in online mapping applications and in projects where maps may be created at various scales. The orientation, size, and style of a label should be balanced against the presentation of other items on a map, to complement the esthetic appeal of the map. For example, a label representing a city center or large expansive area might be placed on a map with the text-oriented horizontally in relation to the bottom edge of the map (Fig. 4.18).

FIGURE 4.18 A portion of the Asheville-Quadrangle illustrates the orientation and style for labeling rivers (for example, the French Broad River) in blue italicized texts that reflect the path of the river. Asheville and West Asheville are also labeled horizontally in relation to the bottom of the page while road names follow the orientation of the road. Credit: U.S. Department of the Interior, Geological Survey (2019).

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Inspection 4.7 Access the Kenai National Wildlife Refuge (Alaska) cross-country ski and snowshoe trails map that can be found on this book’s website (gis-book.uga.edu). What labels and annotations are provided that will help recreationists understand the difficulty of the trails? For the independent map maker and others who are unaffiliated with government agencies or large corporations, there may be no rules or templates for formatting and placing labels. There may be, however, some common labeling conventions that have been established over time that would seem logical to follow. For example, when a point feature representing a position on dry land is located near a water body, the label for the point feature is usually placed on the land rather than in the water. Similarly, when labeling rivers, the color of the text is commonly blue, the font is italicized, and the label is placed within the boundary of the river. Labels should also never be positioned upside-down or presented so small that they are difficult to read. As mentioned previously, font size and style can often be used to classify the importance of mapped features following a hierarchical system. The names of cities, counties, states, provinces, countries, and other politically defined areas are commonly capitalized. The label for a state, for example, should be larger than a label for a county in that state, which might be larger than a label for a town in that county (Wood, 2000). The goal of the label is to identify mapped features in a clear, logical manner. Effectively labeling features on a map can become a tedious and time-consuming process if the map medium is large (showing broad areas) or the scale of the map is large (showing great detail). A map maker must be careful to ensure that the labels employed do not obscure other important mapped features or labels of other features. While much of the labeling process can be automated in a GIS software program, this process may place labels in undesirable positions on a map or in places where the labels were not intended to be. When this is the case, a map maker may find themselves faced with the task of manually editing or adding labels within a GIS software program or find themselves faced with the task of editing the map graphic produced by a GIS software program within other graphics editing software programs (e.g., Adobe Illustrator) to allow more flexibility in label placement.

Insets Within a well-designed map, insets can be useful tools for orienting map users, providing context for the main area of interest, and perhaps providing more geographic detail. An inset can have the purpose of providing a broader perspective of the land or water body being mapped by illustrating where it resides within a smaller scale (birds-eye) view of the Earth. These are often called locational insets or locator maps. For example, when mapping stands that are suitable for timber production in Kootenai National Forest, an inset map might be used to show where the national forest is located in relation to the

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states of Montana, Idaho, Washington, and the Canadian border (Fig. 4.19). An inset can also have the purpose of providing a closer inspection of specific resources of interest by illustrating these within a larger-scale (more focused) view of a place. These are often called detail maps and provide a map user a more informed perspective of an area of interest (Bettinger et al., 2020). A map of Cumberland Island (Fig. 4.20) uses a detailed map to call attention to a smaller area of the island that has a walking route, camping area, and boat docks. Regardless of whether there are dissimilarities between the scale of the features within an inset and the scale of the main area of a map, an inset can have the purpose of displaying more closely together land or water bodies that are geographically far apart. These are often called continuation insets. In many maps of the United States, the states of Alaska and Hawaii are often placed within a continuation inset positioned on these maps off of the western coast of the mainland (Fig. 4.21). In effect, the vast expanse of space between two places might be ignored by presenting one or both of the areas inside separate windows.

FIGURE 4.19 An example of an inset (lower left of the map) is used to illustrate the location of the Kootenai National Forest in relation to the larger landscape in which it is situated. Credit: U.S. Department of Agriculture, Forest Service (2015).

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FIGURE 4.20 An inset (bottom of the map) is used to highlight a portion of Cumberland Island that includes a walking route, camping locations, and docks. Credit: U.S. Department of the Interior, National Park Service (2018b).

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FIGURE 4.21 An inset (lower left of the map) is used to highlight Alaska, one of the Bureau of Land Management’s administrative units. Credit: U.S. Department of the Interior, Bureau of Land Management (2019).

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Inspection 4.8 An inset can be found on the Swanson River (Alaska) canoe route map that can be accessed from this book’s website (gis-book.uga.edu). Where is the inset located on the map? What kind is it? Can the inset be enhanced? When incorporating an inset into a map, attention should be paid to the overall layout and aesthetic of the map. In many circumstances, a map should be designed to draw attention to the main area of interest (the central theme); other perspectives provided by insets should be positioned to avoid distracting from the main message of the map. As we insinuated, the land or water bodies contained in the insets can be portrayed at a larger or smaller scale, depending on their purpose. Translation 4.3 Access the New Sparta (Venezuela) map from this book’s website (gis-book.uga. edu) and locate the insets. What are these insets attempting to communicate to readers of the map?

Graticule As was noted earlier when discussing map orientation (and in Chapter 3), a graticule is a grid that is placed on a map using lines of latitude and longitude. A graticule is useful in the sense that it provides general location and orientation information (Fig. 4.22). The distance between the lines of latitude and longitude, in degrees, is regular (not random or haphazard across a map) and is based on an assumption decided upon by the map maker. The density or sparseness of the grid should be balanced against the need to precisely know where landscape or water body features are located. Maps that are not used for navigational purposes might benefit from using a graticule (Peterson, 2015). When presented on a map, a graticule can be instructive for illustrating the distortion that results from using certain map projections (Fig. 4.23) (Battersby and Kessler, 2012).

Other components For reference purposes, many different pieces of information might be included in a map. While important, this reference information might be placed toward the margins of the map (in the marginalia) and presented in a nonprominent way (small font, etc.). These include: (a) Name of map maker and date of map. For several reasons, it can be important to add to a map the name of the map maker and the date that a map was created. This provides users of the map the ability to understand who they may contact should questions arise concerning the detail that the map provides. Also, this information provides users of the map a temporal position around which they can compare historical or future knowledge of the landscape or water body. For

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FIGURE 4.22 A map of North America using the WGS 1984 geographic coordinate system overlaid with 30 degrees latitude and longitude units to define location. Credit: Country boundary data from the ArcGIS Hub (2015).

example, a 2010 vintage map of a forest in northern California may not include an imprint of the effects of more recent wildfires; therefore, the map may provide people a glimpse of the landscape conditions that were present prior to the recent fires. Further, in examining a map of potential harvest areas in Bayfield County, Wisconsin, a forester may need to contact the map developer to request adjustments based on first-hand knowledge of the resource. Therefore, knowledge of the person who made the map can be of great value in ensuring the adjustments are made in a timely manner. (b) Data sources. Information regarding the data behind the resources depicted in a map can increase the legitimacy of the product and help a map user understand that the data may have been acquired from a reputable source (Bettinger et al., 2020). Source: U.S. Department of the Interior, National Park Service

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FIGURE 4.23 A map of North America containing a 30 degrees latitude and longitude graticule to define the location and illustrate distortion associated with the Eckert projection for a portion of North America. Credit: Country boundary data from the ArcGIS Hub (2015).

(c) Location of the metadata. Metadata and background information regarding the GIS databases used to develop a map can be of value to map users who are concerned about the quality of the data. Often, these pieces of reference information are connected by Internet links (URLs, uniform resource locators). Internet links should be used sparingly and with caution should a map have an intended life of more than a few years since Internet links are often not stable over long periods of time. Metadata can be accessed at https:// ... (d) Caveats, warranties, disclaimers, and warnings concerning data quality. A caveat is a statement related to certain conditions or limitations of the data used to make a map. A warranty is a guarantee or promise of quality or condition. Often caveats and warranties are included in maps to caution map users about the relative quality of the information. For example, a certain map may indicate the location of an

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electric utility line. A landowner may then rely on the map to accurately describe the location of the utility or gas line. In conducting management activities, the landowner may then accidentally contact the utility line. Subsequently, the landowner may argue that the map developer was responsible for any financial or personal losses as a result of contacting the utility line. Caveats or warranties placed on maps may therefore provide the map maker protection from claims of liability charged by people who may have been hurt (financially, or physically) after using a map for some personal or professional reason. The resources and information displayed in this map arose from a number of data sources that included ____. Considerable effort was made to verify the accuracy, precision, and overall quality of these databases. Any decision made based on the information provided here is solely the responsibility of the map user. This map is distributed “as is.” No warranties of any kind, expressed or implied, are provided by the organization that made the map (____) concerning the validity, accuracy, or completeness of the information presented in the map. Decisions involving the risk of financial loss or physical injury should not be made by relying on this map’s content. Diversion 4.4 Suppose you are a natural resource consultant who primarily develops GIS databases and maps for private landowners who manage forestlands in the Midwestern United States. On one such map, you have placed the location of underground natural gas pipelines, since they are within the area of a rather large forest you have mapped. Create a warranty that would be associated with this map that might be used to protect yourself from claims of liability if someone were to have punctured the pipeline while conducting management activities. Similarly, a disclaimer may be provided on a map to further protect the map developer from legal action taken by a map user based on the quality or content of the map. Disclaimers might refer to potential positional problems in the GIS databases used to create the maps or potential problems related to the attributes of map features (omissions, errors, etc.). The ____ was unable to verify the positional location of ____ contained in this map. The ____ staff cannot fully verify the accuracy of information related to the mapped features. A map may also contain one or more warnings that act to alert map users to the appropriate uses of the map. This map should only be used for ____ purposes. Inspection 4.9 Access the Swanson River (Alaska) canoe route map that can be found on this book’s website (gis-book.uga.edu). What caveat is provided with this map regarding

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the detail that the map provides? Why do you think that this caveat was added to the map? (e) Geographic system employed. The projection and coordinate systems employed to collect, manage, and display the GIS data may be of interest to certain map users. Therefore, it is somewhat common to see this information in the marginalia of a map. UTM Zone 11 (f) File locations. The physical locations of the GIS databases used to develop printed or digital maps can sometimes be found in the marginalia of a map. This information is helpful for ensuring future access to the databases and map files in cases where maps require editing or updating. However, these would only be useful to the map maker and others within an organization who have access to the files. C:\Roads\2022\Forest_Roads.gdb C\Roads\2022\Forest_Roads.mxd (g) Copyright. A copyright provides the originator of a work, such as a map, intellectual property protection. A copyright holder may authorize other entities (people, organizations) to use their work, but generally, the work cannot be used for certain purposes (such as in books or papers) without an agreement from the copyright holder. The Creative Commons system provides an avenue for people to use works assigned copyright when the originator of work is given credit in a manner required by the license. Statements of copyright on a map might be presented in this manner: Copyright © 2022 University of ____ © 2012e22 Center for ____, University of ____ (h) Discrimination policy statement. A statement regarding an organization’s discrimination policy may provide defense for the organization against claims of intolerable behavior and enable the organization to publicly state that these types of behaviors are not tolerated (Bettinger et al., 2020). These statements are rare but are becoming more common on products produced by public agencies and large land management organizations. ____ has the policy to create and maintain a work environment that is free from discrimination and harassment of any kind. Discrimination and harassment against any person due to age, sex, race, religion, veteran status, disability, handicap, sexual orientation, or gender identity will not be tolerated. (i) Corporate or organizational logo. A graphic, emblem, or symbol that is associated with an organization serves to provide brand recognition for that organization (Fig. 4.24). Often, logos are considered works of art, and these serve a communication purpose as people will immediately recognize the brand and associate it with the organization.

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FIGURE 4.24 The logo for Pine Products, Inc., a fictitious company.

Reflection 4.3 Imagine that you are a natural resource consultant who primarily develops GIS databases and maps for private landowners who manage forestlands in the eastern United States. On the standard forest management maps that you develop for larger landowners, which of these “other components” would you consider regularly placing on your maps?

Background image A background image can, in some cases, add significant value to the quality of a map. Background images, also commonly referred to as base maps, are nearly always raster GIS databases that have been created from aerial photographs or satellite imagery, yet they could also include scanned images of topographic maps or gray scale images of the surrounding landscape. The addition of a background image can provide map users with a broader understanding of not only the resources contained within a property but also other resources located outside of the property (Fig. 4.25). These resources outside of a managed property might include local houses, businesses, schools, and other instances of human development that may be important considerations in the management plans of a landowner or land manager. For example, if a land management organization has an active prescribed fire program, the location of houses, businesses, hospitals, schools, roads, and other developments outside of their property may add great value to maps that portray prescribed fire plans. However, the incorporation of a background image into a map is not always recommended. Should the addition of a background image overwhelm the message of a map and add little value to the quality of a map, it likely is unnecessary. Inspection 4.10 Access the Ski Hill trail map that can be found on this book’s website (gis-book. uga.edu). How does the background image enhance the overall message that the map conveys? Based on the imagery provided on the map, what landscape features are located near the trail?

Data visualization Ideally, some basic map components (title, scale, legend, north arrow, date, annotation) that act to assist map users in understanding the message or intent of a map would be included in a map. The spatial arrangement of these within a map product requires balancing the overall distribution of items so that the end result is well distributed across the medium (paper, digital image) and coexists well with respect to the main area of

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FIGURE 4.25 A background image was added to a map of a portion of the Chippewa National Forest (Minnesota), indicating some national forest land is located near Longville Municipal Airport. Credit: Base map from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021).

interest (presented in a somewhat centered manner in the map). A number of suggestions were provided earlier regarding the presentation of various map components. This section of the book describes some general types of maps one might produce and some different ways in which information can be displayed within maps to enhance the communication process.

Map types A more comprehensive discussion of the differences between map types can be found in other texts (e.g., Bettinger et al., 2020). Here, we focus on a few of the more common

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types of maps used in forestry and natural resource management. A thematic map is one that presents a theme, subject, or topic. In this sense, just about any map can be considered a thematic map. However, as you will find in the discussion that follows, some types of maps are more precisely named when used for specific purposes. In general, a thematic map might be developed to describe the occurrence of resources, conditions, or other phenomena across a landscape (e.g., tree basal area) or waterbody (e.g., water temperature) that may be of interest to society (Bettinger et al., 2020). Diversion 4.5 Access the Allegheny National Forest vegetation GIS database that can be found on this book’s website (gis-book.uga.edu). Open the GIS database in your preferred GIS software program. Examine the attribute table associated with this GIS database. Develop a list of five types of significantly different thematic maps that can be made using this data. In developing a map, a map maker would use symbology (color, text, symbols) to describe differences or similarities in those resources, conditions, or other phenomena. For example, the average basal area per acre contained in the various forested stands of the Allegheny National Forest can be displayed as classes (strata) that indicate, relatively speaking, where more dense forests may be found (Fig. 4.26). In this case, ten classes of basal area per acre were defined, and each forest stand fell into only one of them. The color scheme that was employed suggests that the denser forests are darker green in color, while the less dense forests are lighter green. Therefore, the theme described in this map is the denseness of the forests. When developing a thematic map that separates resource characteristics into classes, one can use defined intervals such as those found in Fig. 4.26, natural breaks in the data distribution (Fig. 4.27), the number of standard deviations each data point (e.g., stand average basal area) is distanced from the average (Fig. 4.28), and several other means of illustrating similarities and differences. A thematic map can be based on either quantitative (basal area, age) or qualitative (tree species, soil type) information. A thematic map can also involve the use of points, lines, polygons, and raster data. Point features can be given different colors, shapes, and sizes to reflect importance, similarity, difference, and other qualities. Line features can be applied to different patterns, thicknesses, and colors to emphasize how much variation the GIS data contains. In a similar vein, polygon features can be filled with different colors or patterns to illustrate different characteristics of the area of interest. Raster grid cells can also be colored using templates or color ramps that illustrate similarities or differences in value. The general idea of a thematic map serves as an umbrella under which many other specific types of maps can be classified. For example, when one develops a map that emphasizes differences in just the quantitative characteristics of polygon features, where colors or symbols represent similarities or differences in landscape or water body condition, these general types of maps are considered choropleth maps. The basal area maps presented in this section of the book are technically both thematic maps and choropleth maps that visualize information about a national forest

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FIGURE 4.26 Basal area per acre for forested stands in the Allegheny National Forest (Pennsylvania), using a consistent basal area interval of 20 square feet per acre. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021).

using basal area classes. Where appropriate, classless choropleth maps can also be developed to indicate a broader spectrum of continuous change across a landscape or water body using incremental color combinations (Tobler, 1973). Air temperature and precipitation maps might be examples of these types of choropleth maps.

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FIGURE 4.27 Basal area per acre for forested stands in the Allegheny National Forest (Pennsylvania), using a natural breaks process involving ten distinct classes. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021).

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FIGURE 4.28 Basal area per acre for forested stands in the Allegheny National Forest (Pennsylvania), using a standard deviation process to illustrate variation above and below the mean basal area per acre. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021).

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When broad coverage values associated with a landscape or water body are presented using line symbols on a map, the result is an isopleth map. A topographic map (Fig. 4.29) is technically an isopleth map describing changes in elevation for areas above water. A bathymetric map would be used similarly to describe changes in the elevation of surfaces under water. Land elevations in a topographic map are presented as line features (contour lines), the elevations are distributed throughout the landscape or water body (e.g., sea floor), and the values are quantitative (numbers) and continuous (not binary or integer). When line features are viewed on isopleth maps, the conditions (elevation, air pressure, air temperature) are assumed to be consistent along the line. As one moves from one line to the next, the mapped conditions should either rise or fall. The line features displayed on an isopleth map would ideally never intersect or cross; however, one can imagine possibilities of this occurring when attempting to map the elevation of very steep areas, such as vertical cliffs. In addition to topographic maps, other special cases of isopleth maps include:
isobar mapsdto represent locations of equal atmospheric pressure
isotherm mapsdto represent locations of equal air temperature
isoheight mapsdto represent locations of equal land elevation, as in topographic maps
isobath mapsdto illustrate changes in depth to a solid surface located under water

FIGURE 4.29 A portion of the 2018 Mount Shasta (California) 7.5-minute series quadrangle map. Credit: U.S. Department of the Interior, Geological Survey (2018).

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Reflection 4.4 Try to remember the last time you used a topographic or bathymetric map, a map that includes contour lines representing changes in elevation across a landscape or underwater. What part of the world did the map represent? For what purpose were you using the map? Similar to choropleth maps, a hypsometric map (shaded relief map) involves a raster GIS database that presents information continuously throughout a landscape, using variations in color, or hypsometric tinting, to illustrate changes in elevation here on Earth, or on other planetary objects (Fig. 4.30). Some logic might be employed in selecting the colors for a hypsometric map. For example, on a hypsometric map of the Grand Teton National Park in Wyoming it might be intuitive to conclude that areas colored green are forested or contain vegetation, while areas colored white might be high-elevation mountain tops (Darbyshire and Jenny, 2017). However, as the colors simply represent differences in elevation, these maps will not be perfect in this respect. For example, beige or brown might be selected as the color to represent low-elevation landscapes. At first glance, one might suspect that these beige or brown lands are arid and generally void of vegetation, yet in real life these areas might be situated in lowelevation temperate biomes, for example, and contain rather lush vegetation. FIGURE 4.30 A shaded relief map of the Moon. Credit: National Aeronautics and Space Administration (2006).

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A dasymetric map is a form of a thematic map that utilizes changes in color to express differences in the volume or density of attributes across a landscape. Statistical analyses are used to estimate the spatial distribution of volumes or densities where real observation values are lacking. In these maps, some areas may contain no data (zero density) in cases where zones of relative homogeneity suggest the phenomena of interest should not be present. So, rather than illustrate changes in elevation, these maps illustrate changes in the density of phenomena such as the number of humans living in each place (Fig. 4.31). Planimetric maps involve precise, professional surveys of land areas here on Earth and elsewhere (Fig. 4.32). They illustrate the horizontal equivalent position of landscape or water body features as if viewed from above. These maps often involve the use of vector GIS databases. They may include roads and other transportation-related features, buildings, powerlines, sidewalks, water bodies, forests, and other features such as storage tanks, administrative (city, county) boundaries, and parcel boundaries (Bettinger et al., 2020). These maps usually do not include contour lines or aerial images and are often presented as panchromatic maps containing points, lines, polygons, and annotation. Planimetric maps support urban planning and land tenure administration efforts. When a high level of accuracy and precision are evident, they can serve as legal plat maps that describe land ownership. In the United States, plat maps are filed at the county courthouse clerk’s office and act as the official legal descriptions of plots of land. Translation 4.4 Imagine that you are visiting relatives, and at dinner one night your aunt indicates that she is considering selling her home in which she has lived for over 50 years. However, she comments that she first needs to acquire the plat to better understand the extent of her property. Until recently your aunt was aware of the property’s deed, yet she was unaware of a plat. In a few simple sentences, as if you were talking to your aunt, describe this type of planimetric map. We conclude this section of Chapter 4 with a short discussion of a new type of map, the online map that has only within the last decade or so become popular among society in general. The art and science of creating online maps using Internet-based mapping services and programming code are beyond the scope of this chapter; however, the use of online mapping services may become a set of skills that require development. As an example, the Web Soil Survey of the U.S. Department of Agriculture allows a person to create a simple soil map of an area of interest (Fig. 4.33). This map can accompany or be included in a forest plan as it supports the forest plan for this area. This, of course, is only one of many options that involve using an online mapping service and cloud-based GIS data to create a map that can be downloaded or printed. Online maps can also be directly embedded into websites (Fig. 4.34), shared across map servers, and interacted with through smartphones or tablets. In many of these cases, the map itself is not downloaded or printed, only displayed on the device that sent the instructions to the mapping service. Today, the ease of access to maps through the Internet may have a

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FIGURE 4.31 A dasymetric map of the San Francisco Bay area (California). Credit: U.S. Department of the Interior, Geologic Survey (2000).

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FIGURE 4.32 Planimetric map of Apollo 16 Geology Station two on the Moon. Credit: National Aeronautics and Space Administration (1972).

positive impact on increasing society’s ability to understand maps and, perhaps, increase their comprehension of how landscape features are spatially related (how the world works). In the United States, many government agencies serve their GIS databases and maps through online viewers such as the National Map Viewer hosted by the U.S. Geological Survey. Through these types of systems, GIS databases can be downloaded, and various GIS data layers may also be viewed. A user of these services can often also develop a legend, draw shapes, measure lengths and areas, create elevation profiles, and print maps that are created.

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FIGURE 4.33 A soil map of a forested area in Idaho. Credit: Imagery and soil data from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021c).

Inspection 4.11 Using the Internet, navigate to the U.S. Forest Service’s Interactive Visitor Map. The interactive map allows you to find recreation activities such as camping areas, horseback riding trails, fishing spots, and hiking trails. Find a national forest that interests you. Zoom into your preferred scale and create a GeoPDF map of the area of interest using the interactive map tools. Write a brief memorandum describing, which map components are present on the GeoPDF. Are there map components included in the web interface that are not included in the GeoPDF? Are there ways to improve both the interactive web map and the GeoPDF? Online mapping programs provide new ways to interact with spatial data that include enabling or disabling map data layers, changing the scale viewed through zooming tools, and changing orientation by rotating the map. Via online mapping, some map viewers can easily switch from overhead, or vertical, to birds-eye, or oblique, perspectives.

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FIGURE 4.34 An online map of the Pahranagat National Wildlife Refuge (Nevada). Credit: U.S. Department of the Interior, Fish and Wildlife Service (2016).

Google Earth is an example of an online mapping program that exists outside of a web browser. Google Earth, like many other online mapping platforms, can accommodate both vector and raster GIS database models. Mapping systems such as this are powerful aggregators of satellite imagery and aerial photographs, making them readily accessible to everyone (Peterson, 2014). While the cartographic abilities offered through online platforms may be limited compared to those that are available in GIS software programs, the basic components of a map (title, north arrow, legend, and scale) can often be accessed through online platforms. What online mapping tools may lack in terms of expansive cartographic options, they make up for with their ease of use and flexibility. Online maps may allow the user to change the base map, or the background map, which

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may include imagery or topographic data layers, or 3-dimensional models that provide perspective and dimensionality. Further, these systems are commonly linked to servers or cloud computing infrastructure that, combined with image and vector tiling, may allow map rendering to be performed faster than when a traditional desktop GIS is used (Peterson, 2014). Additionally, cloud computing facilitates more timely updating of online maps, and potentially changes can be completed in near-real-time.

Conclusions The art of making a map is fostered by experience and observation. As the number of maps created increases, one should develop a sense of the appropriate sizes and colors (styles) of different mapped features in different mapping conditions and become more confident in the feng shui of modern cartography. Further, as a review of the number of maps made by others increases, one should be able to both become aware of new ideas or approaches for making a map and become aware of approaches that do not seem to work well. Depending on the intended use of a map, a certain set of map elements discussed in this chapter would be required. For disposable, one-off maps, perhaps only the minimum number of elements are needed (title, scale, north arrow, annotation). For maps that will likely be used for a longer period of time, or perhaps will be available to the general public, then a larger set of the map elements we described would seem necessary. Diversion 4.6 Using the Chippewa National Forest GIS data that can be accessed through this book’s website (gis-book.uga.edu), make a thematic map that graphically displays the ages of the red pine (Pinus resinosa) forests. While other GIS data may complement the development of this map, try to make the best map possible that emphasizes the red pine forest resources. Exercises exploring the concepts covered in this book using ArcGIS or QGIS are available on the book website: gis-book.uga.edu.

References ArcGIS Hub, 2015. Countries WGS84. https://hub.arcgis.com/datasets/a21fdb46d23e4ef896f314 75217cbb08_1?geometry¼-126.562%2C-89.998%2C126.562%2C-79.513 (accessed 20.12.21). Battersby, S.E., Kessler, F.C., 2012. Cues for interpreting distortion in map projections. Journal of Geography 111 (13), 93e101. Bettinger, P., Merry, K., Boston, K., 2020. Mapping Human and Natural Systems. Academic Press, London. Darbyshire, J.E., Jenny, B., 2017. Natural-color maps via coloring of bivariate grid data. Computers and Geosciences 106, 130e138. Geo H. Walker & Co, 1908. Birds-eye View from Summit of Mt. Washington, White Mountains, New Hampshire. www.loc.gov/resource/g3742w.ct005519/ (accessed 20.12.21).

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National Aeronautics and Space Administration, 1972. Planimetric Map of Apollo 16 Geology Station 2 on the Moon. https://commons.wikimedia.org/wiki/File:Apollo_16_PSR_Figure_6-29_Planimetric_ map_of_Station_2.jpg (accessed 20.12.21). National Aeronautics and Space Administration, 2006. Moon - Shaded Elevation Map Layer. https:// commons.wikimedia.org/wiki/File:Moon_worldwind.jpg? (accessed 20.12.21). National Aeronautics and Space Administration, 2009. Aster Imagery. U.S. National Aeronautics and Space Administration, Washington, D.C. www.nasa.gov/topics/earth/features/20090629.html (accessed 20.12.21). Peterson, G.N., 2015. GIS Cartography: A Guide to Effective Map Design. CRC Press, Boca Raton, FL. Peterson, M.P., 2014. Mapping in the Cloud. The Gilford Press, New York, NY. Sullivan, R., 1859. Geography Generalized. Marcus and John Sullivan, Dublin, Ireland. Tobler, W.R., 1973. Choropleth maps without class intervals? Geographical Analysis 5 (3), 262e265. U.S. Department of Agriculture, Forest Service, 2002. Forest Service handbook, Northern Region (R1), Missoula, MT, FSH 2409.21e e Timber Management Control Handbook Chapter 100. U.S. Department of Agriculture, Forest Service, Missoula, MT. U.S. Department of Agriculture, Forest Service, 2015. Kootenai National Forest Timber Suitability for the Land Management Plan Final Record of Decision (ROD). www.fs.usda.gov/Internet/FSE_ DOCUMENTS/stelprd3826688.pdf (accessed 20.12.21). U.S. Department of Agriculture, Forest Service, 2021. Allegheny National Forest Geospatial Data. www.fs. usda.gov/main/allegheny/landmanagement/gis (accessed 20.12.21). U.S. Department of Agriculture, Natural Resources Conservation Service, 2021a. Geospatial Data Gateway, Elevation. https://gdg.sc.egov.usda.gov/GDGHome_DirectDownLoad.aspx (accessed 20.12. 21). U.S. Department of Agriculture, Natural Resources Conservation Service, 2021b. Geospatial Data Gateway, Direct Data/NAIP Download. https://gdg.sc.egov.usda.gov/GDGHome_DirectDownLoad. aspx (accessed 20.12.21). U.S. Department of Agriculture, Natural Resources Conservation Service, 2021c. Web Soil Survey. https://websoilsurvey.sc.egov.usda.gov (accessed 20.12.21). U.S. Department of the Interior, Bureau of Land Management, 2018. Bureau of Land Management Recreation Guide. www.blm.gov/documents/wyoming/public-room/map/blm-wyoming-recreationguide (accessed 20.12.21). U.S. Department of the Interior, Bureau of Land Management, 2019. Bureau of Land Management Administrative Units 2019. https://www.blm.gov/documents/national-office/public-room/map/ bureau-land-management-administrative-units-2019 (accessed 20.12.21). U.S. Department of the Interior, Fish and Wildlife Service, 2004. Swanson River Canoe Route. www.fws. gov/refuge/Kenai/map.html (accessed 20.12.21). U.S. Department of the Interior, Fish and Wildlife Service, 2016. Pahranagat National Wildlife Refuge, Nevada. www.fws.gov/refuge/Pahranagat/map.html (accessed 20.12.21). U.S. Department of the Interior, Geologic Survey, 2000. Dasymetric Population Density. www.usgs.gov/ media/images/dasymetric-population-density (accessed 20.12.21). U.S. Department of the Interior, Geological Survey, 2005. Topographic Map Symbols. https://pubs.usgs. gov/gip/TopographicMapSymbols/ (accessed 20.12.21). U.S. Department of the Interior, Geological Survey, 2018. topoView - Mount Shasta Quadrangle, California e Siskiyou County 7.5-minute Series. https://ngmdb.usgs.gov/topoview/ (accessed 20.12.21). U.S. Department of the Interior, Geological Survey, 2019. topoView - Asheville-Quadrangle, North Carolina e Buncombe County 7.5-minute Series. https://ngmdb.usgs.gov/topoview/ (accessed 20. 12.21).

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U.S. Department of the Interior, National Park Service, 2016. Isle Royale National Park Map. www.nps. gov/carto/app/#!/maps/alphacode/ISRO (accessed 19.06.21). U.S. Department of the Interior, National Park Service, 2018a. Map Symbols & Patterns for NPS Maps. U. S. Department of the Interior, National Park Service, Washington, D.C. www.nps.gov/carto/app/ #!/maps/symbols? (accessed 16.05.21). U.S. Department of the Interior, National Park Service, 2018b. Cumberland Island National Seashore Map. www.nps.gov/carto/app/#!/maps/alphacode/CUIS (accessed 20.12.21). U.S. Department of the Interior, National Park Service, 2019a. Grand Teton National Park Map. www. nps.gov/grte/planyourvisit/maps.htm (accessed 20.12.21). U.S. Department of the Interior, National Park Service, 2019b. Yellowstone National Park Map. www.nps. gov/yell/planyourvisit/maps.htm (accessed 20.12.21). U.S. Department of the Interior, National Park Service, 2021. Mount Rushmore National Monument Facilities Map. www.nps.gov/moru/planyourvisit/images/MORU-parkmap-2021_2.jpg (accessed 20. 12.21). Wing, M.G., Bettinger, P., 2008. Geographic Information Systems: Applications in Natural Resource Management. Oxford University Press, Don Mills, ON. Wood, C.H., 2000. A descriptive and illustrated guide for type placement on small scale maps. The Cartographic Journal 37 (1), 5e18. Xiao, J., Wang, P., Lu, H., Zang, H., 2020. A three-dimensional mapping and virtual reality- based humanrobot interaction for collaborative space exploration. International Journal of Advanced Robot Systems 17 (3), 1e10.

5 Geographic data collection Introduction Many people who have just begun using geographic information system (GIS) software assume that the GIS databases they need to make maps (a) have already been developed, and (b) are available for every place in the world, for every theme, or resource of interest. Sadly, this is often not the case. Generally, those needing GIS databases face three scenarios (Bettinger and Wing, 2004): 1. The GIS databases needed do not exist. 2. The GIS databases needed exist but might not be appropriate for the task(s) considered or have not been collected at a scale appropriate for the analysis. 3. The GIS databases needed exist and are appropriate for the task(s). When the third scenario is elusive, alternatives for acquiring or creating GIS databases must be explored. Unless interest lies in national-level lands, such as U.S. national forests, GIS databases are generally scarce or unavailable (often privately owned and proprietary). States often maintain GIS data clearinghouses, but they tend to vary in quality and data themes offered, and at times these databases are not freely available. Much, but not all, of the data developed to support national-level land planning in the United States, is considered to be in the public domain. These are works that are not protected by trademark, copyright, or patent, and therefore not protected from free use by the general public through laws related to intellectual property. The idea that works developed by national organizations are in the public domain varies by country, however. In using works that are placed in the public domain, no permission is required. On the other hand, one cannot acquire works from the public domain and assert ownership over the data. If one cannot locate public domain GIS databases to support their land and resource management needs, other databases will either need to be purchased (where possible) or independently developed. Reflection 5.1 In a short, 200-word paragraph, describe how you feel about the general concept of GIS database availability. Should all GIS databases be freely available? Should a government agency be charged with developing all of the GIS databases that public agencies and private individuals use? Why might a private institution or organization not want their GIS databases available to the public? Form several arguments that support your opinion on this matter. Geographic Information System Skills for Foresters and Natural Resource Managers. https://doi.org/10.1016/B978-0-323-90519-0.00006-6 Copyright © 2023 Elsevier Inc. All rights reserved.

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This chapter delves into the issues concerning GIS database acquisition and development. In one sense, these issues are related to project planning, which involves a specific effort to address an issue of importance to an organization. Broader issues related to planning the implementation of a GIS program include hardware and software requirements, metadata standards, training opportunities, annual budgeting, and others. However, planning for the use of GIS might also focus on issues concerning GIS database acquisition and development. In either event, the focus of this chapter is on the development of GIS databases and the associated data requirements, which can amount to as much as 80% of a GIS program budget when personnel time and acquisition costs are considered (Somers, 2020).

GIS database development planning One of the first steps in developing a GIS database is to define the data needed for the type of analysis that is intended, or the scope of the data (Fig. 5.1). In this sense, one would consider the spatial extent (just inside the property being managed, or further outward) and the depth of the data. Here, depth refers to the information needed to support current forest and natural resource management concerns, and with a little foresight, the information needed to support future management concerns. A second step would be to define the acceptable standards for developing a GIS database. For example, if a timber stands GIS database was being developed, one might construct standards and protocols associated with the GIS database for both the attributes (e.g., acceptable inventory protocols and procedures) and spatial features (e.g., minimum mapping area or the smallest quantity of resource available in the GIS database). These decisions will affect and impact the quality of the final product.

FIGURE 5.1 A general thought process for developing a GIS database.

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Once a general idea about the type of GIS database desired has been established, a list of the data required to complete the development of the GIS database should be created. One might then explore the various sources of data that are freely available or that can be purchased or otherwise acquired from forest management consultants and other organizations (Fig. 5.2). Now, one finds themself in the main data development phase, which suggests a significant amount of basic work may be needed to create a GIS database (digitizing using a digital orthophotograph as a background) or to massage and rework other data that has been developed by others. If a GIS database has been acquired from another source, the features and attributes may need adjustment through format changes, editing efforts, and perhaps modeling processes. Further, a verification process should be employed to establish whether the resulting GIS database meets the needs initially outlined in the scoping process. One should also consider whether the effort to create the GIS database is of value to the organization (e.g., the cost of development is exceeded by the benefit to the organization). Finally, it might be necessary to consult with the end-users of the GIS database (foresters and others) concerning the usefulness of the data in helping them conduct their jobs. This form of summative analysis can be conducted with inperson interviews (conversations) or simple Internet-based (or e-mail) surveys.

FIGURE 5.2 A Venn diagram illustrating database acquisition options. Credit: U.S. Department of the Interior, Geological Survey (2021).

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The next part of this chapter investigates some of the processes used in the main data development phase of creating a GIS database. These processes could involve digitizing, scanning, collecting global positioning systems (GPS) positions, or acquiring imagery through sensors mounted in drones, airplanes, and satellite vehicles.

Creating GIS databases Envision a distinct environmental edge that is formed between a recently harvested forest and a mature forest (Fig. 5.3). Perhaps there is a need to somehow describe this edge in a GIS database so that a map can be made to illustrate the current status of the landscape. This action would involve updating the records that concern the management of different land classes. The end product could be a vector GIS database containing X and Y vertices describing the shape of the edge (Fig. 5.4), or a raster GIS

FIGURE 5.3 Raster aerial image indicating different vegetation conditions. Credit: Base map from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021).

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FIGURE 5.4 Digitized vector polygons separate the different vegetation conditions. Credit: Base map from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021).

database where the grid cells have different values (e.g., 1 ¼ mature forest, 2 ¼ cleared forest) depending on the land class. If the end product were a vector GIS database, the various data creation methods one could employ include:






Digitizing a hand-drawn map. Digitizing using a raster image as a base. Collecting vector features with GPS. Collecting vector features with a drone. Collecting structured and unstructured data.

If the end product were a raster GIS database, the various data creation methods one could employ include:
Scanning a map.
Capturing an aerial image, then perhaps classifying the image.

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Capturing a satellite image, then perhaps classifying the image.
Capturing a drone image, then perhaps classifying the image. As one might imagine, there are some overlapping ideas here. Imagery can be captured and stored as a raster GIS database, and then used as a base upon which a vector GIS database can be created. What may not be evident is that the vector data drawn or captured within GIS can also be rasterized and subsequently used as a raster GIS database. Understanding the desired end product can help in developing a strategy for the data collection part of the database development process.

Digitizing a hand-drawn map Digitizing, or line following is a process often used to develop a vector GIS database (Ansoult et al., 1990). The formal process of digitizing features from maps involves affixing the maps to a digitizing table (Fig. 5.5), registering the map to the table using known positions from a coordinate system, and then tracing the vector features (points, lines, edges of polygons) using a digitizing pen or puck (pointing device). A digitizing puck is similar to a computer mouse, yet it sends a signal to the digitizing table each time a person presses one or more buttons indicating the location of a vertex. This type of process for creating a GIS database is often termed heads-down digitizing due to the need to constantly look down at the materials affixed to the digitizing table. To ensure a high-quality end product (vector GIS database) during heads-down digitizing, a minimum of four reference points are needed. These reference points need to be identifiable on the map, and the real-life geographic coordinates of the

FIGURE 5.5 The office of a U.S. Forest Service employee, with a digitizing table in the background. Credit: U.S. Department of Agriculture, Forest Service (1989).

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positions, as well as the map coordinates, need to be known. For these reasons, road intersections, corners of buildings or sidewalks, and other permanent and easily identifiable landscape features are often used as reference points. Unfortunately, in rural landscapes, the ability to locate four suitable reference points may be difficult. Further, determining the coordinate positions of the reference points can be problematic unless a professional surveyor has been employed. While GPS positions might be used for this purpose, coordinates of points determined using a GPS receiver may contain positional error, particularly if lower-cost, recreation-grade receivers are employed (Bettinger and Fei, 2010). Coordinates of points determined using a topographic map may also contain map interpretation errors. In any event, once four or more well-distributed reference points have been selected, they are identified on the map affixed to the digitizing table using the digitizing puck, and the values of the coordinates are entered into the software program being used. These points are often referred to as tics, and the process of associating the map or image points with the digitizing table is referred to as registration. Irregularities in the positions noted on the map or image and the positions identified using the digitizing puck (and coordinates) are described using a root mean squared error (RMSE) measure (the difference between reference position coordinates and tics) that ideally would be very small when reflective of a decent registration effort. Once a map has been registered to a digitizing table, vector GIS features can be created using the control buttons on the digitizing puck, which send a signal to the digitizing table (and further, to the computer) that identifies the landscape position of the digitized feature. Digitizing can occur in point mode or stream mode (Frith, 1997). Point mode digitizing, as its name suggests, is where the person doing the digitizing identifies each point that needs to be saved as a GIS feature. These could be individual points (e.g., trees, culverts, cell phone towers) or they could be the individual vertices that describe the curvature of a line or a side of a polygon. The person doing the digitizing would press the appropriate button on the digitizing puck at each location where a point is necessary. In stream mode, the puck is engaged to collect data continuously as a person endeavors to digitize by essentially collecting point features on a regular time interval as the person carefully traces lines or sides of polygons. Stream mode is not appropriate for creating individual points but may be more efficient for creating line and polygon shape information. In digitizing maps, errors might be created through the attention to detail (or lack thereof) that the person conducting the effort applies to the development of the new vector features. Further, imperfections in the media (e.g., maps) upon which the digitizing effort is based could lead to digitizing error, as well as the age and moisture content of the media, which can act to shrink or swell the area of interest. If hand-drawn features are being digitized from a map, the quality of the drawn features may also interject some inaccuracies into the GIS database under development (Bettinger and Wing, 2004).

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Digitizing on a computer using a raster image as a base In GIS software systems, it is possible to open and view a raster image (i.e., an aerial photograph or satellite image) and use it as a base map, or background, upon which spatial features can be drawn. This process allows one to use a normal personal computer or tablet, along with a computer mouse or stylus, to create vector features based on what can be viewed in the raster image. In essence, a raster GIS database would be used as the basis for creating a vector GIS database. This form of digitizing is common today and is known as heads-up digitizing since the person digitizing is not looking down at a digitizing table but rather looking up at a computer screen while creating GIS databases. Any vector-based theme of interest that can be imagined when viewing a raster image or map can be created with this method. For example, imagine that an aerial image is used as the base map from which vector GIS databases are created (Fig. 5.6). Some of the features that can be recognized in this aerial image include:
Homes
Roads
Railroads

FIGURE 5.6 Digitizing the boundary of an agricultural field south of Oshkosh, WI. Credit: Base map from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021).

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Agricultural fields Forest patches Pond perimeters Wetlands

Empty vector GIS databases can be created in GIS software, and these can then be populated with digitized points (e.g., urban trees), lines (e.g., roads), and polygons (e.g., ponds, harvested areas) using a computer mouse or a tablet stylus. An empty GIS database would have a name and would have been designated with the feature types that would be stored there. Ideally, it would also have been assigned a coordinate and projection system. However, an empty GIS database would contain no spatial features until someone creates them. Once created, these databases can be further enhanced through the development of appropriate attribute fields (e.g., road surface type) in a table that can be populated by entering into the system the appropriate characteristics of each digitized feature, according to the formats that were defined. For example, if an attribute field for an urban tree GIS database was meant to represent the species of individual trees, the format for this information would likely be defined in such a way to prevent multiple potential representations of the attribute (e.g., to prevent Quercus alba, Q. alba, and white oak all being used to represent different white oak trees). As mentioned in Chapter 2, one method for standardizing attribute entries uses domains, which can be defined during the data creation process. Diversion 5.1 Locate the Clifton Court Forebay (California) GeoTIFF data located on this book’s website (gis-book.uga.edu). Using one of the resources located there as a base map, digitize the boundary of the bay (not the surrounding canals) with your preferred GIS software program. You may have to create a new (unattributed) GIS database prior to digitizing the bay. In the UTM system, this area is located in Zone 10. In digitizing the boundary of the bay, how many vertices did you require to adequately describe the shape of the water feature? Reflection 5.2 Imagine you are developing a map that requires creating GIS databases to represent timber stand types and roads. Creating a GIS database on a computer, using heads-up digitizing methods, can require a lot of work. At some point during this laborious process, your mind wanders, and you might begin to think that it would be easier to make the map you need by drawing it on a piece of paper using a pencil. Discuss the trade-offs in developing a map using GIS databases you might create through digitizing versus a map that you might draw by hand. One advantage of this type of digitizing is the ability to create vector GIS features without having to register a hard-copy map to a digitizing table, which also suggests that a digitizing table is not necessary. Assuming a digital orthophotograph has been

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registered adequately enough, it can act as a valuable base map for the development of data necessary to describe a managed landscape, and further, one can change the scale at which the digitizing occurs to focus on areas of the landscape that require the development of highly detailed line or polygon features. As an example of the use of a raster GIS database as a basis for creating vector GIS databases that support forest management activities, consider the property illustrated in Fig. 5.7. Here, we have a digital orthophotograph acquired from the U.S. Department of Agriculture’s National Agricultural Imagery Program (NAIP), and a simple vector GIS database that represents the boundary of a property of interest. Using the split function in GIS (described in Chapter 7), this property (parcel, polygon) can be subdivided into stands (management units) through heads-up digitizing methods, based on changes in vegetation that are evident in the orthophotograph (Fig. 5.8). Further, if roads are evident, the orthophotograph can be used as a base map for developing (digitizing) the associated vector GIS database. In fact, any distinct type of landscape feature that is evident in the orthophotograph can serve as a geographic basis for the development of GIS databases, including nearby houses, streams, and other features of interest to the management of the property.

FIGURE 5.7 A digital orthophotograph of a property (red polygon) in Cass County, Minnesota. Credit: Base map from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021).

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FIGURE 5.8 A digital orthophotograph of a property (red polygon) in Cass County, Minnesota, with wetlands being delineated. Credit: Base map from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021).

Diversion 5.2 Access the “Property” GIS database that is associated with this Diversion from the book’s website (gis-book.uga.edu) along with the Cass County, Minnesota NAIP county mosaic imagery. Using your preferred GIS software, create a vegetation (stands) GIS database for this property using heads-up digitizing techniques.

Collecting vector features with GPS Through the reception of four electronic signals, GPS, more commonly known around the world as global navigation satellite systems (GNSS), determines (calculates) a position on Earth to some relative horizontal and vertical accuracy. The technology that has been developed to capture the signals emitted by GNSS satellites uses a process of trilateration (like triangulation) to determine positions. Satellites emit GNSS signals at the speed of light, and the amount of time from emission to capture by a GNSS receiver is about 1/15 of a second. Currently, there are over 100 GNSS satellites orbiting the Earth, each emitting a signal that is freely available for receiver manufacturers to use in determining positions on Earth. As of 2021, the U.S. NAVSTAR GPS program had 31 operational satellites. Managed by the U.S. Space Force, the NAVSTAR GPS satellites orbit the Earth on six orbital planes (outer space highways) about 12,550 miles (20,200 km) above our heads.

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As with other GNSS systems, the NAVSTAR GPS satellites emit freely available and encrypted signals only available to the U.S. military. The control that the U.S. government has over the NAVSTAR GPS program is likely why other nations have developed their own GNSS systems. In 2021, the Russian GLONASS system included 23 operational GNSS satellites, the Chinese Beidou (COMPASS) program included about 35 GNSS satellites, and the European Union GALILEO system included 22 operational GNSS satellites. The India Regional Navigation Satellite System (IRNSS or NavIC) had seven operational satellites in 2021 while the Quasi-Zenith Satellite System (QZSS) developed by Japan had four operational satellites. The QZSS system is interesting because it was meant to complement other systems, to enhance positioning efforts within Japan’s urban canyons (cities with tall buildings). Although one might tend to assume so, GIS databases that are based on GNSS data are not perfect, in terms of the accuracy or precision of the features they contain. The type of GNSS receiver employed (and the associated cost) can provide an indication of the quality of GIS data developed. Thinking specifically of point data, recreation-grade GNSS receivers, those that cost generally less than $1000 U S. dollars (USD), have consistently been shown to capture horizontal positions that are 6e10 m accurate, on average, in forests even after augmentation. These devices include small Garmin and Magellan receivers as well as cell phones and GNSS-enabled watches (Lee et al., 2020; Bettinger and Fei, 2010). The antennas within these devices are as small as 0.25 inches (0.62 cm) square. The GNSS capabilities in a typical smartphone are equivalent in cost and accuracy to a recreational-grade GNSS receiver (Merry and Bettinger, 2019). Mapping-grade GNSS receivers, on the other hand, have larger antennas (2e3 inches (5e7 cm) in diameter), and often include software solutions that reject low-quality and multipathed signals from being used. The relative horizontal accuracy of points collected with these devices under forested conditions is generally 1e3 m. Trimble, Juniper Systems, and other manufacturers develop these types of GNSS receivers, which can cost between $1000 and $10,000 USD. Survey-grade GNSS receivers, which often cost more than $10,000, offer the highest accuracy among GNSS receivers, which even in forested conditions would result in submeter positional accuracy. However, these systems may require extended data collection times and a tripod on which to mount the antennas. These types of GNSS receivers are most useful in locating or establishing property lines and property corners, but they are less portable than other devices are for uses such as timber cruising. The positions determined by GNSS signals can be augmented (improved) in several different ways. First, differential correction can be applied to positions collected with a GNSS receiver. Differential correction involves postprocessing the positions (much later than the date they were collected, if necessary) using known correction values from a base station. A base station has a high-quality stationary GNSS receiver that continuously collects data over a known, surveyed position. The positions that a base station receiver determines are then compared to the known position. Any discrepancies between the two might be attributed to atmospheric issues among other possible causes (Dai et al.,

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2003). The difference in distance and direction for each point in time, perhaps for each second of each day, are saved in a base station database. A person can then download from the Internet the base station file for the day and time when their data was collected. With differential correction software, data collected in the field can then be adjusted using the information contained in the base station data. Unfortunately, the base station needs to be within about 200 miles (320 km) of the place where data were collected to be of value. Diversion 5.3 Assuming you are located in the United States, using an Internet browser, navigate to the CORS Map website maintained by the National Geodetic Survey. Search for your location. If you are not located in the United States, select a place of interest and do the same. How many base stations are within 250 km of your location? Choose a base station near your location and investigate the type of base station data that is provided. What other information about the base station is available? How often is the base station collecting a location sample? The second form of augmentation would be, where possible, to access satellitebased differential correction signals that enable near-real-time position adjustments. In North America, the Wide Area Augmentation System (WAAS) provides augmentation signals from two stationary satellites. WAAS was developed by the U.S. Federal Aviation Administration to assist in the landing of airplanes. Many different types of GNSS receivers can now access these signals to improve the determination of positions almost immediately. In forested conditions, WAAS has been shown to improve horizontal positions by a few meters (Danskin et al., 2009). Similar to WAAS, some larger airports are developing Local Area Augmentation Systems (LAAS), which provide a near-real-time correction signal from a ground-based system. A GNSS receiver needs to be within about 30 miles (48 km) of an airport to access and use LAAS signals if it has the capability to do so. Similar to WAAS and LAAS, a differential GPS (DGPS) system accesses a network of ground-based broadcast stations that emit signal correction information. One final augmentation system involves a real-time kinematic (RTK) GNSS system. An RTK system needs a stationary GNSS receiver that is positioned over a known point in a local area. This receiver would need to be able to emit a correctional signal to wandering rover receivers (i.e., those held in people’s hands) in the local area. The roving receivers would also need to have the ability to receive the correction signal and use it accordingly to augment the positions that are being determined. Errors associated with GNSS data can arise from a number of sources. Multipath errors are perhaps the most important to consider when working within and around forests (Danskin et al., 2009). These errors involve the use of GNSS signals (by a GNSS receiver) that have bounced off of, or been deflected by, terrain surfaces, water bodies, buildings, or parts of trees. In effect, the signal used by the GNSS receiver may have taken slightly longer to reach the receiver than necessary, resulting in an inaccurately

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determined position. Some GNSS receivers in the mapping-grade and survey-grade classes have the ability to recognize multipathed signals and selectively avoid using them. However, most recreation-grade GNSS receivers do not have this ability; thus, average horizontal position accuracy is generally lower when using these receivers. Other potential sources of error include atmospheric issues (water vapor, electrically charged particles, particulates), which may be partially alleviated through the use of differential correction. Further, forest type, ground slope, clock errors, and ephemeris (a table of parameters to locate the actual flight of a satellite) inconsistencies can be the cause of the positional error. It is therefore important to consider not only the type of GNSS device that will be used to capture positions forming the foundation of a GIS database, but also the conditions under which the data will be collected (time of day, time of year, device parameter settings). Translation 5.1 A colleague of yours collected some GPS data with a recreation-grade GPS receiver to make a GIS database of roads that you manage. They tell your boss that while the data is accurate, the GIS database may need to be edited to remove some multipath errors. Your boss looks confused. Briefly explain the concepts of multipath errors and positional accuracy in a manner that your seasoned boss might understand. GNSS receivers collect vector data. As you may recall, vector data are most generally points, lines, and polygons. To collect point data, one would walk or drive to a specific place, then engage the GNSS receiver to record a waypoint or position fix. Then, one would walk or drive to the next place, and again engage the GNSS receiver to record a second waypoint or position fix (Fig. 5.9). Types of point GIS databases developed using a GNSS receiver might include:







Inventory sample points Wildlife nest locations Water point sources Archeological sites Urban trees Culverts

When collecting line or polygon data with a GNSS receiver, the receiver is constantly determining positions and storing them as vertices that form connected features. Types of line GIS databases developed using a GNSS receiver might include:






Roads Streams Hiking trails Powerlines Gas pipelines

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FIGURE 5.9 Capturing a point position in the Whitehall Forest (Athens, Georgia) with a GPS receiver. Credit: Pete Bettinger.

