Remote Sensing of Land Use and Land Cover in Mountain Region: A Comprehensive Study at the Central Tibetan Plateau [1st ed. 2020] 978-981-13-7579-8, 978-981-13-7580-4

Table of contents :
Front Matter ....Pages i-xvi
Introduction (Duo Chu)....Pages 1-24
Study Area (Duo Chu)....Pages 25-40
Land-Use Status (Duo Chu)....Pages 41-66
Spatial Distribution of Land-Use Types (Duo Chu)....Pages 67-80
Land-Use Change (Duo Chu)....Pages 81-116
Land-Use Change Scenario (Duo Chu)....Pages 117-132
Land-Cover Change (Duo Chu)....Pages 133-154
Ecoregion Classification (Duo Chu)....Pages 155-180
Land-Cover Classification (Duo Chu)....Pages 181-194
Fractional Vegetation Cover (Duo Chu)....Pages 195-207
Aboveground Biomass of Grassland (Duo Chu)....Pages 209-227

Citation preview

Duo Chu

Remote Sensing of Land Use and Land Cover in Mountain Region A Comprehensive Study at the Central Tibetan Plateau

Remote Sensing of Land Use and Land Cover in Mountain Region

Duo Chu

Remote Sensing of Land Use and Land Cover in Mountain Region A Comprehensive Study at the Central Tibetan Plateau

Duo Chu Tibet Institute of Plateau Atmospheric and Environmental Sciences Tibet Meteorological Bureau Lhasa, China

ISBN 978-981-13-7579-8 ISBN 978-981-13-7580-4 https://doi.org/10.1007/978-981-13-7580-4

(eBook)

© Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Mountains cover about one fourth of the Earth’s land surface and are home to over one tenth of the global population, providing the essential ecosystem services and playing the role of water towers to billions of people living in downstream. Meanwhile, mountain regions are most vulnerable and sensitive to environmental change and global warming, offering a unique opportunity to monitor and study these change processes and consequences. The Tibetan Plateau, the highest and largest plateau on the Earth and well known as “the roof of the world,” is a massive mountainous region on the Eurasian continent and covers over 2.5 millions of square kilometers, with an average elevation of over 4000 meters above sea level. Land use and land cover is one of the important environmental aspects of the plateau. However, our understanding of land-use and land-cover change (LUCC) and their dynamic processes on the plateau is very limited compared to the plateau uplifting and its profound dynamic and thermal forcing on the Asian monsoon system. Therefore, in this publication, based on the author’s doctoral dissertation research and various studies that have been conducted recently, the spatial and temporal dynamics of land use and land cover in the central Tibetan Plateau during the last two decades are presented from different perspectives using various satellite data, long-term field investigation, and GIS spatial techniques. Further, it demonstrated how remote sensing can be used to map and characterize land use, land cover, and their dynamic processes in mountainous regions and to monitor and model relevant biophysical parameters. After an overview of the background and introduction to LUCC and research importance in the first chapter, the study area is introduced in detail from geographical location, physical environment, and socioeconomic conditions in Chap. 2. Landuse status, spatial distribution patterns of different land-use types, and land-use dynamic changes and driving forces in the study area are subsequently analyzed based on the time series of land-use data and digital elevation model using GIS spatial analysis techniques in Chaps. 3, 4, and 5. Scenario analysis with land-use change models can support the analysis of the causes and consequences of land-use dynamics. Therefore, the Markov chain model is applied to project land-use changes in the future for the study area based on the v

vi

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land-use change dynamics and transition probability matrix, and comparison analysis between areas from land-use planning and Markov model projection is presented in Chap. 6, which has a significant implication in planning and optimal use of land resources and harnessing the nonnormative changes in the future. In Chap. 7, land-cover change based on long-term series of the normalized difference vegetation index (NDVI) derived from the NOAA AVHRR is investigated, and its sensitivity to climate conditions is discussed, followed by quantitative analysis on vegetation phenologies and dynamics using the discrete Fourier transform (DFT). Land-cover classification is one of the most important application areas of satellite remote-sensing data. However, deriving thematic map from satellite imagery through classification approaches is not a straightforward task, especially from high-resolution satellite imagery. Thus, based on the main physical environmental and climatic indicators affecting ecological region in the study area, ecoregion classification is carried out using principal component analysis (PCA) and artificial neural networks (ANN) techniques in Chap. 8, while land-cover classification is successfully implemented for the study area based on the Terra/MODIS multispectral composite image using maximum likelihood classifier in Chap. 9. The grassland is a predominant land-use type in the central Tibetan Plateau covering over 70% of the total area. Fractional vegetation cover (FVC) and biomass are important parameters of grassland ecosystems, play a critical role in the sustainable use of grassland resources and the global carbon cycle, and are often used to evaluate and monitor vegetation degradation and desertification as well as soil erosion, while satellite remote sensing offers the only feasible approach to estimate FVC and aboveground biomass (AGB) at large spatial scales. Hence, remotesensing-based empirical models of FVC and AGB estimation are developed for the central Tibetan Plateau based on the relationships between field measurements and corresponding vegetation indices from the Terra/MODIS in the last two chapters, which is particularly important to timely monitor grassland productivity and dynamic changes for effective management of grassland resources to realize sustainable development of grassland ecosystems since grassland degradation and desertification are currently the major threat to the environmental conservation in the Tibetan Plateau. This publication is my long-time research efforts that I have conducted over the past 10 years and one of the highlights in my professional career and research achievement. The book was initially written in 2007 and 2008 when I worked at the International Centre for Integrated Mountain Development (ICIMOD) in Kathmandu, Nepal, and a thorough and complete revision and update were made during my visiting at the University of Colorado at Boulder in 2017 and from that time on. The studies in the book from field observation and sampling to final publication were made possible through the support from different funding sources. Especially, I would like to acknowledge the financial support from the China Meteorological Administration (CMA), National Natural Science Foundation of China (NSFC), Department of Science and Technology of Tibet Autonomous Region (TAR), and ICIMOD. MODIS data acquired from the NASA’s Land Processes Distributed

Preface

vii

Active Archive Center (LP DAAC) are very much appreciated. All meteorological data is provided by the Meteorological Information and Network Center of Tibet Meteorological Bureau. Finally, I am grateful to my parents; wife, Lhakpan; and daughter, Seldron, for their continued support and encouragement. It is my hope that the publication of this book will help and facilitate students and professionals to use remote-sensing technology in understanding environmental change relevant to land use and land cover in mountainous regions around the globe and provide them with a valuable reference and guide. Lhasa, Tibet, China

Duo Chu

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Importance of LUCC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Background of LUCC Research . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Land-Use and Land-Cover Concepts and Linkages . . . . . . . . . . 1.4 LUCC Driving Forces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Global Consequences of LUCC . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Approaches to LUCC Research . . . . . . . . . . . . . . . . . . . . . . . . 1.7 Remote Sensing Applications in LUCC . . . . . . . . . . . . . . . . . . 1.8 LUCC in Mountain Regions . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 3 4 5 6 9 11 13 17

2

Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Geographical Location and Administrative Division . . . . . . . . . 2.2 Social and Economic Conditions . . . . . . . . . . . . . . . . . . . . . . . 2.3 Topography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Topographic Characteristics . . . . . . . . . . . . . . . . . . . . 2.3.2 Landform Conditions . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Vegetation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Basic Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Spatial Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1 Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2 Precipitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.3 Solar Radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

25 25 26 28 28 28 29 29 30 32 32 36 39 39

3

Land-Use Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Land-Use Mapping Method . . . . . . . . . . . . . . . . . . . 3.2.2 Accuracy Assessment for Land-Use Status Mapping . . 3.3 Land-Use Status Classification System . . . . . . . . . . . . . . . . . .

41 41 42 42 43 44

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ix

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3.4

Land-Use Status at Prefecture Level . . . . . . . . . . . . . . . . . . . . 3.4.1 Overall Land-Use Structure . . . . . . . . . . . . . . . . . . . . 3.4.2 Cultivated Land . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.3 Horticultural Land . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.4 Forest Land . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.5 Grassland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.6 Settlement and Built-Up . . . . . . . . . . . . . . . . . . . . . . 3.4.7 Transportation Land . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.8 Water Body . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.9 Unused Land . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Land-Use Status at County Level . . . . . . . . . . . . . . . . . . . . . . 3.5.1 Cultivated Land . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.2 Horticultural Land . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.3 Forest Land . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.4 Grassland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.5 Settlement and Built-Up . . . . . . . . . . . . . . . . . . . . . . 3.5.6 Water Body . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.7 Unused Land . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Conclusion and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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48 48 50 50 51 52 53 53 53 55 55 55 55 56 56 59 59 59 65 66

4

Spatial Distribution of Land-Use Types . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Digital Elevation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Spatial Analysis of Topography . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Elevation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Slope Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3 Aspect Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Spatial Distribution of Land-Use Types . . . . . . . . . . . . . . . . . . 4.4.1 Spatial Distribution of Cultivated Land . . . . . . . . . . . . 4.4.2 Spatial Distribution of Forest Land . . . . . . . . . . . . . . . 4.4.3 Spatial Distribution of Grassland . . . . . . . . . . . . . . . . . 4.4.4 Spatial Distribution of Settlement and Built-Up Land . . . 4.4.5 Spatial Distribution of Water Body . . . . . . . . . . . . . . . 4.4.6 Spatial Distribution of Unused Land . . . . . . . . . . . . . . 4.5 Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

67 67 68 70 70 71 71 72 72 74 75 77 77 78 79 80

5

Land-Use Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Location of Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Data Source and Methods . . . . . . . . . . . . . . . . . . . . . 5.3.2 Background of the Project “Integrated Environmental Assessment and Monitoring