Types of polygon GIS databases developed using a GNSS receiver might include:






Harvest areas Wildlife habitat patches Ownership boundaries Pond perimeters Management areas Diversion 5.4 Use your cellular phone and an application (app) such as Avenza Maps to mark the location of a few trees outside your home or office. Find a way to save these point positions as a GIS database that can be opened in GIS software or Google Earth. In general terms, how accurate (spatially) are the points that represent the trees?

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Collecting vector features with a drone or an aircraft Drones or unmanned aerial vehicles (UAVs) can be navigated to specific locations within a landscape, and their position can be recorded as a point feature. These point features can be transferred to a computer, and within GIS software can be used without adjustment, or they can be connected to form a line or polygon features. After the development of an orthophotograph, or a bundled mosaic of individual drone-captured vertical images, algorithms can be employed to detect the edges of distinct features within these images and convert these to vector polygon features. The more obvious distinct features, such as openings in forests, buildings, roads, and lawns not covered by tree canopies would be suitable for this purpose. These feature classes are recognized and created based on similarities in the RGB (red, green, blue) and infrared spectrums of the electromagnetic spectrum through an image classification process. The edges of the feature classes are then mathematically determined and converted to lines that form the edges of polygons. More than likely some smoothing and transformation (separation of seemingly connected yet unrelated features) is necessary during the processing of the drone imagery to more successfully create vector GIS databases (Sahu and Ohri, 2019). LiDAR (light detection and ranging) technology can be used to develop point clouds (a large collection of points) from LiDAR sensors mounted on drones or aircraft (or held in one’s hand). A point cloud contains point features that are georeferenced both in terms of horizontal coordinates (X and Y, easting and northing) and a vertical coordinate (Z, height or elevation). The points are created from a laser pulse emitted by the LiDAR sensor mounted on a drone or an aircraft (or held in one’s hand), and the sensed return or reflected energy from the same pulse. The time it takes to emit and sense the reflected pulse of energy, along with the position of the drone or aircraft (or person) when this occurs, allows the system to mathematically create the points that represent the tops of trees, the boles of trees, the ground, and buildings, for example. The intensity of the returned or reflected pulse of energy is an attribute assigned to each point.

Collecting structured and unstructured data Unstructured data comes from many sources such as spreadsheets, printed maps, documents, and other information where the spatial context is not present, whereas structured data includes spatial context and may be more easily transformed into a GIS format (Willmes et al., 2017). Some vital unstructured information of interest may have no geographic reference, yet if it could be referenced, it could be used to address environmental and socio-cultural issues of interest to society (Willmes et al., 2017). This information could be integrated with a GIS database through the development and population of attribute fields associated with the appropriate GIS features. A join process in GIS (described in Chapter 7) might also be employed if the size and depth of the data are too extensive to allow one to enter the information by hand. This process would require a GIS database and some other spatial or nonspatial data file that share a common field (column) of data providing a linkage between the two. For example, one

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could join the nonspatial information from a timber cruise (forest sample) with the spatial features that represent the places where the trees were sampled (the plot centers or the prism points) if the GIS database containing the plot centers or prism points contained a field with the plot or point number, and the nonspatial data had the same type of field. Structured data might involve the collection of coordinates from hard-copy maps or other sources such as spatial browsers available through the Internet. Once geographic coordinates of features have been acquired, they can be converted to GIS databases. To do so, X (easting) and Y (northing) coordinates, along with information about the coordinate system, can be used to create a GIS database. For example, the following coordinates may represent the four corners of a parcel of land in western Oregon that is legally described using the Public Land Survey System (PLSS) as SW 1/4 SE 1/4 Section 32 Township 11 South Range seven west, Willamette Meridian (Oregon), has the Benton County tax lot number 117320000201, and is owned by the State of Oregon and managed by the Department of State Lands. 44.5681 N 44.5681 N 44.5644 N 44.5644 N

123.5669 W 123.5618 W 123.5618 W 123.5669 W

The coordinates can be reformatted in a number of ways that would allow the creation of a GIS database within GIS software systems. For example, the following text file format (sometimes referred to as a flat file) is useful for creating a point feature class (point GIS database). Y,X 44.5681,123.5669 44.5681,-123.5618 44.5644,-123.5618 44.5644,-123.5669 An unattributed (empty) polygon GIS database can then be created, within which the boundary of the parcel can be drawn by connecting the points through heads-up digitizing (Fig. 5.10). Diversion 5.5 Take the information noted in the flat file concerning the four corners of a parcel of land in western Oregon and create a point GIS database. Then, create a polygon GIS database by drawing a polygon that connects the four points. How large is the land area?

Scanning a map With respect to GIS and other similar data management concerns, scanning is the process of converting quantities of light reflected from aerial images or maps into electrical analogs, which are then converted to a digital record in binary form that

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FIGURE 5.10 The four points represent the corners of a parcel in western Oregon, along with the polygon that was drawn to connect these points.

facilitates processing the information within a computer system (Williamson, 1992). The act of scanning could produce vector GIS databases (for roads, streams, etc.) through a raster-to-vector conversion process, or simply produce a raster GIS database (aerial image, thematic map). With respect to the latter, the resulting graphic file pixel density is important. A minimum of 250 dpi (dots per square inch), up to about 500 dpi, seems reasonable for creating a raster database through scanning (Shawa, 2006). The spatial resolution (pixel size) of the graphic file depends not only on the scale of the original map and the quality of detail desired but also on the desired pixel size. Larger pixel sizes result in smaller computer files yet also result in detail farther from the original shape of features than when smaller grid cell sizes are used. Scanning resolution is therefore important (Ansoult et al., 1990) and may dictate the resulting pixel size. However, higher scanning densities (dpi) assumed during the scanning process can increase computer file size exponentially. For example, an image file scanned using 500 dpi actually contains four times as much data as an image file scanned using 250 dpi. An image file scanned using 1000 dpi would contain four times as much data as a 500-dpi scanned image, and 16 times as much data as a 250-dpi scanned image. It is for these reasons that the compression ratio of file formats is also important. Shawa (2006) suggested that a 10:1

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FIGURE 5.11 A portion of a 1938 aerial image of the Whitehall Forest, near Athens, GA, rotated, stretched, and referenced the current road, railroad, and powerline features.

compression ratio seemed appropriate given the quality of the scanned image and the resulting file size. Other issues that may need to be considered involving scanning maps or images include the number of colors allowed (e.g., 8-bit or 256 colors, 24-bit or 16777216 possible color variations) and whether efforts to balance the colors or to clean the scanned image are necessary. When scanned or digitized databases contain distortions throughout, a process called rubber sheeting may be necessary to stretch or shrink certain areas of a GIS database to conform to the true positions of landscape features (Doytsher and Gelbman, 1995). Rubber sheeting transforms the positions of all features within a GIS database according to their original position relative to high-quality reference positions. For example, within some GIS software programs, one can import an unregistered aerial image, and using known ground positions from a second, trusted GIS database, locate the corresponding registration points in the unregistered image. In effect, the previously unregistered image

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FIGURE 5.12 Large format aerial image camera. Credit: U.S. Department of the Interior, Geological Survey (2014a).

may then be mathematically rotated and warped so that the registration points in common between the two databases match as closely as possible (Fig. 5.11). A process of affine transformation is employed in an attempt to fit the image to positions within a previously developed GIS database, yet this does not preserve distances or angles in the rotated and warped raster image.

Capturing an aerial image, then perhaps classifying the image Aerial systems are those that use small airplanes or helicopters and expensive, largeformat cameras (Fig. 5.12) to capture images of landscape features from 5000 to 20,000 feet (1500 to 6000 m) above ground. This type of imagery is collected by aerial imagery professionals using systems that ensure that the images are vertical (no more than 3 degrees tilt from the land), that proper overlap among images along and between flight lines is ensured, and that the amount of haze, clouds, and other atmospheric issues are minimized. Small airplanes such as a Cessna are used as vehicles for capturing aerial images. The cameras can capture images on large format film (9  9 inches, 23  23 cm) such as Kodak Plus-X Aerographic film, as well as capture images using digital cameras such as a Zeiss RMK or Leica DMC III. Private landowners, timber companies, and governmental agencies all use aerial imagery professionals to conduct this work. It is possible, but rare these days, that a company or agency will directly own airplanes and employ pilots to capture aerial images except for purposes of responding to cases of great emergency (fires, hurricanes). Aerial imagery is often captured as individual frames (Fig. 5.13) along a predesigned flight line (Fig. 5.14). With an appropriate amount of overlap (endlap along a flight line or sidelap between adjacent flight lines), landscape

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FIGURE 5.13 Individual frame image of a place in the Dinosaur National Monument (Utah). North is to the right. The position is approximately 40.4512 north latitude and 109.2407 west longitude. Credit: U.S. Department of the Interior, Geological Survey (2014b).

FIGURE 5.14 A flight line index for a survey of the Dinosaur National Monument (Utah) in 2001. Credit: U.S. Department of the Interior, Geological Survey (2014b).

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features can be viewed in stereo (three dimensions) resulting in a small, yet realistic, model of the landscape that can be very informative for timber harvesting and forest management purposes. Aerial views of a landscape can be captured and stored on film or by digital sensors. Digital sensor imagery is most common today, and often the charged coupled device (CCD) or complementary metaleoxideesemiconductor (CMOS) devices within a camera record the amount of electromagnetic energy that is reflected or emitted from landscape features. Often, the energy is split and recorded separately in the blue, green, red, and infrared energy spectrums. Vertical aerial images are often used to make aerial image composites that people view on Google Earth and other Internet-based programs that rely on aerial imagery. The NAIP program is one organization that captures aerial imagery for broad expanses (states) every 2e4 years during the spring and summer months. There exist other public programs and privately-owned companies that will also capture this type of imagery and develop digital orthophotographs (images that have been differentially rectified to remove topographic displacement). An orthophotograph may consist of a single frame image (Fig. 5.15) or a composite of single-frame images (Fig. 5.16). Once an aerial image has been georeferenced, or has undergone the assignment of real-world X and Y coordinates to features on the photo, and rectified, it is considered an orthophotograph and can be opened in GIS software and positioned in the correct place on Earth. When this process has been conducted appropriately, most of the distortion and geographic displacement within the original aerial images have been removed. Orthophotographs FIGURE 5.15 A single frame digital orthophotograph with a property boundary superimposed. Credit: Base map from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021).

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FIGURE 5.16 A 2020 national agricultural imagery program (NAIP) county mosaic for Winnebago County, Wisconsin. Credit: County mosaic from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021).

FIGURE 5.17 Steve Stroud (left) a Department of the Interior Office of Aviation Services instructor pilot, Jason Duke (middle), and John Edwards (right), practice flying the 3DR Solo UAS. Credit: U.S. Department of the Interior, Fish and Wildlife Service (2017).

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have the planimetric accuracy of a map (scale is uniform throughout the image) with the pictorial quality of a printed photograph, and therefore they are useful as base maps for various GIS activities, particularly those that involve creating GIS databases. Fixed-wing or propeller-based drones are commonly used today as remotely piloted vehicles that collect aerial imagery. Other forms of flying systems that have been (and perhaps still are) used to capture small format aerial imagery include kites, blimps and balloons, rockets, and birds. A drone is a physical aircraft that flies without human passengers (Fig. 5.17). When the larger system is considered (systems for communication, control, etc.), a drone is a component of an unmanned aircraft system (UAS), just as a computer is a component of a GIS. Aerial images captured by drones or other remotely piloted vehicles can have a greater spatial resolution, and perhaps be developed at a larger scale, than aerial imagery collected with cameras mounted in airplanes. The type of cameras used in remotely piloted vehicles is generally much smaller and lighter than the cameras mounted in airplanes. However, the flying height above the landscape is much lower when using a drone. Often today, aerial images collected by remotely piloted vehicles are captured using digital cameras; however, 35 mm film-based cameras can also be used in kite and balloon systems.

Capturing a satellite image, then perhaps classifying the image Certainly, there are many more instances of airplane and drone flights than there are satellite missions. One reason is that once satellites are launched, they remain in orbit until they are decommissioned to the graveyard orbit (further outward from most other satellite orbits), or they burn up on their return through the atmosphere. Although a drone or aircraft can be regularly flown under various time and weather conditions within the troposphere of Earth, sending a vehicle into outer space to collect information about Earth’s resources is a much more complicated and expensive endeavor. Perhaps surprisingly, as of August 2021, there were 6523 satellites in orbit around the Earth (United Nations Office for Outer Space Affairs, 2021). In contrast to drone or airplane missions, many of these satellites continuously capture images of Earth. Of course, many of the 6000þ operational satellites have purposes other than collecting information about Earth’s land and water resources. Either operating as programmatic satellites or as sensors aboard other satellites, Landsat, ASTER, SPOT, MODIS, Sentinel, GeoEye, Siwei, EROS, Ikonos, and others continuously collect information on land and water surface conditions as they orbit Earth. The Landsat 9 satellite, launched in 2021, orbits Earth every 99 min at an altitude of 438 miles (705 km); the orbit shifts a little each time, and therefore repeatability of the exact path around Earth is 16 days (National Aeronautics and Space Administration, 2020). The information collected is based on sensed electromagnetic energy and is stored in raster grid cells of various sizes. The red, green, and blue bands (ranges of energy sensed) of Landsat imagery, for example, are available with a 30 m spatial resolution.

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Satellite imagery raster GIS databases are either available free of charge or may require a purchase fee, but like aerial and drone imagery, imagery derived from satellites can be used as base maps from which other GIS databases can be created. In contrast to aerial and drone imagery, satellite imagery has not been used as frequently as a base map for digitizing operations. Rather, satellite imagery is often used in conjunction with algorithms to create other raster GIS databases (Wulder et al., 2019). Satellite imagery can be subjected to processes that derive information about Earth that would ordinarily not be evident. The development of a normalized difference vegetation index (NDVI) is but one example. The reflectance values sensed in the red and near-infrared (NIR) bands are used to derive NDVI ((NIR-Red)/(NIR þ Red)). Indices such as NDVI are covered in greater detail in Chapter 9. Many satellite imagery products provide worldwide coverage at very short time intervals (weeks or days), which would not be possible to develop in a timely or extensive manner with aerial or drone systems. With respect to practical applications in forestry and natural resource management, even though the grid cells within satellite imagery are rather large, some satellite imagery has been found to be particularly useful for monitoring forest health (Fig. 5.18), urban growth, and wildfires (Newman, 2020).

Where to find GIS databases As previously mentioned, some of the GIS databases created by governmental agencies are freely available to other governmental agencies, private and nonprofit organizations, and universities and colleges. However, some especially sensitive GIS databases, such as those representing locations of endangered species or archeological sites, are generally not freely available beyond a certain scope of organizations and departments. Data sharing in this sense facilitates accessibility to standard databases, promotes the use of these standard databases, reduces costs related to data development, and establishes partnerships among public and private organizations (Federal Geographic Data Committee, Wetlands Subcommittee, 2009). Many U.S. national forests freely offer GIS

FIGURE 5.18 Relative forest health condition of forested landscapes of the Uinta Mountains (Utah) in 1992 (left) and 2010 (right) through classified Landsat imagery. Dark green reflects healthy forests, dark red reflects forests disturbed by mountain pine beetles (Dendroctonus ponderosae). Credit: U.S. Department of the Interior, Geological Survey (2016).

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databases through the Internet, FTP sites, database servers, or cloud-based applications. Further, there are a number of online repositories, such as the Florida Geographic Data Library (2021) and the U.S. Geological Survey’s federal website, The National Map, where one can download GIS databases. These portals for GIS databases often are accompanied by disclaimers concerning the accuracy, completeness, and timeliness of the data, and indicate that users accept all risks, financial and otherwise, when using the data. Other GIS databases can be purchased from consultants, and similarly, GIS databases can be commissioned from a consultant. Most forestry companies that are not engaged in selling GIS databases consider those that they create to be proprietary. A request for proprietary GIS databases can be made from the owners, which may require one to sign a Memorandum of Understanding (MOU) on the use and security of the GIS databases. Proprietors are generally under no obligation to share their databases with the general public, academic institutions, or public agencies. However, they may engage in datasharing endeavors if these seem necessary to meet a broader objective of great interest to society. Inspection 5.1 Using an Internet browser, access the California State Parks GIS Data portal and locate the webpage that provides links to the open and public GIS data. What do the terms of use, warranty, and disclaimers communicate to people who potentially download and use these GIS databases? In the United States, GIS databases can be developed at the national, state, regional or metropolitan, county, and city-level and used by local decision-makers and planners (Nedovic-Budi c et al., 2009). These databases have varying degrees of public availability, quality, coverage, and accuracy. Often, these databases are aggregated from various agencies and stored in repositories called clearinghouses (Strasser, 1998). Types of GIS databases available at the local level might include transportation, land use and parcels, administrative boundaries, and hydrology. GIS programs can be a powerful tool for increasing citizen engagement in planning and decision-making at the local level, which further underscores the benefit of creating and maintaining spatial data. Today, crowdsourced GIS databases, referred to as volunteered geographic information (VGI) (Goodchild, 2007), have become more readily available. As the name suggests, these are GIS databases created and shared by members of the public (Basiri et al., 2019). These databases are commonly developed through online mapping interfaces or phone apps and are created for many purposes such as mapping the locations of public services like schools and hospitals, road networks, and places important when public health and natural disasters are being considered. GIS databases have been created globally through Internet-based interfaces such as OpenStreetMap and Wikimapia, and in the U.S. through the U.S. Geological Society’s National Map Corps. The existence of more locally specific GIS databases may be bolstered by the increase in citizen science programs, which facilitate public

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participation in GIS database creation (Thompson, 2016). Applications exist for more localized GIS database generation, including such things as reporting the locations of road potholes or sightings of rare birds. In natural resource management, urban forestry has taken advantage of citizen scientist inputs to build tree location databases in several U.S. cities (Foster et al., 2017). Wildfire mapping has also benefited from VGI in combination with geotagged social media activity (Tavra et al., 2021). Care and caution should be applied when using a VGI database since the spatial data are not always created by GIS professionals or experts in a specific field. These databases may be subject to error due to incorrect decision-making by the participant (Comber et al., 2016), or because they are incomplete and not representative of the full extent of a spatial phenomenon. Translation 5.2 One Friday night you find yourself hanging out with friends at a restaurant in the city or town where you live. The conversation is lively, and you want to contribute, so you blurt out that you are developing a crowdsourced GIS database of Sasquatch sightings. Since your friends are not very savvy with GIS, in just a few words, elaborate on the concept of a crowdsourced GIS database.

Data quality, accuracy, and errors in GIS databases For developing GIS databases, some organizations provide standard instructions that employees should follow. These procedure sheets attempt to ensure database development consistency among multiple people in a group by listing the desired procedures to conduct, from digitizing to error checking (Nugent, 1995). In the GIS database development process that was noted at the beginning of this chapter, assessments of quality, accuracy, and error can occur after one has acquired the preliminary data needed to create a GIS database, and after the final GIS database has been created. A policy or a set of protocols may therefore be beneficial in providing guidance to those entrusted with collecting or developing GIS databases. For many larger organizations, particularly large governmental agencies, standards have been developed for GIS database collection, development, acquisition, and postprocessing. The standards may include language on metadata requirements, positional accuracy, data exchange protocols, and other issues related to the development and maintenance of GIS databases. The Wetlands Mapping Standard (Federal Geographic Data Committee, Wetlands Subcommittee, 2009), for example, indicates the type of aerial imagery that should be used as a base map for digitizing wetlands (color infrared digital orthophotograph with a 1 m spatial resolution), the amount of information required to meet a completeness standard for attributes (ecological system, subsystem, class, subclass, water regime, and special modifiers), the minimum mapping unit size, and instructions for edge matching and for ensuring high levels of logical consistency and positional accuracy, among other concerns.

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Reflection 5.3 Throughout this book, we refer to and use GIS databases that were created by the U.S. Forest Service for three national forests (Allegheny, Chippewa, Francis MarioneSumter). No enhancements were made to these GIS databases. If you were employed in a job that required you to use data such as these, how do you feel about the quality of data that was acquired from the national forests? Do you feel a need to assess the quality and accuracy of this data? Why or why not? As discussed in Chapter 2, errors in the spatial features and attributes of GIS databases can be systematic (having a pattern), gross (usually large, singular blunders), or random. They can relate to logical consistency (e.g., multiple values in an attribute field that mean the same thing), completeness (as opposed to incomplete representations of landscape features), positioning problems, and computational problems that arise from the use of calculations (e.g., incorrectly calculating a per-acre value from per-acre volumes) or the use of spatial tools (e.g., applying the wrong buffering tool or buffer width to represent riparian areas around streams). Errors in GIS databases can range from those that are relatively harmless to those that can be quite critical. For example, a relatively harmless error might involve the mislocation of a point that represents an owl nest tree; perhaps it is 10 m away from its true position on Earth. Unless precise habitat evaluations are based on this representation of the owl nest location, its misrepresented position in a GIS database likely has a limited impact on the management of the landscape in which it resides. However, if the elevations within a contour elevation GIS database are 10 m in error, one way or the other, and they are used for floodplain mapping and management of urban development activities, these errors can have great consequences. Some of the logical consistency and edge matching concerns noted in the Wetlands Mapping Standard (Federal Geographic Data Committee, Wetlands Subcommittee, 2009) include: Polygons intersecting the border of a project area shall be closed along the border. Segments make up the outer and inner boundaries of a polygon tie end-to-end to completely enclose the area. Line segments shall be a set of sequentially numbered coordinate pairs. Neither duplicate features nor duplicate points exist in a data string. Intersecting lines are separated into individual line segments at the point of intersection. All nodes are represented by a single coordinate pair, which indicates the beginning or end of a line segment.

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Wetland mapping units . will be edge-matched with [previous] interpretations . within the project area. All linear and polygon features will be edited to ensure an identical or coincident transition across images in the entire project area. A GIS database, like all models, will never be a perfect model of a real-world system. Users of GIS databases have two choices when it comes to the inherent error: either ignore the error (accept or absorb it) or try to reduce the error (collect more data, edit the GIS database, etc.) (Hunter and Goodchild, 1995). Ideally, in developing GIS databases, one would want to minimize errors and mistakes in digitizing and populating attributes, and ultimately, one would prefer GIS databases to be both complete and correct (Nugent, 1995). In assuring themselves that they have sufficient high-quality data to work with, a person may need to conduct a formal or informal assessment of errors in these databases. Some of these errors might be considered errors of commission, which occur through the creation of a GIS database. These often involve a spatial feature being created more than once, or the creation of sliver polygons through digitizing or other automated functions (overlay, buffer, etc.). Others, such as errors of omission, can also occur when landscape features that were meant to be created were overlooked somehow during the GIS database development process (Nugent, 1995). Some of these errors can be located through sorting of the attribute data. For example, slivers can be detected by examining the areas of polygons (slivers have very small areas), while missing attribute data can be detected by applying a query (described in Chapter 7) to locate empty cells in an attribute table. Inspection 5.2 Some GIS databases evolve as management activities, inventories, and other management issues arise, and as people have the time to investigate and address spatial feature and attribute conditions. Open the Allegheny National Forest vegetation stand GIS database in your preferred GIS software. This database can be accessed from the book’s website (gis-book.uga.edu). In the attribute table of this GIS database, select four fields (columns) and describe the consistency and completeness of the data found there. In developing or acquiring GIS databases, some editing of the spatial features or attributes is often necessary. Perhaps a GIS database acquired from another source may need to be reprojected. Once this occurs, verification of the spatial positioning would likely be necessary. In other cases, a newly acquired or developed GIS database may need to be edge-matched to other existing GIS databases, particularly the boundary of a property, to ensure that it is consistent with other established data. In at least one case, an Internet-based data management input tool has been devised to standardize data collection and GIS database development efforts, in an attempt to avoid issues of duplication of effort, missing data, and mismatched data features (e.g., some more

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accurately defined than others) (Dhamaniya et al., 2016). As a final note, to mitigate some of these data quality issues and to provide all levels of society access to the information they contain, GIS databases might be developed to comply with standards such as the Open Geospatial Consortium (OGC) standards to facilitate integration with a variety of GIS software programs (Willmes et al., 2017).

Conclusions More often today, there is a need for GIS databases to assist with the management of forests, the organization of research endeavors, and with the delivery of educational content. The collection of GIS data we provide with this book for certain national forests in the United States is an example of GIS databases that assist with the management of forests. Often, national-level public agencies may freely provide the GIS databases that they have created. Acquiring similar GIS databases from the state, county, and city levels of public administration of lands is much more problematic and uncertain. Further, acquiring GIS databases from private individuals and forestry companies is not very likely without an MOU that outlines the uses of the data, and the security measures employed to prevent public access to the data. Therefore, when faced with the management of a newly acquired forested area, the availability of GIS databases to assist with the management of these forests may be limited. This chapter explored a number of ways in which one could develop GIS databases. The GIS database development process not only involves capturing a description of landscape features through the use of vector or raster data structures, but also involves managing this data through processes that are aimed at correction, enhancement, edge-matching, and others that ensure completeness and consistency with other GIS databases. Exercises exploring the concepts covered in this book using ArcGIS or QGIS are available on the book website: gis-book.uga.edu.

References Ansoult, M.M., Soille, P.J., Loodts, J.A., 1990. Mathematical morphology: a tool for automated GIS data acquisition from scanned thematic maps. Photogrammetry Engineering and Remote Sensing 56 (9), 1263e1271. Basiri, A., Haklay, M., Foody, G., Mooney, P., 2019. Crowdsourced geospatial data quality: challenges and future directions. International Journal of Geographical Information Science 33 (8), 1588e1593. Bettinger, P., Fei, S., 2010. One year’s experience with a recreation-grade GPS receiver. Mathematical and Computational Forestry & Natural-Resource Sciences 2 (2), 153e160. Bettinger, P., Wing, M.G., 2004. Geographic Information Systems: Applications in Forestry and Natural Resources Management. McGraw-Hill, Inc., New York. Comber, A., Mooney, P., Purves, R.S., Rocchini, D., Walz, A., 2016. Crowdsourcing: it matters who the crowd are. The impacts of between group variations in recording land cover. PLoS ONE 11 (7), e0158329.

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Dai, L., Wang, J., Rizos, C., Han, S., 2003. Predicting atmospheric biases for real-time ambiguity resolution in GPS/GLONASS reference station networks. Journal of Geodesy 76 (11e12), 617e628. Danskin, S.D., Bettinger, P., Jordan, T.R., Cieszewski, C., 2009. A comparison of GPS performance in a southern hardwood forest: exploring low-cost solutions for forestry applications. Southern Journal of Applied Forestry 33 (1), 9e16. Dhamaniya, A., Sonu, M., Krishnanunni, M., Praveen, P., Jaijin, A., 2016. Development of web based road accident data management system in GIS environment: a case study. Journal of Indian Society of Remote Sensing 44 (5), 789e796. Doytsher, Y., Gelbman, E., 1995. Rubber-sheeting algorithm for cadastral maps. Journal of Survey Engineering 121 (4), 155e162. Federal Geographic Data Committee, Wetlands Subcommittee, 2009. Wetlands Mapping Standard. Federal Geographic Data Committee, Reston, VA. FGDC Document Number FGDC-STD-015-2009. Florida Geographic Data Library, 2021. Welcome to the FGDL Metadata Explorer. www.fgdl.org/ metadataexplorer/explorer.jsp (accessed 29.12.21). Foster, A., Dunham, I.M., Kaylor, C., 2017. Citizen science for urban forest management? Predicting the data density and richness of urban forest volunteered geographic information. Urban Science 1 (3), Article 30. Frith, M.W., 1997. Measuring Error in Manually Digitized Maps. Master of Science Thesis. University of Regina, Regina, Saskatchewan. Goodchild, M.F., 2007. Citizens as sensors: the world of volunteered geography. Geojournal 69, 211e221. Hunter, G.J., Goodchild, M.F., 1995. Dealing with error in spatial databases: a simple case study. Photogrammetry Engineering and Remote Sensing 61 (5), 529e537. Lee, T., Bettinger, P., Cieszewski, C.J., Gutierrez Garzon, A.R., 2020. The applicability of recreation-grade GNSS receiver (GPS watch, Suunto Ambit Peak 3) in a forested and an open area compared to a mapping-grade receiver (Trimble Juno T41). PLoS ONE 15 (4), e0231532. Merry, K., Bettinger, P., 2019. Smartphone GPS accuracy study in an urban environment. PLoS ONE 14 (7), e0219890. National Aeronautics and Space Administration, 2020. Landsat 9, Earth from Space. U.S. National Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, MD, NP-2020-11621-GSFC. Nedovic-Budi c, Z., Knapp, G.-J., Budhathoki, N.R., Cavri c, B., 2009. NSDI building blocks: regional GIS in the United States. URISA Journal 21 (2), 5e23. Newman, T., 2020. National Land Imaging Program. https://pubs.usgs.gov/fs/2020/3034/fs20203034.pdf (accessed 29.12.21). Nugent, J.L., 1995. Quality control techniques for a GIS database development project. Photogrammetry Engineering and Remote Sensing 61 (5), 523e527. Sahu, M., Ohri, A., 2019. Vector map generation from aerial imagery using deep learning. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5, 157e162. Shawa, T.W., 2006. Building a system to disseminate digital map and geospatial data online. Library Trends 55 (2), 254e263. Somers, R.M., 2020. GIS project planning and implementation. In: Bauzer Medeiros, C.M. (Ed.), Encyclopedia of Life Support Systems, Advanced Geographic Information Systems, Vol. II. United Nations Educational Scientific and Cultural Organization, Paris, France. www.eolss.net/samplechapters/c01/E6-72-03-01.pdf (accessed 29.12.21). Strasser, T.C., 1998. Geographic information systems and the New York State Library: mapping new pathways for library service. Library Hi Tech 16 (3/4), 43e56.

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Tavra, M., Racetin, I., Peros, J., 2021. The role of crowdsourcing and social media in crisis mapping: a case study of a wildfire reaching Croatian city of Split. Geoenvironmental Disasters 8, Article 10. Thompson, M.M., 2016. Upside-down GIS: the future of citizen science and community participation. Cartographic Journal 53 (4), 326e334. United Nations Office for Outer Space Affairs, 2021. Online Index of Objects Launched into Outer Space. United Nations Office for Outer Space Affairs, United Nations Office at Vienna, Vienna, Austria. https://www.unoosa.org/oosa/osoindex/search-ng.jspx?lf_id¼ (accessed 29.12.21). U.S. Department of Agriculture, Forest Service, 1989. Tommy Gregg at the Regional Office, with an Early Digitizing Table behind Him. https://commons.wikimedia.org/wiki/File:1989._Tommy_Gregg_with_ an_early_digitizing_table_behind_him._Tommy_led_the_first_efforts_to_automate_the_Region_6_ aerial_detection_survey_GIS_data._Portland,_OR._(35432849605).jpg (accessed 29.12.21). U.S. Department of Agriculture, Natural Resources Conservation Service, 2021. Geospatial Data Gateway, Direct Data/NAIP Download. https://gdg.sc.egov.usda.gov/GDGHome_DirectDownLoad. aspx (accessed 29.12.21). U.S. Department of the Interior, Fish and Wildlife Service, 2017. Unmanned Aircraft Systems Take Flight in Southeast Region. www.fws.gov/southeast/articles/unmanned-aircraft-systems-take-flight-insoutheast-region (accessed 29.12.21). U.S. Department of the Interior, Geological Survey, 2014a. Using High-Resolution Digital Aerial Imagery to Map Land Cover. https://pubs.usgs.gov/fs/2014/3009/pdf/fs2014-3009.pdf (accessed 29.12.21). U.S. Department of the Interior, Geological Survey, 2014b. USGS-NPS Vegetation Characterization Program, Dinosaur National Monument. www.sciencebase.gov/catalog/item/54b6d03be4b03ff52 7031686 (accessed 29.12.21). U.S. Department of the Interior, Geological Survey, 2016. Landsat Pine Beetle Pair. www.usgs.gov/ media/images/landsat-pine-beetle-pair (accessed 29.12.21). U.S. Department of the Interior, Geological Survey, 2021. Data Acquisition Methods. www.usgs.gov/ products/data-and-tools/data-management/data-acquisition-methods (accessed 29.12.21). Williamson, A.N., 1992. Map scanning for GIS applications. Photogrammetry Engineering and Remote Sensing 58 (8), 1199e1202. Willmes, C., Becker, D., Verheul, J., Yener, Y., Zickel, M., Bolten, A., Bubenzer, O., Bareth, G., 2017. PaleoMaps: SDI for open paleoenvironmental GIS data. International Journal of Spatial Data Infrastructures Research 12, 39e61. Wulder, M.A., Loveland, T.R., Roy, D.P., Crawford, C.J., Masek, J.G., Woodcock, C.E., Allen, R.G., Anderson, M.C., Belward, A.S., Cohen, W.B., Dwyer, J., Erb, A., Gao, F., Griffiths, P., Helder, D., Hermosilla, T., Hipple, J.D., Hostert, P., Hughes, M.J., Huntington, J., Johnson, D.M., Kennedy, R., Kilic, A., Li, Z., Lymburner, L., McCorkel, J., Pahlevan, N., Scambos, T.A., Schaaf, C., Schott, J.R., Sheng, Y., Storey, J., Vermote, E., Vogelmann, J., White, J.C., Wynne, R.H., Zhu, Z., 2019. Current status of Landsat program, science, and applications. Remote Sensing of Environment 225, 127e147.

6 Geographic data management Introduction The management of geographic information system (GIS) databases involves many concerns that include obtaining and documenting the data, using the data, storing the data and its derivatives, and maintaining the security of the data (Rhodes, 2013). So far, this book has explored various data types, reference systems, and collection methodologies used in the course of creating and using GIS databases. In future chapters, examples will be provided concerning the complex geographical analyses one can conduct with GIS databases. Going forward, this book will provide examples of methodological and processing techniques involving spatial databases. In the course of these discussions, many different GIS databases will be created, some will be considered temporary (the result of intermediate processing steps), and others will be considered permanent (final). Therefore, this seems like a good point in time to discuss the issues of GIS data management. Everyone has their favorite computer file storage locationsdsome people store files on their desktop or laptop devices (Fig. 6.1), some store files in a documents folder on a server or cloud storage system, while others prefer to store data on one or more external drives. What seems effective and efficient (what works) for one person may not be viewed the same way by another. Security, accessibility, and cost are elements that should be considered when determining what storage is best suited. Understanding the

FIGURE 6.1 A file local storage area for a Chippewa National Forest GIS database. Geographic Information System Skills for Foresters and Natural Resource Managers. https://doi.org/10.1016/B978-0-323-90519-0.00004-2 Copyright © 2023 Elsevier Inc. All rights reserved.

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risks associated with file storage options and developing a personal disaster recovery plan for the unlikely (yet potential) event that data might be lost are two exercises an individual should undergo when using GIS data. One may also need to acknowledge that the organization employing them may have developed standard protocols for storing, managing, accessing, and sharing GIS databases and that these protocols must be followed regardless of any personal preferences for storing computer files. GIS database management involves records management activities, those formal or informal systems people use for asserting control over the organization, maintenance, use, access, dissemination, and disposition of GIS-related computer files. In these acts, file naming conventions, accession rules (for moving files to an official file storage location), versioning, archiving, and other processes may be involved in a formal file management system. Further, the physical location of files is important as files locally stored, centrally stored on corporate or official servers, or stored in cloud-based services all have advantages and disadvantages with respect to cost and efficiency. In many larger organizations, there are centralized or corporate GIS databases that are considered the official records of the land management organization. The management responsibility for these is often assigned to a single person or group such as a team of information technologies specialists. Decentralized GIS databases include those that are copied (and perhaps modified) versions of the corporate databases and many other GIS databases that have been created by individual people for temporary or permanent use during their employment. As with important printed records, consideration for computer file management should be given to the volume of file space needed, the value of the contents, the ease and frequency of use, the number of potential users, and the type of activities that will be associated with the files (viewing, updating, etc.) (U.S. Department of Housing and Urban Development, 1971). As you may come to find, GIS database management includes not only the acquisition and storing of computer files but also the rendering or manipulation of the data in an efficient manner. The physical location where files are stored is important, especially if other users need to find and access data. For personal-use data or stand-alone projects, it may be simple enough to keep a data dictionarydwritten or digitallydto document each file name, storage location, and a brief description of contents. For larger projects involving multiple users who are possibly working from various locations, GIS databases may be best maintained on a server or in a cloud storage system. By way of example, assume someone has several aerial photographs and a few GIS databases that describe fields and forests that their family owns. These data files may easily be stored as stand-alone files in a directory called “familyproperty” on a personal computer. The computer (central processing unit (CPU)) and its random access memory (RAM) may also be sufficient to process and display the data very quickly. But, if this person works for an organization that has just acquired LiDAR (light detection and ranging) data for forests that are located in several different states or provinces, the same personal computer may be unsuitable for these tasks. First, storing the data may require terabytes (TB) of storage space; second, multiple people may need to simultaneously access the data; and third, the RAM and CPU speed may both be insufficient for efficiently managing and processing the data.

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Storage and file size Digital data exists as a series of numbersdgroups of ones and zeroes, known as bits (Fig. 6.2). With respect to satellite or aerial imagery (raster data), the number of bits of data associated with each grid cell provides evidence of the range of valid values for the image. Given that the values are binary, 2n represents the range of valid values, where n represents the number of bits. For an eight-bit image, each cell can be represented by one of 256 (28) values or numbers from 0 to 255. Eight bits are equal to 1 byte, and 1 byte can best be thought of as a single character or number (0e255), so representing GIS would require 3 bytes. A byte is the standard unit for storing computer-related information and facilitates the manner in which computers process information. Personal computers today are likely either 32-bit or 64-bit computing machines; this refers to how much information can be processed at one time (4 bytes or 8 bytes, respectively). The size of a computer file can typically be viewed following the names of those files listed in computer folders (subdirectories). These file size values represent the units being stored, and they are important as they may be too large for certain storage media. For example, 30 kB is 30 kilobytes, 30 MB is 30 megabytes, and so on. Systems using base-10 standard terminology, accepted as International System of Units (SI) standards, have computer storage capacities that are:





Kilobyte ¼ 1000 bytes (one thousand) Megabyte ¼ 10002 or 1,000,000 bytes (one million) Gigabyte ¼ 10003 or 1,000,000,000 bytes (one billion) Terabyte ¼ 10004 or 1,000,000,000,000 bytes (one trillion)

Similar in magnitude, computer storage systems using base-2 terminology have computer storage capacities that are:
Kilobyte ¼ 210 or 1024 bytes
Megabyte ¼ 220 or 1,048,576 bytes

FIGURE 6.2 Relative comparison between (A) a bit (1e0/binary), (B) a byte (eight bits), and (C) 1 kilobyte (1024 bytes).

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Gigabyte ¼ 230 or 1,073,741,824 bytes
Terabyte ¼ 240 or 1,099,511,627,776 bytes Inspection 6.1 Download the Allegheny National Forest GIS data from this book’s website (gisbook.uga.edu). How many files are contained in this set of data? How much computer memory is required to store the databases on the computer you are using? Which is the largest of the files with respect to the memory required? When GIS databases are obtained or created, they are placed into the memory of a computer. The computer develops a path for them so that GIS software can find them. For example, the paths to two files might be: C:\GIS_Data\ChippewaNF\Chippewa_stands.dbf E:\GIS_Data\AlleghenyNF\Allegheny_roads.shp Here, “C:\” is the root directory on the computer, or the “C drive” as it might be called. Alternatively, files might be stored on an external drive, such as “E:\” or some other letter. In this case, the path may be “E:\GIS_Data.” These directories and paths matter and this is why, with some GIS software, that data seems to have disappeared when storage directories are renamed or changed (or a folder e.g., folder/level). Some GIS software packages are sensitive to how file directories (and files) are named and refuse to access data in folders that include spaces in their nomenclature as opposed to underscores (“C:\GIS Data” compared to “C:\GIS_Data”). As one gains familiarity with different GIS programs, one will become more familiar with these idiosyncrasies and be able to adjust naming conventions accordingly when storing data. Diversion 6.1 Using the Internet, locate downloadable remotely sensed imagery, perhaps from the U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) program or from the Landsat program. What type of raster GIS data did you find? Who hosted the data? What were the conditions for downloading the data? How large is (are) the file(s)? What computer memory issues could arise in storing and processing this data? Where and how did you store this data? These storage issues are important, as they not only can tax the systems designed to provide access to single or shared users of GIS databases but also when designed appropriately, they may reduce confusion within a single user or a shared group of users concerning the location of GIS databases and what they might contain. Conceivably, it is possible to maintain stand-alone GIS databases for each feature that needs to be included in each map, with each file stored in a specific directory (named by project or property, for example). However, doing this can result in a system that requires a large amount of storage space. Further, doing this is likely inefficient causing people who need to access the data to expend extra time in locating the information that they need. Lastly, the use of stand-alone GIS databases for each feature that needs to be included in each

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map may result in confusion and decision-making or presentation errors. One could endeavor to organize or consolidate GIS databases with similar themes into a single subdirectory and use additional lower subdirectories to provide more refinement on themes of data found there. For example, one might create a political boundary subdirectory that contains further subdirectories representing states, counties, municipalities, zip codes, voting districts, and others. If this were a shared resource, other users could then more easily locate the information that they need. It is worth mentioning here that, particularly within large organizations, one should develop a working relationship with their information technology (IT) professionals to develop a solution to the data storage and management problem. Hopefully, the following offers some insights into data types and storage and management options that are available. As noted in Chapter 2, there are many formats for geospatial data files. These can vary depending on the software being used, the source of the data, the type of features that are represented, and the preference of the person who developed the data. Commonly, GIS database file formats include:





Vector files (e.g., .shp, .kml, etc.) Raster files (e.g., .sid, .tif, etc.) Geographic database files (e.g., geodatabase, map files, etc.) Relational databases (e.g., .dbf, SQLServer files, etc.) Diversion 6.2 With an Internet browser, visit The Ultimate List of GIS Formats and Geospatial File Extensions (GISGeography, 2021). Review the list of GIS-related databases. How many of these have you encountered so far in your career or education? Compared to your friends or colleagues, how do you rank?

The size of a GIS database depends on the method for storing the data it contains, and any sort of compression process that has been applied. Keyhole Markup Language (KML) files, for example, which are used in Google Earth and other software programs have an upper limit size of about 10 gigabytes, while shapefiles have an upper limit size of about 2 gigabytes (equivalent to about 70 million point features) (Esri, 2020). Some GIS processes, such as queries and overlays, can operate relatively slower or faster depending on the size of the database that is being used. Raster GIS database files can be exceedingly large and often require the application of a compression algorithm to store them on a computer. Importing several large, single, raster files and performing operations with them can therefore be a slow-going, tedious process. As GIS processes are conducted, the output files that are created can be very large as well. If, for example, GeoTIFFs are being produced that are 1.5 gigabytes each in size, one would need to consider where they will be stored, how many could be stored at that location, and how the information will be backed up (replicated elsewhere). Within GIS, file geodatabases assist in addressing some of these problems as they act as a repository and can therefore include several different formats. A geodatabase has no

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size limit, each GIS database contained within a geodatabase can be as large as one terabyte in size (Esri, 2019). Part of the functionality of a geodatabase is that they allow for the structuring and organization of data into groups or classes. This would allow one to, for example, combine feature classes for interstate highways, state highways, county roads, logging roads, waterways, and railroads into a dataset containing all modes of transportation. These types of storage systems allow multiple users to access files simultaneously and allow for versioning that tracks the various editions created from editing operations. Geodatabases are typically stored on a server that users can map or link to in order to access and query the data. When managing the storage, access, and maintenance issues of a large collection of GIS databases, some questions to consider include:






What are the GIS databases and who needs to access them? How many people need to view or edit the database? What are the heaviest access times, and can the system accommodate the traffic? How much data needs to be made available to all of the users? How much will the system cost?

Some understanding of the storage limitations and various formats for managing spatial data can sometimes be confusing. Capacity, functions, uses, costs and necessity will vary by the organization (Table 6.1). While users and organizations do not want to overcomplicate the process, all wish to maximize efficiency with regard to accessibility and processing. So, where does a GIS database physically reside? Phrases like “stored locally,” “on your machine,” “on a flash/stick/jump/external drive” are all common, as is “the cloud.” The next few sections delve further into these storage location options.

Table 6.1

Data storage options, capabilities, costs, and functions.

Type

Capacity

Cost

Function/use

Hard disk

18 TB

w$600

External drive

100 TB w1 GB w8 TB 1 TB

w$40,000 w$10 $400  $100

NAS

1 TB

 $250

Data storage; can purchase multiple drives for data storage Building toward NAS-level storage, web hosting Portable data storage; sizes range from keyring fob to w200  0.500 e 300  500 Technically portable but large enough to remain static; book-sized. Used for data storage. Expandable, networkable storage. Used for “personal cloud” applications, data storage, sharing, and backup. File geodatabasedpersonal/project storage. Reduces file size and allows for the organization. Enterprise: Multiuser systems with versioning; data storage and sharing. Cost varies based on the amount of data stored, processes performed, files added to and sent from storage, location, etc.; data accessible anywhere. No limit to data storage, etc.

Database $15,000þ Cloud

w$0.025/GB

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Reflection 6.1 Consider the last computer file that you created. Where was it saved?

Local disk Local computer disk space refers to the storage space available inside a computer, in places commonly labeled as the “C” or “D” drives. In many cases, it is possible to add or upgrade local disk drives to a personal computer, but ultimately these should be considered a finite means of data storage. At times, local disk drives can contain what seems like a significant amount of memory space; it is not uncommon presently to find 500 gigabytes or one terabyte hard drives located inside personal computers. Increasingly, these can be solid-state drives where data is stored in semiconductor cells, or hard disk drives, where data is stored on magnetic spinning disks, like the one illustrated in Fig. 6.3. When only one local disk drive exists in a personal computer or laptop, the operating system and all software applications will run and operate from and use the memory of, that drive. This may be a concern as some GIS software programs can require significant computer file storage space, and some GIS databases can be quite large, so caution is urged when using GIS software on a computer or laptop that has a single local disk. Therefore, it may be preferable to use a personal computer or laptop that contains more than one local disk, as this option offers an opportunity to increase processing speeds and increase the amount of data that can be made locally available. Diversion 6.3 Imagine that you work for a large natural resource management consulting agency. In this job, you are fortunate to have the ability to purchase a new computer. However, you need to develop a list of specifications for the purchase order. Through the Internet, locate the website of a company that sells personal computers. How much money would it cost for the personal computer you wish to purchase? How much additional money would it cost to add a second (or third) internal hard drive to the computer you wish to purchase? FIGURE 6.3 An example of a hard disk drive. Credit: Hard drive image from William Warby through Wikimedia Commons (https://commons. wikimedia.org/wiki/File:Hard_Drive_ (11644419853).jpg).

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More than likely, storing too much GIS data on a single local disk drive will slow down the processing speed of a computer. To address this, one might consider developing a routine, manual or scripted, that periodically backs up data files and clears or defragments the local disk space. Also advisable would be the development of a system where only the data immediately necessary are stored on a local disk drive, and all other data are stored remotely in the cloud or on external drives.

External drives External computer storage drives can be attached and detached from a personal computer or laptop relatively easily, and thus provide a varying amount of storage capacity and portability for maintaining GIS databases. Devices for external storage of data have evolved rapidly over the last 4 decades (Fig. 6.4) and their associated costs have decreased dramatically. Many of these options for data storage are no longer used nor accommodated on modern computers, as technology has advanced, allowing greater amounts of data to be stored on increasingly smaller devices. Today, external storage options exist where 32 gigabytes (or more) of data can be stored on a device that fits onto a keychain. Other external storage devices are larger, maybe the size of a deck of cards, and can store several terabytes of data (Fig. 6.5). Some individuals and smaller companies may opt for larger hard disk external drives that are capable of storing 35e40 terabytes of data. Another external storage device option is a network-attached storage (NAS) system (Fig. 6.6). These are commonly used in organizations that operate a network that functions as a scaled-down version of a cloud storage system. NAS drives are physically larger, typically higher capacity, external drives that serve as shareable, computer file storage locations. However, they may be less portable than other external computer storage drive options. Reflection 6.2 Either in your office or in your home, estimate how many different types and instances (numbers of each type) of removable or external computer storage devices you have. Compare your collection to the collections of your classmates or colleagues. The winner (least number of storage devices) buys the others a coffee. Generally, the use of external storage drives causes some processing time delays when GIS databases are stored there, with the exception of perhaps a NAS system. Further, in the absence of a set of data management records, such as a data dictionary and proper metadata, the use of external storage devices can cause confusion and difficulties in locating GIS databases, particularly when multiple external drives are used. Larger capacity external storage drives may alleviate the need to store GIS databases on several different drives, but again, these may be less portable options.

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FIGURE 6.4 External storage devices: tapes (A), floppy disk (B), compact disc (C), 3.5-inch disk (D), ZIP disk (E), and USB flash drive (F).

Cloud storage When one hears the word cloud, it typically evokes an image of a mass of condensed water vapor floating around in the sky. In computer parlance, the term cloud should evoke the idea of a remote place where data is stored. Instead of collecting water vapor and raining precipitation down upon local areas, computer clouds collect data and allow access to that data anywhere in the world where Internet access is possible. The cloud

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FIGURE 6.5 External storage devices with USB plugs that provide data portability.

FIGURE 6.6 An example of a NAS drive. Credit: NAS drive image from Buffalo Americas through Wikimedia Commons (https://commons.wikimedia.org/wiki/File:TS6400DN_Front_View.png).

has existed in one form or another since the 1960s, yet the more modern evolution has been around for 10e15 years. In very general terms, cloud computing refers to computer servers, networking capabilities, and software that are stored in a remote warehouse, yet this system provides access to the information stored there anywhere and at any time. More appropriately, the cloud refers to a cluster of computers or a server farmda building full of rows upon rows of computers (like library stacks) (Fig. 6.7). These are set up so that each one knows about the other, and the applications replicate and store data, and split and share processes for fast computing.

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FIGURE 6.7 A large data center composed of multiple computing devices. Credit: U.S. General Services Administration (2016).

Cloud-based computing removes the need to install and maintain software programs on individual servers, personal computers, and laptops. In effect, a user opens some computer program on a terminal (personal computer, laptop, cell phone, tablet, etc.) and instead of the terminal using resources (processing power, etc.) locally, the work is divided across an array of cloud-based serversdin effect, the computer program runs in the remotely-managed server farm. This capability allows a user to access and process data from anywhere, at any time. Cloud-based data storage works in much the same way. Cloud-based data storage removes the need for storing GIS databases on local or external hard drives, as the data can be made available at any time through a connection to the cloud-based system (e.g., DropBox, Google Drive, One Drive, iCloud, etc.). In effect, a user will process data and save a file (an object), which is then copied and spread across multiple locations within the data warehouse. Typically, a user will enable a secure protocol (e.g., https) to retrieve and access a file, and perhaps download it locally (i.e., onto a computer). The file can be manipulated in the cloud using cloud-based computing resources, but if it was downloaded and manipulated locally, the file can be uploaded back to the cloud relatively quickly. When using cloud-based services, users typically need to create an account and access the services through a secure login process (password-based). Inspection 6.2 Using the Internet, investigate the cost of storage options for services such as the Amazon Simple Storage Service (S3). If you were to use services such as these, and you needed to store 10 terabytes of data in the cloud, what would be the monthly storage price?

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There are some distinct advantages to storing GIS databases in the cloud. First, it can be more efficient to use cloud-based file storage than to use an external drive or local storage, for the simple reason that something tangible does not need to be managed. External drives are small and can be easily lost (or forgotten in a computer lab or coffee shop), and at times they can mysteriously become corrupt or lose data. As mentioned previously, within the cloud, data or files can be accessed anywhere Internet service is available, providing versatility for workflows without the burden of maintaining an array of hardware. Further, depending on the options available, cloud-based storage has theoretically an infinite capacity, space for storage should never be exceeded and it should be protected (through a replication process), which should prevent disasters (lost data) from occurring. A few potential drawbacks to cloud-based storage of GIS databases are data security (data that is compromised or hacked), the need for an Internet connection (a reliable hotspot may alleviate this to some extent), and the cost. Free cloud-based storage may provide 2e5 gigabytes of storage space, but beyond that amount, a fee is typically charged. At a minimum, this fee could be $10 (USD) per month. Although cloud-based storage space may need to be rented, it reduces or eliminates the need to maintain a series of servers or multiple external drives, which may help one conclude that a cloud-based solution is a right option. Multiple software companies, commercial and open-source, provide cloud-based options for processing and storing data. It is likely that in the future we will move even further in the direction of cloud-based, or even quantum computing. As data sets become increasingly larger and our questions increasingly more complex, solutions such as those offered by cloud computing will provide an enhanced capability for data access, processing, and storage. This will aid in moving the management of data from simple stand-alone computer files to advanced visualization platforms, and beyond (Breunig et al., 2020). Translation 6.1 As you sit around the dinner table during a holiday break with your family, someone mentions that they recently lost their thumb drive. It contained all of their most important information, files, and presentations. In simple language that everyone might understand, compare local, external, database, and cloud storage options, and describe how each might promote (in a bad sense) or solve (in a good sense) issues related to data storage.