81 81 83 83 83

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in Central Tibetan Plateau Using GIS and Remote Sensing” . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Land-Use Change at Prefecture Level . . . . . . . . . . . . . . . . . . . 5.4.1 Land-Use Change from 1990 to 1995 . . . . . . . . . . . . 5.4.2 Land-Use Change from 1995 to 2000 . . . . . . . . . . . . 5.4.3 Land-Use Change from 1990 to 2000 . . . . . . . . . . . . 5.4.4 Land-Use Change from 2000 to 2007 . . . . . . . . . . . . 5.5 Land-Use Change at County Level . . . . . . . . . . . . . . . . . . . . . 5.5.1 Land-Use Change in Region of City . . . . . . . . . . . . . 5.5.2 Land-Use Change in Taktse County . . . . . . . . . . . . . 5.5.3 Land-Use Change in Tolung Dechen County . . . . . . . 5.5.4 Land-Use Change in Lhundup County . . . . . . . . . . . . 5.5.5 Land-Use Change in Medro Gongkar County . . . . . . . 5.5.6 Land-Use Change in Nyemo County . . . . . . . . . . . . . 5.5.7 Land-Use Change in Chushur County . . . . . . . . . . . . 5.6 Analysis of Driving Forces . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

7

Land-Use Change Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Scenario Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Validation of Markov Chain Model . . . . . . . . . . . . . . 6.3.2 Predicting Land-Use Change . . . . . . . . . . . . . . . . . . . 6.3.3 Comparison Between Areas from Land-Use Planning and Markov Model Projection . . . . . . . . . . . 6.4 Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xi

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85 85 86 87 95 98 100 100 100 100 107 107 107 107 111 114 115

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117 117 119 121 121 123

. 123 . 130 . 131

Land-Cover Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 NOAA AVHRR Data . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 NOAA AVHRR NDVI . . . . . . . . . . . . . . . . . . . . . . . . 7.2.3 NOAA AVHRR GVI . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.4 GVI Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.5 Meteorological Data . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Land-Cover Change and Climate Impacts . . . . . . . . . . . . . . . . . 7.3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Domain-Averaged Monthly-Mean NDVI Trend . . . . . . 7.3.3 Relationships Between NDVI and Climatic Variables . . . 7.3.4 Average Annual Cycle . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Vegetation Phenologies Using the Discrete Fourier Transform . . . 7.4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

133 133 135 135 135 136 137 138 138 138 140 143 145 145 145

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7.4.2 Discrete Fourier Transform . . . . . . . . . . . . . . . . . . . 7.4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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147 148 150 151

Ecoregion Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 An Overview of Spatial Interpolation . . . . . . . . . . . . . 8.2.2 Interpolation Methods for Climate Variables . . . . . . . 8.2.3 Creating DEM and Gridded Latitude and Longitude Layers . . . . . . . . . . . . . . . . . . . . . . . . 8.2.4 Developing Regression Equation for Climate Variables with Grid Format . . . . . . . . . . . . . . . . . . . . 8.3 Topographic and Climate Variable Zoning . . . . . . . . . . . . . . . 8.3.1 Elevation Zoning and Coding . . . . . . . . . . . . . . . . . . 8.3.2 Aspect Zoning and Coding . . . . . . . . . . . . . . . . . . . . 8.3.3  0 C Accumulated Temperature Zoning and Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.4 Moisture Index Zoning and Coding . . . . . . . . . . . . . . 8.3.5 PET Zoning and Coding . . . . . . . . . . . . . . . . . . . . . . 8.3.6 Annual Precipitation Zoning and Coding . . . . . . . . . . 8.3.7 Annual Mean Temperature Zoning and Coding . . . . . 8.4 Principal Component Analysis . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Ecoregion Classification Using Artificial Neural Network . . . . 8.5.1 Artificial Neural Networks . . . . . . . . . . . . . . . . . . . . 8.5.2 Ecoregion Classification . . . . . . . . . . . . . . . . . . . . . . 8.5.3 Regional Environmental Characteristics . . . . . . . . . . . 8.5.4 Results of Ecoregion Classification . . . . . . . . . . . . . . 8.6 Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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155 155 157 157 159

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161 165 166 166

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166 167 167 168 168 169 172 172 173 174 175 178 179

9

Land-Cover Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Methods and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.2 MODIS Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.3 Reference and Ancillary Data . . . . . . . . . . . . . . . . . . 9.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.1 Land-Cover Classification . . . . . . . . . . . . . . . . . . . . . 9.3.2 Accuracy Assessment . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Conclusion and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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181 181 183 183 185 186 187 187 189 191 192

10

Fractional Vegetation Cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195

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10.2

Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.1 Empirical Model Development . . . . . . . . . . . . . . . . 10.3.2 Comparison with Carlson and Ripley Algorithm . . . . 10.3.3 Spatial Distribution of Vegetation Coverage . . . . . . . 10.4 Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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197 197 198 199 199 202 203 204 205

Aboveground Biomass of Grassland . . . . . . . . . . . . . . . . . . . . . . . 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.1 Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.2 Remote Sensing Data . . . . . . . . . . . . . . . . . . . . . . . 11.2.3 Field Measurements . . . . . . . . . . . . . . . . . . . . . . . . 11.2.4 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3.1 AGB Magnitude . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3.2 AGB Estimate Model . . . . . . . . . . . . . . . . . . . . . . . 11.3.3 Model Validation . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3.4 Spatial Distribution of AGB . . . . . . . . . . . . . . . . . . 11.4 Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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209 209 211 211 212 213 215 216 216 218 221 222 224 225

About the Author

Duo Chu was born in Panam, Shigatse, Tibet, China, in 1969. He received his B.S. degree in atmospheric science from the Nanjing Institute of Meteorology, Nanjing, in 1990, and his Ph.D. degree in physical geography from the Institute of Geographic Sciences and Natural Resources Research of the Chinese Academy of Sciences, Beijing, in 2003. He is the first Ph.D. degree holder who returned to work in Tibet Meteorological Bureau, where he is currently a research scientist and vice director of Tibet Institute of Plateau Atmospheric and Environmental Sciences, after graduation. He worked at the International Centre for Integrated Mountain Development (ICIMOD) from 2007 to 2008 in Kathmandu, Nepal, as country coordinator of HKKH Partnership Project from China. Moreover, he was the visiting scholar in the Norwegian Meteorological Institute at Tromsø in 2000, the senior visiting scholar in the University of Colorado at Boulder in 2017 in the USA, the adjunct professor at Tibet University and Chengdu University of Information Technology, a council member of the China Society on Tibetan Plateau, and a member of the Chinese Meteorological Society. His primary research interests are remote sensing of land cover and land use, surface biophysical parameters retrieval, and snow cover. He has extensive experiences working on remote-sensing applications in environmental change and monitoring in high mountain regions in Tibetan Plateau, has published more than 60 scientific papers in peer-reviewed journals and two books

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About the Author

(Remote Sensing of Terrestrial Parameters in the Tibetan Plateau, China Meteorological Press, 2015; Snow Atlas of Tibetan Plateau, China Meteorological Press, 2018), and is coauthor of two books. He received a number of awards, including the Fifth National Excellent Scientific and Technological Worker in 2012 from the China Association for Science and Technology, Outstanding Young Talent in West China in 2008 and 2010, and Scientific and Technological Leading Talent in 2013 and 2016, from the China Meteorological Administration(CMA), one First Class Award (2004) and three Third Class Awards (2004, 2011, 2016) of Science and Technology Progress from the People’s Government of Tibet Autonomous Region (TAR), one Second Class Award of Science and Technology Progress of Lhasa Municipality in 2012, and one Second Class Award of CMA Scientific and Technological Achievement Application in 2005.

Chapter 1

Introduction

Abstract Land-use and land-cover change (LUCC) plays a pivotal role in global environmental change and significantly affects Earth-atmosphere interactions, ecosystem services, climate change, biogeochemical cycles, and biodiversity. Understanding their dynamic processes and impacts is crucial to better use and manage precious land resources and to realize sustainable development. Global mountain regions cover one fourth of the Earth’s land surface and are most vulnerable and sensitive to environmental change and global warming, which provide unique opportunities to monitor and study environmental change processes and consequences. Meanwhile, remote sensing provides spatially continuous observations on the Earth’s surface from space. The integration with geographic information systems (GIS) is an effective approach to characterize, map, and monitor land-use and land-cover change and dynamic processes. With the advancement in remote sensing, GIS, and computer technology, it is now possible to monitor, map, and assess land cover and land-cover changes at multiple spatial and temporal scales with more spatially and temporally explicit ways. Keywords LUCC · Global significance · Remote sensing · Mountain region

1.1

Importance of LUCC

Land use has generally been considered a local environmental issue, but it is becoming a force of global importance (Foley et al. 2005). Land cover is a fundamental variable that impacts on and links many parts of the human and physical environments. Land cover change is, for example, regarded as the single most important variable of global change affecting ecological systems (Vitousek 1994; Penner 1994; Findell et al. 2017) with an impact on the environment that is at least as large as that associated with climate change (Skole 1994). Changes in land use and land cover considerably alter the Earth’s energy balance and biogeochemical cycles,

© Springer Nature Singapore Pte Ltd. 2020 D. Chu, Remote Sensing of Land Use and Land Cover in Mountain Region, https://doi.org/10.1007/978-981-13-7580-4_1

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1 Introduction

which contributes to climate change and in turn affects land surface properties and the provision of ecosystem services (Foley et al. 2005; Alkama and Cescatti 2016; Song et al. 2018). Worldwide changes to forests, farmlands, rangelands, and waterways are being driven by the need to provide food, fiber, water, and shelter for more than seven billion people. Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity (Foley et al. 2005; Newbold et al. 2015). Such changes in land use have enabled humans to appropriate an increasing share of the planet’s resources, but they also potentially undermine the capacity of ecosystems to sustain food production, maintain freshwater and forest resources, regulate climate and air quality, and ameliorate infectious diseases (Foley et al. 2005; Findell et al. 2017; Song et al. 2018). We are facing the challenge of managing trade-offs between immediate human needs and maintaining the capacity of the biosphere to provide goods and services in the long term (Foley et al. 2005; Giri 2012; Bonan and Doney 2018). Land-use activities whether converting natural landscapes for human use or changing management practices on human-dominated lands have transformed a large proportion of the planet’s land surface (Foley et al. 2005). By clearing tropical forests, practicing subsistence agriculture, intensifying farmland production, or expanding urban centers, human actions are changing the world’s landscapes in pervasive ways (Defries et al. 2004; Smith et al. 2016; Vitousek et al. 1997). Although land-use practices vary greatly across the world, their ultimate outcome is generally the same: the acquisition of natural resources for immediate human needs, often at the expense of degrading environmental conditions. Several decades of research have revealed the environmental impacts of LUCC throughout the globe, ranging from changes in atmospheric composition to the extensive modification of Earth’s ecosystems (Otterman 1974; Sun et al. 2016; Houghton and Nassikas 2017). For example, land-use practices have played a role in changing the global carbon cycle and, possibly, the global climate. Since 1850, roughly 35% of anthropogenic CO2 emissions resulted directly from land use (Houghton and Hackler 2001). Land-cover changes also affect regional climate through changes in surface energy and water balance (Yin et al. 2018; Lawler et al. 2014). Humans have also transformed the hydrologic cycle to provide freshwater for irrigation, industry, and domestic consumption (Vörösmarty et al. 2000). Furthermore, anthropogenic nutrient inputs to the biosphere from fertilizers and atmospheric pollutants now exceed natural sources and have widespread effects on water quality and coastal ecosystems (Bennett et al. 2001). Human land-use activity has also caused declines in biodiversity through the loss, modification, and fragmentation of habitats; soil and water degradation; and overexploitation of native species (Newbold et al. 2015).

1.2 Background of LUCC Research

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Background of LUCC Research

Our concerns about LUCC emerged in the research agenda on global environmental change several decades ago with the realization that land surface processes influence climate. In the mid-1970s, it was recognized that land-cover change modifies surface albedo and thus surface-atmosphere energy exchanges, which have an impact on regional climate (Charney and Stone 1975; Sagan et al. 1979). In the early 1980s, terrestrial ecosystems as sources and sinks of carbon were highlighted; this underscored the impact of LUCC on the global climate via the carbon cycle (Woodwell et al. 1983; Houghton et al. 1985). Later, the important contribution of local evapotranspiration to the water cycle—that is precipitation recycling—as a function of land cover highlighted yet another considerable impact of LUCC on climate, at a local to regional scale (Eltahir and Bras 1996; Lambin et al. 2003; Mahmood et al. 2014). Considering the significance of LUCC in global environment change especially in global climate change, the International Geosphere-Biosphere Program (IGBP) and the International Human Dimensions Program on Global Environmental Change (IHDP) jointly developed a long-term international research initiative land-use and land-cover change (LULCC) (Turner II et al. 1995). Subsequently, many international organizations, research institutes, and countries developed their own research plans and projects on LULCC (Turner II et al. 1994), such as the US Climate Change Science Program and Land-Cover and Land-Use Change (LCLUC) program of the National Aeronautics and Space Administration (NASA). LULCC is an interdisciplinary project designed to improve the understanding and projections of the dynamics of land-use and land-cover change. The LULCC community of scientists aims for new integrated and regional models, informed by empirical assessments of the patterns of land use and case studies that explain the processes underpinning such configurations of land-use and land-cover change over varying spatial and temporal scales (Lambin et al. 1999). In 2005, IGBP and IHDP jointly facilitated the Global Land Project science plan and implementation strategy to improve the understanding of land system dynamics in the context of Earth system functioning (Ojima et al. 2005), which is now renamed into the Global Land Programme (GLP). The International Council for Science (ICSU) and International Social Science Council (ISSC) launched the Future Earth research program in 2012. GLP is a core project of Future Earth, and its science plan and implementation strategy for 2016–2021 is being put into practice (Verburg et al. 2016). The measurement of land-use and land-cover spatiotemporal processes has become important to deepen the understanding of the research plan of the dynamic planet and to achieve future sustainability objectives (Future Earth 2013).

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1.3

1 Introduction

Land-Use and Land-Cover Concepts and Linkages

Land use is defined as the way or manner in which the land is used or occupied by humans (a set of human actions), while land cover is the biophysical state of the Earth’s surface and immediate subsurface, which is an integrated expression of the physical, climatic, and biotic environment as well as the history of land use by humans (Meyer and Turner 1992; Turner II et al. 1995). Land use is high variability in time and space in biophysical environments, socioeconomic activities, and cultural contexts that are associated with land-use change (Mahmood et al. 2014). Land use involves both the manner in which the biophysical attributes of the land are manipulated and the intent underlying that manipulation—the purpose for which the land is used (Turner II et al. 1995). The term land use is used in the sense of the social and economic purposes for which land is managed (e.g., grazing, timber extraction, and conservation). For instance, forestry, parks, livestock herding, and farmlands are classes denoting intent or purpose. By contrast, biophysical manipulation refers to the specific way that human uses vegetation, soil, and water for the purpose in question: for instance, the cut-burn-hoe-weed sequence in many slashand-burn agricultural systems; the use of fertilizers, pesticides, and irrigation for mechanized cultivation on arid and semiarid lands; or the use of an introduced new grass species for pasture and the sequence of movement of livestock in a ranching system (Turner II et al. 1993, 1995). Land-use change refers to a change in the use or management of land by humans, which may lead to a change in land cover (IPCC 2013). Quantitative land-use and land-cover changes refer to increases or decreases in the aerial extent of a given type of land use or land cover. However, land-cover changes may result either from land conversion, which refers to a change from one cover type to another, or land modification, referring to alterations of structure or function without a wholesale change from one type to another, or even maintenance of land in its current condition against agents of change (Turner II et al. 1990a, 1995; Allen and Barnes 1985). Likewise, land-use change may involve either conversion from one type of use to another (i.e., changes in the mix and pattern of land uses in an area) or modification of a certain type of land use (i.e., changes in the intensity of use or alterations of its characteristic qualities/attributes). Land-use and land-cover changes are strongly linked; the environmental impacts of land-use change and their contribution to global change occur through physical processes associated with land-cover change (Steffen et al. 1992; BAHC 1993; Holligan and de Boois 1993). The process of LUCC is very complex and has different forms, with differences in magnitude and rate (Lambin and Meyfroidt 2011; Fuchs et al. 2015). The detection, measurement, and explanation of LUCC depend on the spatial and temporal level of specific analysis (Fuchs et al. 2015). Small changes in LUCC cannot be detected at high levels of spatial and temporal detail; for instance, conversion of a mall wheat field to a tourist complex is not discernible at the regional or national level. Likewise, long-term trends of LUCC cannot be identified within short time spans and small spatial units; for instance, conversion of agricultural land on the urban fringe into

1.4 LUCC Driving Forces

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suburbs cannot be detected in a 1- or 2-year period or in an area of a few hundreds of hectares. The specification of the spatial and temporal levels of detail is crucial as it guides the selection of the types of land use or land cover and determines the factors influencing the types, processes, and impacts of LUCC within particular spatial or temporal frames (Turner II et al. 1993, 1995).

1.4

LUCC Driving Forces

LUCC has important impacts on the functioning of socioeconomic and environmental systems with important trade-offs for sustainability, food security, biodiversity, and the vulnerability of people and ecosystems to global change impacts (Newbold et al. 2015; Foley et al. 2005), and it is driven by changes in multi-scale interacting driving factors such as biophysical conditions of the land, demography, technology, affluence, political structures, economy, and people’s attitudes and values (Roy et al. 2015). LUCC is the result of the interplay between socioeconomic, institutional, and environmental factors. The key to understanding LUCC is to recognize the role of individual decision-makers bringing about change, through their choices, on land resources and technologies. A unifying hypothesis that links the ecological and social realms, and an important reason for pursuing integrated modeling of LUCC, is that humans respond to cues both from the physical environment and from their sociocultural and economic contexts. Therefore, much LUCC research is devoted to the analysis of relations between land use and the socioeconomic and biophysical variables that act as the “driving forces” of land-use change (Turner II et al. 1993; Lambin et al. 2001). LUCC driving forces are generally subdivided into two groups: proximate causes and underlying causes (Verburg et al. 2016). Proximate causes are the activities and actions that directly affect land use, e.g., wood extraction or road building (Geist and Lambin 2002). Underlying causes are the fundamental driving forces that underpin the proximate causes, including demographic, economic, technological, institutional, and cultural factors (Geist and Lambin 2002). In most cases, a wide range of factors is used to represent the underlying causes; examples include soil suitability, population density, rainfall, and accessibility (Turner II et al. 1995). They can also be differentiated into “driving” forces that are expected to change over time, such as population density and market conditions, and “conditioning” factors that are relatively stable over time but may be spatially differentiated, such as agroclimate and cultural context (Turner II et al. 1993). This allows differentiation into spatial and temporal expectations of change (Geist and Lambin 2002). At different scales of analysis, the different driving forces of LUCC have a dominant influence on the land-use system: at the local level, this can be the local policy or the presence of small ecologically valuable areas; at the regional level, the distance to the market, road, port, or airport might be the main determinant factor of land-use change (Verburg et al. 2003). Land-use change is always caused by multiple interacting factors originating from different levels of organization of the coupled human-environment systems (Turner II

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1 Introduction

et al. 1995). The mix of driving forces of land-use change varies in time and space, according to specific human-environment conditions (Verburg et al. 2003). LUCC driving forces can be slow variables, with long turnover times, which determine the boundaries of sustainability and collectively govern the land-use trajectory, such as the spread of salinity in irrigation schemes or declining infant mortality, or fast variables, with short turnover times, such as food aid or climatic variability associated with El Niño oscillation. Biophysical drivers may be as important as human drivers for LUCC (Giri 2012). The former define the natural capacity or predisposing conditions for land-use changes. The set of abiotic and biotic factors that determine this natural capacity varies among localities and regions. Trigger events, whether these are biophysical, such as a drought or hurricane, or socioeconomic (e.g., economic crisis or a civil war), also drive land-use changes. In general, changes in land use and land cover are driven by a combination of factors that work gradually and factors that happen intermittently (Lambin et al. 2001).