Internet of Things and online mapping Readers of this text doubtlessly have heard the adage “work smarter, not harder.” This advice is not to be viewed as an invitation to slothfulness but viewed as a reminder that being efficient does matter. Timber harvesting, for example, has generally moved from hand (axe) felling of trees and animal-powered skidding (dragging) of logs to multifunction machines quickly moving more trees to a loading dock. The evolution of

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technology, particularly the speed, processing capabilities, and power requirements of sensor technology, have allowed sensors to be incorporated into just about everything, and these sensors can monitor a myriad of variables. Combined with location technology (i.e., global positioning systems (GPS)), these sensors become georeferenced data sources, and the information that they develop can be analyzed to produce actionable intelligence for customers, clients, scientists, and the general public. Some years ago, a fitness application (app) being used by soldiers was discovered to provide location data that mapped the perimeters of military installations, the places where soldiers were housed, and other information related to living and working on a military base (BBC, 2018). This type of information can be used to inform decisions concerning how to manage land and infrastructure resources and perhaps save an organization time and money. Loggers who receive real-time information regarding vehicular traffic may use this information to temporarily haul a lighter load (for those roads that require lighter loads) or to take an alternate route to a mill to avoid delay in the receipt of raw materials at processing facilities. This real-time adjustment will help truck drivers maintain a sense of efficiency in light of the delays related to vehicular traffic congestion. Sensors may also be incorporated into trucks or equipment to feed data to a company-owned equipment management system (dashboard), providing managers information on the status of equipment so they can better plan repair or replacement delays. In other applications, this type of data can be used to determine where and how to ship certain products (and quantities). What makes this possible is the broad, mysterious world of the Internet of Things (IoT). The IoT is, quite literally, a wide variety of sensors that are somehow connected to the Internet (Fig. 6.8) and it is almost that simple, at least at first glance. There are, as of this writing in 2021, over 13 billion Internet-connected devices globally, and it is projected that this amount will rise to over 30 billion by 2025 (Statista, 2021). Fortunately, researchers are developing machine-learning algorithms capable of handling these vast quantities of data (known as Big Data) and capable of integrating information (e.g., precipitation, traffic, temperature, storm tracks, etc.) from a variety of sources and sensors. The use of so many different data sources of information may allow real-time analytical processes to provide accurate, timely information to decision-makers in situations that range from disaster recovery operations to emergency service (police, fire) calls and even the status of machines and equipment (Tieman and Morakot, 2017). The IoT infrastructure can range from a series of sensors that detect light availability, precipitation, humidity, and temperature to cameras, tracking collars, RFID (radio frequency identification), dendrometers, and unmanned aerial vehicles (UAV) that detect other types of phenomena. The information gained from devices equipped with IoT sensors can aid in the detection of wildfires, illegally harvested timber, and poached wildlife (Singh et al., 2021). There is also a recent development concerning the idea of the Internet of Trees, where a host of sensors relay data such as air temperature, precipitation, and humidity to a server, and where analyses can then be conducted to

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FIGURE 6.8 The Internet of Things. Credit: U.S. Department of Commerce (2021).

efficiently monitor and manage a forest (Gabrys, 2020). Forest-based sensors can be routed through cellular or low-power wide-area networks (e.g., LoRa Alliance, 2021) to a radio service (like a cell phone tower) and then to communication and analysis servers. The results can then be viewed on a dashboard application (Fig. 6.9) to allow one to make real-time forest management decisions regarding harvesting options, equipment repair, and other important silviculture or forest health issues.

FIGURE 6.9 National Water Dashboard illustrating rainfall events and the flow status of various stream monitoring stations in southeastern Texas on January 8, 2022. Credit: U.S. Department of the Interior, Geologic Survey (2021).

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The IoT provides a powerful, cutting-edge resource for developing mappable features. The raw data collected by IoT sensors and the results of analyses using this information can be mapped and distributed to the public using local or cloud-based resources. The generation of all of this data again brings up the concept of Big Datadlarge and diverse datasetsdand how maps might be created from this data (Robinson et al., 2017). For example, there are a host of interactive online maps that provide society with nearly realtime access to GIS databases related to forest management. One such interactive online map, the Forest Management Map and Data Dashboard involves the U.S. national forests and includes management activity information (U.S. Department of Agriculture, Forest Service, 2021). Further, online mapping applications such as MapQuest, Google Maps, Streetmap, and a multitude of others provide a means for public access to data for locational information and for navigation purposes. The rise of open data and the integration of cloud-based GIS software with online, organizational maps has facilitated the development of interactive maps that can be created, hosted, updated, and operated via open-source and commercial software packages and servicesdeach offering their own set of advantages and disadvantages (see Smith, 2016). Data mapped online can also be summarized using dashboard applications, which offer a concise way to represent some variables of interest. An organization might have an online map of all the forested areas that they manage and a complimentary dashboard that shows how much land has been harvested and replanted, the current carbon stock, areas with endangered species or riparian areas, or any number of resources or issues of interest. Organizations that wish to host online mapping services should balance data availability and integrity, hosting costs, and scalability with the understanding that the use of mobile technology will likely increase, and the user experience may become more demanding (Roth et al., 2017).

Conclusions Both people (if working alone) and organizations must decide upon the most efficient means of storing and accessing GIS databases. This includes the use of the local, external, server, and cloud-based storage options that allow multiple modes and opportunities for data retrieval. The accessibility of GIS databases is paramount to project completion, particularly in situations where remote access to data for work performance, communication, and computational power may be necessary. The recent COVID-19 pandemic (which began in March 2020) is a great example of a situation where remote access to data was necessary for many people to conduct their job-related activities. Individuals who work remotely will need to ensure they have secure access to data and software programs where storage and processing power may not be locally available or sufficient to meet the needs of the projects they are working on. The proliferation of LiDAR, hyperspectral and high-resolution imagery, and the IoT and their increasing use in forestry and natural resource management necessitate that

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data analysts and managers be prepared to integrate large volumes of data into their analyses. Larger data sets will lead increasingly to the need to store data and leverage the computational capacity of cloud-based systems for timely, broad-scale data analyses. More than likely, computer-related processing power will continue to increase, as will opportunities to expand both personal and organizational analytical capabilities as organizations attempt to reach beyond what they consider to be a traditional client base. Individuals, GIS analysts, project managers, and organization leaders should consider carefully their needs and the associated costs of expanding their capabilities with advanced technologies. Students and early career professionals should work toward a basic understanding of the present and future capabilities, so they are prepared to provide insight into processes that integrate existing and future technological capacities with organizational workflows. Translation 6.2 Assume you work as a GIS analyst for a timberland investment management organization (TIMO) and the manager for your region asks you how to best store the GIS databases, forest inventory files, and miscellaneous information related to threatened or endangered species on the lands that the TIMO manages. They also mention that they might need this information as part of a company-wide audit of forest management activities. How might such data be stored? Exercises exploring the concepts covered in this book using ArcGIS or QGIS are available on the book website: gis-book.uga.edu.

References BBC, 2018. Fitness App Strava Lights Up Staff at Military Bases. www.bbc.com/news/technology42853072 (accessed 15.01.22). Breunig, M., Bradley, P.E., Jahn, M., Kuper, P., Mazroob, N., Ro¨sch, N., Al-Doori, M., Stefanakis, E., Jadidi, M., 2020. Geospatial data management research: progress and future directions. ISPRS International Journal for Geo-Information 9 (2), Article 95. Esri, 2019. File Geodatabase Size and Name Limits. https://desktop.arcgis.com/en/arcmap/10.3/ manage-data/administer-file-gdbs/file-geodatabase-size-and-name-limits.htm (accessed 15.01.22). Esri, 2020. Geoprocessing Considerations for Shapefile Output. https://desktop.arcgis.com/en/arcmap/ latest/manage-data/shapefiles/geoprocessing-considerations-for-shapefile-output.htm (accessed 15.01.22). Gabrys, J., 2020. Smart forests and data practices: from the Internet of Trees to planetary governance. Big Data and Society 7 (1), 1e10. GISGeography, 2021. The Ultimate List of GIS Formats and Geospatial File Extensions. https:// gisgeography.com/gis-formats/ (accessed 15.01.22). LoRa Alliance, 2021. What Is LoRaWANÒ Specification. https://lora-alliance.org/about-lorawan/ (accessed 15.01.22). Rhodes, G., 2013. The Optimum Framework for Managing E&P GIS Data. www.landmark.solutions/ Portals/0/LMSDocs/Whitepapers/2013-03-optimum-framework-for-managing-eandp-gis-datarhodes-whpaper.pdf (accessed 15.01.22).

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Robinson, A.C., Demsar, U., Moore, A.B., Buckley, A., Jiang, B., Field, K., Kraak, M.-J., Camboim, S.P., Sluter, C.R., 2017. Geospatial big data and cartography: research challenges and opportunities for making maps that matter. International Journal of Cartography 3 (Suppl. 1), 32e60. Roth, R.E., C ¸ o¨ltekin, A., Delazari, L., Filho, H.F., Griffin, A., Hall, A., Korpi, J., Lokka, I., Mendonc¸a, A., Ooms, K., Elzakker, C.P.J.M., 2017. User studies in cartography: opportunities for empirical research on interactive maps and visualizations. International Journal of Cartography 3 (Suppl. 1), 61e89. Singh, R., Gehlot, A., Akram, S.V., Thakur, A.K., Buddhi, D., Das, P.K., 2021. Forest 4.0: digitalization of forest using the Internet of Things (IoT). Journal of King Saud University-Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2021.02.009 (accessed 15.01.22). Smith, D.A., 2016. Online interactive thematic mapping: applications and techniques for socio-economic research. Computers, Environment and Urban Systems 57, 106e117. Statista, 2021. Internet of Things (IoT) and Non-IoT Active Device Connections Worldwide from 2010 to 2025. www.statista.com/statistics/1101442/iot-number-of-connected-devices-worldwide/ (accessed 15.01.22). Tieman, G., Morakot, P., 2017. Mapping the Internet of Things. In: Hader, C., Brown, C. (Eds.), The ArcGIS Book-10 Big Ideas about Applying the Science of Where, second ed. ESRI Press, Redlands, CA, pp. 133e146. U.S. Department of Agriculture, Forest Service, 2021. Forest Management Map and Data Dashboard. www.arcgis.com/apps/webappviewer/index.html?id¼100f81e3161d4cf19175e1c3815f7280 (accessed 15.01.22). U.S. Department of Commerce, 2021. Internet of Things. www.commerce.gov/images/internet-things (accessed 15.01.22). U.S. Department of Housing and Urban Development, 1971. Files Management, General Instructions. U.S. Department of Housing and Urban Development, Washington, D.C. Handbook 2225.1A. U.S. Department of the Interior, Geologic Survey, 2021. National Water Dashboard. https://dashboard. waterdata.usgs.gov/app/nwd (accessed 15.01.22). U.S. General Services Administration, 2016. A Fresh Approach to Optimizing Federal Data Centers. www. gsa.gov/blog/2016/08/10/a-fresh-approach-to-optimizing-federal-data-centers (accessed 15.01.22).

7 Geographic data processingdvector data Introduction Some of the most important and widely used geographic information system (GIS) databases contain vector features. Points that represent a singular place on a landscape such as inventory plot locations or wildlife nest locations are important reference materials for management activities. Lines that represent linear features including streams or roads assist natural resource professionals in understanding how to navigate the landscape as well as give an indication of aspect, slope, and the potential flow of water. Polygons, closed areas on the landscape that may represent different soil types and vegetation types, are equally important in understanding the condition of natural resources and the potential to host important habitat or to produce important commodities. Vector GIS databases are often created and modified by the people who use themdforesters and natural resource management professionals. Vector GIS databases can also be manipulated to address some very complex management issues and decision-making processes through some of the spatial analysis processes presented in this chapter, allowing one to ask where, across the landscape, are the resources of great interest? In this chapter, we illustrate several methods for processing vector GIS data. We begin with some of the more common ways that a natural resource manager may interact with vector GIS databases. These include selecting features, querying features, and buffering features. Further on in the chapter, we delve into methods for combining two or more vector GIS databases through overlay processes. We also describe various ways one can massage the shape and size of individual vector features through editing processes. Editing the attributes of spatial features, querying, buffering, combining, and splitting spatial features are frequently employed tasks by foresters and natural resource management professionals (Merry et al., 2007, 2016).

Physical selection of spatial features Using the mouse of a personal computer, the touch pad of a tablet, or even the stylus (or your finger) of a touchscreen device such as a cellular phone, one can physically select spatial features from a digital display (Fig. 7.1). Often, there is also a way to further select additional nonadjacent features, perhaps by holding down the SHIFT key on the Geographic Information System Skills for Foresters and Natural Resource Managers. https://doi.org/10.1016/B978-0-323-90519-0.00014-5 Copyright © 2023 Elsevier Inc. All rights reserved.

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FIGURE 7.1 A water feature selected from the stands GIS database of the Chippewa National Forest, Minnesota. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021b).

keyboard of a personal computer (Fig. 7.2). If one were to open and view the associated attribute table of the shapefile used in Fig. 7.1, it should also be evident which attribute records are associated with the selected landscape features (Fig. 7.3). Within the attribute table there may be, depending on the GIS software used, multiple ways to physically select the attribute records, and this would also allow viewing the associated landscape features in the map display window. Within the map display window of a GIS software program, there may also be other ways to physically select landscape features. Perhaps one could draw a rectangle, which would allow the selection of any features inside or touching the edge of the rectangle (Fig. 7.4). This type of process for physically selecting landscape features may be of value when many features within a distinct area need to be selected. A more precise manner of selecting multiple landscape features within an area may be to use (draw) an irregularly shaped polygon defining that area. Using this method, any features located inside this polygon, or that touch the edge of the polygon, would be selected (Fig. 7.5).

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FIGURE 7.2 Several water features selected from the stands GIS database of the Chippewa National Forest, Minnesota. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021b).

FIGURE 7.3 The attribute record associated with the water feature selected in Fig. 7.1. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021b).

Some GIS software programs also allow one to physically select landscape features using circular areas (Fig. 7.6) and line intersect methods (Fig. 7.7). The choice of method to use will depend on the arrangement of landscape features in physical space, as well as a decision concerning which landscape features need to be selected. Once landscape

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FIGURE 7.4 Several stands selected from the stands GIS database of the Chippewa National Forest, Minnesota, using a rectangular selection area. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021b).

FIGURE 7.5 Several stands selected from the stands GIS database of the Chippewa National Forest, Minnesota, using an irregular polygon selection area. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021b).

features are physically selected, by whichever method employed, the following actions can occur:
A summary of some of the characteristics (such as areas) of the selected features can be conducted through the attribute table.

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FIGURE 7.6 Several stands selected from the stands GIS database of the Chippewa National Forest, Minnesota, using a circular selection area. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021b).

FIGURE 7.7 Several stands selected from the stands GIS database of the Chippewa National Forest, Minnesota, using a line-intersect method. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021b).


The values of the selected features contained in certain columns of the attribute table can be edited.
The selected features can be deleted from the GIS database.
The selected features can be saved as a separate GIS database under a different name.

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Translation 7.1 Imagine you are explaining GIS to your parents or siblings. In general terms, describe for them the concept of selecting features, and the various ways that this can be accomplished.

Attribute query A query is a question posed of a person, organization, database, or some other entity. A query could be rather simple or quite complex. In GIS, a query might be used to ask a question about the nonspatial (tabular) attribute data contained within a GIS database. In general, a query will select the records or features (points, lines, polygons) whose attributes correctly match the answer(s) with the question(s) posed. A query requires the identification of an attribute field (column) in the attribute table, an appropriate operator (¼, , ), and a desired value or string of text. Some general examples include Age  40 Basal area  100 Timber type ¼ ‘Loblolly pine’ Acres  25 Individual criterion queries, like those noted above can be connected through the use of Boolean operators (and, or, not) to create a compound query. Some general examples include: Age  40 AND Basal area  100 Age  40 AND Basal area  100 AND Timber type ¼ ‘Loblolly pine’ Age  40 AND Basal area  100 AND Timber type ¼ ‘Loblolly pine’ AND Acres  25 Within GIS, queries should be composed to specifically address the answers that are desired; a vague query may be difficult to construct or may provide results that are not informative. For example, perhaps someone is interested in the number of older forest patches with higher amounts of basal areas per acre. Their general query might be Age ¼ ‘old’ AND Basal area ¼ ‘high.’ This seems vague and provides little guidance on how the actual query of the database would be constructed. A more specific query might be Age  140 AND Basal area  200, but this would require someone to define the age and basal area criteria that represent the class of older forests. One should also keep in mind that queries within GIS are quite literal. If you were searching for “Pinus taeda” in an attribute field, the query would likely not return records containing “P. taeda” “Pinustaeda” or other various forms that would make sense to the person asking the question but are not an exact match to the text string that was used for the query. Knowing and understanding the design of a databases is important for accurate queries. Unlike the Internet and other computing processes, where queries (searches) might return inflected forms of words (singulars, plurals, and possessives of nouns), variations on verbs (present tense, past tense), and records where some of the requested text or data is lacking (Berul, 1969), a GIS query is exactly answered rather than satisfactorily answered.

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As an example of applying a query, if one wanted to select all of the timber stands in the Allegheny National Forest that had a major black cherry (Prunus serotina) component, one would first locate the attribute field that contained this information (EV_NAME), then examine all of the unique forest types to determine which of these describes black cherry forests (Black cherry-white ash/yellow poplar and sugar mapleblack cherry). A compound query (question) would then be constructed: “EV_NAME” ¼ ‘Black cherry-white ash/yellow poplar’ OR “EV_NAME” ¼ ‘sugar maple-black cherry’ As you can see in this example, this compound query contains two separate criteria that could act independently if this were the desire: “EV_NAME” ¼ ‘Black cherry-white ash/yellow poplar’ “EV_NAME” ¼ ‘sugar maple-black cherry’ The Boolean OR operator appropriately connects these, because each timber stand can contain one of the two forest types OR the other (or neither). Had the AND operator been used to develop the compound query, no timber stands would have been selected, since none of the timber stands possess both forest types as attributes in the EV_NAME field. The outcome of applying this compound query (Fig. 7.8) is a set of polygons that indicate the location of the black cherry forests but also identify about 110,036 acres (44,531 ha) or 21.7% of the national forest characterized as having these types of forests. This type of compound query (containing two or more criteria) is very commonly employed when examining large, complex GIS databases used for forestry and natural resource management purposes. In employing the compound query noted above, a newly selected set of features was created. However, it may be possible, depending on the GIS software used, that one could also (a) add selected features to a previously selected set of features, (b) remove selected features from a previously selected set of features, and (c) further select features from a previously selected set of features. As an example of the latter of these (select features from a previously selected set of features), consider the following question posed of the GIS data: BASAL_AREA  100 Had this query been applied to the entire set of timber stands in the Allegheny National Forest stands GIS database, the selected set of features would have contained 14,677 stands (63.2% of the polygons) and 360,274 acres (145,801 ha, 71.1% of the land area). However, if the goal were to select the set of black cherry stands that have a basal area greater than or equal to 100 square feet per acre (23 m2 per hectares) these two steps might be followed: 1. Apply the query: “EV_NAME” ¼ ‘Black cherry-white ash/yellow poplar’ OR “EV_NAME” ¼ ‘sugar maple-black cherry’ 2. From the selected set of features, apply the query: BASAL_AREA  100 From the previously selected set of black cherry stands, the selected set of features with a basal area greater than or equal to 100 square feet per acre would have contained 2808 stands (12.1% of the polygons) and 360,274 acres (23,565 ha, 11.5% of the land area).

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FIGURE 7.8 Timber stands on the Allegheny National Forest that have a major black cherry component. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021a).

Diversion 7.1 Open the Allegheny National Forest stands GIS database that can be accessed from this book’s website (gis-book.uga.edu). Using attribute queries, try to answer these questions about the data: 1. How many red maple (Acer rubrum) stands are there on this forest? 2. How many red maple stands have an average age greater than or equal years? 3. How many red maple stands have an average age greater than or equal years and are 100 acres or larger in size? 4. How many red maple stands have an average age greater than or equal years, are 100 acres or larger in size, and have a basal area greater than per acre (23 m2 per ha)?

to 100 to 100 to 100 100 ft2

Translation 7.2 Imagine once again that you are explaining GIS to your parents or siblings. In general terms, describe for them the concept of querying a GIS database, and the various ways that criterion can be connected.

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Buffer Buffering operations applied to points, lines, and polygons are often called boundary tracing processes. In essence, a physical boundary (buffer) around some vector features is created. The size and shape of the polygon formed is contingent on the distance required from each point, line, or polygon feature (Fig. 7.9). Buffering processes involve creating parallel lines around each straight-line segment formed between two vertices. Depending on the choice, a circle may be drawn around each end point (Fig. 7.10) or a perpendicular line to the parallel lines can be drawn through each endpoint (Fig. 7.11). The lines and curves formed are inspected for intersection points and then recombined into a contiguous polygon, where internal line segments are removed (Jiechen et al., 2009). The various buffering capabilities within GIS allow one to develop zones of proximity around landscape features. For example, creating buffer zones around streams allows one to visualize riparian management areas. And buffer zones around wildlife nest points allow one to visualize areas where limited management may only be allowed. These processes require information regarding: a. The features around which a buffer zone (polygon) will be created.

FIGURE 7.9 Buffers applied to vector features: (A) 100 m (328.1 feet) buffer around houses, (B) 30 m (98.4 feet) buffer around streams, and (C) 100 m buffer around an older forest stand of trees.

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FIGURE 7.10 One process for creating a buffer zone around a line segment involves constructing circles or curves around the end points using a radius based on the fixed buffer distance (b). Parallel lines (a1 and a2) are then created to touch and connect the extreme edges of these circles and the resulting interior line segments are removed to create a buffer polygon around the original line feature.

b. The distance or width desired from the edge of the features to the outside edge of the buffer zone. c. The desire to eliminate overlapping buffer polygons. d. Other parameters that may be adjusted to create different buffer polygon shapes. Reflection 7.1 Consider a work or school environment without GIS. How would you develop the stream buffers to represent the riparian areas for an area as large as a 500,000 acre (202,347 ha) national forest? Would you do it yourself? How long might it take to complete the task? In some GIS software programs, when one or more features (e.g., one or more streams) in a GIS database are selected, only those selected features will be buffered.

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FIGURE 7.11 A second process for creating a buffer zone around a line segment, where parallel lines (a1 and a2) are created around the line feature based on a fixed buffer distance (b), lines perpendicular to the parallel lines are created that intersect the end points (c1 and c2) of the original line segment, and the resulting interior line segments are removed to create a buffer polygon around the original line feature.

Interestingly, it is often the case that when no features are selected in a GIS database, all of the features in the GIS database will be buffered. One would obtain the same result if all of the features within a GIS database are selected. For the following examples, a few streams within the streams GIS database of the Allegheny National Forest (Fig. 7.12) will be used to demonstrate the outcomes of various buffering processes. These include intermittent and perennial streams as well as state-level (Pennsylvania) Class A trout streams. Within the streams GIS database, the FCODE field can be used to identify the intermittent and perennial streams (Table 7.1), and the CLASS_A field can be used to identify the Class A trout streams

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FIGURE 7.12 A portion of the streams GIS database of the Allegheny National Forest. Credit: Stream data from the U.S. Department of Agriculture, Forest Service (2021a).

Table 7.1 Stream types to consider in the approximation of area that might be found within riparian zones of the Allegheny National Forest, as noted in the National Hydrography dataset (U.S. Department of the Interior, Geological Survey 2021). Feature code (FCODE)

Feature code name

Stream type on the Allegheny National Forest

33400 46003 46000 46006 55800

Connector Stream/river Stream/river Stream/river Artificial path

Intermittent Intermittent Perennial Perennial Perennial

(where CLASS_A ¼ 1). Regardless of the class or designation of the streams, buffers can be developed around all streams using a single buffer width. When applying a buffer process, a buffer zone is created around each feature, or piece of stream in this case,

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separately. In the example provided in Fig. 7.13, you will find a case where (a) the overlapping buffer areas for each stream feature have been eliminated, and (b) where they have not been eliminated. At the stream junctures, you can see where there may be overlapping buffer polygons. Diversion 7.2 Access the Allegheny National Forest streams GIS database from this book’s website (gis-book.uga.edu). Buffer all of the streams using a 50 m rule (on both sides). Inspection 7.1 Carefully examine the 50 m stream buffers that were applied to the Allegheny National Forest streams. Are there any places across the landscape that represent islands of uplands completely surrounded by riparian areas? Venturing a guess, about how much (percent) of the national forest lands are considered to be in the riparian area when using a single 50 m buffer width rule? As an example of different buffer area shapes that can be produced from some GIS software, the choice of how to handle the ends of line segments can be changed from rounded ends (as shown above) to squared ends (as described in Fig. 7.11). In this case, one may find that the ends of the line segment buffers may not connect as expected (Fig. 7.14). These issues related to the end type of a buffer created around a line are not important when one applies a buffer process to a point feature. In this case, the buffer simply represents an area described by a radius around the point, and the resulting circle has no end point. Further, when a buffer process is applied to polygons, the changes in direction of the lines that describe the corners of the polygons are represented by circles or curves created using a radius based on the fixed buffer distance (Fig. 7.15). One final example of using a buffer process involves variable width buffers. Imagine a GIS database that contains lines that represent streams, perhaps the Allegheny National

FIGURE 7.13 A 30 m buffer of the streams GIS database of the Allegheny National Forest, where (A) the overlapping buffer areas have been eliminated, and (B) where they have not. Credit: Stream data from the U.S. Department of Agriculture, Forest Service (2021a).

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FIGURE 7.14 A 30 m buffer of the streams GIS database of the Allegheny National Forest, where the end type is a squared or flat shape, rather than a rounded or circular shape. Credit: Stream data from the U.S. Department of Agriculture, Forest Service (2021a).

Forest streams GIS database, and within this database the streams have been classified in different ways by biologists and hydrologists. The attribute table of the GIS database would likely contain the information on stream classifications. A person who desires to create buffers for the streams that are different sizes, and based on the attributes of each stream, can select each set of interest and apply a single-width buffering process like the examples provided in this chapter thus far. However, based on the character of each stream, the line(s) that represent each stream class can be buffered different distances in one efficient buffer process that preserves the buffer distance in the GIS records. To enable a variable-width buffering process, a field needs to be created (if it is not already present) that would contain the appropriate buffer distance for each line segment. The units of these distances should be the same as the units of the coordinate system assigned to the GIS database. Once established, a buffer process can be applied to each line segment to create a buffer that is based on the value for that line segment contained within the attribute table. For this example, a field was created in the Allegheny National Forest streams GIS database. This field was then populated with a buffer width. In this attribute field, intermittent streams were given a value of 15.24 m (50 feet). Perennial streams were

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FIGURE 7.15 Examples of buffers created around simple polygon features.

given a value of 30.48 m (100 feet). Class A trout streams were given a value of 60.96 m (200 feet). These are the suggested riparian widths for these types of streams within the national forest. They are represented as meters in the attribute table since the GIS database uses a UTM coordinate system where northings and eastings are presented using the metric system. The resulting variable-width buffers (Fig. 7.16) are created in one seamless process by utilizing the values in the buffer width column. Other variations of buffering processes include buffering only one side of a line. In addition, a buffer can be developed for the inside area of polygons. Lastly, some GIS software programs have the ability to create multiple ring buffers which delineate areas further and further away from the target features. In the example illustrated by Fig. 7.17, five 100 m (328.1 feet) buffers were created from each house near a managed forest. From the buffer polygons created for each house, the overlapping zones were eliminated. Further, the zones closer to each house maintained precedence over zones farther away from each house. In this sense, the 0e100 m zone near one house was given higher importance than a 401e500 m zone that may have arisen from a buffer process applied to another house, which overlapped the 0e100 m zone of the first house.

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FIGURE 7.16 Variable width stream buffers for the Allegheny National Forest. Credit: Stream data from the U.S. Department of Agriculture, Forest Service (2021a).

FIGURE 7.17 Multiple ring buffer around houses, in 100 m (328.1 feet) intervals up to 500 m.

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Split Splitting vector features in GIS involves selecting one line or polygon feature and dividing it into two or more pieces. In forestry and natural resource management, it is often useful to split vector features to provide as much detail as possible in representing a real-world system, and therefore to capture the essence of the management or biological situation (McConnell et al., 2000). For example, if a timber stand were selected as an area where forthcoming harvest or site preparation activities will take place, within GIS the timber stand might be split along a line that represents the actual area of activity rather than the total area of the originally drawn polygon. As another example, lines that describe roads might be split at various places to represent the beginning or ending point of different road management conditions. This act of splitting a feature not only results in more accurate records of activities, but also results in a better estimate of the area or distance involved, values which may be incorporated into legal contracts. The splitting of a vector feature (line or polygon) involves the identification of the place(s) where the splitting should occur, the insertion of new vertices along lines, lines that represent edges of polygons, or within polygons, and a reconstruction of the topology of the old and new features. In an automated fashion, without the intervention of humans (drawing an irregularly shaped interior location of the intended split), a polygon can often be split (Fig. 7.18) by selecting two nonadjacent vertices on the lines that form the edge of the polygon and drawing a line between them (Zhao et al., 2020). When humans intervene, the splitting process may involve the insertion of new vertices on the lines that form the edge of the polygon, and others places at different angles throughout the original polygon and drawing lines between them (Fig. 7.19). One common activity of foresters is to type-map a forested property. In this process, the objective would be to describe a property by the major land uses or forest types that currently can be found there. Certainly, this sort of activity could also be conducted for past land uses or forest types using old maps or historical aerial images. Nonetheless, ideally a polygon which represents the closed area of the property is first developed or acquired (Fig. 7.20). Then this polygon, again ideally, would be split along the edges of FIGURE 7.18 The splitting of a polygon by connecting a line between two nonadjacent vertices of the line segments that form the edge.

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FIGURE 7.19 The splitting of a polygon by connecting an irregularly shaped line between two places on the line segments that form the edge.

FIGURE 7.20 A 192.5-acre (77.9 ha) piece of farmland in Kittitas County, Washington (USA), (A) before land uses have been split from the original parcel polygon, and (B) after land uses have been split from the original parcel polygon. Credit: Base map from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021).

distinct land uses or forest types. If conducted correctly, the final GIS database that is being edited or created should have these properties: 1. The sum of the areas of each land use or forest type should equal the size of the property. 2. No gaps exist between the land uses or forest types in the GIS database. 3. No overlapping land use or forest type polygons exist in the GIS database. If the sum of the areas of each land use or forest type does not equal the size of the property, then points two or three (or both) are false, and either gaps or overlapping polygons exist in the GIS database. Had one simply drew the new, complex-shaped land use or forest type polygons inside the property (without using a split process), it is almost

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certain that points 2 and 3 would be false. Therefore, it is far easier and less troublesome to split a larger polygon into smaller pieces than to attempt to build a GIS database by digitizing each piece, hoping there would be no gaps and no overlapping polygons in the final product (the GIS database being edited or created).

Clip A clip process is a form of overlay analysis within GIS, or a way to derive spatial and attribute information about specific areas. In a clip process, we are interested in the subset of subject features (points, line, or polygons) that lie inside of an area described by a clip polygon. In this process, each of the subject features in a GIS database is represented by minimum and maximum Y (northing) and minimum and maximum X (easting) coordinates. Points will only have one set of coordinates (minimum and maximum X values are the same and similarly for the Y values). The clip polygon (an area) is either created or selected from a GIS database, and it will be used as a cookiecutter of sorts. Through the use of a clipping process, one is only interested in those subject features that have coordinates inside the clip polygon. The clip polygon may contain one or more edges (line segments), and the minimum and maximum Y (northing) and minimum and maximum X (easting) coordinates of these are also known. The sets of coordinates of both the subject features and the clip features are sorted. Then, the subject features that are entirely outside of the clip polygon are located and discarded (Fig. 7.21). A set of rules such as this might be followed: A. Any of the subject features that have a maximum Y coordinate less than the minimum Y coordinate of the clip polygon are discarded. B. Any of the subject features that have a maximum X coordinate less than the minimum X coordinate of the clip polygon are discarded. C. Any of the subject features that have a minimum Y coordinate greater than the maximum Y coordinate of the clip polygon are discarded. D. Any of the subject features that have a minimum X coordinate greater than the maximum X coordinate of the clip polygon are discarded. For the remaining points, lines, or polygons, a process ensues that compares the coordinates (vertices) of each against the coordinates of the line segments that form the clip polygon. For point features, the decision to include them in the output GIS database (the clipped features GIS database) is relatively simple: does each point lie inside or outside of the clip polygon? For subject features that are lines or polygons described by line segments, those pieces of the subject feature lines lying inside the clip polygon are determined by comparing the coordinates of each vertex to the lines formed by coordinates of the clip polygon vertices, and in effect the subject feature lines are split at the intersection with the clip feature line segments. Those subject feature line segments lying outside the clip polygon are discarded. In the case of subject feature polygons, to

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FIGURE 7.21 Subject features, their relationship to a clip polygon, and those subject features that are obviously outside of the area of the clip polygon by way of the four rules noted above.

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maintain closed areas of the pieces that remain inside the clip polygon, some of the character of the shape of the clip polygon boundary may be used to connect the temporarily dangling edges of the subject (clipped) edges. As an example of a clip process, perhaps it is necessary to understand how much of the Francis MarioneSumter National Forest is located within 0.5 miles (804.7 m) of the small crossroads town of Bethera, South Carolina. Knowing the approximate location of Bethera (612,974 m east, 3,674,309 m north in UTM zone 17), a point GIS database can be created. Then around this point a 0.5-mile buffer can be created (Fig. 7.22). The polygon that represents the buffer can then be used as a clip polygon, and the national forest stands GIS database can be used as the subject features. A clip process retains all of the stands (and parts thereof) located within the clip polygon. Therefore, those subject feature polygons that straddle the boundary of the clip polygon (buffer) are broken. The pieces outside of the clip polygon are discarded, and the edge of these polygons is replaced with the appropriate line segment defining the edge of the buffer (Fig. 7.23). Diversion 7.3 Using the 50 m stream buffers for the Allegheny National Forest that were developed earlier, clip the national forest lands that fall inside these buffer features. How many acres and what percentage of the national forest fall within 50 m of a stream?

FIGURE 7.22 Francis MarioneSumter National Forest lands (light green) and their relation to the crossroads town of Bethera, South Carolina. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021c).

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FIGURE 7.23 Those parts of the Francis MarioneSumter National Forest (light green) that are up to 0.5 miles (804.7 m) from a location denoting the crossroads town of Bethera, South Carolina. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021c).

Erase Conceptually, an erase process is the opposite of a clip process. In an erase process, we are interested in the subset of subject features (points, line, or polygons) that lie outside of an area described by an erase polygon. The erase polygon (an area) is either created or selected from a GIS database, and used as an eraser, removing from the subject features any that fall inside the area defined by the erase polygon. Here, again we are only interested in those subject features that lie outside the erase polygon. The subject features that are located entirely inside of the erase polygon are discarded. This is a straightforward analysis when dealing with point features, and with line and polygon features that fall entirely inside the erase polygon. Those line and polygon features that straddle the boundary of the erase polygon are split at the intersection with the line segments that define the boundary of the erase polygon. Lines are simply shortened in this process. The shape of some subject feature polygons may change, by erasing part of their area, and replacing parts of line segments that define their boundary with parts of line segments from the erase polygon. As an example of an erase process, imagine that we are interested in understanding how much of the Francis MarioneSumter National Forest is located beyond 0.5 miles

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(804.7 m) of the small crossroads town of Bethera, South Carolina. Using the polygon that represents a buffer around this town, and the national forest stands GIS database as the subject features, an erase process removes all of the stands inside the buffer. Those polygons that straddle the boundary of the buffer are broken, and their boundaries are replaced with the appropriate edge of the erase polygon. Any pieces that fell inside the area defined by the erase polygon are removed (Fig. 7.24). Diversion 7.4 Using the 50 m stream buffers for the Allegheny National Forest that were developed earlier, erase these from the stands GIS database. Which forest type is most widely represented (by acreage) in what might be considered the uplands of this forest? In conjunction with other geographic processes, an erase process can be very helpful in estimating the extent and amount of natural and managed resources that reside outside of protected or sensitive areas. Foresters and natural resource managers may use a hierarchical process to understand those parts of a landscape that are relatively unrestricted for the implementation of management activities. Erasing, buffering, clipping, and other processes can be very helpful in estimating where these areas are found. The land classification system used by the forest lands managed by the Oregon Department

FIGURE 7.24 Those parts of the Francis MarioneSumter National Forest (light green) that are beyond 0.5 miles (804.7 m) from a location denoting the crossroads town of Bethera, South Carolina. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021c).

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of Forestry (Oregon Secretary of State, 2013) is one example of how a set of erase processes could be useful in determining the general stewardship class once the rules for defining higher level classes have been developed. First, high-valued conservation areas would be identified and erased from a lands GIS database. Then, special use areas would be identified and erased from the GIS database. Finally, focused stewardship areas (e.g., riparian buffers and others) would be identified and erased from a lands GIS database, resulting in the set of lands that are considered to be general forest management areas. As another example, in conjunction with a series of buffering processes, Dar et al. (2019) employed several erasing processes to help locate suitable places for landfills across a landscape in India. Translation 7.3 In the continued effort to explain GIS to your parents or siblings, in general terms, describe for them the differences in the concepts of clipping and erasing.

Intersect For forestry and natural resource management purposes, there may be an interest in combining two GIS databases to arrive at a third independent, yet fully informed GIS database. One of several overlay processes can be used to achieve this desired result. An intersect process therefore involves an overlay of two GIS databases and, conceptually, an intersect process concerns the application of two consecutive clip processes. However, in contrast to a clip process, the information from both GIS databases is maintained in the resulting GIS database. In the following example, A, B, and C are GIS databases containing polygons. 1. One set of subject features (A) is clipped using a second set of features (B), resulting in a temporary set of features (C) (Fig. 7.25) that contains the attributes of set A. / B clips A, resulting in C 2. The result (C) of this first clip process then becomes a set of clip features that are applied to the original set of clip features (B), resulting in a GIS database that contains the attributes of both set A and set B. / C clips B, resulting in the intersected GIS database The outcome is an intersected GIS database that contains only the areas in common between the two original GIS databases selected to undergo the intersect process. In addition, the attribute table of the intersected GIS database contains the attributes of both GIS databases (A and B) selected to undergo the intersect process. A practical use of this overlay process might include foresters and natural resource managers overlaying databases to understand the forest resources contained within the riparian area of a forest. An intersect process applied to these two GIS databases will only retain the areas that they have in common (Fig. 7.26). An examination of the areas and forest types would then reveal the data desired.

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FIGURE 7.25 Conceptual model of the intersect process.

FIGURE 7.26 A riparian buffer GIS database and a timber stand GIS database prior to the use of an intersect process (A), and afterward (B).

Diversion 7.5 Using the stands and soils GIS databases that cover District 3 (Long Cane Ranger District) of the Francis MarioneSumter National Forests, intersect these two databases. Which types of soils are most widely found in loblolly pine (Pinus taeda) forests? Other variations of the intersect process include intersecting lines with polygons, as in the case of Fig. 7.27 where the outcome of the intersection of streams and timber stands is displayed. One might also notice that this outcome is essentially the same as what could be achieved by clipping the streams using the timber stands polygons. In addition, another process might involve intersecting lines with lines, as is the case of Fig. 7.28, where the intersections of the roads and the streams have been identified, producing a GIS database of point features.

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FIGURE 7.27 The result (broken streams) of intersecting a streams GIS database (line features) with a timber stands GIS database (polygon features).

FIGURE 7.28 The result (purple points) of intersecting a streams GIS database (line features) with a roads GIS database (also line features).

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Union Similar to the intersect process, a union involves an overlay of two or more polygon GIS databases. However, in contrast to the intersect process, in this case the process acts like the merging of GIS databases rather than the clipping of two or more GIS databases (Fig. 7.29) as all of the spatial boundaries of the landscape features contained within the selected GIS databases are retained. As the input databases are brought together, not only are the attributes of each contained in the resulting product, but also any overlapping areas are removed, and the overlapping areas become one or more separate polygon features (Fig. 7.30). In the resulting GIS database, the imprint of the original GIS databases should be evident. The attributes of each feature contained in the resulting product may be valid for each of the original input databases, depending on whether there was a physical correspondence (overlap) among the original polygons. In other words, if there were two input GIS databases:
Some of the resulting polygons will have attributes of polygon features from both of the input GIS databases.

FIGURE 7.29 Conceptual model of the union process.

FIGURE 7.30 Before (A) and after (B) a union process is applied to a stream buffer GIS database and a house buffer GIS database.

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Some of the resulting polygons will have attributes of polygon features from one input GIS database.
Some of the resulting polygons will have attributes of polygon features from the other input GIS database. Reflection 7.2 Had there not been the invention of GIS software, how would you have accomplished a union process task between three different types of maps (timber stands, soils, wildlife habitat areas)? Union processes are often selected as tools to use in complex landscape analyses. For example, in their assessment of habitat conservation areas for a potential Habitat Conservation Plan, the Oregon Department of Forestry (2008) used a union process to bring together polygon GIS databases that represented:
Riparian areas
Steep, unique, and visual areas
Core areas for threatened and endangered species The goal of this GIS analysis was to ensure that no double- or triple-counting of areas occurred in the GIS database that contained all three types of areas requiring special acts of forest stewardship. In other words, in places where two or more polygons from the different GIS databases overlap, a GIS database created by the union process recognizes the overlap, but only represents the overlap with a single polygon. However, the attributes of the polygons in the unioned GIS database contain the information from each of the polygons that originally overlapped.

Identity In an identity process, one polygon-based GIS database serves as the basis, or input database, whose extent will not change in the outcome of the effort. A second GIS database will be overlaid on top of the input GIS database, and the imprint of the lines or polygons from this second GIS database will be enforced on the input database (Fig. 7.31). In the resulting GIS database, the imprint of the features from the second GIS database should be evident (Fig. 7.32). The attributes of each feature contained in the resulting product may be valid for each of the original two GIS databases. Translation 7.4 At some point in the future, you may be working with other professionals on a complex spatial analysis of forest and natural resources. In general terms, describe for them the differences in the concepts of intersect, union, and identity.

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FIGURE 7.31 Conceptual model of the identity process.

FIGURE 7.32 A riparian buffer GIS database and a timber stand GIS database prior to the use of an identity process (A), and afterward (B).

Merge In the management of vector GIS databases, there are two types of merging processes that can be applied: one that combines individual features within a single GIS database and another that combines two or more databases. The merging process that combines individual features takes a set of selected features of the same type (generally lines and polygons) within a single GIS database, each with their own attribute records (rows in the attribute table) and combines them into a single spatial feature with a single attribute

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FIGURE 7.33 Two similar polygons that share an edge are merged into a single polygon. Credit: Base map from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021).

record. If the issue involves merging polygons, any shared or overlapping edges are erased or dissolved (Fig. 7.33). Further, if the features happen to be spatially distinct (share no common edge, point, or place), the features are also combined, yet they become what is known as a multipart feature with a single record (attribute row) in the attribute table. One of the conceptual challenges when merging features is to decide which attribute record in the database, from the various records of the premerged features, will remain to represent the postmerged feature. Any number of features from two to the full extent of a database can be merged. Merging features in this sense is often very helpful when editing features. For example, when editing the boundaries of polygons, it may be evident that part of one polygon should in reality be part of a second polygon (Fig. 7.34). One might split the first polygon into two pieces, and merge one of these with an adjoining polygon to better represent current landscape conditions. The merging process combines two or more GIS databases involving the selection of two or more GIS databases of the same feature type (points, lines, or polygons) and merging them into a single GIS database. In this case, any overlapping areas (in the case of polygons) are not dissolved and removed, as one might expect in an intersect, union, or identity process. Further, the various GIS databases can contain different attribute fields, and one may need to select which of these will be present in the merged GIS database. When conducting this process, some of the attribute fields in the merged GIS database will contain no data for those features which came originally from GIS databases that lacked these fields. This type of merging process is of value if one wanted to erase or clip features from a certain GIS database that were contained within several different areas of interest. In forestry for example, one might be interested in

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FIGURE 7.34 A piece of one polygon is split from the larger part of it, and then merged into an adjoining polygon. Credit: Base map from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021).

understanding those areas of a managed forest that were relatively unconstrained by riparian management rules, wildlife habitat requirements, and other issues. In other words, to understand the location of those areas outside of riparian areas, wildlife habitat zones, and so on. These restricted areas could be defined, merged into a single GIS database, and then erased from a vegetation database to arrive at the areas available for general forest management activities. In this process, it would not matter that the polygons in the merged GIS database overlapped. The merged GIS database would be seen as a temporary tool to help address the management issue.

Dissolve A dissolve process in GIS acts to merge polygons that (a) have the same specific characteristics or values in an attribute table, and (b) that are adjacent or physically touch each other (van Roessel and Pullar, 1993). The intent of the dissolve process may be to decrease the number of features contained in a GIS database; however, in conducting a dissolve process, the number of spatial features within a GIS database may stay the same if there are no conditions where adjacent polygons share the same characteristics or values. While one advantage of applying the dissolve process to a GIS database may be to reduce the number of records managed, one disadvantage is that some diversity will be lost, as the dissolve process is based on polygons with similar values within a limited set of attributes, and not similar values of all attributes. At the polygon level, a dissolve process is similar to a merge process; however, the main difference is that any two adjacent features can be combined in a merge process

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regardless of whether they had similar values in any of the attribute fields. The dissolve process only allows combining adjacent polygons when they are similar in at least one respect. As examples of employing the dissolve process, we will focus on the Long Cane Ranger District of the Francis MarioneSumter National Forest in South Carolina. The vegetation GIS database for this area (Fig. 7.35) contains 1416 polygons. One simple dissolve process would be to combine (remove the boundaries between) timber stands that have the same vegetation type, regardless of the age of the vegetation or any other characteristic or value. In conducting this dissolve process, we find that the resulting GIS database has only 622 polygons, or about 44% of the original number of polygons (Fig. 7.36). From the birds-eye view of the Long Cane Ranger District, it does not appear that much has changed; however, closer inspection of the landscape reveals the various polygon boundaries that have been removed (Fig. 7.37). Had the dissolve process

FIGURE 7.35 Forest vegetation types in the Long Cane Ranger District, Francis MarioneSumter National Forest. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021c).

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FIGURE 7.36 Forest vegetation types in the Long Cane Ranger District, Francis MarioneSumter National Forest after dissolving the share edges of polygons that had the same vegetation type. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021c).

involved vegetation type, year of origin, and site index as the characteristics or values that the polygons must share before being combined, the number of polygons would have been reduced only from 1416 to 1310 (92.5% of the original number) since the criteria for dissolving the shared edges of polygons was much more restrictivedit only applied to polygons with the same vegetation type, site index, and year of origin.

Generalize Several forms of generalization are described next, but the basic process simplifies the shapes of lines and polygons by removing some of the vertices that define their shape. In the example provided in Fig. 7.38, the polygon that defines the shape of the outer edge of the athletic track contains vertices that are spaced about 3 m apart around the curves of

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FIGURE 7.37 Pre- and post-dissolve condition of a portion of the forest vegetation types in the Long Cane Ranger District, Francis MarioneSumter National Forest. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021c).

FIGURE 7.38 A reduction in vertices that describe the outer edge of an athletic track at Central Washington University, Ellensburg, WA. Credit: Base map from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021).

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the track. When a generalization process is applied, a limit is placed on the difference that the location of the generalized features (the modified edge of the polygon) with respect to the original features (the original polygon in this case). In the example, the number of vertices around the curves of the track is reduced to about 22% of the original number of vertices after specifying that the edge of the modified polygon could not vary more than 1 m from the original edge of the polygon. And, of course, due to this generalization process, the shape of the outer edge of the track has changed. Translation 7.5 At some point in the future, you may be discussing GIS database management with other professionals in the fields of forestry and natural resource management. Someone mentions the need to generalize a GIS database. In simple terms, what would a generalize process do to the features contained in the GIS database?

Simplify In contrast to a formal generalization process, which removes vertices yet retains the shape of a line or polygon as long as the new lines (or polygon edges) formed do not deviate x m from their original location, a simplify process removes vertices based on rules that do not relate to the original position of lines or edges. For example, a simplify process may remove vertices that are less than 2 m apart while retaining (as well as possible) the original shape of the feature (Fig. 7.39). A simplify process may also be employed to retain critical shapes or changes in direction of the original landscape features. The distance or tolerance one chooses for the simplify process can obviously have a great impact on the resulting shape of a simplified line or polygon. However, a simplify process may be of value for situations where the density of vertices is much greater than what may be necessary to describe spatial

FIGURE 7.39 The reduction in vertices that occurs when a simplify process removes vertices that are less than 2, 5, or 10 m apart for a polygon that describes the outer edge of an athletic track at Central Washington University, Ellensburg, WA.

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features given the scale at which these features were created. Thinning the vertices may result in a loss of detail but may also provide a more manageable set of features for future uses. Diversion 7.6 Access the Allegheny National Forest vegetation GIS database that is available from this book’s website (gis-book.uga.edu). Open the GIS database in your preferred GIS software program. Select one very complex polygon, based on its shape. Save this one polygon as a separate GIS database. Apply a simplify process to the vegetation polygon and describe how it may have changed in shape and size.

Densify In contrast to a simplify process, which removes vertices from line and polygon features, a densify process adds vertices to line and polygon features. A densify process will break long lines or edges originally formed by only two vertices into lines or edges that have many vertices spaced apart according to the tolerance that is specified. In some cases of curved lines, a densify process will replace curves with straight lines between vertices, in effect losing some detail that may have been provided in the originally created features. Often, this process may use existing data points and fit new points using a spline function. As is suggested with a densify process, the number of vertices will likely increase, perhaps dramatically (Fig. 7.40). With respect to the example we have been developing,

FIGURE 7.40 The increase in vertices that occurs when a densify process adds vertices in gaps that are greater than two or 5 m wide for a polygon that describes the outer edge of an athletic track at Central Washington University, Ellensburg, WA.

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when a 2 m densify process is employed, where any gaps larger than 2 m between vertices have a new vertex inserted, the number of vertices increases from 89 in the original GIS database to 301 in the densified GIS database, an increase of 238%. For the 5 m densify process, there is an increase of 53 vertices, from 89 to 142 (a 59% increase). Inspection 7.2 Access the streams GIS data from the Allegheny National Forest that was associated with the buffering exercises presented earlier in this chapter. Would a densify process be of value in managing this database? Provide a concise, one paragraph argument for your decision on this matter.

Smooth The process of smoothing a spatial feature requires examining more than a few vertices; therefore, this process is not appropriately applied to points or to small or short lines and polygons. A smoothing process examines the selected features and then manipulates what it believes to be sharp angles or turns in direction (Fig. 7.41). A smoothing tolerance can be employed with some smoothing processes, such as the polynomial approximation with exponential kernel (PAEK) process, to focus the outcome of the smoothing

FIGURE 7.41 The changes in shape for a polygon that describes the outer edge of an athletic track at Central Washington University, Ellensburg, WA, when a 10 m smoothing tolerance is assumed.

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effort to a specific distance along the line or polygon that is affected. As the smoothing tolerance is lengthened, less of the fine detail from the original feature(s) is retained. With respect to our athletic track example, when a 10 m PAEK smoothing process is employed, the number of vertices increases from 89 in the original GIS database to 202 in the smoothed GIS database, an increase of 127%. Inspection 7.3 Access the National Wetlands Inventory for Crenshaw County, Alabama, and open the GIS database in your preferred GIS software program. The wetlands data is a U.S. government product (U.S. Department of the Interior, Fish and Wildlife Service, 2020) created from high altitude aerial imagery and other visible landscape features and could contain some error. Closely inspect the geographic features. If you were to use the data for personal, private purposes, would you consider applying a smoothing process to it? Translation 7.6 Develop a simple device (table or chart) that easily describes the differences between a generalize process, a simplify process, a densify process, and a smoothing process.

Join When conducting an attribute table-based join operation in GIS, we need to consider a source database, a destination database, and a join item. With a join process, some data from the source database will be transported over to the attribute table of the destination database based on the presence of the join item in each. Imagine a nonspatial table that contains habitat quality for every timber stand in the Allegheny National Forest. Consider this to be the source database. If this table contains the stand number, and if there is an Allegheny National Forest GIS database that also contains the stand number, then both the source and the destination database have something in common (the stand number). The stand number is therefore the join item. Using this join item (the stand number), the information from the habitat quality table can be connected to the destination GIS database. Often this association is temporary, and not permanently saved in the destination file. Joins within GIS can be considered one-to-one, where both the source database and the destination GIS database contain the same number of features (Fig. 7.42), and each record (row) in the source database relates to only one record (row) in the destination. A one-tomany join would involve a sparse source database that contains fewer records than the destination database. Based on the quality of an attribute in the destination database, one of the records in the source database could be associated with many different records in the destination. Imagine a streams GIS database where the stream type (river, perennial stream, intermittent stream, ephemeral stream) is noted in a particular attribute field (Fig. 7.43). For the perhaps hundreds or thousands of stream line segments, each would be attributed with one of these four stream types. For example, there may be 2000 line

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FIGURE 7.42 A one-to-one association between the source file and the destination file.

FIGURE 7.43 A one-to-many association between the source file and the destination file.