1.5

Global Consequences of LUCC

The pace, magnitude, and spatial reach of human alterations of the Earth’s land surface are unprecedented. Changes in land cover (biophysical attributes of the Earth’s surface) and land use (human purpose or intent applied to these attributes) are among the most important (Turner et al. 1990a; Lambin et al. 1999; Liu et al. 2017). LUCC is so pervasive that when aggregated globally, they significantly affect key aspects of Earth system functioning (Newbold et al. 2016). They directly impact biotic diversity worldwide (Sala et al. 2000); contribute to local and regional climate change (Alkama and Cescatti 2016; Chase et al. 1999) as well as to global climate warming (Houghton et al. 1999); are the primary source of soil degradation (Tolba and El-Kholy 1992; Lambin and Meyfroidt 2011); and, by altering ecosystem services, affect the ability of biological systems to support human needs (Vitousek et al. 1997; Grekousis et al. 2015). Such changes also determine partly the vulnerability of places and people to climatic, economic, or sociopolitical perturbations. Since humans have controlled fire and domesticated plants and animals, they have cleared forests to wring higher value from the land. About half of the ice-free land surface has been converted or substantially modified by human activities over the last 10,000 years. A study estimated that undisturbed (or wilderness) areas represent 46% of the Earth’s land surface (Mittermeier et al. 2003). Forests covered about 50% of the Earth’s land area 8000 years ago, as opposed to 30% today (Ball 2001). Agriculture has expanded into forests, savannas, and steppes in all parts of the world to meet the demand for food and fiber. Agricultural expansion has shifted between regions over time; this followed the general development of civilizations, economies, and increasing populations (FAO 2001a). A study shows that the area of cropland has increased globally from an estimated 300–400 million hectares in 1700 to 1500–1800 million hectares in 1990, a 4.5 to fivefold increase in three centuries and a 50% net increase just in the twentieth

1.5 Global Consequences of LUCC

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century. The area under pasture increased from around 500 million hectares in 1700 to around 3100 million hectares in 1990. These increases led to the clearing of forests and the transformation of natural grasslands, steppes, and savannas. Forest area decreased from 5000–6200 million hectares in 1700 to 4300–5300 million hectares in 1990 (Lambin et al. 2003). According to the global forest resources assessment 2000 (FAO 2001b), the world’s natural forests decreased by 16.1 million hectares per year on average during the 1990s, that is, a loss of 4.2% of the natural forest that existed in 1990. However, some natural forests were converted to forest plantations. Gains in forest cover arose from afforestation on land previously under nonforest land use (1.6 million hectares per year globally) and the expansion of natural forests in areas previously under agriculture, mostly in Western Europe and eastern North America. The net global decrease in forest area was therefore 9.4 million hectares per year from 1990 to 2000. The total net forest change for the temperate regions was positive, but it was negative for the tropical regions. Steppes, savannas, and grasslands also experienced a rapid decline, from around 3200 million hectares in 1700 to 1800–2700 million hectares in 1990. Recent study on global land-change dynamics during the period 1982–2016 shows that tree cover has increased by 2.24 million km2, and this overall net gain is the result of a net loss in the tropics being outweighed by a net gain in the extratropics, while global bare ground cover has decreased by 1.16 million km2, most notably in agricultural regions in Asia. Of all land changes, 60% are associated with direct human activities and 40% with indirect drivers such as climate change (Song et al. 2018; Bonan and Doney 2018). LUCC is environmentally significant in its own right (Lambin and Meyfroidt 2011). It alters the land capacity for sustainable use and ability to regain its original status (Turner II et al. 1995). Land-cover changes and their impacts can become widespread that they are identified as global change in themselves. This kind of change has been referred to as globally cumulative change (Turner II et al. 1990b). Widespread biodiversity loss is one of typical examples. LUCC has led to, or is leading to, significant losses in species numbers and varieties worldwide (Newbold et al. 2016). Wilson (1992) estimates the loss in tropical forests alone to be on the order of 27,000 species annually, although much higher estimates exist. Ecosystem structure and function, long-term ecological processes, and genetic diversity are also at risk in biodiversity loss. Biodiversity losses take place at multiple levels (landscape, ecosystem, species, and gene) and in multiple dimensions (structural, functional, and processual) and, therefore, are particularly important for the structure and function of large-scale ecological processes, with implications for land use as well as other forms of global change (Schulze and Mooney 1993). The transformation from natural ecosystems to agricultural land use and its continued intensification has led to extensive losses in biodiversity and ecosystem services, resulting in the degradation of human well-being (Barnes et al. 2014; Gilbert 2012). Land conversion can alter regional climates through its effects on net radiation, the division of energy into sensible and latent heat, and the partitioning of precipitation into soil water, evapotranspiration, and runoff (Vitousek 1994). Modeling studies demonstrate that land-cover changes in the tropics affect climate largely through water-balance changes, but changes in temperate and boreal vegetation

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influence climate primarily through changes in the surface radiation balance (Penner 1994; Findell et al. 2017). Large-scale clearing of tropical forests may create a warmer, drier climate, whereas clearing temperate and boreal forest is generally thought to cool the climate, primarily through increased albedo (Foley et al. 2005). Urban heat island effect is an extreme example that how land use affects regional climate. Impervious land surface, vegetation cover decreasing, and high buildings in cityscapes lead to lower evaporative cooling, store heat, and warmer surface air. A study in the United States shows that the main portion of the temperature increase over the last several decades were attributed to urbanization and other land-use changes (Kalnay and Cai 2003; Newbold et al. 2015). Land-use activities change air quality as well by changing emissions and the atmospheric conditions, affecting reaction rates, transport, and deposition of air pollution. Land-use practices largely determine air pollution sources such as dust sources, vehicle emission, biomass burning, etc. Moreover, the effects of land use on regional meteorological condition, particularly in urban area, affect air quality, which showing higher urban temperatures generally cause O3 to increase (Sillman and Samson 1995; Lawler et al. 2014). Similarly, impacts of land cover on biogeochemical cycles are significant. Landcover conversion is an important historical and contemporary component of other forms of global change (Turner II et al. 1995). The historical conversion of natural systems to agriculture and other human uses of the land has resulted in a net release of carbon dioxide to the atmosphere (Houghton et al. 1985), one roughly equivalent to the release from fossil fuel burning over the last 150 years, although the current release of carbon dioxide from land-cover change is approximately 30% of fossil fuel combustion. Data both from land-use and land-cover changes are important for determining the biogeochemical cycling of carbon, nitrogen, and other elements at regional to global scales (Houghton et al. 2015). The estimates of carbon released from land clearing and biomass burning combined with the estimates of oceanic uptake of carbon cannot now be reconciled in a balanced global budget. Land-cover data are integral to analyses of other gas dynamics. Natural ecosystems determine the dynamics of many important species such as CH4 and N2O. Ecosystem conversion results in changes in trace gas dynamics. Conversion of tropical forest to pasture seems to be an important factor in trace gas dynamics for years after pasture formation (Baccini et al. 2012). Land is often converted through biomass burning, which may be an important source of CH4, CO, and other relatively important trace gases (Crutzen and Andreae 1990; Houghton et al. 2015). The atmospheric concentrations of CO2 and other trace gases are closely linked to each other through their involvement with and interactions in chemical processes in the atmosphere (Prinn 1994). When compiling a list of the sources and sinks of these gases, it is apparent that both the land-cover and land-cover changes play major roles in determining their actual emissions and thus final atmospheric trace gas concentrations (Leemans et al. 1995; Baccini et al. 2012). Land-cover change has an important influence on water and energy balance. Land cover determines surface roughness, albedo, and latent and sensible heat flux, and changes in the distribution of land cover alter the regional, and possibly global, balance of these fluxes (Pielke 2001; Lambin and Meyfroidt 2011. Mahmood et al.

1.6 Approaches to LUCC Research

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2014; Eichelmann et al. 2018). Land cover is an important parameter for general circulation models (GCMs). These models use coarse grids of 200 km spatial resolution. Therefore, a general distribution of land cover might be sufficient. However, the aggregate sum of various boundary layer transfers for each coarse grid would be dependent on a sub-grid parameterization (Turner II et al. 1995). Latent heat flux, for instance, is mediated by evapotranspiration. Actual evapotranspiration (AET) is a function of land-cover type, soil moisture, and climate variables, such as temperature. Changes in vegetation cover mediate the water balance and therefore influence AET. AET is a function of whole plant and xylem water potentials, leaf area and stomatal closure, rooting depth, and canopy structure across the soil-plant-atmosphere continuum. AET and water use vary spatially across different ecosystems and temporally as one land cover is converted to another. They also present strong seasonality. In these terms, geographically referenced land-cover datasets with a seasonality component at regional or global levels are important for climate modeling beyond their simple utilization as a means to parameterize sensible heat flux (Turner II et al. 1995). Since water balance and physiological controls on latent heat flux mediated by vegetation occur seasonally and at finer spatial scales, there is a need to develop the appropriate land-cover datasets as coarse inputs to GCMs. LUCC also have impacts on sources and sinks of greenhouse gases or other properties of the climate system and may thus give rise to radiative forcing and/or other impacts on climate, locally or globally (IPCC 2014). Global hydrological cycle involves the movement of water over large continents. In this cycle, plants act like water pumps, extracting water from soils and returning it to the atmosphere through evaporation and transpiration. Water recycling in the Amazon rain forest is exemplary; the present precipitation patterns observed are partly a function of the vegetation cover (Victoria et al. 1991). Changes in surface land cover may trigger changes in the hydrological cycle, which in turn would have significant implications for land use and relevant environmental changes (Turner II et al. 1995). However, the impacts of the hydrological cycle caused by LUCC and their consequences in the Amazon region are not yet adequately assessed. As one of the few such assessments, the study shows that a significant regional decrease in evaporation and precipitation would follow a large amount of removal of rain forest in the Amazon region (Shukla et al. 1990; Wang et al. 2009). Likewise, at the global scale, LUCC has been shown to have an important impact on atmospheric circulation (Foley et al. 1994; Snyder 2010; Mahmood et al. 2014) and regional climate extremes (Findell et al. 2017).

1.6

Approaches to LUCC Research

Scientific discipline and tradition have caused two distinctly different approaches to emerge in the field of land-use studies. Researchers in the social sciences have a long tradition of studying individual behavior at the microlevel, some of them using qualitative approaches (Bilsborrow and Ogondo 1992) and others using the

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quantitative models of microeconomics and social psychology. Rooted in the natural sciences rather than the social, geographers and ecologists have focused on land cover and land use at the macroscale, spatially explicated through remote sensing and GIS, and using macro-properties of social organization in order to identify social factors connected to the macroscale patterns (Turner II et al. 1995; Verburg et al. 2004). Scientists associated with the LUCC research community share a common, geographic, interest: the future of the land, while LUCC simulation models are effective and reproducible tools for analyzing both the causes and consequences of future land-use dynamics under various scenarios. Much of the integration of knowledge on land-use change takes place through spatial models that aim at explaining the causes, locations, consequences, and trajectories of land-use change (Verburg and Veldkamp 2005). The variety in disciplinary origin of the researchers contributing to LUCC study has led to a wide range of different modeling approaches and techniques to support the analysis of the causes and consequences of land-use changes in order to better understand the functioning of the land-use system and to support land-use planning and policy. Models are very useful for disentangling the complex suite of socioeconomic and biophysical forces that influence the rate and spatial pattern of land-use change and for estimating the impacts of changes in land use. Furthermore, models can support the exploration of future land-use changes under different scenario conditions. In a word, land-use models are useful and reproducible tools, supplementing our existing mental capabilities to analyze land-use change and to make more informed decisions (Costanza and Ruth 1998). A wide range of models are used for spatial analysis of LUCC, including dynamic systems models, discrete finite state models, optimization models, Markov chain models, and cellular automata models (Verburg et al. 2004). Many empirical methods are often applied to the analysis of spatial patterns of LUCC, including principal component analysis, factor analysis, canonical correlation analysis, and cluster analysis for exploratory spatial data analysis in terms of data reduction and structure detection (Lesschen et al. 2005; De Almeida et al. 2003). Linear regression is a frequently used technique; however, in LUCC modeling, this regression is less popular because linear regression can only be applied for continuous dependent variables. Instead logistic or multinomial regression is used, because land use is normally expressed as a discrete variable. Other regression analysis methods applied to land-use modeling include ordered logit, Tobit analysis, simultaneous regression, etc. Artificial neural networks are powerful tools that use a machine learning approach to quantify and model complex behavior and patterns. They are used for pattern recognition, prediction, and classification in a variety of disciplines, such as economics, medicine, landscape classification, and remote sensing. It is not a statistical technique, but in its functioning, it is related to regression models. The use of neural networks has increased substantially over the last several years because of the advances in computing performance. A number of applications in land-use-related research have been published (Pijanowski et al. 2005).

1.7 Remote Sensing Applications in LUCC

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Data constraints have been identified as one of the key factors that have limited the development of practical land-use change models that directly account for economic factors and human behavior. Another characteristic of many land-use change models is that they are not independent executables but an integrated component of a larger model that may include demographic, ecological, economical, and hydrological modules as well. Such integrated model design would require the parameterization, calibration, and execution of the overall model. Examples of these models include the CLUE model, which has have well-developed ecological sectors and extensive human decision-making elements as well as feedback among sectors (Veldkamp and Fresco 1996), and NELUP model extension, which is a farm-level model that includes the impact of farming decisions on changes in intensity of land use and in land cover. The general NELUP model has ecological and economic components and farming decisions and can serve as a decision support tool to provide feedback on the impact of collective-level policies (Agarwal et al. 2001). A new LUCC model FLUS is developed and is used for simulating multiple land-use scenarios by coupling human and natural effects (Liu et al. 2017).

1.7

Remote Sensing Applications in LUCC

Remote sensing (RS) is the science of obtaining and interpreting information from a distance, using sensors that are not in physical contact with the object being observed. It may be broadly defined as “the collection of information about an object without being in physical contact with the object” (Chuvieco and Huete 2009). Remote sensing techniques allow taking images of the Earth surface in various wavelength regions of the electromagnetic spectrum (EMS) to acquire and interpret information of the Earth surface (Smith 2001; Morain 1991). Satellite remote sensing, in conjunction with GIS, has been widely applied and been recognized as a powerful and effective tool in detecting LUCC (Ehlers et al. 1990; Weng, 2002; Giri 2012). It provides cost-effective multispectral and multitemporal data and turns them into information valuable for understanding and monitoring land development patterns and processes and for building land-use and land-cover datasets, while GIS technology provides a flexible environment for storing, analyzing, and displaying digital data necessary for change detection and database development. Remote sensing offers several advantages. It is a relatively inexpensive and rapid method of acquiring up-to-date information over a large geographical area owing to its synoptic coverage and repetitive measurements. Remote sensing data usually acquired in digital form are easier to manipulate and analyze; they can be acquired not only from visible but also from spectral ranges that are invisible to human eyes; they can be acquired from remote areas where accessibility is a concern; and they provide an unbiased view of land use and land cover (Giri 2012). With the recent advancement in remote sensing and GIS and computer technology, it is now possible to assess and monitor LUCC at multiple spatial and temporal

12

1 Introduction

scales (Giri 2012; Weng 2011). For example, the USGS (US Geological Survey) National Land Cover Database (NLCD) 2011 is an integrated database encompassing land-use and land-cover change products at various thematic, spatial, and temporal resolutions. Historically, aerial photography interpretation (API) has been the basis for mapping land use and land cover in a region (Donnay et al. 2001). The advantage of high spatial resolution (1-3 m/pixel) aerial photography is the accuracy of the interpretation in a complex landscape and the ability to distinguish different types of land use as well as land cover. Multispectral satellite imagery (MSSI) is also used to classify land use and land cover (Zhu and Blumberg 2002; Wentz et al. 2006). The advantages of using satellite imagery are that data can be collected and analyzed at time intervals more frequently, and due to the higher information content of multispectral data, there is less subjective interpretation than with aerial photographs for land cover. The disadvantage is that because of the subpixel complexity of the land surface at the spatial resolutions of most readily available satellite datasets (10–90 m/pixel), certain land-use and land-cover categories are difficult to classify accurately using simple classification approaches (Foody 2002). In contrast with API and MSSI classification techniques, ground observations of land use and land cover can only identify land use and land cover at point locations and are not the appropriate tool with which to map an entire region. Instead, ground observations are frequently used to verify classifications made by other methods. Among the multispectral satellite imagery, MSS and Thematic Mapper (TM) data have been extensively used for various land-cover analysis since the Landsat program began in 1972. However, the relatively low resolution of the MSS (79 m/ pixel) and TM (28.5 m/pixel) data only allows classification of land cover to Levels 1–2 of the Anderson system (Anderson et al. 1976). Higher spatial-resolution sensors have been used for land-cover mapping studies and allow a land-cover classification to Levels 2–3 of the Anderson system. These instruments include the 10–20 m/pixel SPOT, as well as airborne scanners with 3–15 m/pixel resolution such as the Thermal Infrared Multispectral Scanner (TIMS) and the TM simulator (NS001) (Quattrochi and Ridd 1998). ASTER, ETM+, or increased (1 m/pixel or less) spatial resolution (IKONOS, QuickBird) allow even more precise land-cover classification at a local scale. As the most frequently used data at a regional scale, Landsat MSS and TM will continue to be used as a historical global database (Stefanov et al. 2001; Bartholomé and Belward 2005). In the past four decades, land-cover mapping at continental and global scales has been revolutionized by the use of remote sensing imagery from the NOAA/AVHRR satellites. The first global land-cover map was made using data composited to 1 by DeFries and Townshend (1994). Tucker et al. (1985) made a land-cover map of Africa based on principal components of 12 monthly NDVI values. Townshend et al. (1987) likewise described 16 South American vegetation types. Loveland et al. (1991) developed a land-cover dataset for the conterminous USA based on multitemporal NDVI data supplemented by ancillary data. The USGS produced the global land-cover classification for the IGBP with 17 classes (Loveland et al. 2000). The University of Maryland (UMD) produced global land-cover classification with