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segments attributed with the value “perennial” as the stream type. A sparse source database containing the appropriate buffer width might only contain a header and four records: Stream type, buffer River, 100 Perennial, 30 Intermittent, 20 Ephemeral, 10 This source database (table) indicates the buffer width (meters) that each type of line segment might need in defining a riparian area. The join operation would assign to each of the hundreds or thousands of line segments one of these four buffer widths, hence the one (from the source database) to many (to the destination database) association of information. Reflection 7.3 Think of the last instance where you had to connect or relate knowledge from one source of information to a second source of information. This may involve any type or form of data, rather than GIS data. What was the source of information and the destination of information? What was the join item, or the thing that both the source and destination of information had in common? It is possible that a many-to-one join operation could be allowed. For example, in the source database there may be several wildlife habitat values associated with a single stand. Obviously, this duplication in the source database could be a mistake on the part of the analyst. However, if there are many instances of joinable information in a source database, and only one record to which they could be associated in the destination database, this attempted many-to-one join would only allow one of these from the source database to be associated with each destination database record (likely the first or last instance in the source database). Relational database join operations can also be conducted using more than one matching item that exists within two different databases (Siegfreid and Vaidya, 1993). Most often, join operations are very rigid; the join items in the source and destination databases have to match exactly. Differences in how text-based join items are spelled, in how aliases are used, in the codes that might be used, in the data format of the column representing the join item, and other intricacies that may seem to make sense to the user of the databases will likely not make sense to the computer. However, methods have been devised to join two records from different databases that are similar based on some understanding of their conceptual meaning (Guha, 1999).

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Spatial join A spatial join process involves two GIS databases. Both act as the source of information. Interestingly, in this case the join item is not some value contained in a field in their attribute tables, but rather the spatial position of the features in both the source and destination GIS databases. The question asked in this process is whether the features in both GIS databases share the same space. The outcome of this process, a new GIS database, contains the original features of one of the source GIS databases, and the attributes of both GIS databases. As an example, consider two GIS databases, one contains the locations of water sources (points) and the other contains vegetation polygons, or timber stands (Fig. 7.44). The attribute table of the water source GIS database is sparse (Fig. 7.45), while the attributes of the vegetation GIS database contain several important characteristics of each stand or management unit (Fig. 7.46). A spatial join in this case would conduct what is FIGURE 7.44 The vegetation polygons and water sources related to the Brown Tract in western Oregon.

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FIGURE 7.45 The attribute table of the water source GIS database.

FIGURE 7.46 The attribute of the timber stands GIS database.

commonly called a point in polygon operation, where the position of each point in the water source GIS database is compared to the area contained by each polygon in the vegetation GIS database. When it is determined that a point is contained within a polygon, the attributes of that polygon are associated with the point. In a new GIS database, each point would now have its original attributes along with the associated polygon attributes. As one might notice, when a point does not fall within a polygon, it lacks any associated polygon information in the output GIS database (Fig. 7.47). A similar spatial join process can be conducted where points contain information of the closest

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FIGURE 7.47 The attribute table of the spatially joined water source and timber stands GIS database, where the spatial features are points (water sources) that also have as attributes the condition of the timber stands within which they are located.

polygon, regardless of whether the two features coincide or share the same space. Further, a spatial join process can be conducted between GIS databases containing points, lines, or polygons, associating the features in one GIS database with features that are contained, intersect, or are closest to those in another GIS database.

Conclusions As one may have found in reviewing this chapter and in engaging with the diversion, translation, inspection, and reflection exercises, there are a number of very interesting things we can do with (or to) vector GIS databases. These processes for examining and manipulating point, line, and polygon features make natural resource management much more efficient than it was three of four decades ago when paper maps were the norm. For example, if one were managing a 250,000-acre forest (about 100,000 ha) and needed to determine the best places to fertilize the pine stands, one would have to overlay paper maps of timber stands over top of paper maps of soils. Then one would need to identify the riparian areas and ensure that these would not receive a fertilizer application. This process for deciding which stands to fertilize, based on paper maps, could take several hours (or days). However, within GIS today, once the appropriate data has been created incorporating high quality forest stands, soils, and riparian area GIS databases are available, this process would require just a few minutes. An understanding of the many different ways vector GIS data can be massaged is therefore an important tool contemporary foresters and natural resource managers should acquire. Exercises exploring the concepts covered in this book using ArcGIS or QGIS are available on the book website: gis-book.uga.edu.

References Berul, L.H., 1969. Document retrieval. In: Cuadra, C.A., Luke, A.W. (Eds.), Annual Review of Information Science and Technology. Encyclopedia Britannica, Inc., Chicago, IL., pp. 203e227 Dar, S.D., Wani, M.A., Shah, S.A., Skinder, S., 2019. Identification of suitable landfill site based on GIS in Leh, Ladakh Region. Geojournal 84 (6), 1499e1513.

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Guha, R.V., 1999. Method and system for performing conceptual joins across fields of database. Official Gazette of the United States Patent and Trademark Office 1225 (4), 4406. Jiechen, W., Qing, Y., Yanming, C., 2009. A novel method of buffer generation based on vector boundary tracing. In: 2009 International Forum on Information Technology and Applications. IEEE Computer Society, Washington, D.C., pp. 579e582 McConnell, T.J., Johnson, E.W., Burns, B., 2000. A guide to conducting aerial sketchmapping surveys. U. S. Department of Agriculture, Forest Service, Forest Heath Technology Enterprise Team, Ft. Collins, CO. FHTET 00-01. Merry, K.L., Bettinger, P., Clutter, M., Hepinstall, J., Nibbelink, N.P., 2007. An assessment of geographic information system skills employed by field-level natural resource managers. Journal of Forestry 105 (7), 364e370. Merry, K., Bettinger, P., Grebner, D.L., Boston, K., Siry, J., 2016. Assessment of geographic information system (GIS) skills employed by graduates from three forestry programs in the United States. Forests 7 (12), Article 304. Oregon Department of Forestry, 2008. Draft Elliott State Forest Habitat Conservation Plan. Oregon Department of Forestry, Salem, OR. Oregon Secretary of State, 2013. Department of Forestry (Chapter 629), Division 35, Management of State Forest Lands. https://secure.sos.state.or.us/oard/viewSingleRule.action?ruleVrsnRsn¼161831 (accessed 16.01.22). Siegfried, R.H., Vaidya, N., 1993. Automated Incident Management Plan Using Geographic Information Systems Technology for Traffic Management Centers. Texas Transportation Institute, Texas A&M University, TX: College Station, TX. Research Report 1928-1F. U.S. Department of Agriculture, Forest Service, 2021a. Allegheny National Forest Geospatial Data. www. fs.usda.gov/main/allegheny/landmanagement/gis (accessed 16.01.22). U.S. Department of Agriculture, Forest Service, 2021b. Chippewa National Forest Geospatial Data. https://www.fs.usda.gov/main/chippewa/landmanagement/gis (accessed 16.01.22). U.S. Department of Agriculture, Forest Service, 2021c. Francis Marion and Sumter National Forest Geospatial Data. www.fs.usda.gov/main/scnfs/landmanagement/gis (accessed 16.01.22). U.S. Department of Agriculture, Natural Resources Conservation Service, 2021. Geospatial Data Gateway: Direct Data/NAIP Download. https://datagateway.nrcs.usda.gov/GDGHome_DirectDownLoad.aspx (accessed 16.01.22). U.S. Department of the Interior, Fish and Wildlife Service, 2020. National Wetlands Inventory, Download Seamless Wetlands Data. https://www.fws.gov/program/national-wetlands-inventory/datadownload (accessed 16.01.22). U.S. Department of the Interior, Geological Survey, 2021. National Hydrography Dataset. https://www. usgs.gov/national-hydrography/national-hydrography-dataset (accessed 16.01.22). van Roessel, J., Pullar, D., 1993. Geographic regions: a new composite GIS feature type. In: McMaster, R. B., Armstrong, M.P. (Eds.), Proceedings of the Eleventh International Symposium on ComputerAssisted Cartography (AUTO-CART0 11). American Society for Photogrammetry and Remote Sensing and American Congress on Surveying and Mapping, Bethesda, MD, pp. 145e156. Zhao, Q., Zhang, H., Wang, G., Li, Y., 2020. Irregular tessellation and statistical modeling based regionalized segmentation for SAR intensity image. Remote Sensing 12 (5), Article 753.

8 Geographic data processingdraster data Introduction In general, a raster geographic information system (GIS) database might be envisioned as a perfect sheet, quilt, or mosaic of grid cells that are all the same shape and size, with no holes or otherwise missing grid cells, and no overlapping grid cells within the collection. The grid cells within a raster GIS database are often referred to as pixels, which is a contraction or shortened version of the term picture elements. Some basic concepts related to raster GIS databases include the following 15 points, many of which were derived from the thoughts of Grabau (1976) almost 50 years ago: 1. Each grid cell in a raster GIS database has an identical shape (square, rectangle, triangle, hexagon, etc.). 2. Square grid cells are the most common shapes in raster GIS databases. 3. Each grid cell in a raster GIS database has a fixed, specific size (meter, acres, hectares) and dimension (e.g., 1
1 m, or 30
30 m). 4. All grid cells within the same raster GIS database have the same size and dimension. 5. Each grid cell is internally homogenousdone value (color, tone, or other code) is used to describe the entire grid cell. 6. The location of grid cells in a raster GIS database is identified by the row and column where they are found in the larger grid (e.g., row 639, column 11,297). 7. The location of a grid cell within the larger grid simply notes the position of the cell with respect to other grid cells. 8. A Cartesian coordinate pair (northing and easting) can be determined for a corner or for the center of a grid cell, but the coordinate pair does not represent the area spatially covered by the grid cell. 9. The image created by a raster GIS database is made possible through a planimetric arrangement of the grid cells (two-dimensional view where elevation is not evident). 10. Perception of a landscape or water body represented by a raster GIS database is inversely related to how closely one views the information. a. Generally, when viewed closely, the real-life landscape or water body represented by the collection of grid cells can be obscured (one is not certain what the grid cells represent in real life). Geographic Information System Skills for Foresters and Natural Resource Managers. https://doi.org/10.1016/B978-0-323-90519-0.00009-1 Copyright © 2023 Elsevier Inc. All rights reserved.

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b. Generally, when viewed from greater distances, the real-life landscape or water body represented by the collection of grid cells emerges more clearly. As the size or dimension of grid cells increases, real-life landscape or water body features become more obscured. As the size or dimension of grid cells increases, contrast between real-life landscape or water body features generally declines, and features become more difficult to identify. A raster GIS database has a spatial resolution that is reflective of the grid cell size (i.e., if the grid cells are 30
30 m in size, the spatial resolution is 30 m). A raster GIS database may have a spectral resolution that is reflective of the range of electromagnetic energy represented as the value of each grid cell. This would only be appropriate for raster GIS databases that are direct outcomes from remote sensing (satellite, aerial, drone, etc.). A raster GIS database has a temporal resolution that reflects the date on which the grid cell values represent the condition of the landscape or water body. Reflection 8.1 Which of the 15 concepts related to raster GIS databases most concerns or confuses you? What questions come to mind as you ponder your concern or confusion? Inspection 8.1 Open the Itasca County (Minnesota) county composite image (digital orthophotography) that is available from this book’s website (gis-book.uga.edu) in your preferred GIS software program. What is the shape of the grid cells in this raster GIS database? What is the spatial resolution of the grid cells? How many grid cells are contained in this raster GIS database? Roughly speaking, what percent of the raster GIS database actually contains real information? How much storage space is required to host the database?

Suppose that it becomes important for an organization to determine historical forest composition and subsequent changes across a broad landscape, or to determine the amount of forest damage arising from a large natural disturbance such as a fire or hurricane (tropical cyclone). When conducted inside of GIS, these analyses will most likely involve the use of raster GIS databases that represent forest cover types and other land and water resources or weather conditions. These databases are often the most accessible as collections of satellite data or data derived from other remotely sensed sources and can often be downloaded not long after they were collected. Raster GIS data, as was suggested earlier, commonly has a simple data structure that consists of grid cells (i.e., intersecting columns, and rows) where each is represented by a single value. The continuous or discrete values assigned to each grid cell can be used in a GIS software program to determine information needed (e.g., slope, forest type) to assess the

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condition of land and forest resources across a broad, continuous landscape. As one should find in reading this chapter, there are numerous methods for manipulating raster GIS databases, and these methods involve the field of spatial statistics. Spatial statistics are mathematical analyses that take into consideration the spatial characteristics of data and use this to identify patterns inherent to data or resulting from spatial analysis processes (Unwin, 1996). Spatial statistical analyses can be as simple as determining the sum of the area of a forest using an area-related field in an attribute table associated with a vector forest stands GIS database. Similarly, spatial statistics can include calculating the frequency, mean, range, and standard deviation of a spatial attribute (Bernhardsen, 2002). With respect to raster GIS databases, spatial statistics underlie spatial analysis processes of nearest neighbor analyses and interpolation methods, including inverse distance weighting and kriging covered later in this chapter, as well as modeling spatial relationships between spatial variables. Spatial statistical analyses may become increasingly advanced ranging from regression analysis to algorithms calculated in machine learning environments to find patterns and statistically significant variables in complex spatial databases. Reflection 8.2 What was the last raster GIS database you used? Perhaps you viewed a weather map on your computer or cell phone. Some aspect of this weather map likely involved a raster GIS database. Perhaps you viewed an aerial image within an online map viewer. For the last raster GIS database you used, how do you think the database was developed? Raster GIS databases can represent land and water areas that are very small or very vast. Small raster GIS databases (perhaps only 10
10 m in total size) might be obtained from unmanned aerial vehicles (UAV) that fly in low altitudes (i.e., 100 hundred meters) over the surface of the ground. Large raster GIS databases (100
100 km in total size) might be obtained from satellites that orbit several thousands of meters above the Earth. Individual raster GIS databases (scenes, tiles, grids) may be combined or may be reduced in size if an analysis needs to be conducted across a broader, or only within a smaller, area. As with vector GIS databases, some queries can be applied to raster GIS databases to address particular questions of interest. With raster GIS databases, it is also possible to use a mask to extract (clip) raster data that represents a specified area of interest. For example, if general land cover classes were needed for the Talladega National Forest in Alabama, a vector GIS database representing the boundary of the national forest could be used as a mask to extract raster data from a land cover database, limiting analysis to only those grid cells located within the boundary of the national forest (Fig. 8.1). When using raster GIS databases, analyses can be performed on the entire database, on only those queried or masked grid cells, or in a moving window fashion centered on single grid cells. Mathematical processes might also involve raster GIS databases obtained from various sources and represented by various spatial resolutions. This chapter will address

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FIGURE 8.1 Land cover data for the Talladega National Forest (A) not masked by the boundary of the national forest and (B) masked by the boundary of the national forest. Credit: Land cover data from the U.S. Department of the Interior, Geological Survey (2018).

introductory concepts for processing raster GIS databases, beginning with data resolution, then leading to elevation and terrain analysis, data conversion and reclassification, map algebra, data interpolation, and image classification.

Resolution When working with raster GIS databases, particularly with satellite or aerial imagery, four types of resolution are often addresseddspatial, temporal, radiometric, and spectral. The radiometric resolution of a raster database refers to the number of differentiable levels or values of data. For example, each grid cell in an 8-bit raster GIS database has potentially 256 levels which can be represented by a single value ranging from 0 to 255. The spectral resolution of a raster database, where it is appropriate, refers to the range (band) of electromagnetic energy that has been observed using a particular sensor. Here, the relative intensity of reflected blue light, for example, in the range of 0.4e 0.5 micrometers (mm) might be stored with each grid cell, and this range of the energy represents the spectral resolution of the GIS database. The temporal resolution of a raster GIS database represents a measure of repeatability, or of how often data is obtained. This measure of resolution refers to time; sometimes it refers to the date of database development, but more often it refers to the time period that elapses between subsequent versions of the GIS database. Inspection 8.2 Open the Itasca County (Minnesota) county composite image (digital orthophotography) that is available from this book’s website (gis-book.uga.edu) in your preferred GIS software program. What are the spectral and temporal resolutions of the raster database?

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While the three resolutions noted above are important to consider when using raster GIS databases, the spatial resolution is often the primary concern. Spatial resolution refers to the size of a grid cell (pixel). In short, the spatial resolution of a raster GIS database is a metric representing the amount of land or water that one grid cell represents, or what one can see by using a single grid cell to represent some area of interest (Fig. 8.2). This is an important concept for all raster GIS databases and for all data processing considerations because the value (relative reflectance, land cover, etc.) associated with each grid cell is often assumed to refer to the average condition within the grid cell. Thus, smaller grid cells provide better precision and heterogeneity when describing a landscape or water body, yet this added specificity may be accompanied by greater variation in grid cell values. Further, inadequate spatial resolution can lead to inaccurate outcomes of spatial analyses and misleading conclusions. For example, some analyses may suffer because the fine-scale detail that was required when using a smaller spatial resolution was not provided through some particular raster GIS database.

FIGURE 8.2 Spatial resolution is an important consideration in raster data analysis. It allows the analyst to “see” features at varying scales and viewed as a scene, maybe providing insights into an area of interest. Credit: Base map from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021).

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As an example, a 30 m spatial resolution raster GIS database would likely not allow as precise determinations of tree crown dimension as a raster GIS database containing 1 m grid cells. Importantly, though, when using smaller spatial resolution databases, an analysis will likely require more than one adjacent (connected) grid cell to determine the true character of certain landscape features, such as tree crowns. When performing analyses using raster GIS databases, one may have to aggregate (add together) or resample (transfer values) grid cells so that a common spatial resolution and spatial position of the grid cells within the various GIS databases can be obtained. A word of caution, resampling to develop a smaller spatial resolution GIS database requires some complex methodologies and is not advisable for everyday analysis. Further, resampling does not change the values associated with the resulting grid cells. For example, if a 2 km grid cell were resampled (split) into four 1 km grid cells, each value associated with the resampled grid cells would have the same value as the original (2 km) grid cell (i.e., changing the spatial resolution does not change the observed or interpolated data associated with the original grid cell) (Fig. 8.3). Translation 8.1 One night at a dinner you are attending with close friends, the subject of your recent work activities arises. You mention the need to resample some raster databases. In one short, clear, general sentence, describe what resampling a raster database accomplishes. When selecting raster GIS databases to address a natural resource management issue, an understanding of the various resolutions of the data is important. Consider the temporal resolution (or age) of the data; it may be imprudent to analyze active timber stands for a carbon assessment if the associated data is a decade old and has not been updated to recognize recent disturbances (harvests, storm impacts, diseases, etc.), for example. Also important is the minimum mapping area of a project, or the smallest area or feature being mapped, and how it relates to the overall geographical extent of the analysis. If one were assessing land use and land cover types for a state or province, a moderate spatial resolution (30 m or lower) may be adequate. However, if one were assessing an urban area for street-tree canopy cover, building footprints, and other finescale features, a smaller spatial resolution may be needed (e.g., 1 m or less). Further, one consideration in the processing of raster GIS databases involves the desired spatial resolution of the output. This will generally consist of the largest grid cells that can be produced to obtain the most meaningful interpretation of the results. For example, if one FIGURE 8.3 Example of resampling grid cells in order to achieve a desired spatial resolution. In this case, a 2 km grid cell with a value of 10 is resampled into four 1 km grid cells, each with a value of 10.

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were integrating in an analysis three raster databases with 1 m, 10 m, and 20 m spatial resolutions, the spatial resolution of the resulting output may need to be 20 m or greater to provide meaningful results.

Elevation and topography Across the various fields that comprise natural resource management, elevation may represent different ideas for describing the natural world. Foresters, for example, may think of elevation as the height of the ground surface above sea level, or the height of an object above the surface of the ground (i.e., a tree top). This is very closely related to the concept of topography, or the study, description, and visualization of landscape features with respect to other features around them. Topographic information can represent the extent and character (shape, slope, etc.) of anthropogenic features as well as natural water bodies, mountains, plains, and others. Importantly, topographic maps provide an indication of elevation or the height of a surface above or below sea level. Those who have used topographic maps, or their digital equivalents, should be familiar with contour lines (described in Chapter 4) and the elevations that are suggested by these features. Alternatively, other resource managers may take a land surveying approach to the concept of elevation, where one considers a reference ellipsoid and a geoid height to arrive mentally or physically at a “true” (orthometric) elevation. Digital elevation data, acquired directly from a sensor or derived from other sources, is an important raster GIS database for natural resource managers (Szypuła, 2019). Raster GIS databases containing elevation information can be used in GIS software to visualize surfaces above or below sea level, as each grid cell provides a relative elevation value in Imperial (English) or metric units. It should come as no surprise that the spatial resolution of these raster GIS databases is therefore important. For example, if a raster GIS database representing elevations has a 30 m spatial resolution, everywhere within each 30 m grid cell the elevation of land is considered static (fixed). More precise descriptions of elevation require a greater spatial resolution database (one with smaller grid cells), yet this desire will likely increase data storage needs, processing times, and other considerations as the number of grid cells in a GIS database increases nonlinearly as a function of the inverse of the desired amount of grid cell size reduction. Example 1: One uses a 10 m grid cell GIS database but desires a database of grid cells that are 1/2 the size (5 m). The number of grid cells in a 5 m raster GIS database will contain (2/1)2 ¼ 4 times the number of grid cells in the 10 m raster GIS database.t Example 2: One uses a 30 m grid cell GIS database but desire a database of grid cells that are 1/30 the size (1 m). The number of grid cells in the 1 m raster GIS database will contain (30/1)2 ¼ 900 times the number of grid cells in the 30 m raster GIS database. In any event, digital elevation data not only provides a way to understand the shape of the Earth, but also to derive additional landscape information which may aid in making management decisions or in modeling plant and animal species habitat conditions.

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Diversion 8.1 Using the Internet, conduct a literature review. Describe, in a short paragraph, the main characteristics of the ETOPO1 global relief model. Elevation-related GIS databases are often referred to as digital elevation models (DEMs) or digital terrain models (DTMs). The digital portion connotes its creation from digitized surveys or maps (like USGS Topographic maps), or data collected by sensors (e.g., LiDAR (light detection and ranging)), synthetic aperture radar (SAR), etc.). DEMs can be created from scanned topographic maps as well, and from high quality aerial images. In this latter case, the topography is established with vertical and horizontal control points and the contours are digitized using a special device called a stereo plotter (Wiche et al., 1992). The vertical and horizontal accuracy of a DEM is based partially on the source of the topographic information and partially on the quality and density of the surveyed control points within the map (Elassal and Caruso, 1983). DEMs can also be created from the information (signal returns) of LiDAR devices mounted in drones or other aircraft (Fig. 8.4). More recent raster DEM databases use updated vertical datum and geoid information to increase the accuracy and precision of the databases. As for availability, generally medium spatial resolution, global databases, and often smaller political unit (state, province, county, etc.) databases are commonly freely available through various government agencies. Higher spatial resolution DEMs may need to be purchased or obtained through a geodetic survey. High-accuracy, high-resolution DEM databases are important sources of landscape information, but it should be noted that, FIGURE 8.4 A 1 m DEM showing a portion of Allegheny National Forest derived from LiDAR collected as part of the 3D Elevation Program (3DEP). The area shown is from the eastern side of the Allegheny Reservoir near the border between Pennsylvania and New York with an elevation ranging from 2127 to 1327 feet. Credit: Elevation data from the U.S. Department of the Interior, Geological Survey (2017).

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like most models, errors may have been introduced during the creation of a DEM and then these might propagate further through any derived products. Higher spatial resolution elevation data facilitates the development of more effective, less biased models in simulations and processes that use these databases (Hawker et al., 2018; Rocha et al., 2020). For small areas of interest, it may be possible to use an UAV, or drone, to develop an elevation database that has a spatial resolution of less than one foot (25 cm). A DEM on its own provides useful elevation information, yet products that can be derived from a DEM allow for the delineation of watersheds (catchments, drainages) and streamside management zones (riparian areas, SMZs) (Rocha et al., 2020), along with other areas that might have prohibitively steep ground slopes. These derived products may be useful in planning management activities, such as for identifying lands too steep for ground-based forest harvesting systems, or for devising a flood management program (Hawker et al., 2018). Ground slope (Fig. 8.5) is often referred to as the first derivative of a DEM. It allows one to understand the relative change in elevation of a landscape over some corresponding horizontal distance and is often reported in degrees (0e90 degrees) or percent (rise or fall in elevation/horizontal distance (e.g., 100 feet)). The information provided by a DEM on ground slope conditions can be beneficial to forestry operations. If one were planning a timber harvest, a ground slope of 1%e5% throughout a potential harvest area would likely be less difficult to plan than a harvest area with a ground slope of 30%e50%, as the latter would likely cause forest managers to consider several additional logistical and environmental issues. A DEM can also provide the slope information

FIGURE 8.5 Ground slope (5 m) derived from the 1 m DEM shown in Fig. 8.4. Slope ranges from 0 degrees (green) to approximately 90 degrees (red). Credit: Elevation data from the U.S. Department of the Interior, Geological Survey (2017).

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necessary for preliminary road planning processes. Further, since topography can influence soil development, a DEM can also be used to inform the development of soil maps. Ground slope is also an important component for determining water flow direction, and therefore can inform forest managers of the potential for soil erosion during and after harvesting activities. The calculation of ground slope (%), as many remember it, is the rise over the run. More specifically, ground slope is the change in elevation between two points (increase of decrease) divided by a horizontal distance between those two points. When developing a ground slope GIS database, the ground slope for a grid cell considers the elevation of neighboring grid cells. The change in slope (in degrees) between two neighboring grid cells is: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  2  2ffi DZ DZ Slope ðdegreesÞ ¼ tan1 þ
57:29578 Dx Dy

where tan1 is the inverse tangent function, DZ Dx is the change in elevation in the x DZ direction (east-west), and Dy is the change in elevation in the y direction (north-south). Typically, a 3
3 grid cell window (9 grid cells, with the grid cell of interest in the middle) is used to determine average ground slope in a local area from a DEM. The determination of ground slope might also only use the four neighboring cells in cardinal directions (north, south, east, west) or all cells in a predefined larger neighborhood using a third order finite difference (Bolstad, 2012). Reflection 8.3 Although we have not come very far with the discussion and use of DEMs, of two options for describing ground slope (a raster DEM and vector contour lines), which do you prefer? Why? What would change your opinion? Another useful product derived from a DEM is the aspect of a landscape (Fig. 8.6), which allows one to understand the direction that the ground faces. In other words, it allows one to understand the direction that water will flow downslope when it rains. The aspect of a grid cell in a DEM is quantitatively presented as an azimuth. Azimuths range from 0 to 360 degrees (both of which represent north). Aspects begin at north and increase in value as one progresses from east (90 degrees) to south (180 degrees) and then west (270 degrees) in a clockwise manner. An indication of aspect can be important in the consideration of tree species selection (or preference) for regenerating a harvest area, as aspect can provide a relative understanding of the potential amount of sunlight an area of interest might receive during a given year or season. Further, the information provided by elevation, ground slope, and aspect can be useful in predicting woody vegetation biomass and tree species presence (Stage and Salas, 2007; Yang et al., 2020). For fire management purposes, a DEM might be used to provide aspect, elevation, and slope information that can be used to improve the accuracy of weather condition databases and GIS databases describing forest fuels (Loveland and Ramey, 1986).

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FIGURE 8.6 Aspect (5 m) derived from the 1 m DEM shown in Fig. 8.4. Aspects here include both flat areas and ground facing direction for areas of elevation. Credit: Elevation data from the U.S. Department of the Interior, Geological Survey (2017).

Diversion 8.2 Using the DEM that is provided on this book’s website (gis-book.uga.edu) and your preferred GIS software program, create a raster GIS database of the aspect (directional flow) of the landscape. Then, create a map that professionally presents this landscape. A DEM can also be used to create a three-dimensional vector surface that represents changes in the surface of a landscape. This triangulated irregular network (TIN) (Fig. 8.7) is a collection of nonoverlapping triangles that are developed from the elevations provided by a DEM, resulting in a three-dimensional view of the landscape. TINs are often referred to as an independent data model, or a special case of the vector data model (Bolstad, 2012; Chang, 2019). Regardless of which category the data model belongs, a TIN provides a three-dimensional visualization of the terrain, allowing features to be delineated based on changes in the elevation of their surface. As was noted in Chapter 2, a TIN is a database of points where both elevation and location are known, and where a triangular mosaic is created by connecting nearby points. Often, a TIN is converted to a

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FIGURE 8.7 A TIN derived from the 1 m DEM shown in Fig. 8.4. Credit: Elevation data from the U.S. Department of the Interior, Geological Survey (2017).

lattice, and then perhaps a grid where the centers of grid cells are described by a point (McConville, 1995). One can create a TIN from a DEM, and interestingly, one can create a DEM from a TIN. Further, slope and aspect can be derived from the information contained within a TIN. Ridgelines can be identified by the negative ground slope values on either side of the diffluent (flowing away) edges, and valleys can be identified by the positive slope values on either side of confluent (flowing together) edges (Elmes et al., 1993). Thus, when using a TIN, one can visualize slope and aspect and determine potential water flow direction along a gradient. A TIN can also be created to allow one to visualize the canopy of a stand of trees, or the surface of other interesting or important features (e.g., buildings) (Liu and Wu, 2020). Translation 8.2 In an attempt to impress your friends with the knowledge and experience you have gained while using GIS, you mention the ability to describe the shape of the surface of a landscape using a TIN. In one short, clear, general sentence, describe what comprises a TIN.

Reclassification Either through interpolation or image classification processes, it is somewhat common to transform a raster GIS database, where each grid cell contains a continuous value (a

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fraction of a number, such as air temperatures presented to 0.1 degree), so that each grid cell contains a discrete class value. A discrete class value is represented by an integer. In conducting a reclassification process, the original raster GIS database is not affected, as a new raster GIS database is created from the act of reclassifying the values of each grid cell. For example, one might transform the grid cell values within a precipitation raster GIS database into discrete classes such as these: Grid cells with precipitation levels from 0.001 to 1 inch are given a value of one. Grid cells with precipitation levels from 1.001 to 2 inches are given a value of two and so on . Subsequent to this, the resulting reclassified GIS database might be used in a qualitative classification to display areas of land that have received light to heavy precipitation. Reclassification of a raster GIS database works the same way when the original database contains discrete data. If one were to obtain a forest cover type raster GIS database and decide that they need a more generalized approach for forest cover (e.g., to generalize pine/conifer areas instead of having classified values for loblolly pine (Pinus taeda), slash pine (Pinus elliottii), and eastern red cedar (Juniperus virginiana)) then, similar to the continuous data reclassification above, values could be queried and reclassified. For example, grid cell values originally classified as various conifers might be assigned to class 1, mixed pine/hardwood groups might be assigned class 2, pure hardwood stands might be assigned class 3, and so on. The process of creating a new raster GIS database through reclassification is, in effect, a reassigning of the original grid cell values of a raster GIS database to discrete classes. This act can be viewed as an effective data reduction technique. For example, a raster forest type GIS database can be reclassified so that all forested areas contain a single "forest" code, and all "non-forest" areas contain a different code. This case illustrates that one can group together grid cells that have a variety of similar values and reduce the amount of apparent heterogeneity in the database. While the effect of a reclassification process such as this has on the results of a landscape analysis may vary, depending on the type of analysis, a reclassification process could allow one to easily reduce the potential size and complexity of a map’s legend and allow more flexibility in designing the color scheme. For example, if one were using the 2016 version of the National Land Cover Database (NLCD) for the United States (U.S. Department of the Interior, Geologic Survey, 2019) one would find several land cover classes that refer to developed areas:    

Developed, Developed, Developed, Developed,

open space low intensity medium intensity high intensity

If one were simply interested in developing a map that illustrated developed lands, these four land classes might be reclassified so that the associated grid cells each contain

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a single developed land class code. Then, a map could be developed where a single color might illustrate the developed areas, rather than four different colors (Fig. 8.8). Reflection 8.4 If each grid cell in a raster GIS database can be assigned a single value to represent some phenomenon, would you prefer that value to be an integer or a continuous real number? Perhaps your decision relates to the phenomenon of interest, so select the theme or source of the raster GIS database, then answer the question that was posed here. Would a reclassification process be necessary to convert the data in your raster GIS database to the data format that you prefer?

Map algebra Within GIS software programs, map algebra refers to various mathematical operations and manipulations that can be employed to create new raster GIS database themes. The map algebra operators used in processes such as these may be those which are traditionally considered mathematical (e.g., multiplication, division, addition, subtraction). For example, an operator might be applied to every grid cell in a raster GIS database to convert ground slope from values of degrees to values of percent (e.g., unit conversions). The map algebra operators applied to raster GIS databases might also be relational (e.g., less than, equal to, etc.) or Boolean (i.e., and, or, not). For example, the results of a map algebra process might return true or false (1 or 0, respectively) values for each grid cell based on a question (e.g., Is the ground slope greater than 35%?). Using map algebra for forest management purposes can involve determining average values of some resource for some area of interest. Further, as in the case of reclassification, when map algebra is applied to a raster GIS database, the original GIS database is not affected, and a new GIS

FIGURE 8.8 The 2016 National Land Cover Database for the Duluth, MN and Superior, WI region (A) including multiple land cover classes and (B) reclassified to produce a map showing only the developed portions of the two cities. Credit: National Land Cover Database data from the U.S. Department of the Interior, Geological Survey (2018).

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database is created from the acts associated with the calculations applied to each grid cell. Map algebra processes can be categorized as local, focal, and zonal operations (Tomlin, 1990, 2013), though a fourth class (global operation) is also often recognized. Local map algebra processes refer to those that are applied independently to each grid cell (pixel) on a cell-by-cell basis. These processes can be applied to the grid cells of a single raster GIS database or to the grid cells from multiple GIS databases that lie in the same position on a landscape or water body. For example, a raster GIS database may represent average precipitation values over a landscape, with rainfall values associated with each grid cell presented in millimeters. Through a local map algebra process, the values for each grid cell can be converted to rainfall values in inches by dividing the original grid cell values (millimeters) by a constant (25.4), or by multiplying each grid cell value by another constant (0.0394) (Fig. 8.9). For a multiraster GIS database example, consider the need to determine landscape change within an area of interest. Here, it may be of value to understand whether the landscape conditions at the location of each grid cell had changed from a forested condition to a nonforested condition between two points of time (represented by two raster databases illustrating landscape conditions perhaps a decade apart). In this case, one might first apply a relational map algebra process to both raster GIS databases, coding each grid cell as being either forested (1) or nonforested (0). Then a local map algebra process would be applied at the location of each grid cell to subtract the grid cell value of the second raster GIS database from the grid cell value of the first raster GIS database. If the result of this calculation is 0, then there was no observable landscape change at the grid cell location over the time frame depicted by the two GIS databases. forest in time period 2 (1)dforest in time period 1 (1) ¼ 0 nonforest in time period 2 (0)dnonforest in time period 1 (0) ¼ 0 However, if the result for a grid cell is either 1 or 1, then one might conclude that some form of landscape change has occurred at that location of the grid cell (Fig. 8.10). Again, the original raster GIS database are unaffected by the processes, and a new raster GIS database would be created to reflect the outcome of the map algebra process. forest in time period 2 (1)dnonforest in time period 1 (0) ¼ 1 nonforest in time period 2 (0)dforest in time period 1 (1) ¼ 1

FIGURE 8.9 Conversion of grid cell values in a raster database from millimeters to inches.

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FIGURE 8.10 Changing grid cell values between two time periods.

Diversion 8.3 Access the ANF_8 raster GIS database from this book’s website (gis-book.uga.edu). This is a piece of the Warren County NAIP (National Agricultural Imagery Program) imagery for a portion of the Allegheny National Forest (Pennsylvania). Using your preferred GIS software, first conduct a local area map algebra process whereby 10 units are added to the values from this database. If you compare the outcome of this process to the original NAIP imagery, you should see that it is adding 10 units to the red band reflectance value. Now, one simple map algebra process involves assessing each grid cell in a raster GIS database and asking a single question about it. If the answer to the question is true, a similar cell location in a new raster GIS database contains the value 1 (true), and if the answer is false, a similar cell location in the new raster GIS database contains the value 0 (false). With this in mind, conduct a second map algebra process where grid cells with values in the ANF_8 raster GIS database that are greater than 65 (the red reflectance) are given a value of one and other are given a value of 0. Open the new GIS database. What do you notice happened? Focal map algebra processes, also known as neighborhood processes, involve the determination of a new value for a focal grid cell (base or reference pixel) after considering the original value of the focal grid cell and the original values of the grid cells that surround it. The number of grid cells considered in a focal map algebra process can vary, but the dimension involves an odd number of cells (often 3
3, 5
5, 7
7, or 9
9) so that the middle grid cell in the window is distinct (the focus). For example, a 3
3 neighborhood involves a set of grid cells three wide and three tall. In the middle of this set of nine grid cells is the focal grid cell for which a new value will be determined. The other eight cells, along with the original value of the focal grid cell, may be used to determine the new value for the focal grid cell. Once the computation has been made, the 3
3 neighborhood (window) shifts, often along a row of grid cells, one grid cell, changing both the focal grid cell and the members of the 3
3 grid cell set. As in other cases of map algebra and reclassification, the results of these and other map algebra processes create a new raster GIS database, and do not affect the values of the original raster GIS database(s). Interestingly, in the corners of a raster GIS database, and along the edges, most focal map algebra processes are only applied to the grid cells containing

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FIGURE 8.11 Example of a focal/neighborhood map algebra process. The values of grid cells in a raster GIS database (A) are used along with a 3
3 moving window to create a raster GIS database of the average values (C). The exploded view (B) of the window illustrates the nine values considered in the map algebra process for a certain focal cell, where the average is 4.2.

valid values (Fig. 8.11), so fewer grid cells than what the neighborhood window size implies may be used in these cases. Similar types of moving window processes are used in the analysis of an image (picture), where a neighborhood process is used to sharpen or smooth the feature values (e.g., colors, differences) in the image. Inspection 8.3 In the paper by Wood et al. (2013), which is noted in the references at the end of this chapter and is freely available on the Internet through PLoS ONE, a number of moving windows were applied to two different raster databases to examine image texture and attempt to relate it to avian habitat quality. What were the different moving window dimensions, and what were the spatial resolutions of the raster databases to which they were applied? Zonal map algebra processes are those where an operation is performed only within some predefined zone. This predefined zone might consist of the area within a property boundary or some larger management unit within a forest. A zonal map algebra process is similar to a focal map algebra process except that neighborhoods of irregular sizes and shapes can be used. Within the zones, similar mathematical operations can then be employed. Suppose there existed a raster GIS database of one acre (0.4047 ha) grid cells that contain the estimated average tree basal area per acre, and that it covered the entire United States. One might be curious about the average basal area for the State of

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Georgia, and therefore define the boundary of the state as the zone. Then, all of the grid cells contained within the boundary of the state would be used to satisfy the curiosity of the analyst. Similarly, an interested analyst could determine, at a county-level, the estimated area of different tree species (Fig. 8.12) by defining the boundaries of the counties of interest as the zones and applying a map algebra process (summation in this case). Global map algebra processes are those that are applied to an entire raster GIS database, where the value of each grid cell may impact the desired outcome of an analysis depending on the location of each grid cell and its value relative to other grid cells. One application of a global map algebra process would be for identifying a potential least-cost path (woods road) across a landscape. If one were tasked with determining the location of where to establish a road and the various logging decks (landings) needed for a future timber harvest, this could be accomplished using a DEM and a global map algebra process. If the proposed road were to pass through flat terrain, each grid cell between the beginning and the proposed endpoint of the road might be relatively equal in quality (each grid cell has a very low ground slope value), and therefore a straight-line (Euclidean) distance would likely be suggested as the location of the proposed road between the two endpoints. However, if an SMZ is present in the direct path between the

FIGURE 8.12 Parish-level pine (Pinus spp.) area for the State of Louisiana. Credit: Forest type data from the U.S. Department of Agriculture, Forest Service (2008).

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endpoints, or if the ground slopes present in the direct path between the endpoints are too steep to accommodate heavy equipment and log trucks, these associated grid cells would be assigned a greater “cost.” If these high-cost locations (grid cells) were included in the proposed road, a straight-line distance between the beginning and the end of the road may not be optimal. A longer route with a lower overall cost would need to be identified. Such processes are considered global because all of the grid cell values in the raster GIS database might be considered. The outcome or result in this example would be the identification of a proposed road that was likely less expensive, safer, and of lower environmental impact than other options. Inspection 8.4 In the paper by Sales et al. (2019), which is noted in the references at the end of this chapter and is freely available on the Internet through MDPI.com, a few raster databases were created from a DEM. What were they, and why were they needed? Finally, sometimes it is necessary to conduct map algebra processes using the information contained within multiple raster GIS databases. Assuming the spatial resolution and the locations of grid cells are consistent among different raster GIS databases, the values of their grid cells can be accessed and used as variables in an environmental analysis. For example, some environmental analyses employ simple additive or weighted computations to estimate the condition or character of a landscape. These types of analyses would access the necessary information for each location (grid cell) on the landscape from the multiple raster GIS databases, compute an outcome, then create a new raster GIS database with the same dimension (extent, grid cell size, etc.) as the raster GIS databases used as input to the analysis. For example, when assessing potential windstorm damage across a forested landscape, and environmental analysis might involve the potential maximum wind speed contained in one raster GIS database, the current forest age contained in another raster GIS database, the ground slope from a DEM, and the aspect from a fourth raster GIS database. Then, for each grid cell, the potential maximum wind speed, current forest age, ground slope, and aspect would be considered in an analysis that estimates potential wind damage to the forest. These types of map algebra processes may also be incorporated into spatial statistical analyses.

Interpolation Within some raster GIS databases, as a result of data collection limitations or errors in data collection efforts, certain grid cells may lack valid values. These missing values can be of great concern when a raster GIS database is used in a map or is used in a subsequent data analysis. Imagine a need for a raster GIS database meant to represent soil nutrient levels across a landscape. An actual soil sample might include characteristics of soils from points spaced out several hundred meters apart across a landscape. So, very good information about the soil resource would be known at the location of the sample

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points, yet there would be no information at all regarding soil characteristics in the landscape between the sample point locations. Through mathematical interpolation, it may be possible to create a raster GIS database where each grid cell either contains an actual field measurement (of a soil characteristic, for example) or an estimate of the soil characteristic of interest (an estimate of a soil characteristic, for example). This process of creating the estimates would involve an interpolation of soil characteristics using nearby field measurements. Several local mathematical processes, those that use field samples collected at point locations, can be employed to interpolate grid cell values across an area of interest. The kernel density process can be used to obtain estimates of expected values (magnitude) per unit area (Fig. 8.13). This is accomplished by assigning a search radius around specific points in a raster GIS database to determine an expected value which is based on actual nearby values. A larger search radius results in a smoother outcome. This type of process

FIGURE 8.13 Kernel density interpolation using tornado initiation points per square mile within a 100 mile buffer around Little Rock, AR between 1950 and 2017. Credit: Tornado data from the U.S. Department of Commerce, National Oceanic and Atmospheric Administration (2020).

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can be applied in situations where the count of some objects of interest (trees, buildings, etc.) is desired. The outcomes of this process can be used to provide a broad overview of a landscape and can be employed in resource evaluations including assessing fires (Kuter et al., 2011), thinning decisions (Hung et al., 2005), animal habitat and movements (Pe´ron, 2019), and other conditions or events of interest to a forester or natural resource manager. Translation 8.3 In a staff meeting, your colleague mentioned that a kernel density process might be used to estimate basal area density for a southern pine forest (see Hung et al., 2005). Your boss looked confused when this statement was offered. In a short collection of sentences, explain in common terms what the inputs and outputs of such a process might be. Another, and perhaps more commonly used method of interpolation, is inverse distance weighted (IDW) (Fig. 8.14). IDW estimates weighted average grid cell values using values from known locations across a landscape, and an inverse of the distance between points as the weights. Here, an implied power function controls the influence of the resulting values assigned to each grid cell; the closer a grid cell is to a point with a known value, the more influence or change the known point has on the grid cell’s interpolated value. Using IDW, values of known points have a greater influence on the interpolated values of grid cells, which adheres to Tobler’s First Law of Geography (introduced in Chapter 1) that nearer objects are more similar than objects that are

FIGURE 8.14 IDW interpolation of tree heights. Credit: Base map from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021).

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further away. Interestingly, the resulting raster GIS database provides predicted values for each grid cell that are only within the range of the values associated with the known points, not slightly higher or lower than the minimum or maximum known value, as might be possible in real life. IDW has been used on a very broad scale to estimate the amount of basal area, snags, and downed wood debris per unit area (per acre) and to illustrate the presences and distributions of different tree species (Woodall et al., 2005). Diversion 8.4 Using your preferred GIS software and the data that can be found below, create a point GIS database. The coordinates are from UTM Zone 15. The location is a small area of the Chippewa National Forest. With an understanding that the tree heights are hypothetical, use an IDW process to create a raster GIS database of tree heights. In Fig. 8.14, the output grid cell size was 1 m, and a fixed 200 m radius was employed to create the grid. Try a few IDW options. Which set of parameters seemed to produce your most satisfying raster GIS database? Easting

Northing

Tree height

372,904 372,904 373,062 373,062 373,062 373,062 373,220 373,220 373,220 373,220

5,271,537 5,271,656 5,271,537 5,271,656 5,271,775 5,271,894 5,271,894 5,271,775 5,271,656 5,271,537

62 65 66 53 59 62 60 55 63 66

The process of kriging (Fig. 8.15) is another way to interpolate missing values in raster GIS databases. One advantage of some of these types of interpolation methods is the ability to develop estimates of concentrations of various phenomena over a specific geographic area while also providing a glimpse of the confidence levels (Mason, 1983). Using a linear system of equations, kriging estimates the value and probable variance associated with unsampled places by minimizing the variance of the estimates in conjunction with using a weighted average of known values within a certain neighborhood of the unsampled place (Krige, 1966; Dunlap and Spinazola, 1984). In this way, kriging considers the structure of the known data points and the spatial correlation/dependence (i.e., semivariance) between them. Kriging estimates values for all of the grid cells that do not contain an actual measurement and also provides prediction errors that allow for a determination of the quality and usefulness of the predictions in the output. As such, kriging is considered a spatial statistical method. While one advantage to kriging is that the variance at each point of a map can be provided, one disadvantage is that the mathematics can be cumbersome (Mason, 1983). A

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FIGURE 8.15 A LiDAR point cloud with only those points classified as "ground" (A) used for kriging interpolation (B) creating a 1 m DEM for a portion of the University of Georgia’s campus with identifiable landscape features including a stadium, river, and roadway. Credit: Elevation data from the U.S. Department of the Interior, Geological Survey (2017).

semivariogram can be produced to illustrate the range of influence a sampled location value has on an unsampled location value, based on distance. Further, the semivariogram may provide an indication of how the values at two sample locations might vary based on their distance apart. In the semivariogram, the sill (the total variance when it appears to level off), the range (where sample observations closer than this distance are spatially autocorrelated), and the nugget (the small-scale variability inherent in the system) can be noticed (Fig. 8.16). The spatial dependence observed through a semivariogram provides the ability to interpolate unsampled values with minimum variance and without bias (Vieira et al., 1997). There are a variety of kriging methods that one can employ, such as

FIGURE 8.16 A semivariogram.

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ordinary, indicator (for binary data), universal, and cokriging. Each should be considered as a potential method for analysis, interpretation, and interpolation of raster GIS databases. Among other uses, kriging has been used to estimate the extent of precious metal ore reserves, soil types, and contaminant plumes using data collected at sample points (Mason, 1983). Further, kriging, along with IDW, are common methods for interpolating elevation values from LiDAR point clouds for the creation of DEMs. Reflection 8.5 The interpolation methods described here essentially estimate a landscape or water body value for places where no direct measurement is available. What is your comfort level with using a raster GIS database which contains interpolated data values?

Classification Classification is a very useful tool for organizing the contents of a landscape, determining the area and location of landscape features, and estimating changes in the landscape over time using raster GIS databases collected from aerial photographs and satellite imagery. Image classification can be helpful in answering questions such as How much water is in the state of South Carolina? Where are prairie lands located in Colorado? and Where has forest loss occurred in Oregon? With the relatively high temporal frequency of image capture done by modern satellites and flights contracted by different agencies and organizations, years of imagery may be classified creating an invaluable archive of current and past landscape features. The goal of the classification processes is to generate raster GIS databases that can be incorporated into other spatial analysis processes (Blaschke, 2010). Raster image classification often relies on spectral reflectance values, representative of portions of the electromagnetic spectrum, contained in each cell and potentially across multiple bands of electromagnetic energy (Fig. 8.17). When thinking about classification, not all similar landscape features will have the exact same spectral reflectance values. For example, all deciduous trees will not have the same relative reflectance values in the red, green, and blue bands of electromagnetic energy. Instead, the classification process takes into account a range or average of spectral values related to a landscape feature. Additionally, when classifying an image, the classes chosen should fit the need of the analysis. For example, if one was interested in identifying the area of different forest types, classes might include broadleaf forests, coniferous forests, mixed forests, and other landscape characteristics such as water and roads present on the image being classified. Generally, there are three types of classification methods: supervised, unsupervised, and object-oriented classification. Inspection 8.5 Using your preferred web browser, navigate to the multiresolution land characteristics consortium’s (MRLC) enhanced visualization and analysis tool website. Choose a location and investigate the available land cover data. In a brief memorandum,

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FIGURE 8.17 Multispectral imagery is imagery that includes more than one band of electromagnetic energy information.

summarize the forest land cover change for the county you selected. What forest land cover classes are used to describe the area? What gains or losses in forest cover have occurred? What might be potential causes of these changes?

Supervised classification Supervised classification is the process of assigning a class value to a set of grid cells in a raster GIS database based on the values of similar grid cells that have been a priori classified as representative of a class or group (Abburu and Golla, 2015). This process is called supervised classification because it requires a person to manually interpret a satellite or aerial image and define groups of grid cells, called training sets, that share similar spectral reflectance values. A training set should include grid cells that are homogenous and representative of various features of interest across a landscape. Using a multispectral image, or an image with multiple bands of electromagnetic imagery, the range of numerical spectral signature values (red, green, blue, near infrared (NIR), and other bands collected by a particular sensor) for each class are used to define which grid cells in an image are members of the same class (Lillesand et al., 2004). These training sets are then used by algorithms or statistical models to assign a class to each grid cell of the image. Training sets can be informed through field surveys, but this is often costprohibitive (Turner et al., 2003) and more often they are defined through image interpretation.

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The accuracy of a resulting classified image is directly related to the quality of the training sets defined by the image interpreter. Commonly, these classes are representative of land cover types including forests, agricultural crops, water, and other classes falling within an area of interest but may also focus on impervious surfaces and tree canopy cover (U.S. Department of the Interior, Geological Survey, 2012). The U.S. Geological Survey maintains a land cover database, the NLCD, covering the conterminous United States created through classification using 30 m satellite imagery from the Landsat satellite system which was most recently updated in 2019 (Fig. 8.18). Commonly, classification uses a maximum-likelihood classifier to determine the probability that a cell belongs to a class and assigns the cell to the class it is most likely to belong to. By delineating training sets, a spectral signature for each class is defined. The maximum-likelihood classifier uses this signature to determine the probability that a grid cell belongs to a class. The success of a maximum-likelihood classifier to accurately sort pixels into the correct classes relies on well-defined training sets and the assumption of a normal distribution of spectral values. The classifier uses the covariance and mean of the spectral signatures included in training sets representing an object to determine its probability for inclusion into a class. While effective at managing overlapping classes, this method can be computationally intensive depending on the number of classes to be defined, the spatial resolution of the image, and the number of bands used in the classification process (Lillesand et al., 2004).

FIGURE 8.18 The 2016 National Land Cover Database for the conterminous United States developed from Landsat satellite imagery. Credit: U.S. Department of the Interior, Geological Survey (2019).

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Other classification methods include parallelepiped and minimum distance classification. A parallelepiped classification method is a decision rule using the average grid cell brightness values and standard deviation threshold computed through training data of a selected number of bands in an image. For simplicity, consider grid cell values for two bands. The average value for that band, plus or minus one standard deviation, would represent the value ranges for each class in the classification. The decision rule requires that the band values for each of the two must fall within the high/low boundaries for each band to be assigned to a particular class. If not, then the grid cell remains unclassified. Conversely, because of the nature of the classes used, there could be overlap in the average values. If this occurs, a grid cell is assigned to the class for which the conditions are satisfied first which has the potential to result in a misclassification of grid cells. The minimum distance classifier uses training set data to compute a band average for each class (e.g., forest, water, agriculture, etc.). Spectral distances for unknown image grid cells are then computed from each class by comparing the spectral distance (using Euclidean distance) to each class. The classifier uses the Pythagorean theorem, deriving the distance to an unknown grid cell based on the shortest (minimum) distance to a class. This classification method is simple, with the resulting classified image having no unclassified grid cells unless a distance threshold is defined. These traditional algorithmic methods for classifying imagery are common and effective; however, accuracy in land cover classifications has also been obtained using deep learning (a type of machine learning) (Yuan et al., 2020) and other advanced algorithmic techniques.

Unsupervised classification Unsupervised supervised classification does not require the development of training sets but is a data-driven process using spectral reflectance values. This classification technique groups, or clusters, similar spectral values using algorithms that find patterns in the underlying spatial data (Duda and Canty, 2002). Those values grouped together should be representative of a class. Depending on the algorithm used for classification, the number of clusters may be defined, and classes will be defined based on a K-means clustering algorithm (Lillesand et al., 2004). For example, an unsupervised classification on Francis MarioneSumter National Forest might be performed using 1 m resolution NAIP imagery then reclassified into three classes: forest, nonforest, and water (Fig. 8.19). There are advantages to using unsupervised classification techniques particularly if an area being classified is unfamiliar to those requiring the classified images. Additionally, the results of the classification are typically quickly derived; however, again, processing time is dependent on the size of the area being classified and the resolution of the imagery.