1.8 LUCC in Mountain Regions

13

14 classes (Hansen et al. 2000). The Boston University produced global land cover using both IGBP and UMD legend (Friedl et al. 2002). Other global datasets include 1 km Global Land Cover 2000 (GLC2000) sponsored by the European Space Agency (ESA), 500 m MODIS land-cover map, and 300 m ESA GlobCover landcover map. Chen et al. (2015a) produced the first 30 m-resolution global land-cover dataset (GlobeLand30). In addition, other coarse-resolution imagery from the SPOT Vegetation instrument and the European Medium-Spectral Resolution Imaging Spectrometer (MERIS) onboard ENVISAT also are important sources of remotely sensed data for global land-cover mapping (Bartalev et al. 2003; Han et al. 2015). Global datasets of land cover are a significant requirement for global environmental change and climate models (Grekousis et al. 2015; Yan and Roy 2015; Weng 2011; Giri 2012). To meet the increasing needs for these datasets, tremendous efforts have been made to produce global land-cover datasets by using coarse spatialresolution, high temporal-frequency data. The following four global land-cover datasets are freely available at present, and these products are widely used by the international science community: (1) IGBP land-cover data, (2) UMD 1 km global datasets, (3) Global Land Cover 2000 (GLC2000), and (4) MODIS global land cover. Although a variety of methods have been used for making these datasets and global land-cover map, for land-cover classification, there are two main approaches that can be adopted: the supervised approach and the unsupervised one (Richards 1993; Bruzzone and Serpico 1997; Ban and Jacob 2013). The former is based on supervised classification methods, which require the availability of a multi-temporal ground truth. The latter performs change detection by making a direct comparison of the two multispectral images considered, without relying on any additional information. Despite the supervised approach requiring the availability of ground truth information for both considered dates, it exhibits some important advantages over the unsupervised one: (1) capability of explicitly recognizing the kind of land-cover transitions which occurred in the investigated area, (2) robustness to the different atmospheric and light conditions at the two acquisition times, and (3) ability to process multi-sensor/multisource images.

1.8

LUCC in Mountain Regions

Mountain regions occupy about one fourth of the Earth’s surface; they are home to approximately one tenth of the global population and provide goods and services to about half of humanity (Messerli and Ives 1997). Forty percent of global population lives in the watersheds of rivers originating in the planet’s different mountain ranges. Mountains are a source of inspiration and recreation for a crowded world, but they also serve as the “Water Towers of the World,” and with a growing emphasis globally on water resource issues, this function is crucial for human well-being. Accordingly, they received particular attention in “Agenda 21,” a program for sustainable development into the next century adopted by the United Nations

14

1 Introduction

Conference on Environment and Development (UNCED) in June 1992 in Rio de Janeiro. Chapter 13 of this document focuses on mountain regions and states: Mountains are important sources of water, energy, minerals, forest and agricultural products and areas of recreation. They are storehouses of biological diversity, home to endangered species and an essential part of the global ecosystem. From the Andes to the Himalayas, and from Southeast Asia to East and Central Africa, there is serious ecological deterioration. Most mountain areas are experiencing environmental degradation.

Furthermore, the strong altitudinal gradients in mountain regions provide unique and sometimes the best opportunities to detect and analyze global change processes and phenomena as the following reasons. 1. Meteorological, hydrological (including cryospheric), and ecological conditions (in particular vegetation and soils) change strongly over relatively short distances. Consequently, biodiversity tends to be high, and characteristic sequences of ecosystems and cryospheric systems are found along mountain slopes. The boundaries between these systems (e.g., ecotones, snowline, and glacier boundaries) may experience shifts due to environmental change and thus can be used as indicators; some of them can even be observed at the global scale by remote sensing. 2. For many mountain ranges, snow and ice are a key component of the hydrological cycle, and the seasonal character and amount of runoff are closely linked to cryospheric processes. In addition, because of the sensitivity of mountain glaciers to temperature and precipitation, the behavior of glaciers provides some of the clearest evidence of atmospheric warming and changes in the precipitation regime, both modulated by atmospheric circulation and flow patterns over the past decades. Change in climate has been shown to result in shifts in seasonal snow pack (Haeberli and Beniston 1998). 3. Many mountain ranges, particularly their higher parts, are not affected by direct human activities. These areas include many national parks and other protected, “near-natural” environments, including biosphere reserves. They may serve as locations where the environmental impacts of climate change alone, including changes in atmospheric chemistry, can be studied directly. 4. Mountain regions are distributed all over the globe, from the Equator almost to the poles and from oceanic to highly continental climates. This global distribution allows us to perform comparative regional studies and to analyze the regional differentiation of environmental change processes in mountains as characterized above. Moreover, mountain regions typically offer a wide variety of ecosystems within a small geographical area, thus providing a small-scale model for latitudinal changes (Becker and Bugmann 2001). In the UN 2030 Agenda for Sustainable Development, it is further recognized that mountain and upland areas have a universal importance: they provide water and other global goods and services to humanity. However, mountain ecosystems are highly vulnerable to climate change, extreme weather events, and land degradation and recover slowly from disasters and shocks. In this Agenda, member states

1.8 LUCC in Mountain Regions

15

pledged to leave no one behind and stressed the importance of reaching those furthest behind first and, by 2030, ensure the conservation of mountain ecosystems, including their biodiversity, in order to enhance their capacity to provide benefits that are essential for sustainable development (United Nations 2016). With the rapid industrialization and population growth worldwide, the natural environment has undergone unprecedented changes (Beniston 2003). Although mountains differ considerably from one region to another, one common feature is the complexity of their topography. Orographic features include some of the sharpest gradients found in continental areas. Related characteristics include rapid and systematic changes in climatic parameters, in particular temperature and precipitation, over very short distances (Becker and Bugmann 1997); greatly enhanced direct runoff and erosion; and systematic variation of other climatic (e.g., radiation) and environmental factors, such as differences in soil types. Mountains in many parts of the world are susceptible to the impacts of a rapidly changing climate and provide interesting locations for the early detection and study of the signals of climatic change and its impacts on hydrological, ecological, and societal systems (Wester et al. 2019). LUCC and related environmental deterioration in mountains can be driven by numerous factors that include deforestation, overgrazing by livestock, cultivation of marginal soils, and climate change. On the other hand, mountain ecosystems are susceptible to soil erosion, landslides, and the rapid loss of habitat and genetic diversity. In many developing countries, in part because of the degradation of the natural environment, there is widespread unemployment, poverty, poor health, and bad sanitation (Price and Butt 2000). Mountain forests are changing in extent, structure, and composition at an accelerating rate under the influence of forces associated with both global change and local and regional management strategies (Price and Butt 2000). While forest cover is declining in most of the world’s mountain systems, there are significant deviations from this trend. Over the last 100 years, forests have been reestablished on abandoned agricultural land in eastern North America and Western Europe. Mountain glaciers also are changing rapidly. Since 1850 the glaciers of the European Alps have lost about 30–40% of their surface area and about half of their volume (Haeberli and Beniston 1998). Similarly, glaciers in the Southern Alps of New Zealand have lost 25% of their area over the last 100 years (Chinn 1996), and glaciers in several regions of central Asia have been retreating since the 1950s (Fitzharris et al. 1996). The 7-year average rate of ice loss for several glaciers monitored in the US Pacific Northwest was higher for the period since 1989 than for any other period studied (Hodge et al. 1998). Accelerated retreat has also been reported for the tropical Andes (Thompson et al. 2000). The Tibetan Plateau (TP) and surroundings contain the largest number of glaciers outside the polar regions. These glaciers are at the headwaters of many prominent Asian rivers and are largely experiencing shrinkage, which generally presents decreases from the Himalayas to the continental interior and the least in the eastern Pamir (Yao et al. 2012; Shekhar et al. 2017). The study shows that in the Himalayas, glaciers and glacial lakes are changing at alarming rates. Himalayan glaciers are retreating at rates ranging from 10 to 60 m per year, and many small glaciers (0.13, are evident. The associated peak values of greenness also vary on an interannual basis. The vegetation phenology clearly responds to the seasonal atmospheric forcings, demonstrating strong seasonal cycles. The vegetation in the Lhasa area starts to grow around late May/early June, achieving peak NDVI values around July or August. Then, the NDVI value slowly decreases as vegetation senescence takes over. The lowest NDVI values occur between March and April. The maximum monthly-mean domain-averaged NDVI value from 1985 through 1999 is 0.32, which occurred in June 1999, while the minimum value was 0.01 in March 1997. The average values of

7.3 Land-Cover Change and Climate Impacts

141

0.35

(a)

Domain-average NDVI

0.30 0.25 0.20 0.15 0.10 0.05

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

0.00

250

(b) Precipitation(mm)

200 150 100 50

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

20

1986

1985

0

(c)

Temperature(˚C)

15 10 5 0

1985

-5

Fig. 7.1 (a) Time series of domain-averaged monthly-mean NDVI from 1985 to 1999. The solid line shows a trend of 0.024/decade. (b) Time series of monthly total precipitation from 1985 to 1999. The solid line shows a trend of 6.35 mm/decade. (c) Time series of monthly-mean temperature from 1985 to 1999. The solid line shows a trend of 0.69  C/decade

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7 Land-Cover Change 0.3

5-week smoothed NDVI in 1986(driest year) 5-week smoothed NDVI in 1990(wettest year)

0.25

NDVI

0.2 0.15 0.1 0.05 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 (Week)

Fig. 7.2 Weekly maximum NDVI for 1986 and 1990

Fig. 7.3 (a) Domain-averaged monthly mean NDVI; (b) monthly mean temperature; and (c) monthly total precipitation. All data are from 1985 through 1999

7.3 Land-Cover Change and Climate Impacts

143

maximum and minimum monthly-mean NDVI of each year from 1985 through 1999 is 0.21 and 0.09, respectively. Mean value from 1985 through 1999 calculated by using monthly-mean NDVI is 0.14. Comparison of the meteorological and NVDI data in Fig. 7.3 shows that maximum precipitation is not always accompanied by maximum vegetation growth (see, e.g.,1988). The total accumulated annual precipitation seems to relate more to the magnitude of NDVI than any specific monthly maximum. Temperature also plays an important role; elevated temperature if combined with increased rainfall can lead to prolonged and maximized vegetation growth, as shown in year 1999.

7.3.3

Relationships Between NDVI and Climatic Variables

The relationship between NDVI and precipitation in the Lhasa area has been analyzed from 1985 to 1999. As shown in Fig. 7.1, the resemblance of interannual variation of precipitation and NDVI is remarkable. Both are characterized by high values during summer and low values during spring and winter. This is mainly because the precipitation in the Lhasa area is strongly influenced by warm and humid air mass coming from Indian Ocean, which is driven by the South and East Asian monsoon system, and annual vegetation growth is very sensitive to rainfall patterns. A detailed correlation between NDVI and rainfall over the year, especially during the growing season, is summarized in Table 7.3. The results show that NDVI is more sensitive to rainfall during the growing season (April to August) than that over the entire year. Table 7.3 summarizes the statistical analysis between NDVI, annual total precipitation amount, and relationship between monthly NDVI and monthly precipitation amount for 1985–1999. Generally, the maximum monthly rainfall amount occurs in August or July, and the minimum monthly precipitation falls in November and December. The correlation coefficients between monthly NDVI and corresponding monthly precipitation from 1985 to 1999 are greater than 0.70 (P < 0.05) except for 1988, when the coefficient fell to 0.67 (P < 0.05). Correlation coefficients from April to August are greater than that from January to December. The correlation coefficient between monthly NDVI and monthly total precipitation from 1985 to 1999 is 0.75 (P < 0.01). In conclusion, in the semiarid climatic zone in Tibetan Plateau, the NOAA AVHRR NDVI, and precipitation in the Lhasa area are highly correlated. Temperature also affects vegetation growth over the Tibetan Plateau. The monthly temperature changes also resemble monthly NDVI changes (Fig. 7.1). The correlation coefficient between monthly-mean temperature and NDVI over the period 1985–1999 is 0.63 (P < 0.01). The lower correlation coefficient between monthly temperature and NDVI, compared to 0.75 between the monthly precipitation and NDVI, implies that precipitation is the main factor affecting NDVI variations in the Lhasa area. Since the historical maximum temperature in the Lhasa area is lower than 30  C, the main impact of temperature on vegetation growth in the

Year 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Time

Precipitation (mm) Annual total precipitation 529.8 288.6 386.5 418 364.3 613.8 476.3 291.7 535.9 371.8 517.8 448.9 321.5 580.9 538.1

Temperature ( C) Annual mean temperature 8.1 7.8 8 8.3 8.5 7.6 8 7.7 8.2 8.5 8.8 8.5 7.5 9 9.2

Maximum and minimum monthly NDVI and corresponding months Max Month of Min Month of NDVI max NDVI NDVI min NDVI 0.24 August 0.06 April 0.24 July 0.03 March 0.21 September 0.05 March 0.21 August 0.05 February 0.24 July 0.07 March 0.24 August 0.05 February 0.26 July 0.07 April 0.24 Jul. Aug. 0.08 April 0.24 July 0.07 April 0.26 July 0.07 April 0.24 July 0.07 March 0.29 August 0.08 March 0.2 July 0.01 March 0.3 September 0.09 March 0.32 Jun 0.07 April

Correlation coefficients between monthly total rainfall and monthly NDVI January to April to December August 0.88 0.91 0.72 0.84 0.74 0.68 0.62 0.67 0.79 0.86 0.81 0.8 0.9 0.98 0.89 0.91 0.9 0.98 0.85 0.88 0.71 0.94 0.77 0.76 0.7 0.88 0.81 0.87 0.79 0.74

Correlation coefficients between monthly-mean temperature and monthly NDVI January to April to December August 0.68 0.82 0.68 0.91 0.76 0.9 0.57 0.88 0.71 0.75 0.75 0.71 0.66 0.89 0.73 0.92 0.72 0.91 0.73 0.93 0.21 0.48 0.62 0.76 0.64 0.8 0.81 0.8 0.57 0.91

Table 7.3 Correlation coefficients (P6000 8

Table 8.3 Aspect zoning and codes Range of aspect Direction Code

330 –30 N 1

30 –90 NE 2

90 –150 SE 3

150 –210 S 4

210 –270 SW 3

270 –330 NW 2

environmental assessment in the central TAR using remote sensing and GIS in the 1990s, agroclimate resources zoning in the late 1990s, etc.

8.3.1

Elevation Zoning and Coding

The elevation zoning refers to zone elevation derived from DEM with 300–500 m different intervals and each elevation zone has specific indicating and practical meanings. The eight elevation zones and corresponding coding numbers are shown in Table 8.2 (Zhang and Chu 1998).

8.3.2

Aspect Zoning and Coding

Aspect zoning is also derived from DEM data. Its value ranges from 0 to 360 on clockwise direction with starting aspect ¼ 330 . Under condition of true north as shaded slope and true south as Sun-facing slope, the aspect is classified into six zones (Zhang and Chu 1998), which is shown in Table 8.3.

8.3.3


0 C Accumulated Temperature Zoning and Coding

The daily mean air temperature in spring steadily greater than 0 C is in agreement with local soil thawing at daytime and freezing at night, budding of winter crops such as winter wheat and rape, planting early spring crops, grass germination, starting of farming activities such as spring plowing. The end date that daily mean temperature steadily is greater than 0 C corresponds with ending growth of winter crops, starting of soil freezing, and commencing of grass dormancy. Therefore, the number of days between the beginning date in spring and ending date in autumn of daily mean temperature greater above 0 C are considered as general growing period of crops (including perennial fruit tree), or regarded as farming period. Accumulated temperature during this period is called annual total accumulated temperature and is

8.3 Topographic and Climate Variable Zoning

167

Table 8.4
0 C accumulated temperature zoning and corresponding codes Code 1 2 3 4 5

Range of temperature ( C) < 1000 1000–1450 1450–1800 1800–2500 > 2500

Implication Alpine desert or perennial snow and ice zone Pure pasture or alpine desert Agro-pastoral region Crops with one harvest a year and high yield The early mature maize and chimonophilous crop can be planted; there are extra heat resources for crops with one harvest a year and high yield

an important temperature indicator for agriculture and pastoral animal production (Lin et al. 2001). After occurrence of early frost in autumn in the Lhasa area, crops and pasture stop growing. Accumulated temperature from the beginning date of early forest (daily minimum temperature < 0  C) to the ending date of daily mean temperature
0 C cannot be used by crops. Accumulated temperature zoning in the Lhasa area and its implication are shown in Table 8.4.

8.3.4

Moisture Index Zoning and Coding

The moisture index refers to the ratio of precipitation to evaporation, an indicator representing degree of aridity or moisture in a region (Lin et al. 2001). There are many methods to compute moisture index. Ivanov empirical formula is used here and its expression is as follows:   K ¼ PR=B0 ¼ PR= 0:0018ðT þ 25Þ2 ð100  f Þ

ð8:5Þ

where K is moisture index, PR is precipitation (mm); B0 is evaporation (mm), T is mean temperature ( C), and f is relative humidity (%). The zoning criteria of Ivanov moisture index and corresponding natural landscape are shown in Table 8.5.

8.3.5

PET Zoning and Coding

PET is a representation of the environmental demand for evapotranspiration and represents the evapotranspiration rate of a short green crop, completely shading the ground, of uniform height and with adequate water status in the soil profile. It is a reflection of the energy available to evaporate water, and of the wind available to transport the water vapor from the ground up into the lower atmosphere. Actual evapotranspiration is said to equal PET when there is ample water (Smith 1992). Among different methods, the Penman-Monteith method is most reliable way to

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8 Ecoregion Classification

Table 8.5 Ivanov moisture index zoning and corresponding landscape with codes Range of index 0.3–0.4

Code 1

Name Semi-arid

Natural landscape Desert steppe

2 3

Semi-arid and semimoisture Semi-moisture

0.6–1.0

Arid steppe, desert steppe Steppe

4

Moisture

> 1.0

Forest

0.4–0.6

Farming measures Depends on irrigation Needs irrigation Supplemental irrigation Self-restraint moisture

Table 8.6 PET zoning and codes Range (mm) Code

< 890 1

890–970 2

970–1040 3

1040–1120 4

1120–1200 5

> 1200 6

410–450 5

> 450 6

Table 8.7 Annual precipitation zoning and codes Range (mm) Code

< 290 1

290–330 2

330–370 3

370–410 4

estimate PET under various climates, as it reflects changes in all meteorological factors affecting evaporation and plant transpiration. It is recommended by Food and Agriculture Organization of United Nations (FAO) and is used in this study. The spatial distribution patterns of PET in the Lhasa area generally show decrease from south to north and its zoning is shown in Table 8.6.

8.3.6

Annual Precipitation Zoning and Coding

Water is main component of vegetation organism and is essential to photosynthesis, nutrient transportation, temperature regulation, and biochemical reaction for entire vegetation growing cycle. In the Lhasa area, rainfall is main water source and annual rainfall amount has great regional differences, generally showing increase from west to east and from valleys to mountains. Annual precipitation zoning is shown in Table 8.7.

8.3.7

Annual Mean Temperature Zoning and Coding

Annual mean temperature is an important indicator to represent temperature resource in a region. Climatologically, annual mean temperature is used to present general heat condition in a region.

8.4 Principal Component Analysis

169

The spatial pattern of annual mean temperature in the Lhasa area generally presents decrease from south to north and from valleys to mountains. Its zoning and corresponding codes are shown in Table 8.8.