Object-based classification More recent of the classification techniques is object-based image analysis (OBIA). Remotely sensed images can be divided, or segmented, into groups of adjacent grid cells

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FIGURE 8.19 NAIP imagery (1 m) for Francis MarioneSumter National Forest classified into three classes using unsupervised classification.

based on similarities in spectral reflectance values. Members of these segments are further grouped into objects based on their size, texture, shape, and other characteristics defining a boundary around like objects. This process is called segmentation and is the basis for object-based classification. The process can also be implemented at different scales depending on the purpose of the classification. For instance, OBIA can be used in identifying larger areas of similar land cover or finer scale objects like forest cover types in highly variable landscapes (Zhou and Troy, 2009). The result of an object-oriented classification is commonly a land cover file but instead of being a raster GIS database comprised of individual grid cells assigned to a class, the land cover database is comprised of vector polygons, varying in shape and size, representative of landscape features on the surface of the Earth such as trees, buildings, fields, roads, and others. The output of a classification using object-based processes removes the harsh edges of the grid cells and replaces these with the softer edges of

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complex polygons. Further, by using segments as the basis for developing objects representative of landscape features, a greater amount of spectral information is available for delineating landscape features compared to grid cell-based approaches. Additionally, object-based classification incorporates spatial relationships (i.e., distance, direction, and connectivity) in making determinations of what on the Earth belongs to what object (Blaschke, 2010; Zhou and Troy, 2009). An object-based classification process can involve more types of raster GIS data than what can be derived directly from satellite and aircraft sensors. For instance, digital surface models and DTMs created from aerial LiDAR technology coupled with high-resolution satellite imagery have been incorporated into an object-based classification process to identify forest types, clearcuts, and other land cover types with nearly 90% accuracy (Machala and Zejdova´, 2014). It should be stressed that the success of each of these classification techniques may be sensitive to the spatial resolution of the imagery being used in relation to the size of the objects being defined. For example, if the grid cell and the object are relatively similar in size, then supervised or unsupervised classifications methods should suffice. However, if the grid cell size is much smaller (1 m) than the objects being classified, then object-based classification may be more appropriate (Blaschke, 2010). Further, finer spatial resolution is not always better. Smaller grid cells sizes may dilute the characteristics (spectral signature) of the object of interest (Yu et al., 2006).

Spectral indices An often-used strategy for identifying vegetative conditions at the grid cell level using multispectral remotely sensed imagery involves the use of spectral indices. In addition to vegetative cover, raster GIS databases representing spectral indices can be valuable for identifying biomass and changes in biomass, determining the impact of weather events and other natural disturbances, and assessing plant health. These indices use various spectral bands and often the difference between spectral bands. Such indices are widely used and useful in modeling applications and detecting problems in natural and managed systems. There are enough spectral indices to fill a chapter on their own (see Jensen, 2015) with myriad applications. In the assessment and management of natural resources, it is a good idea to become familiar with a few of those commonly encountered.

Normalized difference vegetation index The normalized difference vegetation index (NDVI) is the ratio of the difference between the NIR band and the red band and the sum of the two. This index is commonly used to quantify greenness as it is an illustration of photosynthetic activity: NDVI ¼ (NIR e red)/(NIR þ red)

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The resulting values range from 1 to þ1, where values closer to one indicate healthy vegetation and values less than one indicate either nonvegetative components of the landscape or unhealthy/less green vegetation (Fig. 8.20). This may be the most widely used vegetation index and is provided by the MODIS satellite system as a product allowing for the continual assessment of greenness globally. Calculating the ratio is often automated in GIS software but is a simple calculation that can also be computed using raster calculation capabilities offered with most GIS systems.

Enhanced vegetation index The enhanced vegetation index (EVI) works in a manner similar to NDVI but with the added benefit that it works more effectively in densely vegetated areas (Fig. 8.21). In addition to the near-infrared and red bands, EVI incorporates reflectance from the blue

FIGURE 8.20 NDVI for the continental United States from the MODIS satellite system. Credit: National Aeronautics and Space Administration, Earth Observatory (2000).

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FIGURE 8.21 EVI for the continental United States from the MODIS satellite system. Credit: National Aeronautics and Space Administration, Earth Observatory (2000).

band to compensate for the scattering of energy from aerosols and to account for soil background signals: EVI ¼ G((NIR e red)/((NIR) þ (C1
red) e (C2
blue) þ L))

where G is a gain factor of 2.5, C1 ¼ 6, C2 ¼ 7.5, and L ¼ 1. It should be noted that C1, C2, and L are constants that can be applied across sensor types including MODIS, Landsat, and Sentinel.

Normalized difference moisture index Using shortwave infrared (SWIR) reflectance, normalized difference moisture index (NDMI) is computationally similar to NDVI, but the ratio incorporates both the nearinfrared and SWIR band: NDMI ¼ (NIR e SWIR)/(NIR þ SWIR)

NDMI indicates leaf water content (or vegetation water content) and can be used as an early indicator of drought stress, in storm or insect damage assessments, and to identify dry areas in fire prone locations. NDMI is highly effective in disturbance detection (Ochtyra et al., 2020). As with the other spatial indices noted above, these are

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computed for each grid cell in a raster GIS database using the original spectral reflectance values of the position on Earth represented by the grid cell.

Data conversion One type of data conversion involving raster GIS databases uses the spatial and nonspatial information described by grid cells to create vector GIS database features. For example, a raster GIS database of land uses might be available, where each grid cell contains a land use code. When a set of contiguous grid cells contain the same land use code, this collection can be envisioned as a polygon, perhaps generated through an object-based classification process. The collection of cells can be used to create a polygon in a vector GIS database through the identification of the outer edge of the collection and the development of connected lines that then define that outer edge. Certain grid cells may further contain a land use code identifying them as representative of roads. In this case, a line might be formed by connecting the center of each of these grid cells, resulting in a line feature in a vector GIS database. Certain grid cells in a land use raster GIS database may even further contain a land use code that relates to physical features we commonly identify with points, such as water towers. In this case, each individual grid cell would be converted to a point feature in a vector GIS database. The quality of the vector GIS features created through this process of vectorization of a raster GIS database (Fig. 8.22) is sensitive to the grid cell sizes of the raster GIS database (Bettinger et al., 1996). The conversion of a vector GIS database to a raster GIS database, or rasterization, involves creating grid cells and assigning them values based on where the original points,

FIGURE 8.22 The result of converting a raster GIS database of forest group types in a portion of the Francis MarioneSumter National Forest to a vector GIS database with cover type defined with polygon boundaries. Credit: Forest group data from the U.S. Department of Agriculture, Forest Service (2008).

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lines, and polygons from a vector GIS database reside. For this process, one would need to specify a grid cell size (spatial resolution) desired for the resulting raster database. Point features from a vector GIS database would be represented as grid cells in a raster GIS database. However, the grid cells, depending on their size, may overestimate the size of the original point features. Line features from a vector GIS database would be represented by a collection of seemingly connected grid cells in a raster GIS database, although no information would be available (other than perhaps a specific line-related code) that suggests these grid cells are actually connected in real life. Polygon features from a vector GIS database would similarly be represented by a set of seemingly connected grid cells. The shape and size of the rasterized features would again be sensitive to the grid cell size assumed (Bettinger et al., 1996). Diversion 8.5 Using the Chippewa National Forest roads GIS database, which is composed of 17,332 lines (arcs), convert this GIS database to a raster database using your preferred GIS software. What was the spatial resolution of the resulting GIS database? In Fig. 8.23, the grid cells representing the rasterized roads are about 22 m in size. How do you feel about the outcome of this vector-to-raster conversion process? One may integrate vector and raster GIS databases for visualization purposes in the development of maps or the presentation of mapped features in online mapping systems. It is possible to create tiles (subsets) of raster and vector GIS databases that can be used, for example, as base maps on a GPS receiver. This allows a GPS receiver to only use the portion of the data necessary to navigate and identify landscape features at some user-defined scale.

FIGURE 8.23 A small subset of the original Chippewa National Forest roads GIS database (black lines) and the rasterized version (green grid cells). Credit: Road data from the U.S. Department of Agriculture, Forest Service (2021).

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Translation 8.4 At dinner one night you are attempting to impress your friends with your knowledge of GIS. In one sentence each, describe what rasterization and vectorization accomplish.

Conclusions Raster GIS databases represent landscapes or water bodies in a different manner than vector GIS databases. The collection of grid cells within a raster GIS database has a consistent size and shape; therefore, these databases may be desirable for certain management purposes. The processing of raster GIS databases needs not to be considered a daunting task, as it is simply a different way of thinking about the manipulation of values that represent landscapes or water bodies. In this chapter, several processes were introduced that involve the development of new raster GIS databases, and the assignment of new values to the grid cells within those databases. Throughout this discussion, it was important to consider both the sources of data and the types of outcomes that were desired. One important point was to be mindful of spatial resolution differences between multiple raster GIS databases used in a single analysis. Elevation and topography are often important considerations in the management of natural resources, in terms of both distance above sea level and relative changes within an area of interest. Raster GIS databases that represent elevations (DEMs) have other practical uses for forestry and natural resource management, such as in determining slope and aspect that may be important when considering operational and silvicultural activities. Raster GIS databases may not always be classified in such a way that is useful for each intended purpose; therefore, reclassification of grid cells to appropriate and useful classes might be necessary. It may also be necessary to assign information to grid cells from sampled locations on the ground, or to convert values, smooth, determine average conditions, and so on. One may not always have the appropriate raster GIS database for a map or for an analysis, but one may be able to create it from sampled data. In such cases, interpolation techniques would be invaluable for estimating missing values throughout a raster database. Additionally, one may have to classify aerial or satellite imagery for analysis or as input into additional spatial analysis processes. Further, it is also possible to convert a vector GIS database to a raster GIS database and vice versa, if these processes seem necessary. A GIS user may not be fluent in every method and tool available to manipulate raster GIS databases, but an awareness of the capabilities and possibilities involving raster GIS database analyses will prove useful in addressing the myriad issues facing the management of natural resources. Inspection 8.6 The U.S. Geologic Survey developed the 3D Elevation Program (3DEP) which produced several products for the United States. These can be visualized using the National Map. Using the Internet, go to the National Map and view the data

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products. Turn on the slope and the elevation databases. If you click the ellipsis (.) beside each layer, you can view a description of each product. How do you think these databases were constructed? How might you be able to use them? Reflection 8.6 If you had to absolutely make a choice between vector and raster data, which would you select as your preferred data type? "It depends" is not acceptable for this exercise, so choose one and explain why it represents your preferred data type. Exercises exploring the concepts covered in this book using ArcGIS or QGIS are available on the book website: gis-book.uga.edu.

References Abburu, S., Golla, S.B., 2015. Satellite image classification methods and techniques: a review. International Journal of Computer Applications 119 (8), 20e25. Bernhardsen, T., 2002. Geographic Information Systems: An Introduction, third ed. John Wiley & Sons, Inc., New York. Bettinger, P., Bradshaw, G.A., Weaver, G.W., 1996. Effects of geographic information system vectorraster-vector data conversion on landscape indices. Canadian Journal of Forest Research 26 (8), 1416e1425. Blaschke, T., 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65 (1), 2e16. Bolstad, P., 2012. GIS Fundamentals: A First Text on Geographic Information Systems, fourth ed. Eider Press, White Bear Lake, MN. Chang, K.-T., 2019. Introduction to Geographic Information Systems, ninth ed. McGraw-Hill Education, New York. Duda, T., Canty, M., 2002. Unsupervised classification of satellite imagery: choosing a good algorithm. International Journal of Remote Sensing 23 (11), 2193e2212. Dunlap, L.E., Spinazola, J.M., 1984. Interpolating Water-Table Altitudes in West-Central Kansas Using Kriging Techniques. U.S. Department of the Interior, U.S. Geologic Survey, Alexandria, VA, Water. -Supply Paper 2238. Elassal, A.A., Caruso, V.M., 1983. USGS Digital Cartographic Data Standards, Digital Elevation Models. U. S. Department of the Interior, Geologic Survey, Reston, VA. Geological Survey Circular 895-B. Elmes, G.A., Liebhold, A.M., Twery, M.J., 1993. Two approaches to landscape characterization of susceptibility to gypsy moth defoliation. In: Liebhold, A.M., Barrett, H.R. (Eds.), Proceedings: Spatial Analysis and Forest Pest Management. U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiment Station, Radnor, PA, General Technical Report NE-175, pp. 172e183. Grabau, W.E., 1976. Pixel Problems. U.S. Army Corps of Engineers, Waterways Experiment Station, Mobility and Environmental Systems Laboratory, Vicksburg, MS. Miscellaneous Report M-76-9. Hawker, L., Bates, P., Neal, J., Rougier, J., 2018. Perspectives on digital elevation model (DEM) simulation for flood modeling in the absence of a high-accuracy open access global DEM. Frontiers in Earth Science 6, Article 233. Hung, I.-K., McNally, B.C., Farrish, K.W., Oswald, B.P., 2005. Using GIS for selecting trees for thinning. In: Proceedings of the 25th Annual ESRI International USER Conference. 25-29 July 2005, San Diego, pp. 1e9.

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Jensen, J.R., 2015. Introductory Digital Image Processing: A Remote Sensing Perspective, fourth ed. Prentice Hall, Upper Saddle River, New Jersey. Krige, D.G., 1966. Two-dimensional weighted moving average trend surfaces for ore evaluation. Journal of the South African Institute of Mining and Metallurgy 66, 13e38. Kuter, N., Yenilmez, F., Kuter, S., 2011. Forest risk mapping by kernel density estimation. Croatian Journal of Forest Engineering 32 (2), 599e610. Lillesand, T.M., Keifer, R.W., Chipman, J.W., 2004. Remote Sensing and Image Interpretation, fifth ed. John Wiley & Sons, New York. Liu, H., Wu, C., 2020. Developing a scene-based triangulated irregular network (TIN) technique for individual tree crown reconstruction with LiDAR data. Forests 11 (1), Article 28. Loveland, T.R., Ramey, B., 1986. Applications of U.S. Geological Survey Digital Cartographic Products, 1979-1983. U.S. Department of the Interior, Geologic Survey, Alexandria, VA. Survey Bulletin 1583. Machala, M., Zejdova´, L., 2014. Forest mapping through object-based image analysis of multispectral and LiDAR aerial data. European Journal of Remote Sensing 47, 117e131. Mason, B.J., 1983. Preparation of Soil Sampling Protocol: Techniques and Strategies. U.S. Environmental Protection Agency, Environmental Monitoring Systems Laboratory, Office of Research and Development, Las Vegas, NV. EPA-600/4-83-020. McConville, D.R., 1995. GIS Application: Spatial Surfacing of Point Data for Myriophyllum Investigations. U.S. Department of the Interior, National Biological Service, Environmental Management Technical Center, Onalaska, WI. Long Term Resource Monitoring Program Report 95-P004. National Aeronautics and Space Administration, Earth Observatory, 2000. Spring Vegetation in North America. https://earthobservatory.nasa.gov/images/696/spring-vegetation-in-north-america (accessed 19.02.22). Ochtyra, A., Marcinkowska-Ochtyra, A., Raczko, E., 2020. Threshold- and trend-based vegetation monitoring algorithm based on inter-annual multi-temporal normalized difference moisture index series: a case study of the Tatra Mountains. Remote Sensing of Environment 249, Article 112026. Pe´ron, G., 2019. Modified home range kernel density estimators that take environmental interactions into account. Movement Ecology 7, Article 16. Rocha, J., Duarte, A., Silva, M., Fabres, S., Vasques, J., Revilla-Romero, B., Quintela, A., 2020. The importance of high resolution digital elevation models for improved hydrological simulations of a Mediterranean forested catchment. Remote Sensing 12 (20), Article 3287. Sales, A., Gonza´les, D.G.E., Martins, T.G.V., Silva, G.C.C., Spletozer, A.G., de Almeida Telles, L.A., Siviero, M.A., Lorenzon, A.S., 2019. Optimization of skid trails and log yards on the Amazon Forest. Forests 10 (3), Article 252. Stage, A.R., Salas, C., 2007. Interactions of elevation, aspect, and slope in models of forest species composition and productivity. Forest Science 53 (4), 486e492. Szypuła, B., 2019. Quality assessment of DEM derived from topographic maps for geomorphometric purposes. Open Geosciences 11 (1), 843e865. Tomlin, C.D., 1990. Geographic Information Systems and Cartographic Modeling. Prentice Hall, Englewood Cliffs, NJ. Tomlin, C.D., 2013. Geographic Information Systems and Cartographic Modeling. ESRI Press, Redlands, CA. Turner, W., Spector, S., Gardiner, N., Fladeland, M., Sterling, E., Steininger, M., 2003. Remote sensing for biodeiversity science and conservation. Trends in Ecology and Evolution 18 (6), 306e314. U.S. Department of Agriculture, Forest Service, 2008. National Forest Type Dataset. https://data.fs.usda. gov/geodata/rastergateway/forest_type/ (accessed 19.02.22).

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U.S. Department of Agriculture, Forest Service, 2021. Chippewa National Forest Geospatial Data. www.fs. usda.gov/main/chippewa/landmanagement/gis (accessed 19.02.22). U.S. Department of Agriculture, Natural Resources Conservation Service, 2021. Geospatial Data Gateway: Direct Data/NAIP Download. https://datagateway.nrcs.usda.gov/GDGHome_DirectDownLoad.aspx (accessed 19.02.22). U.S. Department of Commerce, National Oceanic and Atmospheric Administration, 2020. SVRGIS. www. spc.noaa.gov/gis/svrgis/ (accessed 19.02.22). U.S. Department of the Interior, Geological Survey, 2012. The National Land Cover Database. https:// pubs.usgs.gov/fs/2012/3020/fs2012-3020.pdf (accessed 19.02.22). U.S. Department of the Interior, Geological Survey, 2017. Lidar Point Cloud e USGS National MAP 3DEP Downloadable Data Collection. www.sciencebase.gov/catalog/item/4f70ab64e4b058caae3f8def (accessed 19.02.22). U.S. Department of the Interior, Geological Survey, 2018. National Land Cover Database. www.usgs.gov/ centers/eros/science/national-land-cover-database (accessed 19.02.22). U.S. Department of the Interior, Geological Survey, 2019. National Land Cover Database 2016 Completed and Released. https://eros.usgs.gov/doi-remote-sensing-activities/2019/usgs/national-land-coverdatabase-2016-completed-and-released (accessed 19.02.22). Unwin, D.J., 1996. GIS, spatial analysis and spatial statistics. Human Geography 20 (4), 540e551. Vieira, S.R., Tillotson, P.M., Biggar, J.W., Nielsen, D.R., 1997. Scaling of semivariograms and the kriging estimation of field-measured properties. Revista Brasileira de Cieˆncia do Solo 21 (4), 525e533. Wiche, G.J., Jenson, S.K., Baglio, J.V., Domingue, J.O., 1992. Application of Digital Elevation Models to Delineate Drainage Areas and Compute Hydrologic Characteristics for Sites in the James River Basin. North Dakota. U.S. Department of the Interior, Geological Survey, Denver, CO. Water-Supply Paper 2383. Wood, E.M., Pidgeon, A.M., Radeloff, V.C., Keuler, N.S., 2013. Image texture predicts avian density and species richness. PLoS ONE 8 (5) e63211. Woodall, C., Johnson, D., Gallion, J., Perry, C., Butler, B., Piva, R., Jepsen, E., Nowak, D., Marshall, P., 2005. Indiana’s Forests 1999-2003 Part A. U.S. Department of Agriculture, Forest Service, North Central Research Station, St. Paul, MN. Resource Bulletin NC-253A. Yang, J., El-Kassaby, Y.A., Guan, W., 2020. The effect of slope and aspect on vegetation attributes in a mountainous dry valley, Southwest China. Scientific Reports 10, Article 16465. Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., Schirokauer, D., 2006. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering and Remote Sensing 72 (7), 799e811. Yuan, Q., Shen, H., Li, T., Li, Z., Jiang, Y., Xu, H., Tan, W., Wang, J., Gao, J., Zhang, L., 2020. Deep learning in environmental remote sensing: achievements and challenges. Remote Sensing of Environment 241, Article 111716. Zhou, W., Troy, A., 2009. Development of an object-based framework for classifying and inventorying human-dominated forest ecosystems. International Journal of Remote Sensing 30 (23), 6343e6360.

9 Remote sensing Introduction Remote sensing is the art and science of collecting information about an entity or phenomenon (a landscape) without having direct contact with the entity or phenomenon (Lillesand et al., 2004). In collecting remotely sensed information about a landscape or water body, we are often interested in learning characteristics such as color, texture, temperature, structure, shape, and composition of landscapes and water bodies (Gates, 1967). Remote sensing efforts often result in the development of raster geographic information system (GIS) databases, which we have shown in Chapter 8 can be manipulated and analyzed, or as we suggested in Chapter 5 can be used as base maps in GIS software programs for the development of vector GIS databases. In the field of remote sensing, observations of land and water resources (and other features of interest such as the condition of the atmosphere) are collected using a wide variety of sensors, the most common systems involve satellites and small aircraft equipped with cameras and sensors for collecting reflected or emitted electromagnetic energy. However, unmanned aerial systems, or drones, are becoming quite popular for these purposes. The quality of information collected through remote sensing is in part a function of the radiation (electromagnetic energy) incident upon the phenomena being sensed as well as the external (and perhaps internal) conditions and properties of the phenomena (Gates, 1967). In fact, it is often these conditions and properties that are of most interest to us in forestry and natural resource management. Remote sensing is a very important field that provides forestry and natural resource management organizations a valuable source of geographic data, and the products produced from remote sensing activities can vary greatly in their temporal and spatial availability. While in some circumstances remotely sensed data may be collected only once for an area (i.e., a single flight over an area with a drone or airplane), in other circumstances systems orbiting satellites can collect remotely sensed data on a regular interval (days or weeks), which results in a wonderful temporal archive of the condition of landscape and water resources, and Earth’s atmosphere. One of the great advantages of many remotely sensed data products is that they may be georeferenced to a location on Earth, facilitating their incorporation and use in GIS (Khorram et al., 2012). Reflection 9.1 Thinking more broadly than the application of remote sensors to forestry and natural resource management, what types of remote sensors have been of value to your personal life? Perhaps you dealt with some medical issue that involved a remote Geographic Information System Skills for Foresters and Natural Resource Managers. https://doi.org/10.1016/B978-0-323-90519-0.00001-7 Copyright © 2023 Elsevier Inc. All rights reserved.

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sensor. Perhaps you were involved with a civil issue (adherence to laws or regulations) that involved the use of a remote sensor. Inspection 9.1 Using your preferred Internet browser, navigate to the U.S. Department of Interior, Geological Survey’s Earth Explorer. Data on this website are downloadable and free to the public. Using the information icon associated with each set of data, make a list of the satellite systems with imagery available through Earth Explorer. What other types of GIS data are available? Generally, within the realm of forestry and natural resource management, remote sensing systems utilize sensors that collect energy reflected or emitted from features on the Earth’s surface. A digital remotely sensed image is stored in a raster format, and each grid cell contains a radiance number describing the condition of the landscape or atmosphere. A physical (film-based) remotely sensed image requires scanning to convert the landscape, water, and atmospheric features found on the negative (film) or the positive (print) to digital form. Remote sensing requires an energy source such as the Sun, or a man-made energy source. The source sends out energy, and the transmitted, reflected, or emitted energy related to the source is collected by the sensor. These types of energy could be described in many different ways based on the length of the wavelength of the energy, such as radar (radio energy waves), sonar (acoustic energy waves), visible light, heat, radio waves, cellphone signals, global positioning system (GPS) signals, and others (Lillesand et al., 2004). The focus of this chapter is on those objects on the surface of the Earth that reflect or emit (return) wavelengths to a sensor (Fig. 9.1). There are two general types of remote sensing sensors: passive and active (Fig. 9.2).

FIGURE 9.1 For the purposes of this chapter, remote sensing works by capturing reflected or emitted electromagnetic energy from the landscape, water, atmosphere, or other objects that reside near the Earth’s surface.

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FIGURE 9.2 Active versus passive sensors illustrated. Credit: National Aeronautics and Space Administration (2021d).

Passive remote sensors primarily focus on the visible and infrared portions of the electromagnetic spectrum (Fig. 9.3), and the reflected or emitted energy from the landscape, water, or atmosphere. Here, the Sun is often the energy source. For the purposes of this chapter, active sensors generate their own energy and direct it to a feature of interest, then measure what is reflected back to the sensor. Radar (radio detection and ranging), sonar (sound navigation and ranging), and LiDAR (light detection and ranging) are some examples of active sensors. One way to characterize remote sensing opportunities is to reference them with respect to the portion(s) of the electromagnetic spectrum that might be of interest, rather than other sorts of ions, specks of dust, force fields, or vibrations that might occur in nature (Giacconi and Harris, 1969). Every phenomenon on the surface of the Earth (grass, water, trees, buildings, etc.) emits or reflects energy described by the electromagnetic spectrum. Energy from the Sun passes through the atmosphere in the form of waves. Waves have both peaks and troughs, with the distance between each wave peak called a wavelength, often measured in micrometers (mm) when referring to the visible and infrared portions of the electromagnetic spectrum, or in meters when referring to other types of energy such as radio waves (Sabins, 1997). At a single stationary point, the frequency of a wavelength can be determined by the number of passing peaks of the

FIGURE 9.3 The electromagnetic spectrum includes wavelengths and frequencies. Credit: National Aeronautics and Space Administration (2013).

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wave (Lillesand et al., 2004). Wavelengths range from the shorter (higher frequency) gamma rays to the visible portions of the spectrum, to the longer (lower frequency) microwaves and radio waves. Commonly, the wavelengths captured by passive remote sensing sensors are divided into individual bands (i.e., a red band, a green band, and a blue band). Visually, humans are only able to see the red, green, and blue portions of the electromagnetic spectrum. As previously mentioned, phenomena on the Earth reflect, emit, absorb, and transmit portions of the electromagnetic spectrum differently. For instance, healthy, growing vegetation generally reflects greater amounts of green wavelengths than red and blue wavelengths of energy, which is why most leaves are green in the summertime even though the amount of reflected green wavelengths is quite low due to pigment absorption of light (Gates, 1967). Translation 9.1 One day you find yourself trying to explain the concepts of wavelengths and frequencies to a close friend. On a single sheet of paper, develop a simple conceptual model that briefly and simply communicates these concepts. Electromagnetic energy can be scattered, or dispersed, as it passes through the atmosphere (Khorram et al., 2012), and this may result in haze within a satellite or aerial image. The scattering of energy is the result of several components commonly found in the atmosphere, including ozone, water vapor, and carbon dioxide. These atmospheric interactions can play a role in what can be detected in different bands of the electromagnetic spectrum at any given point in time with different sensors (Lillesand et al., 2004). In digital landscape imagery, each object on the Earth’s surface is defined by a range of spectral reflectance values that are stored in each band (red, green, blue, infrared, and perhaps others) with the combination of values stored in each band comprising an object’s spectral signature. For example, spectral reflectance variation for lodgepole pine (Pinus contorta) is simply illustrated in Fig. 9.4. Here, a graph depicts the average spectral reflectance values for a lodgepole pine stand in Yellowstone National Park. Some remote sensing products (satellite images and aerial images) are composites containing multiple bands of remotely sensed energy levels and are referred to as multispectral databases, as noted in Chapter 8. Similarly, some remote sensing products may be considered hyperspectral, a variation of multispectral imagery, where energy levels are recorded in much smaller ranges of spectral bands including the visible, thermal, infrared, and near-infrared (NIR) portions of the electromagnetic spectrum (Fig. 9.5). A multispectral image might include 10 spectral bands while a hyperspectral image may include 225 spectral bands, for example. Different bands of a multispectral image can be combined (or ordered) based on the requirements of analysis. Combining bands can be beneficial in highlighting different landscape features. For example, if one were interested in assessing forest health, the NIR and red bands of an image may be more valuable inputs for image processing and interpretation. Using Landsat 8 imagery to illustrate the role of different bands, several

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FIGURE 9.4 Spectral signature variability of lodgepole pine (Pinus contorta) in Yellowstone National Park. Credit: U.S. Department of the Interior (2020). FIGURE 9.5 An “image cube” illustrating the large volume of spectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral sensor with each layer representing a range of spectral values collected by the sensor. Credit: National Aeronautics and Space Administration, Jet Propulsion Laboratory (2020).

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common band combinations are useful to note. Natural color composites (Fig. 9.6), depict a landscape as one would see it naturally (similar to a photograph), and use the red, green, and blue bands (in that order). Characteristics of vegetation health across a landscape may be more obvious by ordering the Landsat 8 bands by the NIR, red, and green bands, which is commonly called a color infrared composite. In analyzing urban

FIGURE 9.6 Several different examples of Landsat 8 band combinations include natural color, color infrared, and false-color composites. Credit: U.S. Department of the Interior (2021a).

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landscapes, the two short wave infrared (SWIR) bands (bands 6 and 7) in combination with the red band might be of most value, while in analyzing agricultural land the SWIR, NIR, and blue bands might be of value. These band combinations are an example of false-color composites that use some electromagnetic energy that the human eye cannot detect. Reordering bands of an image may also help one identify vegetation impacted by insect infestations, salinity in soils, and scars on the landscape from forest fires or natural disasters. Diversion 9.1 In your preferred GIS software, open the Cass County, Minnesota National Agriculture Imagery Program (NAIP) imagery that can be accessed from this book’s website (gis-book.uga.edu). What bands of sensed electromagnetic energy are available? Focus on a very specific place, one that you are confident of its land use. Hopefully, the GIS software you use allows you to enable and disable the individual bands of the NAIP imagery. One by one, view each band individually from the others at this specific place that you have chosen. Relatively speaking, describe the intensity of each band at this place, and speculate why it is so. Many satellite and aerial systems can also provide a panchromatic band, or a gray scale (white to black) raster database, which can provide greater detail (higher spatial resolution) than databases formed using the other bands collected during a data acquisition process. A panchromatic band contains information from visible portions of the electromagnetic spectrum, specifically the red, green, and blue bands, but the information from these bands is aggregated to produce a gray scale color, and the resulting image does not include individual spectral band information. Most often, these panchromatic bands are used for a process called pansharpening, where the panchromatic band (with finer spatial detail) is combined with one or more of the other spectral bands (with coarser detail) and the resulting image adopts the spatial resolution of the panchromatic band and potentially facilitates more effective image interpretation of smaller objects on the landscape (Vrabel, 1996). Remotely sensed data, specifically those collected by satellites and aircraft, are commonly used to develop land cover GIS databases when combined with image interpretation or classification processes, as mentioned in Chapter 8. However, remotely sensed data sources can also be invaluable for deriving information about biomass, species distributions, forest inventory and structure, changes in vegetation over time, wetlands, and many, many other issues of interest to forestry and natural resource management professionals. More advanced systems, such as LiDAR, can be used to collect data at a spatial and temporal resolution suitable for developing highly accurate elevation data. LiDAR is an active remote sensing technology initially created by the U.S. National Aeronautics and Space Administration (NASA) (Weng, 2014). LiDAR uses laser pulses, and their subsequent reflected returns, to estimate distances to objects. A LiDAR database contains a 3-dimensional cloud of points defined by X, Y, and Z coordinates, or the coordinate location of the pulse (X,Y) and its elevation (Z) above the ground. The

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time the pulse takes to return to the sensor, along with the speed of light, is used to determine the distance between the sensor and an object. LiDAR sensors can be mounted in aircraft (Fig. 9.7), including unmanned aerial vehicles (UAVs), or held in one’s hand as ground-based systems. In addition to a laser, components of an airborne LiDAR system include GPS and inertial sensors that register the acceleration of the unit as well as its positioning (i.e., pitch and roll) and navigation during flight (Weng, 2014). As previously mentioned, ground-based LiDAR sensors might be handheld or mounted on a tripod or other device and used to scan features on the surface of the Earth. Both orientations (vertical and horizontal) allow for the creation of 3-dimensional spatial datasets. With LiDAR data, spatial resolution is a function of the number of points collected per unit area (U.S. Department of Commerce, National Oceanic and Atmospheric Administration, 2012) and, it should be noted, that one LiDAR scene may contain millions of X, Y, and Z coordinates.

FIGURE 9.7 An airborne LiDAR sensor is often mounted in an aerial vehicle and used to scan the Earth’s surface. Credit: U.S. Department of the Interior (2003).

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Translation 9.2 While visiting relatives one holiday season, they learn that you are familiar with LiDAR. Briefly, in one sentence, describe LiDAR imagery with respect to natural resource management. In one additional sentence, explain how LiDAR might be, conceptually, similar to other types of remote sensing systems that would be more familiar to your family. As referenced in Chapter 8, point clouds derived from LiDAR sensors can be used to determine elevations above sea level (bare ground data) with all above-ground features on the landscape removed, but the point clouds can also provide data about the structure and height of features on a landscape (Fig. 9.8). Further, algorithms can be used to estimate the spaces in between points to create continuous surfaces defined as, for example, digital surface models (DSM) and digital elevation models (DEM). When LiDAR sensors are used to scan an area, they collect multiple signal returns at different elevations or distances from the sensor. One might be able to envision the role this type of active remote sensing application could play in forestry and natural resources. Depending on the scale and spatial resolution at which LiDAR data are collected, one may easily be able to identify the shape and height of buildings, ground elevations, tree characteristics (i.e., height, shape, type), infrastructure, and other landscape features. Several forest and tree-level parameters can be obtained as well. From point cloud returns, a canopy model can be developed, and the difference between the canopy and DSM provides an estimate of tree heights allowing one to view canopy changes throughout a forest (Fig. 9.9). Additionally, crown dimensions can be obtained, which may help in determining forest type. It is also possible to separate point cloud values and

FIGURE 9.8 Airborne LiDAR was collected at Loggerhead Key Lighthouse in Dry Tortugas, Florida. Credit: U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Ocean Service (2021).

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visualize individual trees, perhaps facilitating the measurement of individual tree characteristics such as height, diameter at breast height, and various crown measurements (Evans et al., 2006). Ground-based LiDAR systems may provide many of the same metrics, yet perhaps with a focus on wood quality, where knots from abscised or pruned limbs (for example) or galls formed by fusiform rust (Cronartium fusiforme) might be identified. As a concept, a resolution was first introduced in Chapter 2 and revisited in Chapter 8 to illustrate and explain the structure and functionality of raster GIS databases. As one might recall, the concepts of spatial, spectral, temporal, and radiometric resolution of remotely sensed imagery are important. For aerial and satellite imagery, the spatial resolution is a function of the altitude of the sensor (distance from the sensor to sensed object or landscape) and the sensitivity of the sensor or its field of view (Weng, 2014). Features of a satellite’s orbit, including its flight altitude and swath width (size of the area captured by the satellite during orbit), impact the spatial resolution. Typically, spatial resolution in remote sensing is discussed as low, moderate, or high, with low (course) spatial resolution generally defined as a raster image with a spatial resolution of more than 100 m, while high (fine) spatial resolution is generally used to categorize imagery with a spatial resolution of 10 m or less. The visible bands (red, green, blue) of Landsat imagery are generally considered to be of moderate spatial resolution (about 30 m). When considering which satellite or aerial imagery products to choose, the spatial resolution in relation to the features of interest can be an important issue. In addition to the range of spectral reflectances stored in each grid cell, as noted in Chapter 8, spectral resolution can relate to the number of spectral bands recorded by a sensor. The spectral resolution can determine what features of the Earth’s surface can be identified using a specific sensor. For example, the most recent Operational Land

FIGURE 9.9 LiDAR point cloud highlighting differences in canopy height in a forest. Credit: U.S. Department of Agriculture, Forest Service (2020).

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Imagery (OLI) and Thermal Infrared Sensors (TIRS) from Landsat 9 capture 11 bands of spectral data including the bands of the visible spectrum, a panchromatic band, a NIR band, two SWIR bands, and two thermal infrared bands. Comparatively, the SPOT 7 (Azersky) satellite collects five bands of spectral data including a panchromatic, blue, green, red, and NIR band. Therefore, generally, Landsat 9 has a higher spectral resolution than SPOT 7. However, SPOT 7 has a greater spatial resolution associated with each of these bands (1.5 m for panchromatic, 6 m for the others) than Landsat 9. The increased capacity for capturing high spatial and spectral resolution has increased the capacity of analysts and decision-makers to identify spatial entities that exist as discrete objects, including individual trees (Turner et al., 2003). Temporal resolution, in remote sensing terms, is the time that has elapsed between subsequent image capture of the same landscape features by a remote sensing sensor. For example, the Landsat 9 satellite captures information about the exact same location on the Earth every 16 days. When speaking of satellites in constant orbit, the temporal resolution of a satellite is also a function of its altitude and swath width. Generally, those satellites that capture narrower swaths have a finer spatial resolution but longer temporal resolutions (more swaths are needed) while satellites with wider swaths have shorter temporal resolutions and coarser spatial resolutions (fewer swaths are needed). With modern geospatial sensing technologies, including UAVs and ground-based LiDAR sensors, temporal resolution can be as short, or fine, as minutes or hours when required. Temporal resolution is restricted often by cost, the flight of the sensor, and temporal requirements for analysis. Diversion 9.2 Access Google Earth Pro on a personal computer. Navigate to the Washington Monument in Washington, D.C. Ignoring the fact that the imagery available for this place arises from many different systems, develop an estimate of the temporal resolution of the imagery that is available over the last decade. As noted in Chapter 8, radiometric resolution may best be thought of as a level of precision in the signal received by the sensor and stored for processing. Many have interacted with Landsat TM data (e.g., Landsat 5) and seen the familiar 0e255 values/ colors associated with grid cells as 8-bit data. Systems with a radiometric resolution of 14-bit offer 16,384 levels and 16-bit data, 65,536 levels. These values increase an analyst’s ability to discern features on the surface of the Earth, particularly when combined with greater spatial resolution. Two disadvantages of using remotely sensed imagery with the high radiometric and spatial resolution are (1) the increased amount of data storage space required and (2) the increased data processing time necessary, both of which might be mitigated somewhat by using cloud-based storage and processing capabilities. Inspection 9.2 Open the NAIP 2019 Status Map available from this book’s website (gis-book.uga. edu). Select a state that interests you and compose a brief memorandum

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describing the years of available imagery, the spectral resolution for each year of imagery, the spatial resolution for each year of imagery, and the implied temporal resolution of the imagery. Reflection 9.2 You have been assigned the task of determining the appropriate imagery to use to develop a time series of harvest locations covering 20 years for Francis MarioneSumter National Forest. Describe the steps you would take to accomplish this task including a brief discussion of the advantages and disadvantages of a few sets of imagery with a focus on their spatial, spectral, and temporal resolutions.

Aerial imagery systems Our reference to aerial imagery includes those products (digital or film-based images) that are captured by small flying or floating vehicles that are generally (but not exclusively) about 5000 to 20,000 feet (1524 to 6096 m) above the landscape when images are collected. This collection of efforts represents one of the oldest and most widely used sources of remotely sensed imagery. For example, historically, aerial images have been captured by pigeons (Fig. 9.10), hot air balloons, and kites before airplanes were widely used in society. Today, these images are more commonly captured at a higher altitude using a small airplane, such as a Cessna (Fig. 9.11). To illustrate the widespread nature of these vehicles, it was estimated that there were about 205,000 aircraft in the general aviation fleet of the United States in 2020. Further, it was estimated that there were over 167,000 fixed-wing general aviation aircraft and about 10,000 helicopters in the United States in 2021 (U.S. Department of Transportation, Federal Aviation Administration, 2021). Of these, about 5900 might be considered for-hire or contractable (Salas, 2021), and certainly a smaller subset of these might be used for aerial imaging purposes. Inspection 9.3 Using the Internet, search for the University of Georgia Libraries Aerial Photography collection. A direct link, as of Spring (2022) is http://dbs.galib.uga.

FIGURE 9.10 Pigeons are used for capturing aerial images and examples of the images captured. Credit: Image courtesy of Hans Alder through wikimedia commons. https://commons.wikimedia.org/wiki/File:Pigeon_ photographers_and_aerial_photographs.jpg.

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FIGURE 9.11 A Cessna 172 at the Anson County Airport, NC. Credit: Image courtesy of James Willamor through Wikimedia Commons (https://commons.wikimedia.org/ wiki/File:Charlotte_Aerial_ Photography_-_Cessna_172_ (N73429)_(3560919956).jpg).

edu/gaph/html/. Find and select Clarke County and note the years of each historical aerial image collection. Select one of the years, then see if you can view not only the composite spatial index but also an individual frame. Images from aerial systems may be captured in both true color and panchromatic ranges of the electromagnetic energy spectrum, along with some areas of the infrared or thermal ranges of electromagnetic energy. Aerial system cameras can capture images of landscapes or water bodies on film or digitally within CMOS (complementary metaloxide-semiconductor) sensors or CCD (charged coupled device) sensors. Film-based aerial images are generally limited to the visible and near-infrared energy (0.4e0.9 mm) of the electromagnetic spectrum (Paine and Kiser, 2012; Weng, 2014). Digital aerial images collected by the U.S. Department of Agriculture, Farm Service Agency’s NAIP include the common red, green, and blue bands but may also include a NIR band depending on the year of capture (Ucar et al., 2018). Individual frames (snapshots) of the landscape are often collected along flight lines, and these frames are combined with others nearby to create composite images that can be displayed in GIS software programs. The individual frames can also be printed or used for analysis in GIS software. Aerial photogrammetry is the process of obtaining measurements and information from aerial images. Many aerial image interpretation processes involve the eight image interpretation principles noted later in the chapter. To further optimize, or increase the precision of measurements, methods have been developed to use stereoscopic interpretation. One can view a 3-dimensional model of a landscape using two overlapping images captured subsequently along a flight line or captured in adjacent flight lines through the endlap or sidelap of the images (Fig. 9.12). These stereo pairs, or images representing the same location from different observation points in the sky, allow one to

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view a 3-dimensional landscape using a stereoscope (Fig. 9.13). It is common for aerial images to be captured with endlap of 60%, though in varying terrain, greater overlap may be required (Jensen, 2006). Similarly, aerial images are often captured with approximately 30% sidelap when there are parallel flight lines. The difference in perspective between the two images is known as parallax and involves the displacement of landscape features when viewing the two images (Lillesand et al., 2004). To interpret what is seen on an aerial image, eight basic image interpretation principles are useful, including:









Tone Texture Pattern Shadow Shape Size Location and association (or site) Time (in the case of repeated imagery)

Given the separable hues in a single band of an aerial image, the level of brightness or tone can be discerned. The interaction of energy (e.g., absorption and reflectance) with

FIGURE 9.12 Endlap and sidelap are data collection practices used to provide overlapping aerial imagery for an area that can be used to view imagery stereoscopically.

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FIGURE 9.13 A stereoscope is used to view overlapping pairs (stereo pairs) of aerial photographs in 3-dimensions. Credit: U.S. Department of the Interior (2021b).

various features on the Earth’s surface allows one to use tone to help determine their characteristics. Combining multiple bands of image hue (or color) and using additive colors allows one to benefit from real-world or true color while interpreting features within an image (Jensen, 2006). As an example, instead of using gray scale reflectance values that range from 0 to 255, one could display red, green, and blue bands simultaneously to view a true-color image where vegetation appears green, water features appear blue, and so on. Differences in color can allow one to separate certain deciduous forests from coniferous forests in an image, even during the growing season. Texture, or the spatial arrangement and changes of tones, reflects the variability in tones or the variability of a single tone across an image. Texture can vary based on the scale and resolution of an image. For example, higher spatial resolution images may result in more tonal differences (increased texture) across an image. Generally, one would expect a smoother texture to be observed when viewing roads, the tops of buildings, and some grassy fields or bare areas. Conversely, coarse textures are more commonly observed in images of forested areas, particularly when there are older, taller trees intermixed with shorter, smaller trees. When objects and their arrangement repeat across an aerial image, this is referred to as a pattern. This repetition of a feature in an image may help improve the interpretation of a landscape (Lillesand et al., 2004). One might think of the last time they were in an airplane and looked down to the ground from the window. Depending on the flight path, for example, when flying over Lincoln, Nebraska, and surrounding Lancaster County (Fig. 9.14), one might immediately be able to identify agricultural areas based on their repeating pattern. Similarly, one might observe a linear pattern of trees in an aerial

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FIGURE 9.14 Using pattern as an indicator of land cover may be advantageous in interpreting agricultural areas and hardwood forests. Credit: Base map from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021).

image and conclude that the trees were planted. In some parts of the world, one may also use the pattern of hydrologic features to assume that deciduous forests may be in close proximity. Shadows likely do not need much explanation; however, when aerial imagery is captured during the daytime, shadows will help one understand taller versus shorter features (i.e., a tall tree next to a short house). Or when examining an image of a forest that is situated adjacent to an open field (Fig. 9.15), the shadows cast by trees on the edge of the forest may be helpful in determining the type (species), perhaps deciduous versus coniferous, based on the shape and size of the shadows. Context clues may be useful for one to identify a feature hidden under a shadow. However, without the availability of context clues, one may be unable to identify small saplings, shrubs of interest, or even the presence of impervious surfaces within the shadows. Further, shadows can complicate automated (based on algorithms) image classification, causing confusion between classes with similar spectral signatures, and thus the presence of shadows can potentially lead to lower land use classification accuracy. Certain landscape features have an easily discernible shape or defined form. Consider a building like the Pentagon in Washington, D.C., or a cloverleaf-shaped highway exchange. Buildings within a city may be simple to identify as they are typically square or

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FIGURE 9.15 Taller features (i.e., trees) adjacent to shorter features (i.e., agriculture fields) might result in shadows visible on aerial imagery. Credit: Base map from the U.S. Department of Agriculture, Natural Resources Conservation Service (2021).

rectangular features with similar tones and textures (and perhaps long shadows). However, outside the landscape context of infrastructure or a city grid, smaller buildings may pose more of a challenge to interpret. Natural features, while less uniform, may have discernable shapes. On higher resolution images, tree crowns tend to be circular or slightly elliptical. When 3-dimensional renderings are available or when viewing aerial images in stereo (stereoscopically, described later), tree height might also be estimated. Diversion 9.3 Acquire the 1938 panchromatic individual frame image Whitehall.sid from this book’s website (gis-book.uga.edu). This area is just a few miles south of Athens, Georgia. Using the image interpretation principles described thus far, develop a short paragraph that describes what you think you see in this image. Be specific and relate evidence of potential land uses to the individual principles. The relative or absolute size of features in an image can help one interpret their biological or developmental characteristics. In addition to measurable characteristics of an object determined through the scale of the image, size is an important contextual clue in the image interpretation process. For example, the presence of relatively similarly sized features may be informative, as may the variability in sizes of landscape features. Known sizes of features would be useful in determining the sizes of other objects found in an image. For example, an American football field is 120 yards (360 feet, 110 m) long when end zones are included. If this feature were located next to a house that seemed one-third smaller (based on what one sees in the image), one might conclude that the house is about 120 feet long (36.6 m).

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Location and association (or site) are often critical components of the image interpretation process, specifically when the focus is forestry and natural resources. Location refers to both the geographic and spatial location of landscape and water body features but also the topographic position (top of the hill, the bottom of the hill, etc.). When one considers the ecological characteristics of suitable habitat for a certain tree species (for example), which may include aspect, elevation, or slope, these distinctions can help define the types of tree species that could potentially be identified and limit the number of alternative features that might possibly be interpreted. Time is also an important image interpretation principle, particularly since the availability of Google Earth and other services that provide different temporal perspectives of the same location on Earth. With aerial imagery captured at different points in time, one can form a good idea of the development of a forest, for example, as the various images indicate harvest, site preparation, thinning, and other activities had occurred within a parcel of land over time. As has been noted in previous chapters, scale is the relationship distance between features displayed within an image and the size of those features in real life. Typically, when a film-based camera is used, the scale of an aerial image can be determined by comparing the focal length of the camera used to the flying height of the airplane capturing the image (Fig. 9.16). This is commonly written as: S ¼ f/H

Where, S is the scale of the image, f is the focal length of the camera, and H is the flying height above the surface of the Earth. For example, if the camera focal length is 6 inches and an airplane is flying 5000 feet (1524 m) above the ground, the scale of the image is: S ¼ 0.5/(5000) ¼ 0.5 feet/5000 feet ¼ 1/10,000 or 1:10,000

Another option for determining the scale of an aerial image, whether film-based or digital, is by comparing distances observed within an image to actual distances on the ground. As suggested previously, a known distance can help estimate the size of a

FIGURE 9.16 The scale of an aerial image captured using a film camera can be determined by using the focal length of the camera and the flying height above the ground.

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feature, yet it can also be used to help determine the scale of an aerial image. The following equation can thus be used to determine the scale: S ¼ (image distance/ground distance)

Using the American football field as an example again, assume that the distance on the image (end zone to end zone) is 0.1 inch. Knowing that the actual distance of the football field is 360 feet (4320 inches), one can determine the scale: S ¼ (0.1 inch/4320 inches) ¼ 1/43,200 or 1:43,200

Reflection 9.3 If you had to use a hard copy, printed aerial image to assist with some work or project that captures your attention, how large would you prefer this printed image to be? Given your understanding of scale, what scale would you prefer this printed image to be? Orthophotographs are those aerial images that have been rectified and georeferenced, or assigned a true location on the Earth’s surface, resulting in an image with a consistent scale and the planimetric correctness of a map. In developing an orthophotograph, tilt and topographic displacement inherent in an aerial image are virtually completely removed, allowing users of orthophotographs to fairly accurately digitize features and measure distances on the screen of a personal computer or another device. Additionally, the process of creating orthophotographs removes most of the inherent distortion (Sabins, 1997). The U.S. Department of Agriculture NAIP imagery collection consists of a catalog of orthophotographs collected for the conterminous United States during the leaf-on (or growing) period every three to five years for different parts of the country. Typically, the orthophotographs are offered to the public in the form of digital orthophoto quarter quad (DOQQ) (3.75 degrees latitude and longitude) images or full county mosaics.

Satellite-based imagery systems According to the World Meteorological Organization (2022) there were 288 operational earth observation and meteorological satellites orbiting the Earth in 2022, and another 138 of these types of satellites are in the planning stages of development. In general, there are two types of satellite systems in terms of their orbital paths: sun-synchronous and geostationary. Sun-synchronous satellites orbit the Earth traveling from north to south near the poles in sync with the Sun. By being synchronous with the Sun, these satellite systems stay in a relatively fixed position in relation to the Sun, resulting in satellite sensors that are able to collect information about the same location on the Earth, at the same time of day, with Sun angles that are similar. Due to the altitude that these satellites travel (400þ miles (644 km) above Earth), they have the capacity for

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imaging large swaths of the Earth, but one satellite is unable to provide coverage of the entire surface during a single orbit. Conversely, geostationary satellites are positioned in a fixed location relative to the Earth’s rotation. These types of satellite systems are typically operated at high altitudes and are more commonly associated with communications purposes or with places where a need exists to have constant monitoring or delivery of services. For example, the Wide-Area Augmentation Service (WAAS) of the United States utilizes geostationary satellites to broadcast GPS correction messages to people in North America (Enge et al., 1996). Here, we provide a brief overview of several well-known satellite system programs for imaging landscapes and water bodies, such as the Landsat program. As of 2021, the Landsat program began its ninth Landsat satellite mission (Lulla et al., 2021). The main purpose of each Landsat mission is to monitor the Earth and change conditions across the globe (Fig. 9.17). With the insight and forethought of U.S. government scientists with NASA and the Department of Interior, the origins of the Landsat program began in the 1960s along with the development of the Earth Observation Satellite (EOS) program (Loveland and Dwyer, 2012). The first collection of remotely sensed images captured by Landsat date back to the 1970s collected by Landsat 1, which was launched in 1972 (Loveland and Dwyer, 2012). The Landsat system of satellites has been collecting images of the Earth continuously ever since (Masek et al., 2020). This continuity in remote sensing collection, coupled with the temporal resolution of each sensor, makes Landsat data an invaluable resource for numerous forestry and natural resource management issues that may include monitoring forest loss from changes in land use, informing management policy including the impact of insects and fire, and establishing a reference condition for landscapes (Pflugmacher et al., 2012).

FIGURE 9.17 A Landsat satellite. Credit: National Aeronautics and Space Administration (2021a).

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Inspection 9.4 Using your preferred browser, search the Internet for Landsat imagery, specifically imagery available from the U.S. Geologic Survey image collection. Select one image and describe the extent of the coverage (space - how wide and tall in miles or kilometers), and the types of landscape features that are evident. Over time, the Landsat missions have varied in terms of the sensor systems that they carry. Beginning with Landsat 1 through 3, the satellites were equipped with a camera system, the Return Beam Vidicon (RBV), and the Multispectral Scanner System (MSS), a digital scanning device that collected four bands of spectral information (green, red, and two NIR bands) (Table 9.1). The Landsat 3 mission, which ended in 1983, included an additional thermal infrared band. During the first three satellite missions, spatial resolution for data collection was 60 m with a satellite return time of 18 days, which meant that the same location on the Earth was imaged every 18 days. Beginning with Landsat 4, which was launched in 1982, through Landsat 5, the system no longer included the RBV unit but added the Thematic Mapper (TM) sensor while maintaining the MSS sensor. The TM sensor allowed for the collection of seven bands of spectral data, including the red, green, and blue bands along with two SWIR and a thermal infrared band. Interestingly, thermal infrared energy can be collected during the day or night (Weng, 2014). In addition to increased spectral resolution, these satellite systems also captured imagery at a finer spatial resolution, 30 m, for most bands. Further, the return time for the satellites was reduced from 18 to 16 days (National Aeronautics and Space Administration, 2021b). In 1993, Landsat 6 was unable to reach orbit. This unsuccessful mission was to have carried a new sensor, the Enhanced Thematic Mapper (ETM), which would have captured the same seven bands as the MSS and TM systems but would have also included a 15 m panchromatic band. This sensor served as the precursor to the ETM þ system launched with the Landsat 7 satellite in 1999. During the tenure of Landsat 7, the scan line collector (SLC) of the ETM þ instrument failed in 2003 leading to gaps on the edges of imagery collected and a data loss of 25% (Williams et al., 2006) (Fig. 9.18). With more than one Landsat satellite system in orbit at a time, these data gaps can potentially be augmented with images captured from a different satellite’s sensor (Masek et al., 2020). Diversion 9.4 Using your preferred GIS software, open the Landsat image that is available from this book’s website (gis-book.uga.edu). Where is the image located? How much land area does it cover? What kinds of questions can this image help you answer? Landsat 8 and 9 are currently in operation, with Landsat 9 launching in 2021, using a new sensing system, the OLI along with the TIRS. The OLI sensor records electromagnetic energy levels in the visible and infrared portion of the electro magnetic spectrum while the TIRS sensor records electromagnetic energy levels in the

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Table 9.1 Spectral and spatial resolution of Landsat satellite systems (U.S. Department of the Interior, Geological Survey, 2021c). MSS Landsat 1e3 Band 4 Band 5 Band 6 Band 7

Wavelengths (mm) 0.50e0.60 0.60e0.70 0.70e0.80 0.80e1.10

Resolution 60 m 60 m 60 m 60 m

Wavelengths (mm) 0.45e0.52 0.52e0.60 0.63e0.69 0.76e0.90 1.55e1.75 10.41e12.50 2.08e2.35

Resolution 30 m 30 m 30 m 30 m 30 m 120 m 30 m

Wavelengths (mm) 0.45e0.52 0.52e0.60 0.63e0.69 0.76e0.90 1.55e1.75 10.41e12.50 2.08e2.35 0.52e0.90

Resolution 30 m 30 m 30 m 30 m 30 m 60 m 30 m 15 m

Wavelengths (mm) 0.43e0.45 0.45e0.51 0.53e0.59 0.64e0.67 0.85e0.88 1.57e1.65 2.11e2.29 0.50e0.68 1.36e1.38 10.60e11.19 11.50e12.51

Resolution 30 m 30 m 30 m 30 m 30 m 30 m 30 m 15 m 30 m 100 m 100 m

TM Landsat 4 and 5 Band 1 Band 2 Band 3 Band 4 Band 5 Band 6 Band 7 ETM D Landsat 7 Band 1 Band 2 Band 3 Band 4 Band 5 Band 6 Band 7 Band 8 OLI Landsat 8 and 9 Band 1 Band 2 Band 3 Band 4 Band 5 Band 6 Band 7 Band 8 Band 9 Band 10 Band 11

thermal infrared range (Masek et al., 2020). Additionally, these systems produce panchromatic imagery. Both satellites are the product of collaboration between NASA and the U.S. Geological Survey (USGS). The spatial resolution for both satellite sensors

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FIGURE 9.18 The Landsat 7 scan line collector (SLC) failed resulting in gaps in data collection. Credit: National Aeronautics and Space Administration, Earth Observatory (2009).

is contingent on the spectral band and ranges from 15 to 100 m. Landsat 8 and Landsat 9 have an 8-day lag between their return times leading to an increase in temporal resolution available. The satellite (spacecraft) that carries the latest Landsat sensing system is a LEOStar-3 Bus (Fig. 9.19) that weighs about 4.4 tons (4000 kg), orbits the Earth at a 438 mile (705 km) altitude, and has around a 10-year useful life (Orbital ATK, 2016). Images collected by Landsat are downloaded to Earth using the S-band frequency, which lies in the microwave range (2e4 GHz) of the electromagnetic spectrum. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor is part of the larger Terra spacecraft system (Sato et al., 1999), which was launched in 1999. Terra is a satellite bus that weighs about 5.7 tons (about 5200 kg) and orbits the Earth at a 438 mile (about 705 km) altitude (National Aeronautics and Space Administration, 2022). The Terra satellite follows a sun-synchronous orbit capturing the visible (red, green, and blue) and NIR bands (15 m), six SWIR bands (30 m), and five thermal infrared bands (90 m) (Fig. 9.20). The focus of the Terra satellite system is to understand how the Earth’s atmosphere, surface, and climate interact to better understand climate change along with understanding natural disasters and human activities. It is comprised of five sensors including ASTER and MODIS (noted later). While the Terra satellite is currently operational, as of February 2020, it began drifting off its orbit, impacting each part of the satellite system differently; however, no great impact to data collection is expected to occur (National Aeronautics and Space Administration, 2021e). Additionally, ASTER’s SWIR sensor is no longer functional. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor is one of the main instruments on the Terra satellite. The sensor views the entire Earth every 2 days. MODIS has a relatively high spectral resolution as it captures 36 spectral bands of electromagnetic energy. The bands vary in spatial resolution: 250 m (bands 1e2), 500 m

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FIGURE 9.19 The LEOStar-3 bus that caries the Landsat 9 sensor system. Credit: National Aeronautics and Space Administration, Goddard Media Studios (2019).

FIGURE 9.20 A portion of the Mississippi River was captured by the ASTER sensor in 2017, highlighting the course of the river. Credit: National Aeronautics and Space Administration (2019).

(bands 3e7), 1000 m (bands 8e36). With increased spectral resolution, MODIS data has been used to model carbon exchange in vegetation and soils, to estimate primary production and photosynthesis, measure cloud cover and aerosols (dust, forest fire smoke, volcanic eruptions), water vapor and atmospheric temperature, snow and ice-cover,

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surface temperatures, and to identify vegetation and other types of land cover (National Aeronautics and Space Administration, 2021c). Started by Center national d’e´tudes spatiales (CNES), or the National Center for Space Studies, the French space agency developed the Satellite Pour l’Observation de la Terre (SPOT) satellite system (European Space Agency, 2021c). SPOT is a sun-synchronous satellite that was first launched in 1986. Seven different SPOT satellite missions have been providing continuous coverage of the Earth’s surface through both multispectral and panchromatic bands, with a return period to the same place on Earth every 26 days. In May of 2020, the production of data products from the first five SPOT missions ended. SPOT 1, 2, and 3 used the same type of sensor system that included a High-Resolution Visible (HRV) sensor to capture the green, red, and NIR bands of electromagnetic energy levels at a 20 m spatial resolution and a 10 m resolution panchromatic band (European Space Agency, 2021c). SPOT 4 had similar instrumentation as the first three SPOT missions with the addition of an extra SWIR band. Additionally, SPOT 4 included the VEGETATION sensor that captured electromagnetic energy levels in the blue, red, and infrared portions of the electromagnetic spectrum. The VEGETATION sensor was also included in all later SPOT missions. Advances were made in the spectral resolution with the SPOT 5 satellite collecting much finer resolution imagery, between 2.5 and 5 m for the panchromatic band, 10 m for the multispectral bands, and 20 m for the SWIR imagery. SPOT 6 went further to increase spatial resolution to 1.5 m for both the multispectral bands (blue, green, red, and NIR). Launched in 2014, SPOT 7 carried the same instrumentation as SPOT 6 with the same temporal, spectral, and spatial resolutions. While imagery from SPOT 6 and 7 are provided by the European Space Agency, these two commercial satellites belonged to Airbus Defense and Space (European Space Agency, 2021c). However, SPOT 7 was subsequently sold to the Azerbaijan space agency, Azercosomos, and renamed Azersky (Henry, 2014). As an example of another government-led satellite imaging program, the Sentinel imagery program was developed by the European Union through the European Space Agency for global monitoring of landscape features. The Sentinel-2 system is housed within an AstroBus spacecraft that weighs about one metric tonne (2200 pounds) and has a sun-synchronous orbit at an elevation of 488 miles (786 km) above Earth. The data derived from the Sentinel-2 satellite system is captured by a multispectral imagery sensor, which has a spatial resolution of 10 m in the visible spectrum (4 bands), and infrared spectrum sensors that have a spatial resolution from 20 to 60 m (9 bands) (Martimort et al., 2007). Among many other uses, Sentinel imagery has been used for mapping land cover in forested areas (De Luca et al., 2022) and has been used for estimating soil organic carbon in agricultural areas (Urbina-Salazar et al., 2021). Reflection 9.4 If you were to design a satellite-based remote sensing system, how large would it be and how high above Earth would you like it to orbit? Would you prefer a geostationary or a sun-synchronous approach? What bands of electromagnetic imagery

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would you prefer to collect? How would the imagery be delivered to your computer? And finally, most importantly, how would you get the satellite up into space? While the commercial satellite industry is larger than we have the space to discuss, here we provide a short description of commonly referenced commercial satellites, both those that are still active and those that are no longer operational. For example, IKONOS was revolutionary at the time of its launch in 1999 due to its high spatial resolution. Developed by Lockheed Martin and operated by DigitalGlobe, IKONOS included a multispectral sensor capturing the red, green, blue, and near-infrared portions of the electromagnetic spectrum with a spatial resolution of 4 m and panchromatic imagery with 1 m resolution (Satellite Imaging Corporation, 2021b). IKONOS was a sunsynchronous satellite capturing imagery at one location every 14 days (European Space Agency, 2021b). Outside of military applications unavailable to the public, no commercially available satellite captured imagery at such a high spatial resolution at the time. In 2015, IKONOS was decommissioned due to an accuracy issue. Archives of the imagery are maintained by the European Space Agency. GeoEye-1 (previously called OrbView-5), also operated by DigitalGlobe and launched in 2008, captures imagery across four spectral bands (red, green, blue, and NIR) and panchromatic imagery at high spatial resolution (2 m and submeter (0.5 m), respectively) with a return time to a location of approximately 3 days (Satellite Imaging Corporation, 2021a). As might be suggested here, the GeoEye-1 satellite produces among the finest spatial resolution images of satellites orbiting the Earth. The intent of the satellite system is to develop remotely sensed images of large areas, focusing on not only natural resources but also on transportation, natural disasters, location-based services, and other features of importance (European Space Agency, 2021a). A series of WorldView satellite systems, again operated by DigitalGlobe, were designed for surveying the environment and began operating in 2007. WorldView-1 was the first commercial satellite capturing 50 cm spatial resolution imagery, and uniquely, the satellite only produced panchromatic imagery. WorldView-2, launched two years later, produced images of the Earth’s surface that included 8-band 2 m multispectral imagery and a 0.5 m panchromatic band. In addition to the red, green, blue, and NIR bands, WorldView-2 includes four additional bands that are dedicated to collecting bathymetric data (using the coastal blue band), vegetation-specific data (the red edge and yellow bands), and an additional NIR band (Rapinel et al., 2014). WorldView-3 is similar to WorldView-2 and includes a submeter panchromatic band and an additional eight multispectral bands, yet it orbits Earth at a lower altitude. It was launched in 2014 and is still operational as of 2021. Finally, WorldView-4 (previously named GeoEye-2) was in orbit for approximately 3 years and produced 31 cm panchromatic images and 1 m multispectral resolution images. The satellite was put out of service due to a technical failure (European Space Agency, 2021d).

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Translation 9.3 One afternoon, you are approached by a colleague seeking advice on how to select the appropriate satellite image for the project they are working on. Briefly describe a few components of a satellite system that they should consider when choosing satellite imagery.

Unmanned aerial vehicle imagery systems Unmanned aerial vehicles (UAVs), also commonly referred to as unmanned aerial systems (UAS) or drones, have come into increasing use in the last 15 years as they have become quite affordable, and associated sensors and applications continuously improve (Torresan et al., 2017). Over the long history of human-directed remote sensing of the landscape, unmanned vehicles might have technically included birds and balloons, but here we refer to those platforms for which we can control the flights using computer systems. Arguably many more drones have been manufactured, purchased, and used for recreational purposes, yet as of February 7, 2022, the number of registered drones in the United States was nearly 860,000, and of these about 328,000 were considered drones registered for commercial use (U.S. Department of Transportation, Federal Aviation Administration, 2022), likely for observational and transport purposes. Reflection 9.5 Obviously today there are many drones, and many more are coming in the future. How do you feel about licensed or unlicensed people flying these within the area where you currently live? Would you want to restrict activity, or restrict who can conduct these activities? Drones can carry sensors ranging from video cameras to multispectral and hyperspectral cameras, LiDAR, radar, airborne laser scanners, or sensors for weather and air quality monitoring (Raparelli and Bajocco, 2019). The ability of a natural resource manager to operate these sensors at any time means that the temporal resolution of the data is nearly at will, assuming the local weather conditions are acceptable. Using a drone, one might be limited by regulatory constraints, such as the extent of the operable airspace or the flying height (400 feet (122 m) in the United States), but the benefit is that the spatial resolution can be very high, perhaps 4 inches (10 cm) or less (Na¨si et al., 2018). These systems can also be operated closer to features of interest, allowing for a more accurate and thorough inspection if one were interested in crown health or tree height, for example. Drones can also be operated above or beneath a forest canopy, yet care should be taken to avoid colliding with a tree. In these conditions, it might be possible to assess subcanopy regeneration and plant density to assist in planning, for example, herbicide applications or prescribed fires. These systems allow for a myriad of applications to natural resources management in order to obtain tree measurements, precision forestry applications, urban forest insect damage, site assessment, and a host

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of other applications that provide additional and timely information to natural resources managers (Raparelli and Bajocco, 2019; Na¨si et al., 2018). While these systems are extremely useful and allow instant remote access for natural resources assessment, they are limited by battery life (Terwilliger et al., 2017) and data processing, fusing, analysis, and integration challenges (Sun et al., 2021). Some drones operate as fixed-wing aerial vehicles, and others employ multi-rotor systems. Fixed-wing aircraft (Fig. 9.21) look like traditional airplanes, and they may have a propeller in the front of the aircraft, or one located just behind the wings. Rotor systems employ small blades similar to a helicopter, yet typically they have four (a quadcopter) (Fig. 9.22) or more rotors that lift and propel the aircraft. Fixed-wing aircraft typically have to be launched and recovered (Terwilliger et al., 2017) and can be more difficult to maneuver, particularly near or under objects. Multi-rotor systems can be programmed to lift off and return vertically down to a known landing area. Their ability to navigate through highly complex environments, such as within a stand of trees, is continuously improving. The size of a drone will generally dictate the size (or weight) of a sensor it can carry. Typically, larger drone systems can support larger (or heavier) cameras or other sensors. Diversion 9.5 Imagine you are to recommend the purchase of a UAV to support the endeavors of a forest management group, and that your budget is $10,000. Using the resources freely available through the Internet, develop this recommendation and describe the potential forestry uses of the UAV for forests that are near your current location.

FIGURE 9.21 NASA researchers at Langley Research Center preparing to launch a fixed-wing aircraft. Credit: National Aeronautics and Space Administration (2017).

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FIGURE 9.22 A rotor (quadcopter) flying in an urban environment. Credit: National Aeronautics and Space Administration (2020).

The images obtained from drones are similar to those obtained from aerial systems noted earlier - individual frames. These images, just as in the case of aerial systems, can be combined to produce a landscape composite. However, in the case of drone-based images, the amount of land contained within a single frame is much smaller than what might be contained in an image captured by an aerial system (due in part to the differences in flying heights). Therefore, in general, many more drone-based images would be needed to cover the same area of a single image from a traditional aerial system. To operate a drone commercially (i.e., for compensation) in the United States, one must earn a Remote Pilot Certificate under Part 107 (Small UAS Rule) of the Federal Aviation Administration (FAA). To obtain this certificate, one must be at least 16 years old, able to communicate in English, be in sound physical and mental condition to safely operate an aircraft, and successfully pass the aeronautical knowledge exam at an FAA Knowledge Testing Center. The certificate earned is good for 24 months, after which it must be renewed via online recurrent training offered by the FAA. This certification is important for many reasons, as it informs drone pilots of










The common operations found within and around airports. The proper use of airspace. The need to maintain a line of sight of a drone. Protocols for handling oncoming air traffic. The reasons why permitting processes to operate in certain airspace are necessary. Common air traffic control communications. The need for incident reporting. Methods for assessing local weather conditions. The maximum load limits for a drone.

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The wide variety of safety considerations. Twenty or 30 years ago (and still today) people were able to fly small paper or wooden airplanes, or small airplanes attached to tethers, with relative freedom and ease. There were likely very few people who were qualified and were able to fly computer-assisted drones. As of February 7, 2022, there were about 263,000 certified remote pilots in the United States (U.S. Department of Transportation, Federal Aviation Administration, 2022). Translation 9.4 As a new employee of an established forest management organization, people are looking to you for new ideas. In a very brief way, explain the main differences between remotely sensed images collected by drones, aerial systems, and satellites. Then, select one specific forest management activity, and select a preferred remotely sensed image (from one of the three systems) that would best support the monitoring of the management activity. Develop a short paragraph to briefly support your position.

Conclusions While only a brief introduction to remote sensing has been presented in this chapter, the wide range of technologies available to foresters and natural resource professionals to remotely sense landscapes and water bodies is clear. With the availability of aerial imagery, satellite imagery, imagery collected by drones, and LiDAR point clouds, the composition, structure, and change over time of natural features can be assessed and incorporated into analysis and decision-making. When selecting the appropriate remotely sensed data, the associated spectral, spatial, radiometric, and temporal resolutions are important factors. The increased popularity and availability of LiDAR sensors and UAVs make capturing remotely sensed imagery nearly immune to temporal concerns; however, database management and processing are concerns with sensors that produce large databases with fine-scale (centimeter, inch) spatial resolution. Exercises exploring the concepts covered in this book using ArcGIS or QGIS are available on the book website: gis-book.uga.edu.

References De Luca, G., Silva, J.M.N., Di Fazio, S., Modica, G., 2022. Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for land cover mapping in a Mediterranean region. European Journal of Remote Sensing 55 (1), 52e70. Enge, P., Walter, T., Pullen, S., Kee, C., Chao, Y.-C., Tsai, Y.-J., 1996. Wide area augmentation of the global positioning system. Proceedings of the IEEE 84 (8), 1063e1088. European Space Agency, 2021a. GeoEye-1. European Space Agency, Earth Online. https://earth.esa.int/ eogateway/missions/geoeye-1 (accessed 26.10.21).

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European Space Agency, 2021b. IKONOS-2. European Space Agency. Earth Online. https://earth.esa.int/ eogateway/missions/ikonos-2 (accessed 26.10.21). European Space Agency, 2021c. SPOT. European Space Agency. Earth Online. https://earth.esa.int/ eogateway/missions/spot (accessed 01.12.2021). European Space Agency, 2021d. WorldView Series. European Space Agency. Earth Online. https://earth. esa.int/eogateway/missions/worldview (accessed 26.10.21). Evans, D.L., Roberts, S.D., Parker, R.C., 2006. LiDAR e a new tool for forest measurements? The Forestry Chronicle 82 (2), 211e218. Gates, D.M., 1967. Remote sensing for the biologist. BioScience 17 (5), 303e307. Giacconi, R., Harris, B., 1969. Comments on remote sensing. IEEE Transactions on Geoscience Electronics GE- 7 (4), 179e190. Henry, C., 2014. Azercosmos to take ownership of Airbus’ Spot 7 EO satellite. Via Satellite December 2, 2014. www.satellitetoday.com/innovation/2014/12/02/azercosmos-to-take-ownership-of-airbus-spot7-eo-satellite (accessed 24.02.22). Jensen, J.R., 2006. Remote Sensing of the Environment: An Earth Resource Perspective, second ed. Prentice Hall, Upper Saddle River, NJ. Khorram, S., Kock, F.H., van der Wiele, C.F., Nelson, S.A.C., 2012. Remote Sensing. Springer, New York. Lillesand, T.M., Keifer, R.W., Chipman, J.W., 2004. Remote Sensing and Image Interpretation, fifth ed. John Wiley & Sons, New York. Loveland, T.R., Dwyer, J.L., 2012. Landsat: building a strong future. Remote Sensing of Environment 122, 22e29. Lulla, K., Nellis, M.D., Rundquist, B., Srivastava, P.K., Szabo, S., 2021. Mission to Earth: LANDSAT 9 will continue to view the world. Geocarto International 36 (20), 2261e2263. Martimort, P., Arino, O., Berger, M., Biasutti, R., Carnicero, B., Del Bello, U., Fernandez, V., Gascon, F., Greco, B., Silvestrin, P., Spoto, F., Sy, O., 2007. Sentinel-2 optimal high resolution mission for GMES operational services. In: 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, New York, pp. 2677e2680. Masek, J.G., Wulder, M.A., Markham, B., McCorkel, J., Crawford, C.J., Storey, J., Jenstrom, D.T., 2020. Landsat 9: empowering open science and applications through continuity. Remote Sensing of Environment 248, Article 111968. Na¨si, R., Honkavaara, E., Blomqvist, M., Lyytika¨inen-Saarenmaa, P., Hakala, T., Viljanen, N., Kantola, T., Holopainen, M., 2018. Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft. Urban Forestry & Urban Greening 30, 72e83. National Aeronautics and Space Administration, 2013. Imagine the Universe! https://imagine.gsfc.nasa. gov/science/toolbox/emspectrum1.html (accessed 25.08.21). National Aeronautics and Space Administration, 2017. NASA Testing Technologies to Increase Drone Uses. www.nasa.gov/image-feature/langley/nasa-testing-technologies-to-increase-drone-uses (accessed 10.01.22). National Aeronautics and Space Administration, 2019. NASA/METI/AIST/Japan Space Systems, and U.S. https://asterweb.jpl.nasa.gov/gallery-detail.asp?name¼mississippimap (accessed 10.01.22). National Aeronautics and Space Administration, 2020. City Life Awaits Drones in Final Year of NASA Research. www.nasa.gov/image-feature/city-life-awaits-drones-in-final-year-of-nasa-research (accessed 10.01.22). National Aeronautics and Space Administration, 2021a. Landsat Overview. https://www.nasa.gov/ mission_pages/landsat/overview/index.html (accessed 21.02.22). National Aeronautics and Space Administration, 2021b. Landsat Science. https://landsat.gsfc.nasa.gov/ (accessed 30.11.21).

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National Aeronautics and Space Administration, 2021c. MODIS: Moderate Resolution Imaging Spectroradiometer. https://terra.nasa.gov/about/terra-instruments/modis (accessed 22.10.21). National Aeronautics and Space Administration, 2021d. Remote Sensors. https://earthdata.nasa.gov/ learn/remote-sensors (accessed 10.01.22). National Aeronautics and Space Administration, 2021e. Terra: The EOS Flagship. https://modis.gsfc. nasa.gov/about/ (accessed 22.10.21). National Aeronautics and Space Administration, 2022. About Terra. https://terra.nasa.gov/about/ (accessed 24.02.22). National Aeronautics and Space Administration, Earth Observatory, 2009. The Sorting Problem. https:// earthobservatory.nasa.gov/features/GlobalLandSurvey/page2.php (accessed 20.02.22). National Aeronautics and Space Administration, Goddard Media Studios, 2019. Landsat 9 Spacecraft Animations and Stills. https://svs.gsfc.nasa.gov/13259 (accessed 27.02.22). National Aeronautics and Space Administration, Jet Propulsion Laboratory, 2020. AVIRIS Data e AVIRIS Moffett Field Image Cube. https://aviris.jpl.nasa.gov/data/image_cube.html (accessed 20.02.22). Orbital, A.T.K., 2016. LEOStar-3 Bus Fact Sheet. Orbital ATK, Dulles, VA. Paine, D.P., Kiser, J.D., 2012. Aerial Photography and Image Interpretation, third ed. John Wiley & Sons, Hoboken, NJ. Pflugmacher, D., Cohen, W.B., Kennedy, R.E., 2012. Using Landsat-derived disturbance history (19722010) to predict current forest structure. Remote Sensing of Environment 122, 146e165. Raparelli, E., Bajocco, S., 2019. A bibliometric analysis on the use of the unmanned aerial vehicles in agricultural and forestry studies. International Journal of Remote Sensing 40, 9070e9083. Rapinel, S., Cle´ment, B., Magnanon, S., Sellin, V., Hubert-Moy, L., 2014. Identification and mapping of natural vegetation on a coastal site using Wordview-2 satellite image. Journal of Environmental Management 144, 236e246. Sabins, F.F., 1997. Remote Sensing: Principles and Interpretation. W.H. Freeman and Company, New York. Salas, E.B., 2021. Number of Aircraft in the United States 1990-2021. Statista Inc., New York. https://www. statista.com/statistics/183513/number-of-aircraft-in-the-united-states-since-1990/ (accessed 15.02.22). Satellite Imaging Corporation, 2021a. GeoEye-1 Satellite Sensor. www.satimagingcorp.com/satellitesensors/geoeye-1/ (accessed 26.10.21). Satellite Imaging Corporation, 2021b. IKONOS Satellite Sensor. www.satimagingcorp.com/satellitesensors/ikonos/ (accessed 26.10.21). Sato, I., Watanabe, H., Tsu, H., 1999. Launch-ready status of ASTER ground data system. Proceedings of SPIE 3870, 548e554. Sun, Z., Wang, X., Wang, Z., Yang, L., Xie, Y., Huang, Y., 2021. UAVs as remote sensing platforms in plant ecology: review of applications and challenges. Journal of Plant Ecology 14 (6), 1003e1023. Terwilliger, B., Ison, D., Robbins, J., Vincenzi, D., 2017. Small Unmanned Aircraft Systems Guide: Exploring Designs, Operations, Regulations, and Economics. Aviation Supplies and Academics, Inc., Newcastle, WA. Torresan, C., Berton, A., Carotenuto, F., DiGennaro, S.F., Gioli, B., Matese, A., Miglietta, F., Vagnoli, C., Zalder, A., Wallace, L., 2017. Forestry applications of UAVs in Europe: a review. International Journal of Remote Sensing 38, 2427e2447. Turner, W., Spector, S., Gardiner, N., Fladeland, M., Sterling, E., Steininger, M., 2003. Remote sensing for biodiversity science and conservation. Trends in Ecology and Evolution 18 (6), 306e314. Ucar, Z., Bettinger, P., Merry, K., Akbulut, R., Siry, J., 2018. Estimation of urban woody vegetation cover using multispectral imagery and LiDAR. Urban Forestry & Urban Greening 29, 248e260.

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Urbina-Salazar, D., Vaudour, E., Baghdadi, N., Ceschia, E., Richer-de-Forges, A.C., Lehmann, S., Arrouays, D., 2021. Using Sentinel-2 images for soil organic carbon content mapping in croplands of southwestern France. The usefulness of Sentinel-1/2 derived moisture maps and mismatches between Sentinel images and sampling dates. Remote Sensing 13, Article 5115. U.S. Department of Agriculture, Forest Service, 2020. Celebrate GIS Day 2020 with a Look at Lidar. www.fs. usda.gov/inside-fs/delivering-mission/deliver/celebrate-gis-day-2020-look-lidar (accessed 01.10.22). U.S. Department of Agriculture, Natural Resources Conservation Service, 2021. Geospatial Data Gateway: Direct Data/NAIP Download. https://datagateway.nrcs.usda.gov/GDGHome_DirectDownLoad.aspx (accessed 01.08.22). U.S. Department of Commerce, National Oceanic and Atmospheric Administration, 2012. Lidar 101: An Introduction to Lidar Technology, Data, and Applications. NOAA Coastal Services Center, Charleston, SC. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, 2021. National Ocean Service. What is lidar? https://oceanservice.noaa.gov/facts/lidar.html (accessed 01.12.21). U.S. Department of the Interior, Geological Survey, 2003. Lidar Plane. www.usgs.gov/media/images/ lidar-plane (accessed 01.12.2021). U.S. Department of the Interior, Geological Survey, 2020. Spectroscopy Lab. www.usgs.gov/labs/speclab (accessed 30.11.21). U.S. Department of the Interior, Geological Survey, 2021a. Common Landsat Band Combinations. www. usgs.gov/media/images/common-landsat-band-combinations (accessed 30.11.21). U.S. Department of the Interior, Geological Survey, 2021b. Stereoscope. www.usgs.gov/media/images/ stereoscope (accessed 08.01.22). U.S. Department of the Interior, Geological Survey, 2021c. What Are the Band Designations for the Landsat Satellites? www.usgs.gov/media/images/common-landsat-band-combinations (accessed 20.02.22). U.S. Department of Transportation, Federal Aviation Administration, 2021. Air Traffic by the Numbers. U.S. Department of Transportation, Federal Aviation Administration, Washington, D.C. https://www. faa.gov/air_traffic/by_the_numbers/ (accessed 15.02.22). U.S. Department of Transportation, Federal Aviation Administration, 2022. UAS by the numbers. U.S. Department of Transportation, Federal Aviation Administration, Washington, D.C. https://www.faa. gov/uas/resources/by_the_numbers/ (accessed 15.02.22). Vrabel, J., 1996. Multispectral imagery band sharpening study. Photogrammetric Engineering and Remote Sensing 62 (9), 1075e1083. Weng, Q., 2014. Introduction to remote sensing systems, data, and applications. In: Wang, G., Weng, Q. (Eds.), Remote Sensing of Natural Resources. CRC Press, Boca Raton, FL. pp. 3e19 Williams, D.L., Goward, S., Arvidson, T., 2006. Landsat: yesterday, today, and tomorrow. Photogrammetric Engineering and Remote Sensing 72 (10), 1171e1178. World Meteorological Organization, 2022. OSCAR Observing Systems Capability Analysis and Review Tool. https://space.oscar.wmo.int/satellites (accessed 15.02.22).

10 Advanced applications in forestry and natural resource management Introduction In forestry and natural resource management we often use geographic information systems (GIS) to address pressing management situations and to explore options. As many natural resource management issues have a spatial context, the use of geography and mathematics can help inform our decisions. In this chapter we explore four case studies. The first involves an investigation into the amounts and types of forests found within the riparian areas of a rather large national forest in Pennsylvania. As one may find, the rules that define the scope of a riparian zone are laid out in policies adopted by the national forest, which were likely informed by science and expert opinion of land managers, hydrologists, and biologists. The geographic data employed in the analysis is not perfect - it never is. However, the geographical analysis pursued through GIS is an attempt to closely emulate the policies of the national forest and to reveal the forest resources contained in the riparian area. The second case study involves an analysis of the recreation opportunity spectrum (ROS) land classes within a national forest in Minnesota. Here, the rules that define the recreation opportunity classes were developed by recreation specialists, and as readers will see, they have some interesting spatial aspects that not only reveal the recreation potential of the landscape, but also provide an example of a hierarchy of classes and how one might address this through geographical analyses. The third case study involves an examination of the forest fertilization options for a national forest in South Carolina. Forest fertilization is a potential silvicultural treatment that can enhance the growth of trees, and while doing so, perhaps improve the health of a forest. The rules that define, which areas could be fertilized (pine stands on certain soils that are far from a stream) indicate that some interesting spatial analyses might be needed. The result of this analysis can help forest managers think about the potential for this type of treatment across the landscape that they manage. The final case study introduces the use of a land cover database to determine the area occupied by specific forest cover types within the different compartment boundaries of a national forest, again, in Pennsylvania.

Case study: riparian areas The intent of this case study is to understand the amount and type of forests contained within riparian areas, given the riparian policies that are assumed, as well as the Geographic Information System Skills for Foresters and Natural Resource Managers. https://doi.org/10.1016/B978-0-323-90519-0.00013-3 Copyright © 2023 Elsevier Inc. All rights reserved.

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proportion of forest types within these areas. In general, a riparian area is a piece of land near a stream or water body, but more specifically, one definition of a riparian area is: . an upland, wetland, and/or riparian area that protects and/or enhances aquatic resource functions associated with wetlands, rivers, streams, lakes, marine, and estuarine systems from disturbances associated with adjacent land uses. Office of the Federal Register, National Archives and Records Administration (2009).

The technical extent of a riparian area in this sense can be determined through on-site investigation of the soils and vegetation of a place. Often however, riparian areas are defined through policy as a physical distance on either side of a stream, measured from the stream centerline or the stream edge. As most stream GIS databases contain lines that define the center of a stream, these are often used along with the assumptions from policies to develop maps of riparian areas. For this case study, the analysis will be focused on the Allegheny National Forest in northwest Pennsylvania. The Allegheny National Forest plan (U.S. Department of Agriculture, Forest Service, 2007) indicates that riparian areas (Table 10.1) for the national forest will vary by flow type (river, perennial stream, intermittent stream) and whether streams have been designated as state-level Class A trout streams (Pennsylvania Fish and Boat Commission, 2021). The Allegheny National Forest plan also indicates that riparian area widths may vary depending on the slope of the adjacent uplands and other local conditions. However, these intricacies are not acknowledged in the case study that follows. Therefore, given the data available and the assumptions we have made regarding the policies employed by the national forest, we will proceed with a process to address the goals noted above. Prior to this moment at which we have arrived, several data acquisition and processing steps were conducted:
Acquire the timber stands and streams GIS data from the Allegheny National Forest.
Visit a Pennsylvania Fish and Boat Commission (2021) website to determine, which of these streams are important trout streams (Fig. 10.1).
Develop a GIS database that represents the edge, or high water mark, of the Allegheny River and the Allegheny Reservoir (Fig. 10.2). Within the streams GIS Table 10.1 Riparian area widths for streams that fall within the Allegheny National Forest (U.S. Department of Agriculture, Forest Service, 2007). Water feature

Riparian width (ft)

Allegheny River Class A trout streams Perennial streams Intermittent streams

300 200 100 50

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FIGURE 10.1 Types of streams and rivers within the Allegheny National Forest. Credit: Hydrology data from the U.S. Department of the Interior, Geological Survey (2021).

database of the national forest, the river and reservoir are represented as lines running mainly down the center of channels. Simply buffering these lines would understate the appropriate riparian area around the river and reservoir. Both sides of the river and reservoir needed to be digitized, since the width of the river is more than 300 feet (91.44 m) wide in some places.

FIGURE 10.2 Edge of the Allegheny Reservoir and the line features within the streams GIS database. Credit: Hydrology data from the U.S. Department of the Interior, Geological Survey (2021).

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To address the analysis associated with this case study, we need to employ several GIS processes that involve querying, buffering, overlaying, and summarizing spatial data. One approach to address this analysis is to use the following steps (Fig. 10.3). 1. Add a field to the streams GIS database that will contain a quantitative value representing the appropriate buffer widths for each stream. As one can see in Table 10.1, this field needs to be capable of containing values with several digits, and perhaps one or two decimal places. The values that will populate this field are the width (meters) of the buffer that is needed from the centerline of each stream outward. These units are expressed in meters because soon a variable width buffer process will be enabled, and this requires the buffer widths for each stream segment to be located in the attribute table and also requires them to be represented in the same units as the coordinate system (Universal Transverse Mercator).

FIGURE 10.3 Flow chart representing possible processing steps and GIS databases created and used to assess the extent and condition of riparian areas.

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2. Populate the buffer width field in the streams GIS database with the appropriate buffer widths. For the purpose of this analysis, the FCode field will assist in identifying the perennial and the intermittent streams (Table 10.2). The Class_A field contains a value (1) that represents the Class A trout streams and a second value (0) that identifies those streams that are not Class A trout streams. The Class A trout stream designation overrides the perennial/intermittent designation; therefore, this will be addressed last. First, using a query, select the perennial streams. "FCode" ¼ 46000 OR "FCode" ¼ 46006 OR "FCode" ¼ 55800

Once the perennial streams have been selected, enter the value “30.48” in the buffer width field. This value is 100 feet/3.2808 m per foot, or the metric equivalent of the 100 foot buffer width. Then, select the intermittent streams with a similar query. Alternatively, if possible, invert the selection. After the intermittent streams have been selected, enter the value “15.24” in the buffer width field (50 feet/3.2808 m per foot). Finally, using the Class A stream field values, select those streams that have a value of 1. After the Class A streams have been selected, enter the value “60.96” in the buffer width field (200 feet/3.2808 m per foot). This will override any other value that was previously placed there for these streams. At this point, every stream should have a positive value in the buffer width attribute field. 3. Use a variable width buffer process to buffer each stream according to its appropriate buffer width. The result should be riparian buffers that vary according to the value placed in the buffer field (Fig. 10.4). Inspection 10.1 Buffering is simply a rigid process of drawing a line exactly a certain distance around the selected geographical features. The computer conducts the process with no sense of whether it seems appropriate. Arguably, the opposite of riparian area features on a landscape are the uplands. Take a few minutes and Table 10.2 Stream types to consider in the approximation of area that might be found within riparian zones of the Allegheny National Forest, as noted in the National Hydrography dataset (U.S. Department of the Interior, Geological Survey, 2021). Feature code (FCODE)

Feature code name

Stream type on the Allegheny National Forest

33400 46003 46000 46006 55800

Connector Stream/river Stream/river Stream/river Artificial path

Intermittent Intermittent Perennial Perennial Perennial

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FIGURE 10.4 Varying buffer widths on the Allegheny National Forest. Credit: Hydrology data from the U.S. Department of the Interior, Geological Survey (2021).

review the lands considered uplands through the policies we assumed in this exercise. Are there any areas that seem unreasonable to manage as an upland? Diversion 10.1 How much difference would it make to use rounded ends at the end of each line segment that is being buffered, as opposed to squared ends? Had overlapping areas among the buffers not been erased, how much of an overstatement of buffer area would there have been? 4. Develop a buffer around the edge of the river and reservoir. In buffering the edge of the Allegheny River and Allegheny Reservoir above the Kinzua Dam, we are interested in the part of the 300 foot (91.44 m) buffer that covers land (Fig. 10.5). The edge of the river system had to be digitized because it was not evident in the streams GIS database. The centerline of the river and reservoir was contained in the streams GIS database; however, buffering the centerline 91.44 m would not have reached land in some areas of the landscape. Translation 10.1 Imagine you have traveled home for a holiday celebration. At dinner one night you very proudly mention the elaborate GIS analyses you have conducted recently. However, you receive blank stares and confused looks from your family. In just a few sentences, very clearly describe the idea of a riparian zone and the GIS process of buffering. 5. Combine the various buffers that were created and clean up the data. Once the stream buffers and the river buffer GIS databases have been created, these will be combined to form a single GIS database that represents all of the potential riparian buffer areas. To accomplish this, a union overlay process can be applied, which retains the spatial extent of both GIS databases in a third, combined feature

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FIGURE 10.5 Buffer of the Allegheny River and Reservoir and the varying buffer widths on the Allegheny National Forest. Credit: Hydrology data from the U.S. Department of the Interior, Geological Survey (2021).

database, and any overlapping areas are erased. Further, the interior parts of the combined buffer that lie inside the Allegheny River and Allegheny Reservoir should be erased. The result is a GIS database of polygons (Fig. 10.6) that represent areas inside and outside of the national forest; however, as one might recall, we are only interested in the parts that fall inside the national forest. So, we are not quite finished with this analysis. 6. Extract the timber stands that reside inside the riparian buffers. Using the timber stands GIS database and the combined riparian area GIS database, one of several overlay processes could be used to determine the places that lie inside the riparian areas. For this example, a clipping process is used, where the combined riparian area GIS database is used to clip (like a cookie cutter) the lands of the national forest that fall inside (Fig. 10.7). This is not a perfect analysis, for if one were to examine the national forest area around the Allegheny Reservoir, one will find that some of the timber stands seem to extend into the reservoir. These inconsistencies aside, the areas within the buffers can be estimated by extracting the pieces of timber stands that lie inside the riparian buffers. 7. Recalculate the size of the remaining polygons, if necessary. We would like to summarize the character of the resulting pieces of the original timber stands GIS database, but first, their land areas may need to be recalculated, depending on the GIS software program that is being used. Inspection 10.2 How many polygons are contained in the GIS database that represents pieces of timber stands within the riparian areas? What is the range of size of these polygons?

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FIGURE 10.6 Combined riparian buffers on the Allegheny National Forest. Credit: Hydrology data from the U.S. Department of the Interior, Geological Survey (2021).

FIGURE 10.7 Pieces of timber stand within riparian buffers on the Allegheny National Forest. Credit: Hydrology data from the U.S. Department of the Interior, Geological Survey (2021).

8. Summarize the results. A summary (Table 10.3) of the land areas within the riparian areas on this national forest indicates that the mixed upland hardwoods species group, along with the black cherry-white ash/yellow poplar group and the sugar maple-beech/yellow birch groups dominate areas near the streams and river,

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accounting for 63.7% of the land area. The total amount of land area within the riparian area is 28,470.7 acres (11,521.9 ha), which represents about 5.6% of the national forest. This kind of information can be informative for the development of forest policies, particularly those that relate to decisions regarding riparian widths. Imagine if someday the policies change, if the buffer widths are extended, one Table 10.3 Estimated forest and land area within riparian areas of the Allegheny National Forest. Forest or land type Beech Bigtooth aspen Birch Black cherry-white ash/yellow poplar Black locust Black oak/scarlet oak/hickory Chestnut oak Eastern white pine Eastern white pineehemlock Eastern white pine-northern red oak/white ash Hemlock Lowland shrubs Mixed lowland hardwoods Mixed oaks Mixed upland hardwoods Northern hardwoods-hemlock Northern red oak Norway spruce Oak-aspen Oak-hardwoods Open Quaking aspen Red maple (dry site) Red maple (wet site) Red pine Red pine-oak Spruce Sugar maple Sugar maple-beech/yellow birch Sugar maple-black cherry Sugar maple-northern red oak Upland shrubs White oak White spruce Yellow poplar/white oak/northern red oak a

Acres 18.42 4.08 41.55 4706.58 10.52 12.86 4.44 82.75 238.16 25.31 1814.68 322.48 423.36 611.26 7747.91 19.48 741.49 99.51 1.61 1544.87 2066.68 107.94 862.26 119.36 524.76 6.91 67.17 34.76 5847.60 170.24 2.34 96.01 24.07 32.32 36.96

suggests this class represents less than 0.1% of the riparian area.

Percent of riparian area 0.1 a 0.1 16.5 a a a 0.3 0.8 0.1 6.4 1.1 1.5 2.1 27.2 0.1 2.6 0.3 a 5.4 7.3 0.4 3.0 0.4 1.8 a 0.2 0.1 20.5 0.6 a 0.3 0.1 0.1 0.1

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would expect a greater proportion of national forest land to fall within policydefined riparian areas, and conversely if the buffer widths are contracted, the opposite would likely occur. One could walk through the same processes with different buffer width assumptions and more precisely estimate the impacts of different policies. Diversion 10.2 After overlaying the riparian buffer GIS database on top of the timber stands GIS database, what percentage of each forest type is considered to be contained in a riparian zone? This analysis was rather complex, but not a very difficult GIS analysis to perform. Having good quality data was essential. We assumed that the spatial features were correct, and that the assigned attributes were also correct. However, there are likely some spatial and attribute errors, and depending on one’s tolerance for these, a decision may or may not be made to invest some time to search for these errors and make corrections as necessary. Certainly, as one may gather, this analysis was not perfect, but it does provide an estimate of the riparian resources on this property. Reflection 10.1 Had there not been the invention of GIS software, and the databases needed to conduct this analysis did not exist, how would the amount of area contained within the riparian zones have been estimated?

Case study: recreation opportunity spectrum The ROS was developed by the U.S. Department of Agriculture’s Forest Service and the U.S. Department of Interior’s Bureau of Land Management as a tool for managing recreation and tourism on federal land, and for integrating recreation and tourism with other land uses (Clark and Stankey, 1979). The ROS was developed based on empirical (factual) surveys of recreation preferences for various forms of landscape conditions (Cerveny et al., 2011) and is based on a perceived need for a variety of recreational opportunities across a landscape or within a property (Angelo, 1981). Although the ROS is perhaps the most often used spatial tool for recreation decision-making on federal public lands in the United States, a number of other classification processes for outdoor recreation have also been developed, including the recreation carrying capacity, limits of acceptable change, placed-based planning, visitor impact management, and the tourism opportunity spectrum (TOS) (Cerveny et al., 2011; Butler and Waldbrook, 1991). The latter system is based on the ROS and includes aspects of accessibility (i.e., transportation systems), tourism infrastructure, social interaction, and other nonadventure uses. The ROS is used to describe and identify recreational settings and to illustrate the likelihood of recreational opportunities along a spectrum that is divided into several classes. This system combines the physical landscape characteristics of location and access to allow one to delineate areas of land that may be used for different recreational

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purposes. For example, in the most recent forest plan for the Hiawatha National Forest in northern Michigan (U.S. Department of Agriculture, Forest Service, 2006), it is suggested that recreation-related development, activities, management practices, and access will be consistent with the delineated ROS class for each area. Thus, the recreation opportunities will be provided in a manner consistent with the ROS designation for each management area. The ROS classes represent a wide range of recreational experiences, from those that include a high likelihood of self-reliance, solitude, challenge, and risk, to those that include a relatively high degree of resource development and interaction with other people. A recreation opportunity class therefore is an area of land that may yield certain experiences for recreationists in a specific landscape setting. Consider an activity such as cross-country skiing, a popular recreational activity in western North America. Cross-country skiing experiences in and around cities, such as Bend, Oregon, are likely to result in experiences that are exercise-oriented yet include a high frequency of interaction with other people and developed resources. However, cross-country skiing experiences in the backcountry, such as the nearby Deschutes National Forest, while also exercise-oriented, are more likely to include elements of solitude, risk, personal challenge, and will likely have a lower frequency of interaction with people. Therefore, the same activity, cross-country skiing, can be associated with different experiences in different landscape settings. As a result, there is a need to delineate those areas spatially, so that management activities related to recreational activities (and other management objectives) can be planned accordingly. In this case study, we will assess ROS classes in the Chippewa National Forest in Minnesota. Through the rules and logic devised by the U.S. Forest Service for national forests in Minnesota, we will identify remoteness of lands based on their proximity to roads, and classify lands as rural, urban, and wilderness areas using various spatial assumptions. The original version of the ROS classification (Clark and Stankey, 1979) divided land areas into six classes:







Wilderness (now called Primitive) Semiprimitive nonmotorized (SPNM) Semiprimitive motorized (SPM) Roaded natural Rural Urban

The roaded category has at times been expanded to two classes, roaded natural and roaded modified, although the exact classes used seems to vary from one management situation to the next. In the most recent forest plan for the Chippewa National Forest (U.S. Department of Agriculture, Forest Service, 2004a), the original classes were recognized. Inspection 10.3 Access the Chippewa National Forest plan (Chapter 2 Forest-wide management direction) and locate the desired conditions and ROS class objectives for recreation

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on the national forest. What percent of the national forest system (NFS) lands in the Chippewa National Forest are desired for these classes?






Primitive Semiprimitive nonmotorized Semiprimitive motorized Roaded natural Rural

As one might gather from the names and descriptions of the ROS classes, they infer that specific kinds of recreation activities and experiences might be obtained based on certain physical (e.g., size), social (e.g., encounters with other people), and managerial (e.g., legally designated wilderness area) characteristics. As we suggested, the rules that define the ROS classes can include spatial relationships. For example, in the Minnesota national forests, for land to be considered a primitive area, it must lie more than two miles (3.2 km) from any road (Table 10.4). Further, some of the ROS classes are based on contiguous areas of land that have the same characteristics; therefore, some spatial aggregation of polygons may be necessary. The semiprimitive, nonmotorized ROS class, for example, suggests that there must be at least 500 contiguous acres (202.3 ha) of land of certain forested conditions before land can be classed as such. Here, it may be necessary to add together the area of several contiguous polygons to determine how much area they represent in aggregate. Table 10.4 A subset of rules for delineating Minnesota ROS classes (U.S. Department of Agriculture, Forest Service, 2004a). ROS class

Rule

Urban areas (URBAN) Rural areas (RURAL)

0.25-mile buffer around population centers 0.25-mile buffer around urban areas 0.25-mile buffer around major highways Areas of land greater than 2 miles from a paved, gravel, or dirt road Areas of land greater than 2 miles from a motorized lake Areas of land greater than 2 miles from a motorized trail Minimum contiguous size of 2500 acres Areas of land greater than 0.5 miles from a paved, gravel, or dirt road Areas of land greater than 0.25 miles from a motorized lake Areas of land greater than 0.5 miles from a motorized trail Minimum contiguous size of 1500 acres Areas of land greater than 0.5 miles from a paved road Areas of land greater than 0.25 miles from a gravel road Minimum contiguous size of 1500 acres Areas of land greater than 0.5 miles from a paved road Areas of land greater than 0.25 miles from a gravel road Areas that do not fit into any of the other classes

Primitive (P)

Semiprimitive, Nonmotorized (SPNM)

Semiprimitive, Motorized (SPM) Roaded natural (RN) Roaded, managed (RM)

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With the caveat that we are only using data located inside the boundary of the Chippewa National Forest even though the Minnesota ROS guidelines suggest areas up to two miles (3.2 km) outside the national forest need to be included, this case study will divide the forest into ROS classes based on the subset of the ROS criteria listed in Table 10.4. Using the GIS analysis of the Chippewa National Forest data that can be accessed from this book’s website (gis-book.uga.edu), the intent is to understand how much land area is considered to fall into each ROS class without double-counting any areas and without excluding any areas from the analysis. Prior to this moment at which we have arrived, several data acquisition and processing steps were conducted:
Acquire the timber stands and roads GIS data from the Chippewa National Forest.
Assess the roads GIS database to determine which classes of roads fall into the highway, paved, gravel, etc. classes.
Investigate the presence of population centers and urban areas. Like the other case studies in this chapter, we need to employ several GIS processes that involve querying, buffering, intersecting, editing, and summarizing spatial data. One approach to address this analysis is to use the following steps. 1. Examine the landscape around the Chippewa National Forest to locate the urban population centers. One assumption we make in the analysis is that places, or population centers, may be classified as urban areas if they contained at least 2500 residents (U.S. Department of Commerce, Census Bureau, 1994). In attempting to understand the urban areas surrounding the Chippewa National Forest, we find two cities that fit this description. Grand Rapids is about 10 miles (16.1 km) to the east of the national forest, and Bemidji can be found about 8 miles (12.9 km) to the west. These are too far away, more than 0.25 miles (0.4 km) to assist in our analysis. Other smaller towns can be found very close to the national forest lands (Table 10.5), yet they are not large enough to qualify as an urban area for this analysis. Therefore, we conclude that there are no urban ROS areas in this national forest. 2. Determine the areas that might be definitionally considered rural according to the ROS rules and the GIS processes that might address them (Fig. 10.8). Since there are no urban areas close enough to the national forest to include in this case study, we are only interested in the 0.25 mile (0.4 km) buffer around major highways. For this analysis, we assume these include U.S. Highways 2 and 71 and State Highways 6, 34, 38, 46, 200, 286, and 371 (Fig. 10.9). Further, any spurs of these highways should not be included in this analysis, as they often only represent small offshoots from the main roads. Inspection 10.4 It would be a good idea at this point to understand all of the route names of road segments that qualify as legitimate returned records from a query designed to locate these roads. So, open the roads GIS database associated with the Chippewa National Forest, examine the attribute table, and make a list of the qualifying road names.

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Table 10.5 Small towns situated very close to the Chippewa National Forest (U.S. Department of Commerce, Census Bureau, 2021). Name

Population (2019)

Ball Club Bena Bigfork Black Duck Cass Lake Deer river Federal Dam Longville Remer Walker

165 128 405 884 633 951 98 174 354 938

FIGURE 10.8 A process to assess the rural ROS areas on the Chippewa National Forest.

Inspection 10.5 After selecting the U.S. and State highways in the vicinity of the Chippewa National Forest, it would be a good idea to check the spatial features that

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FIGURE 10.9 Major highways that cross the Chippewa National Forest. Credit: Road data from the U.S. Department of Agriculture, Forest Service (2015).

qualify as legitimate returned records from a query designed to locate these roads. Are there any abnormalities that may need editing? Using the Chippewa National Forest major highways GIS database that was created specifically for this analysis, buffer the roads 0.25 miles (0.4 km). This database was created to rectify a few omissions and inclusions that were found in the query attempted previously. After an overlay process (e.g., clipping), the result (Fig. 10.10) provides an indication of the portions of land on the Chippewa National Forest that might be considered to be included in the rural ROS class. As a result of this analysis, about 28,210.4 acres (11,416.6 ha) might be contained in the rural ROS class. Reflection 10.2 At this point, which GIS processes might you use to understand exactly how much of the Chippewa National Forest might be included in the rural ROS class? 3. Determine the areas that might be definitionally considered primitive according to the ROS rules and the GIS processes that might make sense in addressing these rules (Fig. 10.11). Making the leap of faith that the road features in the roads GIS database fit the description of paved, gravel, or dirt roads, along with motorized lakes or trails in real life, these features (Table 10.6) were first queried in the GIS

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FIGURE 10.10 A 0.25 mile (400 m) blue buffer that was created around the major highways in the southwest portion of the Chippewa National Forest, with vegetation polygons (those with a POLY_ID < 900) noted in gray. Credit: Road data from the U.S. Department of Agriculture, Forest Service (2015).

database, then used to create a two mile (3.2 km) buffer. These buffers are then used to erase land areas from the timber stands GIS database. Whatever lands that remain have to compose at least one contiguous area that is 2500 acres

FIGURE 10.11 A process to assess the primitive ROS areas on the Chippewa National Forest.

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Table 10.6 Assumptions about road types to consider for the ROS analysis within the Chippewa National Forest. Type

FCSUBTYPE

Description

Paved

101 105

Highway (paved) Roaddlight dutydunspecified composition (assume paved) Roaddlight dutydpaved Roaddlight dutydgravel Road unimproved (although not consistent, assume native surface) Roaddlight dutyddirt Traildscenic, historic Airboat trail or canoe byway

Gravel Native surface

517 518 106

Motorized lake

515 525 908

(1011.7 ha) in size. As one might see in the outcome of this analysis, there are no Chippewa National Forest lands large enough, and farther than two miles from a road of the type assumed for the analysis (Fig. 10.12). The largest collection of stands farther than two miles from these roads is about 320 acres (129.5 ha). To this point, we know that there seems to be no urban and no primitive areas within this national forest according to the rules followed and the data utilized.

FIGURE 10.12 A two mile (3200 m) pink buffer that was created around the roads within the Chippewa National Forest, with vegetation polygons (those with a POLY_ID < 900) noted in gray and potential primitive areas in red. Credit: Road data from the U.S. Department of Agriculture, Forest Service (2015), vegetation data from the U.S. Department of Agriculture, Forest Service (2021b).

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4. Determine the areas that might be definitionally considered semiprimitive nonmotorized according to the ROS rules and the GIS processes that might make sense in addressing these rules. One process that might be used to address this issue can be found in Fig. 10.13. First, the roads, with the exception of the motorized lake features, are buffered 0.5 miles (0.8 km). Then, the motorized lake features are buffered 0.25 miles (0.4 km). These buffer features are then merged into one set of buffers (Fig. 10.14). The set of buffers is then used to erase features from the timber stands GIS database, leaving those areas that could potentially serve as semiprimitive nonmotorized ROS areas (Fig. 10.15). Inspection 10.6 Can all of the polygon features within the semiprimitive nonmotorized ROS GIS database of the Chippewa National Forest be included in the semiprimitive nonmotorized class? Why or why not? How would you address this issue if why not was your answer? With some inspection of the contiguous pieces of timber stands from this set, we find that only certain areas (Fig. 10.16) qualify as semiprimitive nonmotorized ROS areas. In fact, about 19,221.7 acres (7778.9 ha) of land, in 8 different blobs might be considered the semiprimitive nonmotorized ROS areas on the Chippewa National Forest. Reflection 10.3 To this point in the analysis, how do you feel about the amount and complexity of work in GIS that is required to assess some of these ROS classes?

FIGURE 10.13 A process to assess the semiprimitive nonmotorized ROS areas on the Chippewa National Forest.

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FIGURE 10.14 A 0.5 mile (800 m) and 0.25 mile (400 m) green buffer that was created around selected roads within the Chippewa National Forest, with vegetation polygons (those with a POLY_ID < 900) noted in gray. Credit: Road data from the U.S. Department of Agriculture, Forest Service (2015), vegetation data from the U.S. Department of Agriculture, Forest Service (2021b).

FIGURE 10.15 Areas (red) that could potentially serve as the semiprimitive nonmotorized ROS class within the Chippewa National Forest. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021b).

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FIGURE 10.16 Areas (red) that serve as the semiprimitive nonmotorized ROS class within the Chippewa National Forest. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021b).

5. Determine the areas that might be definitionally considered semiprimitive motorized according to the ROS rules and the GIS processes that might make sense in addressing these rules. One process that might be used to address this issue can be found in Fig. 10.17. Similar to the previous set of processes employed, here one would query the roads GIS database for paved roads and buffer these roads 0.5 miles (0.8 km). Then, one would query the roads GIS database for gravel roads and buffer these roads 0.25 miles (0.4 km). Then, one might merge these two GIS databases containing the buffers and erase these features from the timber stands GIS database. One additional step would be to further erase the semiprimitive nonmotorized ROS class features, because a piece of land cannot be attributed to two different ROS classes, and semiprimitive nonmotorized ROS class has higher importance. The resulting GIS database (Fig. 10.18) needs to then be edited to contain only those contiguous areas of timber stands that are 1500 acres (607 ha) or more in size (Fig. 10.19). In order to conduct this final procedure, the polygons in this near-final GIS database may need to be converted from multipart shapes to single part shapes, and then their size may need to be recalculated. Further, some effort may be needed to sort through the collections of contiguous stands to locate those that are large enough to meet the criteria.

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FIGURE 10.17 A process to assess the semiprimitive motorized ROS areas on the Chippewa National Forest.

FIGURE 10.18 Prior to editing, areas (purple) that could potentially serve as the semiprimitive motorized ROS class within the Chippewa National Forest. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021b).

After conducting these GIS analyses, one may find that the semiprimitive motorized ROS class contains about 216,791 acres (87,734 ha) of land. 6. Determine the areas that might be definitionally considered roaded natural according to the ROS rules and the GIS processes that might make sense in addressing

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FIGURE 10.19 After editing, areas (green) that could potentially serve as the semiprimitive motorized ROS class within the Chippewa National Forest. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021b).

these rules. One process that might be used to address this issue can be found in Fig. 10.20. Similar to the previous set of processes employed, here one would query the roads GIS database for paved roads and buffer these roads 0.5 miles (0.8 km). Then, one would query the roads GIS database for gravel roads and buffer these roads 0.25 miles (0.4 km). Then, one might merge these two GIS databases containing the buffers and erase these features from the timber stands GIS database. Finally, one would erase from these features the semiprimitive nonmotorized ROS class polygons and the semiprimitive motorized ROS class polygons. There is no size restriction on the resulting pieces of the national forest that fall into the roaded natural class (Fig. 10.21). The result is about 168,485 acres (68,185 ha) in this ROS class. 7. Lastly, the roaded managed ROS class can be developed by taking the features within the timber stands GIS database and erasing from them the features in the rural, semiprimitive nonmotorized, semiprimitive motorized, and roaded natural GIS databases. Diversion 10.3 Create a flow chart, like the ones presented previously in this chapter, to illustrate the steps required to estimate the amount of land in the roaded natural ROS class.

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FIGURE 10.20 A process to assess the roaded natural motorized ROS areas on the Chippewa National Forest.

The end result of this analysis (Fig. 10.22) suggests we find about 231,710 acres (93,772 ha) in the roaded managed ROS class, and the following:





About About About About class.

34.9% of the national forest falls into the roaded, managed ROS class. 25.4% of the national forest falls into the roaded, natural ROS class. 32.6% of the national forest falls into the semiprimitive motorized ROS class. 2.9% of the national forest falls into the semiprimitive nonmotorized ROS

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FIGURE 10.21 Areas (orange) that could potentially serve as the roaded natural ROS class within the Chippewa National Forest. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021b).

FIGURE 10.22 Areas that could potentially serve as the various ROS classes within the Chippewa National Forest (SPNM ¼ semiprimitive nonmotorized; SPM ¼ semiprimitive motorized; RN ¼ roaded natural; RM ¼ roaded managed). Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021b).

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About 4.2% of the national forest falls into the rural ROS class.
None of the national forest falls into the urban and primitive ROS classes.

Case study: fertilization options The intent of this case study is to estimate the amount and location of forest stands that might be eligible for a forest fertilization treatment. Fifty years ago, the broad use of fertilization practices in the southern United States, and South Carolina in particular, for increasing the growth of forests was a relatively new idea, but on certain sites it was acknowledged that forest fertilization might be economically feasible (Knight and McClure, 1969). Today, fertilization is widely practiced within forests managed by the timber industry of the southern United States. Areas with the appropriate timber types (usually pines (Pinus spp.)), of the appropriate ages or conditions (e.g., perhaps immediately after a thinning), and located on soil types that would seem permissible for these actions would be identified in assessing forest fertilization options. For the conduct of this case study analysis, soil types that were noted as not limited with respect to pesticide runoff potential in the SSURGO soils database (U.S. Department of Agriculture, Natural Resources Conservation Service, 2019) were used. This suggests that the potential transmission of pesticides through surface runoff and contamination of surface waters is low in these areas. Further, policy direction for the riparian area buffer widths appropriate for perennial and intermittent streams was obtained from the Francis MarioneSumter National Forest plan (U.S. Department of Agriculture, Forest Service, 2004b). Several data acquisition and processing steps were previously conducted:
Acquire the timber stands and streams GIS data from the Francis MarioneSumter National Forest.
Select timber stands from a portion of the Long Cane Ranger District (District 3) and save them as a separate GIS database (Fig. 10.23).
Using a shapefile that represents the boundary of this area, acquire the U.S. Department of Agriculture SSURGO soils data from the Web Soil Survey (U.S. Department of Agriculture, Natural Resources Conservation Service, 2019), along with some information regarding the limitations of the different soil units. As one might imagine from this description of the problem, there is a need to employ several GIS processes that involve querying, buffering, intersecting, editing, and summarizing spatial data. One approach to address the analysis associated with this case study is to use the following steps (Fig. 10.24). 1. Query the timber stands GIS database for those polygons that have the appropriate range of ages, tree species, and land management emphasis. For the purposes of this case study, assume that these are loblolly pine (Pinus taeda) stands that have a

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FIGURE 10.23 Timber stands from a portion of the Long Cane Ranger District, Francis MarioneSumter National Forest. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021c).

year of origin from 2000 to 2018 (Figure 10.25). Save this set of polygons as a separate GIS database. Diversion 10.4 After conducting the query for loblolly pine stands that have an initiation date of 2000e2018, how many acres are in the returned set of stands? What proportion of the north half of the Long Cane Ranger District is represented by this set? Inspection 10.7 Are the loblolly pine stands that were initiated between 2000 and 2018 throughout the north half of the Long Cane Ranger District distributed randomly, systematically, or are they clumped together?

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FIGURE 10.24 Flow chart representing possible processing steps and GIS databases created and used to assess fertilization options.

2. Query the soils GIS database for those soil types that are reasonable candidates for forest fertilization (Table 10.7). Save this set of polygons as a separate GIS database (Fig. 10.26). The query might be composed in a manner such as this:

"MUSYM" ¼ ’ApB’ OR "MUSYM" ¼ ’CeB2’ OR "MUSYM" ¼ ’CoB’ OR "MUSYM" ¼ ’HsB’ OR "MUSYM" ¼ ’HyB2’ OR "MUSYM" ¼ ’IeA’

Diversion 10.5 After conducting the query for soil types that seem to be most appropriate for forest fertilization, how many acres are in the returned set of soils polygons? What proportion of the north half of the Long Cane Ranger District is represented by this set? 3. Intersect the reduced timber stands GIS database from Step 1 with the reduced soils GIS database from Step 2 to create a GIS database containing only those areas in commondthe appropriate forests and the appropriate soils (Fig. 10.27). Inspection 10.8 Of the pieces of polygons that remain after combining the soils and the timber stands GIS databases, are some of these too narrow to feasibly fertilize, or do some of them contain pieces that are too small for this purpose (Fig. 10.28)?

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FIGURE 10.25 Loblolly pine stands in a portion of the Long Cane Ranger District, Francis MarioneSumter National Forest with a year of origin from 2000 to 2018. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021c).

Table 10.7 Soil types to consider in the first approximation of area that might be fertilized within a portion of District 3 (Long Cane Ranger District) of the Francis MarioneSumter National Forest. Management unit symbol (MUSYM)

Map unit name

ApB CeB2 CoB HsB HyB2 IeA

Appling loamy sand, 2%e6% slopes Cecil sandy clay loam, 2%e6% slopes Coronaca sandy clay loam, 2%e6% slopes Hiwassee sandy loam, 2%e6% slopes Hiwassee sandy clay loam, 2%e6% slopes Iredell sandy loam, 0%e2% slopes

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FIGURE 10.26 Favorable soils for forest fertilization in a portion of the Long Cane Ranger District, Francis MarioneSumter National Forest. Credit: Soil data from the U.S. Department of Agriculture, Natural Resources Conservation Service (2019).

4. Buffer the streams an acceptable width to represent areas that should not be fertilized. According to the Sumter National Forest Plan, riparian zones are 100 feet (30.48 m) wide on either side of a perennial stream (200 feet total), and 50 feet (15.24 m) wide on intermittent streams. Ignoring other issues regarding functional pieces of the uplands that should also be considered, and ignoring any issues related to the attributes assigned to the spatial features in the Fcode field of the attribute table (Table 10.2), for this analysis, the streams GIS database will be subjected to a buffering process using these values (Fig. 10.29). To enable this process, a new attribute field might be added to the streams GIS database that represents the appropriate buffer width for each type of stream segment. Then, a variable width buffering process can be used to accommodate the different buffer widths for perennial and intermittent streams.

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FIGURE 10.27 Intersection of appropriate timber types and favorable soils for forest fertilization in a portion of the Long Cane Ranger District, Francis MarioneSumter National Forest. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021c), soil data from the U.S. Department of Agriculture, Natural Resources Conservation Service (2019).

5. Erase the stream buffer areas from the combined reduced stands/soils database from Step 3. This would be the next logical step in the process. However, upon inspection of the stream buffers and the areas of appropriate timber types and favorable soils for forest fertilization, there are no places across this landscape where they intersect (Fig. 10.30). Therefore, an erasure of the stream buffers from the appropriate timber types and favorable soils for forest fertilization would yield no change to appropriate timber types and favorable soils for forest fertilization. 6. Remove small polygons and edit others as necessary that would not reasonably be fertilized. Ultimately, the areas that could reasonably be fertilized, given the age of the pine trees and the quality of the underlying soils would be apparent. However, some adjustments needed to be made to reduce the size, shape, or number of polygons to reflect operational realities. Had this adjustment not been attended to, an overestimate of the potential areas that could be fertilized would have

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FIGURE 10.28 Intersection of appropriate timber types and favorable soils for forest fertilization in a portion of the Long Cane Ranger District, Francis MarioneSumter National Forest. Credit: Vegetation data from the U.S. Department of Agriculture, Forest Service (2021c), soil data from the U.S. Department of Agriculture, Natural Resources Conservation Service (2019).

FIGURE 10.29 Stream buffers for a portion of the Long Cane Ranger District, Francis MarioneSumter National Forest. Credit: Hydrology data from the U.S. Department of the Interior, Geological Survey (2021).

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FIGURE 10.30 Juxtaposition of stream buffers with an area containing the appropriate timber type and favorable soils for forest fertilization, for a portion of the Long Cane Ranger District, Francis MarioneSumter National Forest. Credit: Hydrology data from the U.S. Department of the Interior, Geological Survey (2021), vegetation data from the U.S. Department of Agriculture, Forest Service (2021c), soil data from the U.S. Department of Agriculture, Natural Resources Conservation Service (2019).

resulted. Further, when seasoned professionals view the map of the potential fertilization areas, they would rather easily see the operational difficulties arising from attempts to fertilize (with helicopters likely) small polygons and narrow pieces of polygons. Therefore, this editing step should not be discounted. 7. Recalculate the size of the remaining polygons, if necessary. 8. Determine the final potential set of forested areas that might be fertilized and estimate the cost of the effort. Translation 10.2 Develop a succinct statement that reflects the outcome of this analysis. Begin the statement with "In analyzing forest fertilization options for the north half of the Long Cane Ranger District, I . " [what did you do]. End the statement with " . and from this analysis it seems that ___ acres could possibly be fertilized." Reflection 10.4 After conducting the series of geographical processes that allowed a determination, roughly, of how much land area could be fertilized, how might the order of processing steps be changed? Could there have been other ways to arrive at the same solution rather than use the order of processes followed here?

Case study: forested area by management unit This case study involves the use of both raster and vector GIS databases for determining the resources (or conditions) contained within specific places across a landscape. For broader landscape analyses and policy discussions, the need may arise to determine the character of forested areas in specific or more general places. Knowledge of land cover

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types, forest cover types, natural features (e.g., rivers, elevation changes, etc.), and the built environment (e.g., roads, cities) would be essential for informing this type of analysis. Vector GIS databases that describe the physical boundary of an ownership (or watershed, or managed area) along with raster GIS databases that have been developed to describe broad physical features (land uses, precipitation patterns, etc.) provide analysts the means for obtaining estimates of resources (or conditions) contained within specific places across a landscape. As was described in Chapter 8, reclassifying a raster GIS database and processes for extracting necessary data can facilitate this analysis. There are several GIS databases that would be useful to obtain information about a specific place (a tract or property), or multiple scales of political boundaries (e.g., state, county, forest, stand, compartment, etc.). In this example, we will estimate the forested area, by compartment (management area), within the Allegheny National Forest. The National Land Cover Database (NLCD) (U.S. Department of the Interior, Geological Survey, 2018) will be utilized, but in practice, any classified raster database could be used assuming it was developed using a reputable process and was relatively accurate in estimating the land cover class of each place across a broad landscape. The following steps will aid in this process. 1. Obtain the 2016 (or more recent) NLCD for the entire continental United States. Inspection 10.9 Using your preferred GIS software, open the NLCD database and navigate to an area of the United States which you are most familiar. What are your impressions of the quality of the NLCD in this area? 2. Extract, or mask out, only those land cover grid cells (pixels) that fall within the boundary of the Allegheny National Forest (Fig. 10.31). Table 10.8 defines each land cover class within the extracted portion of the NLCD using a discrete value. Translation 10.3 You are having lunch with several of your colleagues, and the subject of GIS arises. Imagine that your colleagues are familiar with the processes used to manipulate vector GIS databases. However, during this conversation, you casually use the term mask out in relation to manipulating a raster GIS database. To ease some of the confusion that may be evident, very succinctly and clearly develop a statement that relates the process of masking out to similar process(es) used to manipulate vector GIS databases. 3. Query the attributes of interest or reclassify the raster dataset to obtain the desired land use classes. As is common in GIS analysis, there is more than one way to obtain forest cover grid cells than by simply isolating those grid cells classified as a forest. In this case, we are interested in forest cover by class (deciduous, evergreen, and mixed) and total forest cover. The options would be to:
Extract the attributes for classes. Select the grid cells with values 41 (deciduous forest), 42 (evergreen forest), and 43 (mixed forest).
Reclassify the raster GIS database to create new raster GIS databases for each forest cover type. Reclassify only the deciduous forest grid cells (class 41) to a

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FIGURE 10.31 NLCD classes within the Allegheny National Forest. Credit: Land cover data from the National Land Cover Database (U.S. Department of the Interior, Geological Survey, 2018).

Table 10.8 Land cover classes with the extracted portion of the NLCD (U.S. Department of the Interior, Geological Survey, 2018). Class value

Classification

11 21 22 23 24 31 41 42 43 52 72 81 82 90 95

Open water Developed, open space Developed, low intensity Developed, medium intensity Developed, high intensity Barren land Deciduous forest Evergreen forest Mixed forest Shrub/scrub Herbaceous Hay/pasture Cultivated crops Woody wetlands Emergent herbaceous wetlands

value of 1, and all other grid cells to values representing NoData. Repeat this process for evergreen grid cells and mixed forest grid cells. 4. Once the raster GIS databases have been prepared, a vector GIS database that represents the compartments (management areas) of the Allegheny National Forest

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can be used, along with a zonal statistics process, to estimate the number of grid cells that fall within each compartment. The end result would be a table containing a count (among other statistics including possibly the mean, standard deviation, and range) of the number of grid cells within each compartment boundary. This process is repeated using each raster GIS database that was created to represent each forest cover type. 5. The tables that are generated through the use of the zonal statistics process can then be joined to the vector GIS database representing the compartment boundaries using a field (a join item) that is present in both the compartments GIS database and the tables. The end result of this join process would be three vector GIS databases for each forest cover type that include the number of grid cells (the count) of each cover type for each compartment. Reflection 10.5 When one uses a join process in GIS, three basic things are needed. What are they? 6. In preparation for determining the area of each forest cover type within each compartment, a field should be added to the newly created databases. This field might be labeled "Area," or if the preferred unit is acres or hectares the field name might reflect the unit of area. As the end calculation will be an area measurement, thought should be given as to the type of field used (i.e., integer or float) and the precision necessary for the measurement. 7. Calculate the area of forest cover type within each compartment. Most GIS software systems have a function for calculating values within the attribute table. With the information obtained from the zonal statistics process, it is now possible to calculate area of each forest cover type. Here, the focus is only on the portion of the NLCD database extracted for this case study and assumes that the raster GIS database resulting from reclassifying the NLCD database has the same coordinate system as the compartment GIS database (i.e., NAD UTM Zone 17). Each raster grid cell has a 30 m spatial resolution; thus, the area of each grid cell is 900 square meters. Knowing this, the total area (square meters) for evergreen grid cells contained within a compartment can be calculated using the following relationship: 2

900  the count of evergreen grid cells within a compartment ¼ area (m )

The area estimate for acres would involve using the following relationship: 900  the count of evergreen grid cells within a compartment  0.000247 ¼ area (acres)

With this information, one can now produce maps illustrating area for each forest cover type for each compartment. If no other forest-based information were available, a resource manager could use the NLCD raster GIS database (or other similar raster database) to understand the distribution of forests contained in compartments throughout the Allegheny National Forest. From this case study analysis, they would find

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that compartments have between 26 acres (11.3 ha) and 9198 acres (3722 ha) of deciduous forests (Fig. 10.32). From this analysis, one would conclude that the Allegheny National Forest is composed of far less evergreen forests, from near zero (0) to approximately 903 acres (364.4 ha) in each compartment (Fig. 10.33). Mixed forests range from 3 acres (1.2 ha) to 5264 acres (2130 ha), with the majority located in compartments situated in the western to central portions of the forest (Fig. 10.34). Total forest cover by compartment ranges from approximately 260 acres (105.2 ha) to approximately 10,100 acres (4087 ha) (Fig. 10.35). This information could be useful for planning sales, resource allocation, management activities, timber bids, etc. Operationally, working circles or procurement areas for potentially locating forest product facilities operate in the same fashion. One could buffer the potential facility site and use the same methodology as described here to determine areas of land use cover, or species type, or volume of wood (and so on) if that data were available in a raster format (Fig. 10.36). Diversion 10.6 Determine the percentage (%) of total forest area within each compartment of the Allegheny National Forest and develop a high-quality thematic map to present this information. Diversion 10.7 Using the process described in this case study, or a different process of your own, develop a flow chart that clearly indicates the steps that need to be taken to integrate vector and raster GIS databases to answer the basic question, How much forest area might be contained within each compartment (management area) of the Allegheny National Forest?

FIGURE 10.32 Deciduous forest cover area estimates, by compartment, in the Allegheny National Forest. Credit: Land cover data from the U.S. Department of Interior, Geological Survey (2018), compartment boundaries from the U.S. Department of Agriculture, Forest Service (2021a).

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FIGURE 10.33 Evergreen forest cover area estimates, by compartment, in the Allegheny National Forest. Credit: Land cover data from the U.S. Department of Interior, Geological Survey (2018), compartment boundaries from the U.S. Department of Agriculture, Forest Service (2021a).

FIGURE 10.34 Mixed forest cover area estimates, by compartment, in the Allegheny National Forest. Credit: Land cover data from the U.S. Department of Interior, Geological Survey (2018), compartment boundaries from the U.S. Department of Agriculture, Forest Service (2021a).

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FIGURE 10.35 Total forest cover area estimates, by compartment, in the Allegheny National Forest. Credit: Land cover data from the U.S. Department of Interior, Geological Survey (2018), compartment boundaries from the U.S. Department of Agriculture, Forest Service (2021a).

Conclusions Situations will present themselves throughout the career of a forester and natural resource management professional where complex spatial analyses will be necessary to inform the decisions that must be made. Certainly, decisions can be made without any supporting analysis. Even today, somewhat irrational decisions such as these are made by professionals in the field. However, if enough time is available, if the appropriate data are available, if the GIS systems are available, and if the appropriate confident people are available, a potential management decision can be informed by analyses similar to those presented in this chapter. Addressing contemporary forest or landscape management issues may require that we think about problems in a different manner, using geographical processes to add, subtract, or manipulate space. Thinking about how to address complex landscape problems in the manner of a flow chart or other design diagram can help one select the data and GIS processes that are necessary to arrive at suitable answers. In today’s management environment, with the computer systems we can access, many of these complex problems can be addressed appropriately. Exercises exploring the concepts covered in this book using ArcGIS or QGIS are available on the book website: gis-book.uga.edu

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FIGURE 10.36 A process to determine forest type area for compartments in the Allegheny National Forest.

References Angelo, M., 1981. The recreation opportunity spectrum e a challenge to Canadian foresters. The Forestry Chronicle 57 (2), 55e56. Butler, R.W., Waldbrook, L.A., 1991. A new planning tool: the tourism opportunity spectrum. Journal of Tourism Studies 2 (1), 2e14. Cerveny, L.K., Blahna, D.J., Stern, M.J., Mortimer, M.J., Predmore, S.A., Freeman, J., 2011. The use of recreation planning tools in U.S. Forest Service NEPA assessments. Environmental Management 48 (3), 644e657. Clark, R.N., Stankey, G.H., 1979. The Recreation Opportunity Spectrum: A Framework for Planning, Management, and Research. U.S. Department of Agriculture, Forest Service. Pacific Northwest, Forest and Range Experiment Station, Portland, OR. General Technical Report PNW-98. Knight, H.A., McClure, J.P., 1969. South Carolina’s Timber, 1968. U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station, Asheville, NC. Resource Bulletin SE-13.

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Office of the Federal Register, National Archives and Records Administration, 2009. Code of Federal Regulations 33 Part 200 to End, Revised as of July 1, 2009, Navigation and Navigable Waters, Part 332Compensatory Mitigation for Losses of Aquatic Resources, Section 332.2 Definitions. U.S. Government Printing Office, U.S. Superintendent of Documents, Washington, D.C. Pennsylvania Fish & Boat Commission, 2021. Trout. www.fishandboat.com/Fish/PennsylvaniaFishes/ Trout/Pages/default.aspx (accessed 27.02.22). U.S. Department of Agriculture, Forest Service, 2004a. Appendix B: Minnesota National Forests ROS Mapping Criteria. www.fs.usda.gov/Internet/FSE_DOCUMENTS/fsm9_016231.pdf (accessed 27.02. 22). U.S. Department of Agriculture, Forest Service, 2004b. Final Environmental Impact Statement for the Revised Land and Resource Management Plan, Sumter National Forest. U.S. Department of Agriculture, Forest Service, Southern Region, Atlanta, GA. Management Bulletin R8-MB. U.S. Department of Agriculture, Forest Service, 2006. Hiawatha National Forest, 2006 Forest Plan. www. fs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb5106341.pdf (accessed 27.02.22). U.S. Department of Agriculture, Forest Service, 2007. Allegheny National Forest Record of Decision for Final Environmental Impact Statement and the Land and Resource Management Plan. U.S. Department of Agriculture, Forest Service, Eastern Region, Milwaukee, WI. U.S. Department of Agriculture, Forest Service, 2015. National Forest System Roads. https://data.fs.usda. gov/geodata/ (accessed 27.02.22). U.S. Department of Agriculture, Forest Service, 2021a. Allegheny National Forest Geospatial Data. www. fs.usda.gov/main/allegheny/landmanagement/gis (accessed 27.02.22). U.S. Department of Agriculture, Forest Service, 2021b. Chippewa National Forest Geospatial Data. www. fs.usda.gov/main/chippewa/landmanagement/gis (accessed 27.02.22). U.S. Department of Agriculture, Forest Service, 2021c. Francis Marion and Sumter National Forests Geospatial Data. www.fs.usda.gov/main/scnfs/landmanagement/gis (accessed 27.02.22). U.S. Department of Agriculture, Natural Resources Conservation Service, 2019. Web Soil Survey. https:// websoilsurvey.sc.egov.usda.gov/ (accessed 27.02.22). U.S. Department of Commerce, Census Bureau, 1994. Geographic Areas Reference Manual Chapter 12 Urban and Rural Classifications. U.S. Department of Commerce, Economics and Statistics Administration, Bureau of the Census, Suitland, MD. U.S. Department of Commerce, Census Bureau, 2021. Explore Census Data. https://data.census.gov/ cedsci/ (accessed 27.02.22). U.S. Department of the Interior, Geological Survey, 2018. National Land Cover Database. www.usgs.gov/ centers/eros/science/national-land-cover-database (accessed 27.02.22). U.S. Department of the Interior, Geological Survey, 2021. National Hydrography Dataset. www.usgs.gov/ national-hydrography/national-hydrography-dataset (accessed 27.02.22).

11 Professional practices Introduction Professional standards govern the conduct of people working in their chosen field. They involve knowledge of a subject area, skill in conducting the work in that subject area, practical experience, and professional values (Fig. 11.1). These standards may have been developed by an individual, an organization (e.g., company), a government entity (e.g., state or country), or a professional society. They often appear as part of a code of standards or protocols to manage workflow, work quality, and interactions with the public, and often they are inspired by issues of privacy, accuracy, access, and ownership of intellectual property (Varrax, 2016). Professional standards serve several purposes. One purpose is to promote the presence of the most qualified people in an area of practice, by indicating their association with a group of similarly minded professionals. Another purpose is to indicate that there are guidelines for applying practices in the FIGURE 11.1 A general conceptual model of the issues of concern to professional standards.

Geographic Information System Skills for Foresters and Natural Resource Managers. https://doi.org/10.1016/B978-0-323-90519-0.00012-1 Copyright © 2023 Elsevier Inc. All rights reserved.

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conduct of important work processes. These guidelines may include both the technical and commercial components of one’s work. On a general level, there are a number of symbols or reminders of the need for professional practice, ethics, and character in different fields of work. For example, upon graduation from many medical schools, the ceremony often includes the doctor’s pledge, or the Hippocratic Oath, “. with the promise to do no further harm to their patients.” As another example, in Canada, engineers are awarded a ring upon graduation to symbolize belonging to the engineering profession. The iron ring ceremony is called the Ritual of the Calling of the Engineer and was developed in response to the collapse of a bridge in Quebec that cost the lives of 75 people due to faulty engineering. While undergoing this ritual does not certify a person as a professional engineer in Canada, the ring acts as a reminder of the duty that the profession owes the public for its sound practice. These small rings are commonly worn by Canadian engineers on the little finger of their dominant hand for the remainder of their professional lives. Reflection 11.1 If you were charged with developing a symbol for geographic information system (GIS) practitioners, one that indicates that the practitioner is well-trained, knowledgeable, and conscious of the high value of their work to society and to their employer, what would this symbol look like?

Professional standards One broad example of professional standards involves professions that require credentialing from a national or state government in order to practice in an area. For example, registered foresters in the State of Georgia must demonstrate the educational and experiential requirements of the Georgia State Board of Registration for Foresters, provide five character references, and pass an examination. Codes of practice are also often produced through the creation of a license. These codes both restrict the people who are legally allowed to practice in a profession and impose a set of duties on the license-holders. Ultimately, systems involving credentialing and licensure are often designed to address and promote public welfare. Thus, in some cases, formal education and work experience are not sufficient enough for a person to practice their trade in certain fields. The right to practice may only be allowed by obtaining a license from a governing organization, which often requires an application that illustrates a person’s case. In addition to the licensing of foresters in some states of the United States, other common licenses are held by medical doctors (through medical boards), lawyers (who must pass a bar examination before they are allowed to practice), engineers, surveyors, nurses, architects, auctioneers, pilots, and others. Licenses that may heavily involve the use of GIS practices include several of these, such as engineering and surveying. The rapid advancement of drone photography has recently required licenses issued by the

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U.S. Federal Aviation Administration (FAA) for people in the United States who want to use these in commercial operations. Another broad example of professional standards can be found in association with voluntary professional organizations. These types of standards are usually applied at a national or international level but can also arise from state- or province-level organizations that are self-organized by their professions. Although some qualifications based on education and experience may limit who can join these organizations, membership in these organizations is voluntary; therefore, they do not restrict access to the profession through the need to obtain a license. These organizations provide many services for their members, such as education, lobbying for governments, or accreditation of academic programs. These organizations also often provide a Code of Ethics that members agree to abide by in the conduct of their work activities and profession. Some examples of voluntary organizations that are self-organized by profession include the Society of American Foresters (SAF), the Association of American Geographers (AAG), the American Fisheries Society (AFS), and the Wildlife Society (TWS). One example of a voluntary Code of Ethics was developed by the Society of American Foresters. This code was designed to protect and service society by providing guidance for the conduct of professional practices. The preamble of the Society of American Foresters code of ethics states: The purpose of this Code of Ethics is to protect and serve society by inspiring, guiding, and governing members in the conduct of their professional lives. Compliance with the code demonstrates members’ respect for the land and their commitment to the long-term management of ecosystems and ensures just and honorable professional and human relationships, mutual confidence and respect, and competent service to society. Society of American Foresters (2000).

This Code of Ethics includes six principles: (1) managing land for current and future generations, (2) respecting landowners’ objectives, (3) using science to manage forests, (4) using science to guide policy development, (5) pledging to base personal conduct on honesty, open communication, and respect, and (6) pledging to base professional conduct on honesty, fairness, goodwill, and respect for laws. Thus, membership requires a continual honoring of these Codes of Ethics. Diversion 11.1 Access the Society of American Foresters Code of Ethics through your preferred Internet browser. Briefly, how do you think that the use of GIS could be associated with the six principles noted in the Code? GIS professionals obtain their training from a variety of educational disciplines, and therefore they can be members of many different professional organizations that are

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often closely associated with their field of education. The American Society of Photogrammetry and Remote Sensing (ASPRS) encourages the membership of people from various fields who meet certain geospatial educational requirements. As an example, a common element of work involving GIS includes photogrammetry, and professional photogrammetrists are people with expertise in building analytical models from images and then collecting detailed measurements from those images. In North America, these people are often members of the ASPRS. These types of professional programs may encourage their members to obtain certificates by successfully completing exams, which help identify particular competencies among their members. These types of professional programs may also require their members to obtain a certain amount of continuing education credit within certain periods of time (e.g., 20 credits within a 3-year period) to maintain membership or certification status. However, these efforts rarely provide an exclusive right to practice in these areas. Inspection 11.1 Using the Internet, navigate to the website of the professional organization that most closely aligns with your field of study. If it is available, access the Code of Ethics. How many principles does the Code of Ethics contain? Are there any interesting aspects of the Code of Ethics that surprise or concern you? As of the writing of this book, there are a few professional societies devoted specifically to GIS professionals, although some have argued that a specific code is needed for those who use GIS during the course of their normal work activities (Varrax, 2016). One is the GIS Certification Institute (GISCI), where a person can seek certification as a GIS professional, following a portfolio review and a comprehensive exam, having met the minimum standards for ethical conduct and professional practice established by the GISCI (Fig. 11.2). There are four member organizations in the GISCI: the AAG, the National States Geographic Information Council (NSGIC), the University Consortium of Geographic Information Science (UCGIS), and the Urban and Regional Information Systems Association (URISA). GISCI certification is endorsed by several states in the United States, such as California, Ohio, New Jersey, Oregon, and North Carolina. In addition, the National Association of Counties (NaCo) supports GISCI and the recognition of GISP certification. Although it is estimated that about 675,000 people employed in the United States are considered geospatial specialists, as of December 2021, there were 5314 active GISPs in the United States (GIS Certification Institute, 2021a). Thus, there seems to be a need for increased recognition of the skills possessed by GIS professionals. The GISCI has developed a GIS Code of Ethics to guide GIS professionals and help them make the most appropriate and ethical choices in the course of their work activities. The Code of Ethics (GIS Certification Institute, 2021b) includes four sections that describe (1) obligations to society (do the best work possible, contribute to the community, speak out about issues), (2) obligations to employers and funders (deliver quality work, have a professional relationship, be honest in representations), (3) obligations to

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FIGURE 11.2 A certificate is awarded to a certified GISP stating that the recipient meets ethical and professional standards established by the GISCI.

colleagues and the profession (respect the work of others, contribute to the discipline to the extent possible), and (4) obligations to individuals in society (respect privacy, respect individuals). The Code provides a number of examples of positive actions for moral and ethical behavior of GIS professionals, and obligations to society are of most importance when there are conflicts with other obligations in the Code. Inspection 11.2 Access the GIS Certification Institute through your preferred Internet browser. Locate and review the minimum requirements for obtaining GISP certification. What are the requirements for obtaining GISP certification? At this point in time, would you be interested in seeking GISP certification? Why, or why not? Corporate Codes of Ethics have been developed by many companies as well. A corporate Code of Ethics, or code of professional conduct, often attempts to link business interests and corporate rules with employee ethics and legal compliance (legislation) and are in many cases touted in a manner to enhance an organization’s reputation, status, and identity (Adelstein and Clegg, 2016). In essence, they are designed to formally

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outline and steer the expected behavior of employees, perhaps motivated by a need to illustrate their commitment to ethical practices to people both inside and outside of an organization (van Zolingen and Honders, 2010). Many of these types of programs are designed to reduce the chance or opportunity for unethical behavior on the part of an employee (Jannat et al., 2022); however, it has been argued that the benefit of having a corporate Code of Ethics is unclear, and should not simply be used as window dressing favoring the public stature of an organization (Duong et al., 2022). Nonetheless, noncompliance with organizational codes of professional conduct may at times lead to dismissal from an organization, which is why we mention it here. Regardless of the source of a Code of Ethics, or a code of professional conduct, some of the more important functions of these include (from Varrax, 2016):








They They They They They They They

enhance a profession’s reputation and social trust. may deter unethical behavior. provide professional solidarity and purpose. function as a basis for public expectations. provide legitimate support against improper demands. serve as a basis for adjudicating disputes. are a moral anchor for the practitioner.

Potential legal issues involving GIS When conflicts occur concerning professional practices, there is often a need to resolve them in a court system. The overlap in potential analytical skills between a GIS professional and a land surveyor is one example of where potential legal issues may arise. In Florida, for example, a professional land surveyor and mapper is one who: . determines and displays the facts of size, shape, topography, tidal datum planes, legal or geodetic location or relation and orientation of improved or unimproved real property through direct measurement or from certifiable measurement through accepted photogrammetric procedures. Florida Department of Agriculture and Consumer Services (2022)

Even though property boundaries can easily be mapped or developed in GIS software, they are likely not developed in such a way that the map creator can attest to the quality of the property description, and therefore map products should be accompanied by a disclaimer indicating as such. One example of a real legal issue involving the interface of GIS and land surveying involved a GIS company that developed a process to convert legal descriptions of real property to simple maps that indicated property lines overlaid on satellite images. The company then offered these maps for sale to banking institutions as low-cost alternatives to formal land surveys for land appraisals. While the company defended its practice as

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falling under U.S. First Amendment rights, arguing that using existing information to create new information is a protected type of speech, the Mississippi Board of Licensure for Professional Engineers and Surveyors sued them to stop them from practicing this business model, accusing them of practicing surveying without a license (Mississippi Board of Licensure for Professional Engineers and Surveyors v. Brent Melton and Vizaline, LLC Case: 45CH1:17-cv-00902 (2017); Vizaline, LLC v. Tracy et al. 949 F.3d 927 (2020)). Ultimately, it was determined that using public information to draw property lines on satellite images is a form of free speech, and a person does not need a professional license to do so (Powers, 2020). However, this legal issue was born out of the unclear distinction between what a surveyor is licensed to do, and what a GIS professional is able to do with GIS software. Diversion 11.2 Imagine that you and two of your friends have created a new business, Polygons, Inc. As a group, you decide to create a set of professional principles to guide your business. Each principle begins with In the course of our work we shall seek to . What are five principles you would include in this set of professional principles? With respect to potential legal issues, conflicts concerning who is allowed to do certain types of mapping work can involve many different professions. For example, in California, a civil engineer has the following authority: California’s Professional Engineers Act (xx 6700e6799), 6731.1. Civil engineeringdadditional authority for engineering surveying Civil engineering also includes the practice or offer to practice, either in a public or private capacity, all of the following: (a) Locates, relocates, establishes, reestablishes, or retraces the alignment or elevation for any of the fixed works embraced within the practice of civil engineering, as described in Section 6731. (b) Determines the configuration or contour of the earth’s surface or the position of fixed objects above, on, or below the earth’s surface by applying the principles of trigonometry or photogrammetry. (c) Creates, prepares, or modifies electronic or computerized data in the performance of the activities described in subdivisions (a) and (b). (d) Renders a statement regarding the accuracy of maps or measured survey data pursuant to subdivisions (a), (b), and (c). Thus, one interpretation of this Act involves the creation of a detailed contour map, an effort traditionally performed by mathematically fitting multiple images together in a computer and publishing an associated error for the work. Once the model is developed, ground points are collected by a stereo compiler, generating a series of X, Y, and Z coordinates. This type of detailed work is considered to be within the realm of engineering or surveying in most jurisdictions, as it uses the principles of mathematics in proposing an engineering solution. The U.S. Geological Survey (USGS) performed much of this work to create topographic maps for much of the United States. As a GIS analyst, a person may need to represent the contours of a landscape to determine sites that have a ground slope of less than 60%. As this task is in the planning stage, a team may decide to

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use the appropriate USGS quadrangle map and digitize the contour data, which then becomes the basis of the project. Some may agree that the construction of the original contour information by the USGS was engineering or surveying in nature, while the tracing of the contour lines was not, and therefore could be performed by an unlicensed professional. The argument is that the GIS professional is not creating the original data in this example, the contour data. Therefore, it may be seen as an acceptable practice that would not need to be regulated by an engineering or surveying license. Building off of the previous example, assume that a person has a sophisticated drone that is equipped with a light detection and ranging (LiDAR) sensor. The person establishes a high-quality, local ground control position using a survey-grade global positioning system (GPS) receiver capable of subcentimeter positional accuracy. The person then uses the drone to fly over the area of interest, capturing the LiDAR data. The data is then used in a GIS to develop the contour model that was desired. Even though the collection and processing of the data using mathematical principles remain a practice of engineering or surveying, many GIS professionals can perform this work given the training they have accumulated. Unfortunately, the use of this technology to create a product typically under the realm of other licensed professionals can potentially cause a conflict that may eventually need to be litigated to resolve. Reflection 11.2 Do you feel that a person needs a license to work on projects that develop highly accurate positioning information through the collection of LiDAR and GPS data? Explain your position. Looking forward, those using crowdsourced GIS databases may also face legal issues (Olteanu-Raimond et al., 2017; Schmidt et al., 2021). Issues concerning private property rights and the violation of privacy arise when information is shared about people without their permission. The ability to identify individuals in photos with geotags, however rare or random this seems, has become an issue of concern to some. Information related to disease surveillance, particularly, can be of great concern to one’s privacy as the level of information collected about a person, and the potential for private information to be disclosed publicly can breach the rights of individuals (Blatt, 2015). A distinction of importance here is whether geographic information of a personal nature has been volunteered by the affected individual or contributed by others. Ownership of the data, copyright issues, and permissions to use works developed by others can also arise. Since different data and information are likely brought together to form a crowdsourced GIS database, different rights to the data and information may need to be observed. Finally, the legal liability of a person or organization for damage incurred due to decisions made that were based on crowdsourced data may be unclear. Given the uncertain nature of the quality of volunteered information, the use of it in practice would need to be highly qualified.

Professional responsibility Professional responsibility suggests that one should practice their trade or work effectively in their field with regard to both their clients and to society in general, even though

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at times the demands of efficiency and competitiveness result in conflicting decisions regarding the conduct of work (Solbrekke and Englund, 2011). One unmistakable fact of professional life involves the interaction of GIS professionals with people from other professions both inside and outside of natural resource management. Given the discussion presented earlier, perhaps there needs to be more interaction between leaders of the GIS profession and state-level surveying and engineering boards to help guide these businesses with clarity concerning appropriate work products each can produce. This interaction may mitigate the expense of entering into costly litigation and help develop processes and protocols where all professions can benefit from the expanding geospatial technologies. Further, care should be taken by each GIS professional so as to not practice in restricted fields where licensing is required, as this may result in civil penalties and professional embarrassment when cases become public. Another area of professional responsibility involves how one conducts their commercial business. Obviously, we would expect everyone to operate their business legally, not commit fraud, not violate contracts, and pay the appropriate taxes associated with each transaction. Along these lines, if a person decides to work for themselves as a small business, they may want to form a more complex legal entity rather than a sole proprietorship. Depending on the government in which the person operates, certain types of business structures may act to protect the business, and various personal assets, from being lost due to poor or negligent work. Establishing a business can be a complex endeavor, and guidance from other professionals is therefore suggested. At a minimum, a GIS professional may need to enlist the support of an attorney skilled in assisting small businesses through contracts and human resource issues to create the desired business entity. Second, a GIS professional may need an accountant to supervise their finances, ensure taxes are correctly paid, and correctly manage expenses related to the wages of other employees in the company. Lastly, a GIS professional may need a commercial insurance broker to provide advice concerning the appropriate workers-compensation insurance that covers accidents in the workplace. Other types of insurance include general liability insurance for nonemployees that may be injured in the workplace, and professional liability insurance (similar to malpractice insurance held by doctors or lawyers). The latter protects a business against a mistake made in the work, for example, including misplaced gas lines or powerlines on a map that others rely upon for sound information. This mistake could lead to unexpected damages to others’ property as well as fire suppression costs, which may bankrupt a business or an individual. Translation 11.1 Using the Internet, access the Protected Areas Database of the United States (PADUS Viewer). What does the disclaimer on the map viewer indicate? In addition to the conflicts noted earlier in this chapter, other types of conflicts of interest can also be encountered while operating a GIS business. For example, a conflict of interest may involve cases where a GIS professional represents two different clients

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while working on similar projects. One of the two clients may accuse the GIS professional of favoring the other client (in terms of effort or quality). In addition, working in the GIS profession can cause a person to produce significant amounts of data. Therefore, a common question arises: Who owns the data? Often, the owner is actually the client (the person or organization who paid for the work), but the owner should be clearly defined in contracts that define the scope of the work. Another conflict of interest can occur when a new project is initiated, where a GIS professional uses data created under a previous project (owned by a different client) to address the new work. This may act to violate a confidentiality agreement with the first client. Therefore, before data is shared among different clients, an agreement should be reached regarding the owner, and regarding what can be shared. Finally, an element of professional practice that is often included in a professional Code of Ethics involves self-limitation of one’s practice. A GIS professional should engage in work where they have the professional skills and experience to successfully complete. Thus, if one does not have the training or experience in an area of GIS that is important to a project, and some allowance for gaining that training and experience is not stipulated in a statement of work, it would be professionally irresponsible to attempt such work.

Conclusion Professionalism should guide the work of a GIS analyst (or forester, or natural resource manager) throughout their career. Measures of professionalism can involve an association with a professional society or the attainment of a state license. The multidisciplinary nature of using GIS crosses many professional disciplines, yet recently there have arisen GIS-specific societies and certification programs that can guide professional practices. Admittedly, the rapid development of spatial data technologies may lead to increasing conflicts between GIS professionals and engineering and surveying professionals, who have a long history of collecting data with an established positional quality that addresses the needs of society. These conflicts can only be addressed through communication and cooperation amongst professional groups. Finally, developing a GIS business may require working with additional professionals to guide the business. These interactions may be necessary to avoid the numerous instances where a conflict of interest can arise. Often, these conflicts can be avoided by respecting the ownership of the data.

References Adelstein, J., Clegg, S., 2016. Code of ethics: a stratified vehicle for compliance. Journal of Business Ethics 138 (1), 53e66. Blatt, A.J., 2015. Health, Science, and Place: A New Model. Springer International Publishing, Cham, Switzerland.

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Duong, H.K., Fasan, M., Gotti, G., 2022. Living up to your codes? Corporate codes of ethics and the cost of equity capital. Management Decision 60 (13), 1e24. Florida Department of Agriculture and Consumer Services, 2022. Surveyor/Mapper Licensing. https:// www.fdacs.gov/Business-Services/Surveyors-and-Mappers/Surveyor-Mapper-Licensing (accessed 06.01.22). GIS Certification Institute, 2021a. GIS Certification Institute. www.gisci.org/ (accessed 17.01.22). GIS Certification Institute, 2021b. GIS Certification Institute. www.gisci.org/Ethics/CodeofEthics.aspx (accessed 17.01.22). Jannat, T., Alam, S.S., Ho, Y.-H., Omar, N.A., Lin, C.-Y., 2022. Can corporate ethics programs reduce unethical behavior? Threat appraisal or coping appraisal. Journal of Business Ethics 176 (1), 37e53. Olteanu-Raimond, A.-M., Hart, G., Foody, G.M., Touya, G., Kellenberger, T., Demetriou, D., 2017. The scale of VGI in map production: a perspective on European national mapping agencies. Transactions in GIS 21 (1), 74e90. Powers, M., 2020. Innovative Mississippi Analytics Firm Free to Expand its Business. https://ij.org/pressrelease/innovative-mississippi-analytics-firm-free-to-expand-its-business/ (accessed 17.01.22). Schmidt, F., Dro¨ge-Rothaar, A., Rienow, A., 2021. Development of a Web GIS for small-scale detection and analysis of COVID-19 (SARS-CoV-2) cases based on volunteered geographic information for the city of Cologne, Germany, in July/August 2020. International Journal of Health Geographics 20, Article 40. Society of American Foresters, 2000. SAF Code of Ethics. www.eforester.org/SESAF/CodeofEthics.aspx (accessed 17.01.22). Solbrekke, T.D., Englund, T., 2011. Bringing professional responsibility back in. Studies in Higher Education 36 (7), 847e861. van Zolingen, S.J., Honders, H., 2010. Metaphors and the application of a corporate code of ethics. Journal of Business Ethics 92 (3), 385e400. Varrax, F., 2016. Beyond professional ethics: GIS, codes of ethics, and emerging challenges. In: Delgado, A. (Ed.), Technoscience and Citizenship: Ethics and Governance in the Digital Society. Springer International Publishing, Cham, Switzerland, pp. 143e161.

Appendix: questions 1. ____ is the art and science of collecting information about an entity or phenomenon (a landscape) without having direct contact with the entity or phenomenon. a. cartography b. forestry c. sustainability d. remote sensing 2. Technically, a ____ is one that is specifically able to store, access, analyze, manipulate, and visualize spatial and nonspatial data. a. cloud server b. global positioning system c. geographic information system d. forest inventory database 3. When one’s interest lies in lands other than national-level lands, such as the U.S. national forests, GIS databases are generally ____. a. free of charge b. unavailable c. of poor accuracy d. old 4. Facts, estimates, synthesized knowledge, statistics, and real-world observations are the ____ we use to describe a situation or system and to make decisions. a. computing machines b. conjectures c. opinions d. data 5. ____ GIS data include point, line, and polygon features. a. raster b. modular c. vector d. synthetic 6. Computer-based GIS software has been in development in various companies and organizations around the world for about ____ years. a. 10 b. 20 c. 50 d. 100

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7. A ____ GIS database can be envisioned as a perfect sheet, quilt, or mosaic of grid cells. a. vector b. raster 8. Centralized GIS databases managed by a GIS department, official records of a land management organization, might be considered ____ databases. a. communal b. corporate c. environmental d. open 9. A ____ helps one describe their position on Earth, relative to other places. a. map legend b. north arrow c. map reference system d. map color scheme 10. A grid cell in a raster GIS database might also be referred to as a pixel, which is contraction of the term ____. a. partially external element b. pitted element c. picture element d. Pixar element 11. Depending on your field of work, a ____ may have been developed and could contain principles and pledges related to honesty, fairness, good will, and respect for laws. a. regulation b. code of ethics c. federal law d. local ordinance 12. Much, but not all, of the GIS data developed to support national-level land planning in the United States is considered to be ____. a. proprietary b. old c. of poor quality d. in the public domain 13. Perhaps the best spatial feature one can use to represent the location of a northern spotted owl (Strix occidentalis) nest is a(n) ____. a. grid cell b. point c. polygon d. line

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14. Through the GIS Certification Institute (GISCI), a person with the appropriate training and experience can seek certification as a ____. a. registered forester b. certified photogrammetrist c. mapping expert d. GIS professional 15. A single coordinate pair of latitude and longitude can best be used to describe the location of ____. a. a 1000 acre forest reserve b. a red cockaded woodpecker nest tree c. a 2 mile stretch of the Willamette River in Oregon d. the Okeefenokee Swamp in Georgia 16. Each grid cell in a raster GIS database has a(n) ______ shape. a. interesting b. oblong c. regular d. indescribable 17. ____ is one of the primary energy sources for remote sensing endeavors in forestry. a. Hemp b. The Sun c. The Three Mile Island nuclear power plant d. Coal 18. Computer bits that store GIS data are ____. a. disposable b. tradeable c. binary d. tragic 19. Which of these is NOT true about using a GIS computer program to make maps? a. The symbology (symbols, colors, text) of a map can be adjusted easily b. Maps can be easily reprinted c. Maps often have a less professional appearance than hand-drawn maps d. Map files can be shared with other people who use the same software 20. Internal computer hard drives, a graphics card for monitors, and a printer are three examples of GIS ____ components. a. software b. cloud storage c. hardware d. accessary

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21. ____ are the most common shapes of grid cells in raster GIS databases. a. Triangles b. Squares c. Pentagons d. Hexagons 22. The development of GIS databases can amount to as much as ____ of a GIS program budget when personnel time and acquisition costs are considered. a. 1 b. 10 c. 25 d. 80 23. A series of coordinates that are connected, but do not form a closed area, can best be used to describe the location of ____. a. the Great Dismal Swamp in Virginia b. a 1000 acre forest reserve c. a northern spotted owl nest tree d. a 1000 meter stretch of the Wabash River in Indiana 24. Perhaps the best spatial feature one can use to represent the location of the Pend Oreille Valley Railroad in eastern Washington State is a(n) ____. a. grid cell b. point c. polygon d. line 25. On a computer, 8-bit equals one ____. a. vide b. franc c. bitcoin d. byte 26. The models one can use to describe the Earth’s surface include ____, ____, and ____. a. ellipsoid, datum, geoid b. ellipsoid, datum, passoid c. ellipsoid, graticule, passoid d. ellipsoid, graticule, geoid 27. ____ is the art of making maps. a. Mapography b. Photogrammetry c. Cartography d. Topography

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28. A(n) ____ should be employed to establish whether the resulting GIS database meets the needs initially outlined in the scoping process. a. app b. verification process c. overlay process d. satellite 29. Of the following options, ____ ideally should best guide the work of a GIS analyst throughout their career. a. professionalism b. public opinion c. politics d. freedom 30. If one were developing a raster GIS database, which of the various data creation methods would one usually NOT employ? a. scanning b. capturing an aerial image c. capturing a drone image d. heads-up digitizing 31. In order to physically print a large map, ____ would seem necessary. a. a plotter b. cloud storage c. extra computer RAM d. a digitizing table 32. In describing the shape of the Earth, an ellipsoid, which is essentially a sphere, is flattened by about ____ at the poles. a. 5 feet b. 100 meters c. 3 miles d. 20 kilometers 33. A geoid is irregularly shaped since it attempts to approximate ____ in a manner perpendicular to forces of gravity caused by variations in the density of the Earth. a. minute crenulations b. multipath error c. declination d. mean sea level 34. If a polygon had straight sides, it would need at least ____ of them to form a closed area. a. 1 b. 2 c. 3 d. 4

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35. For a raster GIS database capable of using 8-bit to describe the attribute of a grid cell, it allows one to associate up to ____ values with each grid cell. a. 8 b. 64 c. 256 d. 1024 36. When a sensor sends out an energy signal, then receives and interprets a reflected portion of it, it is terms a ____ sensor. a. passive b. smart c. photovoltaic d. active 37. Perhaps the best spatial feature one can use to represent the location of Walden Pond near Concord, Massachusetts is a(n) ____. a. grid cell b. point c. polygon d. line 38. A datum is essentially a ____ smooth model of the Earth’s surface developed from a large volume of Earth surface measurements. a. politically b. socially c. mathematically d. biologically 39. Within GIS, a characteristic of a spatial feature, often stored in a table, is often called a(n) ____ of that feature. a. attribute b. reflection c. opinion d. fact 40. In terms of the electromagnetic spectrum, which band of energy has the shortest wavelengths? a. red b. green c. blue d. infrared 41. Each grid cell in a raster GIS database is ____. a. homogenous in describing a spatial feature b. divisible into smaller shapes c. balanced against nine others around it d. cleaned each time before being used

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42. The WGS 84, or ____, is a global datum that acts as both an ellipsoid model and a datum. a. World Geodetic System of 1784 b. World Geodetic System of 1984 c. World Geographic System of 1984 d. World Geologic System of 1984 43. Most directly, computer-based map making is facilitated by a(n) ____. a. growth and yield model b. geographic information system c. harvest scheduling model d. inventory management system 44. ____ can be used to describe past, present, and future landscape conditions, and when offered through the Internet, can include real-time information such as weather, traffic, earthquakes, and many other dynamic events. a. Maps b. Inventories c. Harvest schedules d. Global positioning systems 45. The North American Datum of 1983 (NAD 83) is considered a ____ datum. a. global b. local c. editable d. supplemental 46. An aerial image composite of a county in California indicates that its size is 2 GB. What is GB? a. Geobit b. Geobyte c. Gigabit d. Gigabyte 47. There are a number of concerns one would want to consider when selecting a GIS software program, ____ is probably the least important of the ones noted below. a. cost b. documentation c. physical location of company d. training needed 48. A systematic transformation of positions from an irregularly shaped surface, the Earth’s surface, to a mathematically derived surface, is called a ____. a. projection b. selection c. buffer d. declination

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49. An ____ map projection attempts to maintain the relative sizes of areas even though it cannot completely remove distortions of mapped features. a. equal direction b. equal orientation c. equal shape d. equal area 50. A(n) ____ map projection attempts to preserve relative angles and shapes of land areas. a. equal area b. conformal c. equidistant d. cubic 51. As their basic act, maps communicate a(n) ____ to an intended audience. a. ownership claim b. emotion c. preference d. message 52. In terms of the electromagnetic spectrum, which band of energy has the shortest wavelengths? a. thermal b. gamma c. visible d. infrared 53. A DEM, with respect to mapping, is a ____. a. downstream elevation model b. damage evaluation module c. digital elevation model d. disturbance evaluation module 54. One megabyte of GIS data is ____ larger than 1 kilobyte of GIS data. a. 2 b. 10 c. 100 d. 1000 55. Perhaps the best spatial feature one can use to represent the diverse levels of precipitation across the State of Colorado is a(n) ____. a. grid cell b. point c. polygon d. line

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56. If you wanted to physically select a landscape feature from within a GIS software program, you could ____. a. use the mouse of your computer b. request the feature using an ordering system c. contact the user support group d. capture the screen and copy the result into a new window 57. Natural color composite images uses ____ to depict a landscape as one would see it naturally. a. red, green, and infrared bands of electromagnetic energy b. red, green, and microwave bands of electromagnetic energy c. red, green, and ultraviolet bands of electromagnetic energy d. red, green, and blue bands of electromagnetic energy 58. A line following, or ____ process is often used to develop a vector GIS database. a. scanning b. digitizing c. overlaying d. merging 59. The rules that regulate the interactions of the geometries associated with vector data models compose the ____. a. topography b. bathymetry c. sociology d. topology 60. The term heads-down digitizing is different than heads-up digitizing due to ____. a. the need for a special digitizing table and digitizing puck b. the need for special glasses c. the need for a special chair d. the need for a special computer 61. A(n) ____ map projection attempts to preserve the scale of Earth features. a. equal area b. conformal c. equidistant d. cubic 62. When using a ____ map projection, the line of tangency with Earth is usually the Equator. a. conic b. conformal c. cylindrical d. cubic

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63. As the size or dimension of grid cells in a raster GIS database increases, features on a real-life landscape or water body become ____. a. more revealing b. more accurate c. more precise d. more difficult to identify 64. A ____ is a common topology error. a. multipath b. dangle c. mathematical mistake d. sliver 65. When using a raster GIS database, a(n) ____ might be used to extract (clip) raster data that represents a specified area of interest. a. aperture b. mask c. coat d. placard 66. The process of associating a map with a digitizing table is referred to ____. a. introduction b. convolution c. permutation d. registration 67. It is possible to open and view a raster image in GIS software and use it as a(n) ____ for head-up digitizing, where spatial features can be drawn. a. anagram b. base map c. replicate d. hidden layer 68. A raster GIS database contains ____. a. points b. lines c. polygons d. grid cells 69. The ____ resolution of a raster GIS database refers to the number of differentiable levels or values of data. a. radiometric b. spatial c. spectral d. temporal

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70. The ____ resolution of a raster GIS database refers to size of the grid cells. a. radiometric b. spatial c. spectral d. temporal 71. One raster GIS database that contains elevation information might be a ____. a. ASCII b. BIL c. CMOS d. DEM 72. A ground slope value, in percentage terms, often thought of as the ____ over the ____. a. latitude, longitude b. northing, easting c. rise, run d. aspect, orientation 73. The direction "southwest" that might be derived from an analysis of a DEM, refers to a(n) ____. a. aspect b. bearing c. caliper d. declination 74. Most often, a map developed within GIS software provides the map reader with a ____ perspective of the landscape. a. horizontal b. 3-dimensional c. vertical d. hyperspectral 75. A panchromatic image displays landscape features using a(n) ____. a. vector format b. small scale c. gray scale d. infrared transformation 76. Transforming the values of grid cells in a raster GIS database from forest type classes to discrete numeric value classes might involve a ____ process. a. generalization b. overlay c. split d. reclassification

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77. Mathematical operations conducted on the value of grid cells in a raster GIS database might involve a ____ process. a. union b. intersect c. buffer d. map algebra 78. The Mercator map projection is an example of a ____ projection. a. conformal b. cubic s. conic d. cylindrical 79. Someone of great value to the management of a GIS program is an IT professional. What does IT stand for? a. internal topography b. internal technology c. information technology d. information topography 80. Grid cells within a raster GIS database ____. a. can be different sizes b. can be different shapes c. can contain a spectral reflectance value d. can overlap 81. A 3-dimensional representation of a continuous surface commonly used for representing elevation, and comprised of nonuniformly shaped triangles, is called a ____. a. UTM b. TIN c. TPS d. TEM 82. Within GIS software programs, each individual feature, such as a polygon, is represented by ____ rows within the associated attribute table. a. 0 b. 1 c. two d. 10 83. ____ imagery is based on laser pulses from an active remote sensor. a. Sentinel b. Landsat c. LiDAR d. ASTER

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84. On a map illustrating the state forests of Vermont, the ____ would best provide a person a sense of orientation. a. legend b. scale c. north arrow d. title 85. Some GIS software programs also have the ability to allow one to physically select landscape features using ____. a. circles b. hexagons c. irregularly shaped polygons d. lines 86. (Age 300) is an example of a(n) ____. a. spatial join b. union process c. attribute query d. split process 87. Aerial images are typically collected from elevations above Earth that are about ____. a. 400 feet or less b. 5000 to 20,000 feet c. 100,000 feet d. 400 or more miles 88. One megabyte of GIS data is ____ larger than 1 kilobyte of GIS data. a. 2 b. 10 c. 100 d. 1000 89. Someone of great value to the management of a GIS program is an IT professional. What does IT stand for? a. internal topography b. internal technology c. information technology d. information topography 90. Commercial GIS software is often called ____ software. a. off-the-shelf b. avoidable c. incoherent d. open source

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91. The ____ resolution of a raster GIS database indicates the relative size of grid cells. a. temporal b. spectral c. spatial d. ordinal 92. The ____ resolution of a raster GIS database indicates the range of energy associated with grid cells, as recorded by certain sensors. a. temporal b. spectral c. spatial d. ordinal 93. ____ GIS software programs are free of cost to the user and often contain source code that is modifiable. a. Off-the-shelf b. Older c. North American d. Open source 94. A(n) ____ is a question posed of a person, organization, database, or some other entity. a. spatial join b. union c. query d. intersect 95. (Age
40 AND Basal area
100 AND Timber type ¼ ‘Loblolly pine’ AND Acres
25) is an example of a ____. a. compound intersect b. compound union c. compound query d. compound join 96. A ____ map algebra process, also known as a neighborhood process, might involve the determination of a new value for a grid cell in a raster GIS database after considering the values of the eight other cells around it. a. focal b. temporal c. zonal d. global 97. One fundamental concept behind spatial analysis is partially explained through Tobler’s First Law of Geography, which suggests that ____. a. the world is smaller than it seems b. science is hard, practice is easy c. everything is related to everything else, but near things are more related than distant things d. the Earth can easily be portrayed on a flat surface

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98. A map projection commonly used for displaying maps electronically through Internet mapping applications is the ____. a. Web Mercator map projection b. Mollweide map projection c. Robinson map projection d. Albers equal area map projection 99. A ____ conic model map projection involves simply resting the cone on the surface of the Earth. a. conformal b. cubic s. tangent d. parallel 100. The position of the Four Corners Monument in the southwest of the United States can be described by X (longitude) and Y (latitude) ____ of about 109.0452 and 36.999 , respectively. a. objects b. coordinates c. planes d. slopes 101. One might interpolate the values of places across a landscape in a raster GIS database using a(n) ____ process. a. fractal generation b. topographic weighting c. inverse distance weighting d. reclassification 102. The ____ process is associated with a semivariogram, which can illustrate the range of influence a sampled location value has on an unsampled location value, based on distance. a. fractal generation b. kriging c. inverse distance weighting d. reclassification 103. An example of qualitative data found in a GIS database might include which of the following? a. 12 b. 9.87 c. Pinus elliottii c. 0

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104. A discrete value that might range from 32,767 to 32,767 when signed is called a ____. a. long integer b. short integer c. strange integer d. Boolean integer 105. One advantage of heads-up digitizing over heads-down digitizing is there is ____. a. no need to use a digital base map b. no need to register a map to a digitizing table c. no need to use a computer mouse d. an opportunity to use a digitizing puck 106. To determine a position on Earth, a GPS receiver needs ____ electronic signal(s) from GPS satellites. a. 1 b. 2 c. 3 d. 4 107. Currently, there are around ____ GPS satellite orbiting Earth. a. 10 b. 20 c. 50 d. 100 108. A(n) ____ data format only allows the values 0 and 1 in a GIS database. a. long integer b. short integer c. binary d. expressive 109. The ____ of data in a GIS database reflects the degree of exactness of measurements associated with spatial phenomena. a. precision b. succinctness c. accuracy d. range 110. On a map illustrating the forest types of the Chippewa National Forest in Minnesota, the ____ would best provide a person a sense of the different forest types found there. a. legend b. scale c. north arrow d. title

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111. On a map of the Kings Ranch in Texas, when the top of the map represents north, the ____ part of the ranch can be found on the left side of the map. a. northeastern b. western c. eastern d. southern 112. The United States GPS program is formally called the ____ program. a. Beidou or Compass b. NAVSTAR c. Galileo d. GLONASS 113. It is not uncommon today to purchase a computer that would have at least 1 TB of data capacity on an internal hard drive. TB, in this case, stands for ____. a. tetrabyte b. topobyte c. terabyte d. timobyte 114. Of benefit for those who use or develop a lot of GIS data, a NAS system, or ____ system, is used in organizations that operate a network, and it functions as a scaled down version of a cloud storage system. a. network attached storage b. non-attachable storage c. near area storage d. normal adjacent storage 115. With satellite imagery of a very large area of the world, one might best use a ____ process to understand where the has forest areas are. a. map algebra b. overlay c. inverse distance weighting d. classification 116. The ____ of data in a GIS database reflects how well the data represent true values. a. precision b. succinctness c. accuracy d. range 117. GPS point data, perhaps 100 estimates of the same true place on Earth, that are clumped together tightly, yet positioned 2 miles from the correct spot on Earth, would be considered ____. a. precise and accurate b. accurate but not precise c. precise but not accurate d. neither precise nor accurate

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118. A Latitude of 28 25’54’’ N can also be described in decimal degree notation as ____. a. 28.25 b. 28.43 c. 28.71 d. 28.95 119. A conic map projection commonly used for making maps of the United States is the ____. a. Web Mercator map projection b. Mollweide map projection c. Robinson map projection d. Albers equal area map projection 120. Any circle on a map of Earth that divides Earth into two equal halves is called a ____. a. multipath ellipse b. great circle c. asymmetric arc d. oblate spheroid 121. A(n) ____ classification of a set of satellite imagery would require a person to devise some training sets. a. object-based b. supervised c. improvised d. unsupervised 122. Positions on Earth based on angles projected from the center of the Earth to the surface of the Earth, using the Equator as the base edge, are called ____. a. latitudes b. northings c. longitudes d. eastings 123. The image below illustrates a ____ data storage device.

Appendix: questions

124.

125.

126.

127.

128.

129.

130.

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a. flash drive b. cloud c. zip drive d. floppy disk With respect to queries, AND, OR, and NOT are examples of ____. a. proper values b. transfer items c. synthetic conjunctions d. Boolean operators Boundary tracing processes in GIS allow one to create ____. a. attributes b. buffers c. points d. grid cells The European Union GPS program is formally called the ____ program. a. Beidou or Compass b. NAVSTAR c. Galileo d. GLONASS The Russian GPS program is formally called the ____ program. a. Beidou or Compass b. NAVSTAR c. Galileo d. GLONASS Satellite images are typically collected from elevations above Earth that are about ____. a. 400 feet or less b. 5000 to 20,000 feet c. 100,000 feet d. 400 or more miles Drone-based images in the United States are typically collected from elevations above Earth that are about ____. a. 400 feet or less b. 5000 to 20,000 feet c. 100,000 feet d. 400 or more miles With respect to illustrating orientation on a map of the Jefferson National Forest in Virginia, north, south, east, and west are the ____ directions. a. warbler b. chickadee c. robin d. cardinal

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Appendix: questions

131. True north and magnetic north may be different, depending on where a property being managed is found. The difference is called ____. a. grid north b. declination c. dilution of precision d. spatial resolution 132. A ____ buffer process could be used to create riparian areas of different widths if the information to do so is available in the attribute table of a GIS database. a. temporal b. complex c. synthetic d. variable width 133. Many people today store their GIS data in the ____, which refers to a cluster of computers, or a server farmda building full of rows upon rows of computers, where computer files are stored. a. basement b. cloud c. warehouse d. cache 134. One sensor in a digital camera might be a ____. a. CMOS b. CFL d. CESSNA d. CPA 135. The U.S. Department of Agriculture’s national program for collecting imagery of agricultural areas is called ____. a. NFL b. NAW c. NAPW d. NAIP 136. The Chinese GPS program is formally called the ____ program. a. Beidou or Compass b. NAVSTAR c. Galileo d. GLONASS 137. Another term for GPS is ____. a. GS b. GNSS c. GVS d. GIS

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138. Evenly spaced longitude and latitude lines on a map of Canada would be considered a _____. a. source of error b. decision consistent with multipath c. bad choice for representing the country on a map d. graticule 139. ____ represent positions east or west of some reference point, commonly the prime meridian. a. Latitudes b. Northings c. Longitudes d. Westings 140. A(n) ____ classification of a set of satellite imagery would use patterns in the data to separate grid cells into spectral clusters. a. object-based b. supervised c. improvised d. unsupervised 141. Imagine a map of a 360-acre place called the Porterhouse Tract in North Carolina, which is presented with a scale of 1" ¼ 10 chains. Eight inches on this map would represent ____ in real life. a. one mile b. one half of a mile c. one quarter of a mile d. one eighth of a mile 142. A scale bar that can be found on every 7.5-minute topographic map developed for land areas of the United States is a type of a(n) ____. a. representative fraction scale b. equivalence scale c. graphical scale d. app-based scale 143. A ____ helps ensure that all coordinates within a coordinate system are positive. a. false easting b. declination c. conic projection d. map legend 144. Augmentation of GPS signals and improvements on ground positions can involve all of the following EXCEPT ___. a. ground-based differential correction information b. satellite-based differential correction signals

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145.

146.

147.

148.

149.

150.

151.

Appendix: questions

c. signals from a real-time kinematic system d. signals from a NASA radio tower A ____ is a border, or box, that encompasses one or more map elements. a. cute line b. neat line c. projection line d. franchise line The UTM system is more formally called the ____. a. Universal Tangential Metric system b. United Technical Map system c. Universal Thematic Map system d. Universal Transverse Mercator system The most important source of error in GPS-collected positions in forests involves ____. a. nearby water towers b. relative humidity c. multipathed signals d. air temperature The ____ refers to a variety of items that are somehow connected to the Internet. a. Internet of Things b. cloud c. conclave d. GIS library Labels (text) indicating towns, cities, and other places of interest that might be found on a road map of the State of Oklahoma are technically called ____. a. symbols b. annotation c. insets d. titles There are ____ zones in the UTM system. a. 24 b. 60 c. 100 d. 360 The northing and easting measurement units within the UTM coordinate system are ____. a. centimeters b. inches c. feet d. meters

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377

152. In type-mapping a forested property, one can begin with a polygon that represents the boundary of the property, then ____ it into major land uses found there. a. merge b. split c. join d. intersect 153. Each zone in the UTM system is ____ degrees wide at the Equator. a. 1 b. 3 c. 6 d. 12 154. A place with a northing of about 4,000,000 in the UTM system implies what? a. The place is 4,000,000 meters south of the North Pole b. The place is 4,000,000 meters north of the Equator c. The place is 4,000,000 meters north of the South Pole d. The place is 4,000,000 meters south of the Equator 155. A(n) ____ classification of a set of satellite imagery identifies groups of adjacent pixels based on similarities in spectral reflectance values. a. object-based b. supervised c. improvised d. unsupervised 156. If one were interested in developing a roads GIS database containing all the roads in Jackson County, Florida, beginning with all of the roads in Florida, one might use a(n) ____ process to do so. a. clip b. merge c. join d. union 157. A ____ may help the map reader understand the location of a recreation on the Kootenai National Forest with respect to the states of Montana, Idaho, Washington and the Canadian border. a. locational inset b. legend c. title d. north arrow

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Appendix: questions

158. If one were interested in knowing how much land area was outside of the riparian areas within the Allegheny National Forest in Pennsylvania, a(n) ____ process might be used in conjunction with a vector GIS database of the entire forest and a vector GIS database of the riparian areas. a. clip b. merge c. join d. erase 159. A map ____ is a guarantee or promise of the quality or condition of the map. a. caveat b. disclosure c. warranty d. disclaimer 160. NDVI values range from ____ to ____. a. 100, 100 b. 1, 100 c. 1, 1 d. 0, 1 161. A map ____ might refer to potential positional problems in the GIS databases used to create the maps, or potential problems related to the attributes of map features. a. disclaimer b. warranty c. legend d. projection 162. In a U.S. State Plane coordinate system as well as the Public Land Survey System of the United States, coordinates emanate north and south from a ____ and east and west from a ____. a. meridian, base line b. meridian, line of tangent c. base line, line of tangent d. base line, meridian 163. When two aerial images collected during the same aerial image mission contain parts of the landscape that overlap, they can be called ____. a. an anomaly b. cousins c. a stereo pair d. a mistake

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379

164. GPS receivers collect ____ GIS data. a. perfect b. raster c. vector d. liquid 165. Above-ground systems for creating GIS databases DO NOT include which of the following? a. satellites b. drones c. aircraft d. drills 166. How many Internet-connected devices were there estimated to be in 2021? a. 13 billion b. 130 billion c. 1.3 trillion d. 13 trillion 167. LiDAR is an acronym for ____. a. light detection and ranging b. Lillihammer’s demystified arithmetic c. limited dynamic algorithm d. liquid detection and roughness 168. ____ is the process of converting quantities of light reflected from aerial images or maps into electrical analogs. a. Merging b. Digitizing c. Overlaying d. Scanning 169. A statement of copyright placed on a map may provide the originator of the work ____ property protection. a. intellectual b. annual c. organizational d. natural 170. Conceptually, a(n) ____ is the application of two consecutive clip processes. a. intersect b. merge c. join d. buffer

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Appendix: questions

171. According to the World Meteorological Organization (2022), there were ____ Earth observation and meteorological satellites orbiting the Earth in 2022. a. 28 b. 288 c. 2888 d. 28,888 172. Similar to the merging of GIS databases, a(n) ____ process provides the boundary and attributes of each original feature in the resulting product, yet any overlapping areas are removed. a. intersect b. union c. join d. buffer 173. ____ can be used to stretch or shrink certain areas of a GIS database to conform to the true positions of landscape features. a. Rubber sheeting b. Merging c. Overlaying d. Kriging 174. A digital camera used in an aerial system might have a ____ or complementary metaleoxideesemiconductor (CMOS) devices within a camera record the amount of electromagnetic energy that is reflected or emitted from landscape features. a. film b. charged coupled device c. aperture d. focal plane 175. The image interpretation principal that is based on repeated similarities in the position and color of landscape features is the ____ principle. a. time b. pattern c. size d. shadow 176. Any survey of a property boundary that consists of distances and directions is a form of a ____, which often refers to systems with irregularly shaped parcels and parcels of different sizes. a. metes and bounds b. conic projection c. thematic map d. cylindrical projection

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381

177. The rules a GIS database follows for both the physical storage of files and how the data (tables, etc.) relate to and interact with each other, are considered the ____. a. fields b. schema c. shapefiles d. topology 178. The minimum number of files to form a shapefile is ____. a. 1 b. 2 c. 3 d. 10 179. Aerial images that have been rectified and georeferenced are ____. a. impossible to create b. orthophotographs c. stereoscopes d. traditional products from film-based cameras 180. ____ satellites are positioned in a fixed location relative to the Earth’s rotation. a. Geostationary b. Costa Rican c. Inoperable d. Smaller 181. The Public Land Survey System of the United States is based on townships that are theoretically ____ miles wide and tall. a. 1 b. 3 c. 6 d. 24 182. A(n) ____ process can combine individual features within a single GIS database. a. intersect b. merge c. join d. buffer 183. If one were interested in merging polygons that have the same specific characteristics or values in an attribute table, a(n) ____ process might be used. a. buffer b. dissolve c. clip d. join

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Appendix: questions

184. The image interpretation principal that helps us understand heights of landscape features is the ____ principle. a. time b. pattern c. size d. shadow 185. A georeferenced and rectified aerial image might be considered a(n) ____. a. orthophotograph b. geophotograph c. hypophotograph d. limnophotograph 186. One form of text file is the ____ file format. a. binary b. shapefile c. ASCII d. DEM 187. A Section within the Public Land Survey System of the United States theoretically contains ____. a. 10 hectares b. 640 acres c. 1000 hectares d. Six 40-acre square farm fields 188. Of the following, which is a common raster data file format? a. EPS b. JPG c. KML d. SHP 189. A(n) ____ process can be used to simplify the shapes of lines and polygons by removing some of the vertices that define their shape. a. buffer b. dissolve c. generalize d. spatial join 190. A(n) ____ process is very similar to a generalize process, when applied to a vector GIS database of polygons. a. intersect b. simplify c. merge d. buffer

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383

191. An estimate of NDVI at a certain place on a landscape requires information from the ____ and ____ parts of the electromagnetic spectrum. a. blue, green b. red, green c. red, infrared d. blue infrared 192. When the flying height of an airplane is 9300 feet above Earth, and the focal length of a film-based camera is 8.125 inches, the resulting scale of the images collected is ____. a. 1:13,735 b. 1:15,632 c. 1:17,918 d. 1:19,024 193. ____ imagery provides some estimates of the level of electromagnetic energy sensed in grid cells that are 1 km wide and tall. a. Landsat b. Sentinel c. ASTER d. MODIS 194. The N1/2 SW 1/4 SW 1/4 Section 27 T24N R7E within the Public Land Survey System of the United States theoretically contains ____ acres. a. 10 b. 20 c. 40 d. 80 195. ____ describe the content, quality, and condition of data and provide the ability of people to understand the assumptions and limitations of GIS database. a. Topologies b. Metadata c. Geoids d. Shapefiles 196. When the distance between two road intersections in a digital image of a landscape is 0.48 inches and the same distance in real life is 2.39 miles, the scale of the digital image is ____. a. 1:26,290 b. 1:31,510 c. 1:34,980 d. 1:37,650

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Appendix: questions

197. Volunteered geographic information (VGI) databases are more commonly referred to as ____ databases. a. messy b. crowd sourced c. inclusive d. SF (simple and free) 198. A ____ map of the Allegheny National Forest in Pennsylvania might illustrate a subject such as different forest types. a. transportation b. bathymetric c. topographic d. thematic 199. The latest Landsat satellite orbits about ____ above the Earth. a. 4 miles b. 40 miles c. 400 miles d. 4000 miles 200. A(n) ____ process would essentially produce the opposite effect as a simplify process, when applied to a vector GIS database of polygons. a. densify b. classification c. union d. identity 201. A(n) ____ process can be used to manipulate what it believes to be sharp angles or turns in direction in features of a roads GIS database. a. smooth b. densify c. union d. identity 202. A ____ map of Crater Lake in Oregon might illustrate a subject such as different depths of water. a. transportation b. bathymetric c. topographic d. thematic 203. The ____ that was employed in western Canada is similar to the Public Land Survey System of the United States. a. Dominion Land Survey b. Crown Land Survey c. Canada Land Survey d. Manitoba Land Survey

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385

204. A ____ map might use variations in colors to illustrate changes in elevation around Mt. Shasta in California. a. shaded relief b. bathymetric c. dasymetric d. isobar 205. If one were interested in joining two GIS databases where both the source file and the destination GIS database contain the same number of features is called a ____ join. a. temporal b. equality c. cultural d. one-to-one 206. ____ errors in GIS databases have a pattern to them. a. Gross b. Net c. Random d. Systematic 207. The boundary of the Porterhouse Tract in North Carolina might be described in detail on a ____ map. a. topographic b. bathymetric c. dasymetric d. planimetric 208. The SPOT 7 satellite is now called ____. a. Landsat 10 b. WorldView-5 c. MODIS d. Azersky 209. In early 2022, the number of registered drones in the United States was nearly ____. a. 23,000 b. 173,000 c. 860,000 d. 1,549,000 210. Errors of ____ can occur when landscape features that were meant to be created were overlooked somehow during the GIS database development process. a. omission b. multipath c. precision d. accuracy

Index Note: ‘Page numbers followed by ‘f ’ indicate figures those followed by ‘t ’ indicate tables’. A Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor, 291, 292f Aerial imagery systems aerial photogrammetry, 281e282, 282f, 284e285 Cessna, 280e281, 281f digital sensor imagery, 156e158 Dinosaur National Monument, 157f film-based aerial images, 281, 286e287 fixed-wing/propeller-based drones, 159f, 160 interpretation principles, 281e282 large format aerial image camera, 156f location and association, 282, 286 orthophotographs, 287 pattern, 283e284, 284f pigeons, 280e281, 280f scale, 286e287, 286f shadows, 284, 285f shape, 284e285 site, 286 size, 285e286, 286f stereoscope, 281e282, 283f texture, 283 time, 286 vertical aerial images, 158e160 Affine transformation process, 155e156 Allegheny National Forest, 94f, 122, 123fe125f, 193, 194f, 197e201, 198f, 198t, 199fe200f, 202f, 224, 238f, 304, 304t, 305f, 307t, 308fe310f, 311t, 335, 336f, 337e338, 338fe341f Allegheny National Forest plan, 304e305, 304t, 305f

American Society of Photogrammetry and Remote Sensing (ASPRS), 345e346 ASCII file format, 51e52 ASTER Global Digital Elevation Model, 95f Attribute, 29e32, 32f Azimuthal projections, 68e70, 69f B Basal area, 123fe124f consistent basal area interval, 123f natural breaks process, 124f standard deviation process, 125f Beidou (COMPASS) program, 148 Binary data, 44 Binary Large OBject (BLOB), 44 Boolean data types, 44 Boundary tracing processes, 195e196 Buffering processes boundary tracing processes, 195e196 features, 195e196, 195f line segment, 195e196, 196fe197f multiple ring buffers, 201, 202f polygons, 199e201, 201f variable-width buffers, 200e201, 202f C Canadian Dominion Land Survey, 79e81, 81fe82f Cartesian axes, 72 Cartesian coordinates, 72, 81e82 Cartography, 87e88 Centralized databases, 170 Chinese Beidou (COMPASS) program, 148 Chippewa National Forest, 313e320, 316f, 316t, 317fe326f Classification, 254e257 supervised classification, 255e257

387

388

Index

Classification (Continued ) unsupervised classification, 257, 258f object-based classification, 257e259 Clip process, 205e207, 206fe208f Cloud-based data storage, 177e180 Compound query, 192e193 Computer Graphics Metafile (CGM), 53e54 Conformal projections, 64 Conic projections, 67e68 Continuation insets, 111e115, 114f Coordinate systems coordinates, 17e18, 63e64, 70 geographic, 70e72 metes and bounds surveys, 76, 77f projected coordinate systems, 72e81 Copyright, 119 Corporate/organizational logo, 119 Country-specific projection systems, 63 Cylindrical projections, 64e67 D Dasymetric map, 128, 129f Database design, 49 Database management system (DBMS), 51 Data conversion, 262e264, 262fe263f Data errors attribute issues, 48 gross errors, 46 inconsistent data, 47e48 missing data, 47 positional problems, 48e49, 48f random errors, 46 systematic errors, 46 Data format binary data, 44 Boolean data types, 44 continuous data, 41e42 discrete data values, 41e42 float data, 43 integers, 42e43 interval data, 42 nominal data, 42 ordinal data, 42 ratio data, 42 text data types, 43e44

Data models raster data models, 34e39 tabular data models, 25e26, 29e32 topology, 32e34 triangular irregular networks (TINs), 39e40 vector data models, 26e29 Datums, 61e63 Decentralized databases, 170 Declination, 100 Densify process, 222e223, 222f Detail map, 111e115, 113f Differential global positioning systems (DGPS), 148e149 Digital elevation model (DEM), 34e35, 94e95, 94fe95f, 238e242, 238fe239f, 241fe242f, 253f, 277e278 Digital surface models (DSM), 277e278 Digital terrain models (DTMs). See Digital elevation model (DEM) Digitizing, 141e147 Digitized vector polygons, 141f Disclaimer, 118 Discrimination policy statement, 119 Dissolve process, 217e219, 218fe220f Domains, 49 Dominion Land Survey, Canada legal subdivisions, 80e81, 82f metes and bounds, 79e80 section numbering convention, 80e81, 81f Double precision floating point, 43 Drones, 152, 159f, 160, 295e298, 296fe297f Dynamic labeling, 109e111 Dynamic segmentation, 40e41 E Earth Observation Satellite (EROS) program, 288e289 Eckert IV, 67 Eckert VI, 67 Ellipsoids, 61e63, 177f Encapsulated Postscript (EPS), 53e54 Enhanced Thematic Mapper (ETM), 289, 290t Enhanced vegetation index (EVI), 260e261, 261f

Index

Equal area projections, 63e64 Equidistant projections, 64 Equivalence scale, 103 Equivalent projections, 63e64 Erase process, 208e210, 209f Erdas Apollo image files (IMG), 54 External drives, 176, 177f data storage, 176 devices, 177f NAS drive, 174t, 176, 178f network-attached storage (NAS) system, 176, 178f USB plugs, 177fe178f F False easting and northing, 72 Federal Aviation Administration (FAA), 297e298 Federal Geographic Data Committee (FGDC), 46e47 Fertilization buffering, 331e332, 334f editing, 332e334 flow chart, 329f intersection, 329, 332fe333f querying, 327e329, 328f, 330f, 330t, 331f summarizing spatial data, 334 Fixed-wing aircraft, 296, 296f Floating point, 43 Focal map algebra processes, 246e247, 247f Forestry and natural resource management, 2 computerized systems, 3e4 3-dimensional radar image, Glacier Bay National Park, 4f fertilization options buffering, 331e332, 334f data acquisition and processing, 327 editing, 332e334 flow chart, 329f intersection, 329, 332fe333f querying, 327e329, 328f, 330f, 330t, 331f summarizing spatial data, 334 hand drawn timber sale map, 6f mapping elements, 2

389

National Land Cover Database (NLCD) Allegheny National Forest, 335, 336f, 336t deciduous forests, 337e338, 338f evergreen forests, 337e338, 339f flow chart, 337e338, 341f mixed forests, 337e338, 339f querying, 335e336 total forest cover area, 337e338, 340f zonal statistics process, 337 recreation opportunity spectrum (ROS) Chippewa National Forest, 313e315, 316f, 316t, 317e320, 317f classification, 313 cross-country skiing, 313 data acquisition and processing, 315 development, 312 location and access, 312e313 Minnesota national forests, 314, 314t roaded managed, 324, 326f roaded natural, 323e324, 325fe326f road types, 317e319, 318fe319f, 319t semiprimitive motorized, 322, 323fe324f semiprimitive nonmotorized, 320, 320fe322f riparian areas Allegheny National Forest plan, 304e305, 304t, 305f, 310e312, 311t definition, 303e304 flow chart, 306e307, 306f querying, buffering, and overlaying, 306e312, 307t, 308f, 310f sea floor map, Puerto Rico Trench, 5f spatial analyses, 7 spatial features editing, 7e8 types of, 303 Francis Marion-Sumter National Forest, 3f, 207e209, 207fe209f, 218e219, 218fe220f, 257, 258f, 262f, 327e334, 328fe330f, 330t, 331fe334f G Generalization process, 219e221, 220f Geodatabases, 50e51 GeoEye-1 satellite, 294

390

Index

Geographical Resources Analysis Support System (GRASS), 14e15 Geographic coordinate systems latitudes, 70e71 longitudes, 70e71 Geographic data accuracy and errors, 163e166 database design, 49 database development planning, 138f database acquisition options, 138, 139f spatial extent, 138 thought process, 138, 138f databases creation, 140e161 aerial systems, 156e160 digitized vector polygons, 141f digitizingglobal positioning system (GPS), 141e147 see also Global navigation satellite systems (GNSS) governmental agencies, 161e162 heads-down digitizing, 142e143, 142f heads-up digitizing, 144e145 map scanning, 153e156 methods, 140e141 raster aerial image, 140f raster image, 144e147 satellite image, 160e161 structured and unstructured data, 152e153 volunteered geographic information (VGI), 162e163 errors, 46e49 file types, 49e54 format, 41e44 metadata, 55e56 models, 25e41 precision and accuracy, 44e46 project planning, 138 quality, 163e166 resolution, 41 Geographic data management centralized databases, 170 central processing unit (CPU), 170 cloud storage, 177e180, 179f

computer file storage locations, 169e170, 169f decentralized databases, 170 external drives, 176, 177fe178f internet of things, 180e183, 182f larger projects, 170 local disk, 169f, 175e176, 175f personal-use data, 170 physical location, 170 random access memory (RAM), 170 storage and file size bits and byte, 171 C drive, 172 computer storage systems, 171 consolidate GIS databases, 172e173 database file formats, 173 files paths, 172 geodatabases, 173e174 GeoTIFFs, 173 Keyhole Markup Language (KML) files, 173 stand-alone GIS databases, 172e173 Geographic information sciences, 1 Geographic information system Certification Institute (GISCI), 346e347, 347f Geoids, 61e63, 62f GeoPDF, 54 Global map algebra processes, 248e249 Global navigation satellite systems (GNSS), 147e152 augmentation, 148e149 Beidou (COMPASS) program, 148 differential GPS (DGPS), 149 errors, 149e150 GALILEO system, 148 GLONASS system, 148 line databases, 150 NAVSTAR GPS program, 148 point databases, 150 polygon databases, 151 Quasi-Zenith Satellite System (QZSS), 148 receiver, 148 signal emission, 147

Index

Global positioning systems (GPS), 2, 147e151, 180e181, 270e271, 275e277 Gnomonic map projection, 68e70, 69f Google Earth, 14e15, 54, 131e133 Grand Teton National Park, 90f Graphical scale, 102f, 104 Graphic Interchange Format (GIF), 53e54 Graticule, 70e71, 100e102, 115, 116fe117f Grid north, 100, 102f Gross errors, 46 H Hand-drawn map digitizing, 142e143, 142f Hard disk drive, 175f Hardware, 12e13 Heads-down digitizing method, 142e143, 142f Heads-up digitizing method, 144e145 Hill shade model, 94e95, 94f History, geographic information system (GIS) development, 8e11 Hypsometric map, 127, 127f I Identity process, 214, 215f IKONOS, 294 Inconsistent data, 47e48 Insets continuation, 111e115 locational, 111e115, 112f Integers, 42e43 Internet of Things, 180e183, 182f Internet of Trees, 181e182 Interpolation methods characteristics, 249e250 IDW, 251e252, 251f kernel density process, 250e251, 250f kriging, 252e254, 253f Intersect process, 210e211, 211f Interval data, 42 Inverse distance weighted (IDW), 251e252, 251f Isle Royale National Park map, 89f Isopleth map, 126e127, 126f

391

J Join process, 224e226, 225f many-to-one join, 226 one-to-many join, 224e226 one-to-one join, 224e226 Joint Photographic Experts Group (JPEG), 53 K Kernel density process, 250e251, 250f Keyhole Markup Language (KML), 54, 173 Kriging methods, 252e254, 253f L Labels, 109e111, 110f Landsat satellite, 160, 161f, 256f, 272e275, 274f, 278e279, 288e291, 288f, 290t, 291f Large-scale maps, 103 Latitudes, 70e71 Legends, 107 LEOStar-3 Bus, 289e291, 292f Light detection and ranging (LiDAR) technology, 94e95, 152, 238f, 253f, 270e271, 275e278, 276fe278f Line, 26e28, 26f, 32fe33f, 38f Line symbol, 104e105 Local Area Augmentation Systems (LAAS), 149 Local computer disk space, 175e176 Local map algebra processes, 245, 245fe246f Locational insets, 111e115, 112f Long integer, 42e43 Longitudes, 70e71 Long lot approach, 76, 76f M Magnetic north, 100, 102f Map algebra definition, 244 focal process, 246e247, 247f global process, 248e249 local process, 245, 245fe246f zonal process, 247e248, 248f

392

Index

Map projection, 63 Map creation ASTER satellite imagery, 95f background image, 120, 121f caveats/warranties, 117e118 communication devices, 92 components, 97e120, 98f caveats/warranties, 117e118 copyright, 119 data sources, 116 disclaimer, 117e118 discrimination policy, 119 file locations, 119 geographic system, 119 graticule, 115 insets, 111e115 labels, 109e111 legends, 107, 108f map maker name, 115e116 metadata, 117 neat lines, 107e108, 109f orientation, 99e102 scale, 102e104 symbols, 104e107 contour lines, 92 data visualization, 120e133 digital elevation model (DEM), 94e95, 94fe95f hiking trail map, 88, 89f intended audience, 88e92 mental map, 87e88 Mount Washington summit interpretation, 95e97, 96f quick response (QR) code, 88e92, 92f three-dimensional maps, 94e95 types, 121e133 vegetation sample, 93f Mental map, 87e88 Mercator map projection, 64e66, 65f Merging process, 215e217, 216fe217f Metadata, 55e56, 117 Metes and bounds surveys, 76, 77f Missing data, 47

Mixed-pixel problem, 37e39 Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, 160, 260e261, 260fe261f, 291e293 Mollweide map projection, 67 Mount Rushmore map, 101f Mount Washington map, 95e97, 96f Multi-part shapefile, 40, 40f Multispectral Scanner System (MSS), 289, 290t N National Agricultural Imagery Program (NAIP), 36, 54, 55f, 158e160, 159f, 257, 258f, 281, 287 National Land Cover Database (NLCD) Allegheny National Forest, 335, 336f, 336t deciduous forests, 337e338, 338f evergreen forests, 337e338, 339f flow chart, 337e338, 341f mixed forests, 337e338, 339f querying, 335e336 total forest cover area, 337e338, 340f zonal statistics process, 337 Neat lines, 107e108, 109f Network-attached storage (NAS) system, 174t, 176, 178f Nominal data, 42 Nonproprietary open-source GIS software programs, 15e16 Normalized difference moisture index (NDMI), 261e262 Normalized difference vegetation index (NDVI), 161, 259e260, 260f North American Datum of 1983 (NAD 83), 62e63 O Object-based classification, 257e259 Object-based image analysis (OBIA), 257e259 Off-the-shelf software, 13e16, 15t Online map, 128e133, 132f Open-source software, 14e16, 15t

Index

Operational Land Imagery (OLI), 289e291, 290t Ordinal data, 42 Overlay analysis, 205, 210, 213e214 P Pahranagat National Wildlife Refuge, online map, 132f Planimetric maps, 128, 130f Point, 26e28, 26fe28f, 37e39, 38f Point mode digitizing, 143 Point symbol, 104e105 Polygon, 26f, 28e29, 30f, 32fe33f, 33, 37e39, 38f Polygon symbol, 104e105 Portable Network Graphics (PNG) file format, 53e54 Positional error, 48e49, 48f Precision of data, 42e46, 45f Procedure sheets, 163e164 Professional practices conceptual model, 343e344, 343f legal issues, 348e350 professional responsibility, 350e352 professional standards ASPRS, 345e346 code of ethics, 345, 347e348 GISCI, 346e347, 347f U.S. Federal Aviation Administration, 344e345 Professional responsibility, 350e352 Professional standards ASPRS, 345e346 code of ethics, 345, 347e348 GISCI, 346e347, 347f U.S. Federal Aviation Administration, 344e345 Projected coordinate systems, 72e81 Projection systems Azimuthal projections, 68e70, 69f conformal, 64 country-specific, 63 cylindrical projections, 64e67 equal area, 63e64

393

equidistant, 64 map projection, 63 Pseudocylindrical projections, 67 Q Quasi-Zenith Satellite System (QZSS), 148 Query, 192e194 Quick response (QR) code, 88e92, 92f R Radiometric resolution, 234, 279e280 Raster data classification image classification, 254e255 object-based classification, 257e259 supervised classification, 255e257 unsupervised classification, 257, 258f data conversion, 262e264, 262fe263f data models advantages, 37e39 digital elevation model (DEM), 34e35 grid cells, 35, 35f, 231e232, 245e249, 245fe247f land cover, 35e36, 36f elevation and topography definition, 237 DEMs, 238e242, 238fe239f, 241fe242f grid cell size reduction, 237 interpolation characteristics, 249e250 IDW, 251e252, 251f kernel density process, 250e251, 250f kriging, 252e254, 253f map algebra definition, 244 focal process, 246e247, 247f global process, 248e249 local process, 245, 245fe246f zonal process, 247e248, 248f mask, 233e234, 234f reclassification, 242e244 National Land Cover Data (NLCD), 243 resolution, 36, 37f, 234e237, 235fe236f spatial statistics, 233

394

Index

Raster data (Continued ) spectral indices Enhanced Vegetation Index (EVI), 260e261, 261f Normalized Difference Moisture Index (NDMI), 261e262 Normalized Difference Vegetation Index (NDVI), 259e260, 260f Ratio data, 42 Real-time kinematic (RTK) GNSS system, 149 Record, 29e32 Recreation opportunity spectrum (ROS) Chippewa National Forest, 313e315, 316f, 316t, 317e320, 317f, 319t, 321fe324f, 322e327, 326f classification, 313 cross-country skiing, 313 development, 312 flow chart, 316f, 318f, 320f, 323f, 325f location and access, 312e313 Minnesota national forests, 314, 314t roaded managed, 314, 314t, 324, 326f roaded natural, 314, 314t, 323e324, 325fe326f road types, 317e319, 318fe319f, 319t semiprimitive motorized, 314, 314t, 322, 323fe324f semiprimitive nonmotorized, 314, 314t, 320, 320fe322f Reference systems Canadian Dominion Land Survey, 79e81, 81fe82f conversion, 81e82 coordinate systems, 70e81 geographic coordinate systems, 70e72 projected coordinate systems, 72e81 datums, 61e63 ellipsoids, 61e63 geoids, 61e63 projection systems, 63e70 Universal transverse mercator (UTM) system, 72e74 US public land survey system (PLSS), 77e79

US state plane system, 75, 75f Regions, 40, 40f multi-part shapefile, 40, 40f Registration, 142e143 Remote sensing aerial imagery systems aerial photogrammetry, 281e282, 282f Cessna, 280e281, 281f film-based aerial images, 281, 286e287 interpretation principles, 281e282, 285 location and association, 286 orthophotographs, 287 pattern, 283e284, 284f pigeons, 280e281, 280f shadows, 284, 285f shape, 284e285 size, 285e286, 286f stereoscope, 281e282, 283f texture, 283 time, 286 definition, 269e271, 270f electromagnetic energy, 272 electromagnetic spectrum, 271e272, 271f, 273f LiDAR, 238f, 253f, 270e271, 275e278, 276f natural color composites, 272e275, 274f panchromatic band, 275 passive and active sensors, 270e271, 271f satellite-based imagery systems ASTER, 291, 292f GeoEye-1, 294 IKONOS, 294 Landsat satellite, 288e291, 288f, 290t, 291fe292f MODIS, 160, 260e261, 260fe261f, 291e293 Sentinel-2 system, 293e294 SPOT, 293 sun-synchronous satellites, 287e288 WorldView, 294e295 spectral resolution, 41, 278e279 spectral signature, 272, 273f SPOT 7 satellite, 278e279 temporal resolution, 279 UAVs, 295e298, 296fe297f

Index

Representative scale, 102e103 Return Beam Vidicon (RBV), 289 Riparian areas Allegheny National Forest plan, 304e305, 304t, 305f, 310e312, 311t definition, 303e304 flow chart, 306e307, 306f querying, buffering, and overlaying, 306e312, 307t, 308fe310f Rubber sheeting process, 155e156 Russian GLONASS system, 148 S Satellite-based imagery systems ASTER, 291, 292f GeoEye-1, 294 IKONOS, 294 Landsat satellite, 288e291, 288f, 290t, 291fe292f MODIS, 160, 260e261, 260fe261f, 291e293 Sentinel-2 system, 293e294 SPOT, 293 sun-synchronous satellites, 287e288 WorldView, 294e295 Satellite image, 160e161 Satellite Pour l’Observation de la Terre (SPOT), 293 Scale, 102e104 equivalence scale, 102e103 fine-scale, 37e39 graphical scale, 102e104, 102f large-scale maps, 41, 100e103 representative scale, 102e103 resolution, 41 scale bar, 98f, 104 small-scale maps, 41, 100e102 Scale bar, 104 Scan line collector (SLC), 289, 291f Schema, 49 Sentinel-2 satellite system, 293e294 Shaded relief map, moon, 127f Shapefiles, 49e50 Short integer values, 42e43 Shortwave infrared (SWIR) reflectance, 261, 272e275, 278e279, 289, 291, 293

395

Simplify process, 221e222, 221f Single precision floating point, 43 Slope, 237, 239e242, 239f, 244 Small-scale maps, 103 Smoothing process, 223e224, 223f Software, 13e16 Spatial analyses, 15e19 Spatial join process, 227e229, 227fe229f Spatial resolution, 36, 36f, 41, 234e237, 234fe235f Spectral indices, raster data enhanced vegetation index (EVI), 260e261, 261f normalized difference moisture index (NDMI), 261e262 normalized difference vegetation index (NDVI), 259e260, 260f Spectral resolution, 41, 234, 273f Spline text, 109e111 Splitting vector, 203e205, 203fe204f SPOT 7 satellite, 278e279 Stand-alone GIS databases, 172e173 Standard parallel, 67 Storytelling devices, 87e88 Stream mode digitizing, 143 Structured data, 152e153 Sun-synchronous satellites, 287e288 Supervised classification, 255e257 Symbols line symbol, 104e105 maps, 104e107 National Park Service, 105e107, 105f point symbol, 104e105 polygon symbol, 104e105 U.S. Geological Survey, 106f Systematic errors, 46 T Tabular data models, 25e26, 29e32 Tagged image file format (TIFF), 53 Temporal resolution, 41, 234 Terra spacecraft system, 291 Text data types, 43e44 Thematic map, 121e123 Thematic Mapper (TM), 289, 290t

396

Index

Thermal Infrared Sensors (TIRS), 278e279, 289e291 Three-dimensional maps, 94e95 Titanic Mapping Project, 2 Topographic map, 126e127 Topological model connectivity, 33e34, 34f links, 32e33, 32f node and vertex, 32e33, 32f polygons, 33, 33f Topology, 32e34 Transverse Mercator map projection, 66e67 Triangular irregular networks (TINs), 39e40, 39f, 241e242, 242f True north, 100, 102f U Union process, 213e214, 213f Universal transverse mercator (UTM) system, 72e74, 73f Unmanned aerial systems (UAS), 159f, 160, 295e298, 296fe297f Unmanned aerial vehicles (UAVs), 37, 152, 275e277, 295e298, 296fe297f Unmanned aircraft system (UAS), 159f, 160 Unstructured data, 152e153 Unsupervised classification, 257, 258f U.S. Federal Aviation Administration (FAA), 344e345 U.S. Geological Survey (USGS), 349e350 US public land survey system (PLSS), 77e79, 78fe80f US state plane system, 75, 75f V Vector data buffering boundary tracing processes, 195e196 features, 195e196, 195f line segment, 195e196, 196fe197f multiple ring buffers, 201, 202f polygons, 199e201, 201f streams, 197e199, 198f, 198t, 199fe200f variable-width buffers, 200e201, 202f clip process, 205e207, 206fe208f

data models advantages, 29 line, 28 point, 26, 27fe28f polygon, 28e29, 30f densify, 222e223, 222f dissolve, 217e219, 218fe220f drone/aircraft, 152 erase, 208e210, 209f generalization, 219e221, 220f identity, 214, 215f intersect process, 210e211 join process, 224e226, 225f many-to-one join, 226 one-to-many join, 224e226 one-to-one join, 224e226 merging process, 215e217, 216fe217f query, 192e194 simplify process, 221e222, 221f smoothing, 223e224, 223f spatial features, physical selection landscape features, 188e192, 190fe191f water feature, 187e188, 188fe189f spatial join, 227e229, 227fe229f splitting, 203e205, 203fe204f union, 213e214, 213f Volunteered geographic information (VGI), 162e163 W Web Mercator map projection, 66e67, 66f Wide Area Augmentation System (WAAS), 149 World Geodetic System of 1984 (WGS 84), 62e63 WorldView satellite systems, 294e295 Y Yellowstone National Park map, 31f, 88e92, 91f Z Zonal map algebra processes, 247e248, 248f Zonal statistics process, 336e337