8.4

Principal Component Analysis

In view of seven different indicators involved for ecoregion classification in the study, the principal component analysis (PCA) was used to compress data by eliminating redundancy and reducing the number of dimensions without much loss of information (Jolliffe 2005; Pio et al. 1989) and to generate a stack of layers being the principal components of these seven indicators. PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. It is used to transform the data in a stack from the input multivariate attribute space to a new multivariate attribute space whose axes are rotated with respect to the original space. The axes (attributes) in the new space are uncorrelated. PCA provides a roadmap for how to reduce a complex data set to a lower dimension to reveal the sometimes hidden, simplified structures that often underlie it. The result of PCA is a stack with the same number of grids in the output stack as in the original stack. The first component will have the greatest variance, the second will show the second most variance not described by the first, and so forth. Many times, the first three to four grids of the resulting stack from the principal component function may describe over 95% of the variance (Jolliffe 2005; Rahmani and Atia 2017). The remaining grids of the principal component stack can be dropped. Since the new stack contains fewer grids and over 95% of the variance of the original stack is intact, the computations will be faster, and the accuracy is maintained. The essential algorithm of PCA is to calculate eigenvalues of jV  λI jL ¼ 0, where V is the covariance matrix, λ the eigenvalues, I the identity matrix, L the eigenvectors corresponding to eigenvalues λ, and 0 the zero matrix. In this equation, the relative magnitude of elements of covariance matrix V directly influences calculated results of eigenvectors and eigenvalues. If an abnormal value occurs in a variable, the variance and related covariance of the variable will increase accordingly. In ARC/INFO software, PCA can be performed using PRINCOMP command in GRID module using seven indicators (elevation, aspect, annual mean temperature,
0 C accumulated temperature, mean annual precipitation, PET, and moisture index) as factors and inputs. The final results are shown in Table 8.9. It is clear that the first, second, and third components of PCA can describe 48.66%, 27.91%, and 15.42% of the total variance, respectively. The first three components together reach 92%, that is, 92% of the total variance of seven environmental factors involved can be described by the first three components of PCA. The false composite image of first three components is shown in Fig. 8.7.

Range ( C) Code

18 to 16 1

16 to 14 2

14 to 12 3

Table 8.8 Annual mean temperature zoning and codes 12 to 10 4

10 to 8 5

8 to 6 6

6 to 4 7

4 to 2 8

2 to 0 9

0 to 2 10

2 to 4 11

4 to 6 12

6 to 8 13

170 8 Ecoregion Classification

PCA layers Factors PET
0 C Accumulated temperature Elevation Precipitation Moisture index Aspect Annual mean temperature Eigenvalue Description (%) Accumulated description (%)

Table 8.9 Results of PCA PC#1 0.75231 0.45134 0.4478 0.11605 0.2989 0.0868 0.69958 3.36729 48.66 48.66

PC#2 0.45134 0.71162 0.68186 0.18178 0.00691 0.1231 0.85193 1.93106 27.91 76.57

PC#3 0.4478 0.68186 0.91442 0.32894 0.11933 0.15857 1.10833 1.06724 15.42 91.99

PC#4 0.11605 0.18178 0.32894 0.76521 0.23786 0.06559 0.38162 0.28984 4.19 96.18

PC#5 0.2989 0.00691 0.11933 0.23786 0.35623 0.16442 0.05589 0.16712 2.42 98.60

PC#6 0.0868 0.1231 0.15857 0.06559 0.16442 1.91532 0.17221 0.05526 0.80 99.40

PC#7 0.69958 0.85193 1.10833 0.38162 0.05589 0.17221 1.50447 0.04178 0.60 100.00

8.4 Principal Component Analysis 171

172

8 Ecoregion Classification

Fig. 8.7 False color-composite image of the first three components of PCA

8.5 8.5.1

Ecoregion Classification Using Artificial Neural Network Artificial Neural Networks

Artificial neural networks (ANN) are networks comprised of a number of processing units (neuron) which connect each other. The most common feature of ANN is that they are based on a self-organizing structure that resembles the biological neural system of mammalian brains. Most models are composed by simple and highly interrelated processing units (neurons) that are in permanent connection with each other. Generally, neurons are located in different layers, and ANN differentiates on the basis of the number of layers and the training procedures. Connections between processing units are physically represented by weights, and each neuron has a rule for summing the input weights and a rule for calculating an output value. The model is completed by transfer functions that allow communication between layers and the production of an output neuron (Zhou et al. 1999). The first phase of the application of an ANN is represented by the training phase, which differentiates as a function of the type of network (Zhou et al. 1999; Civco 1993). In the present application, the best results have been obtained by networks belonging to the categories of the multilayer perception (MLP). MLP is perhaps the most popular and well-used type of ANN. The processing units are arranged in a layered feed-forward topology. In its basic form, MLP consists of two layers (input/ output), and its complexity increases by the addition of hidden layers. Each

8.5 Ecoregion Classification Using Artificial Neural Network

173

processing unit performs a biased weighted sum of its inputs, and if the totaled score is sufficient, it activates through a transfer function to produce an output. The training phase represents the inner core of the application of an ANN, and the aim is to set the network weights and thresholds of activation in such an order to minimize the errors between the observed and computed outputs. For this reason, the ANN must be trained with an amount of information that constitutes a sort of “experience” broadly resembling those of mammal brains. Training an ANN with the aim of investigating natural processes usually corresponds to feeding the ANN with a series of observations large enough to describe the inner variance of the investigated process (Song et al. 2012; Mather 2003). Variables describing the process, once organized on the basis of input and observed outputs, have to be utilized for training the network. This process basically corresponds to fitting the ANN to the available data set. Once fed by a database of case histories, the ANN weights and thresholds of activation are automatically adjusted by specific algorithms. The MLP supports the back propagation algorithm, one of the best known and utilized worldwide, which was used in this application. The results of the training phase are usually represented by error functions (such as the sum squared error or cross entropy error functions), which give a measure of how the network fits the training set of the observed data. As a general rule, the MLP training phase is aimed at representing the multidimensional nature of the investigated process.

8.5.2

Ecoregion Classification

Omernik (1987) defined the ecoregions of the USA on the principle that ecosystems and their components display regional patterns that are reflected in spatially variable combinations of causal factors such as climate, mineral availability (soils and geology), vegetation, and physiography. These factors interact, but the importance of each factor in determining the character of ecosystems varies from place to place. This approach is based on patterns of terrestrial characteristics and on the premise that relatively homogenous areas exist and that these areas can be defined by simultaneously analyzing a combination of determinants of causal factors. In this approach, ecoregions are regions of relative homogeneity in the patterns of ecological systems. Another fundamental principle of ecoregional classification is the use of multiple characteristics at each level of a typing hierarchy. Ecological regions are then regions within which there is relative similarity in the mosaic of ecosystems and ecosystem components (biotic and abiotic, aquatic and terrestrial). Maps of single characteristics only illustrate regionalities in that characteristic. The delineation of ecological regions requires the evaluation of maps of all geographic phenomena believed to cause or reflect spatial differences in ecosystems (Omernik 1995; Kleynhans et al. 2005).

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Ecoregional classification is a hierarchical procedure that involves the delineation of ecoregions with a progressive increase in detail at each higher level of the hierarchy. It aims to delineate ecological region based on main impact indicators primarily derived from vegetative cover, topographic and climatic conditions to serve environmental conservation, improvement, reconstruction, and restoration for building a harmonious relationship between physical environment and human society, and is increasingly used as a spatial unit for planning and natural resource and environmental management.

8.5.3

Regional Environmental Characteristics

Lhasa area consists of high mountains and low valleys with topography slanting from northwest to southeast. The elevation of Nyainqentanglha mountain range in the northwest is from 6000 to 7000 m above sea level (masl) with extending 140 km in length, while mountain ridges in southeastern area vary from 5300 to 6500 m. The peak of Mt. Nyainqentanglha is 7162 m as the highest point in the Lhasa area, and topography declines from this point to the southeast and the elevation in lower reaches of Lhasa River is below 4000 m. Lhasa River is the largest tributary of Yarlung Zangbo River, and the confluence of two rivers is in southeastern Chushur County where the elevation is 3550 m as the lowest point in the Lhasa area (Chu et al. 2015). The elevation difference from the peak of Mt. Nyainqentanglha to the confluence reaches 3612 m and average elevation is 4616 m. From the climate perspective, like most of other regions in the TP, Lhasa area is characterized by plateau temperate monsoon climate. The southern high mountains in the TP and their directions from east to west largely restrict atmospheric water vapor transport to this region. Precipitation gradually decreases from east to west while temperature in the south is higher than in the north. Vegetation growing condition in the east is better than in the west. The natural forests and shrubs can be found in some regions in the east, whereas mountain grassland is dominant in the west, reflecting general air humidity is higher in the east than in the west. Main vegetation in the south is mountain grassland while it is gradually replaced by alpine meadow in the north and upper parts of high mountains. Farming activity is primarily concentrated in southern river valley as one of main farming production regions in Tibet, whereas in the north the Damshung County is practicing a pure pastoral animal production. According to regional environmental features and differentiation regularity aforementioned above, Lhasa area is a large-scale mountain ecosystem and main influencing factors include elevation, geomorphology, topographic gradient, and climate and vegetation conditions. Meanwhile, the different combinations of these factors in different regions create various ecological environment units. Each unit has unique ecological environment characteristics and problems.

8.5 Ecoregion Classification Using Artificial Neural Network

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Fig. 8.8 Ecoregion classification result in the Lhasa area

8.5.4

Results of Ecoregion Classification

The false composite image of the first three components as shown in Fig. 8.7 was used as input data for ANN classification. In order to save computer resources and running time for iterative convergence for ANN classification, the input data of original resolution of 100  100 m was resampled into lower-resolution data at 200  200 m. Through collecting four typical training sites in relatively homogeneous regions and iterating in ANN, the final classification is shown in Fig. 8.8. Since Lake Namtso is a special land-cover type in northern study area with covering large areal extent, this cover type was overlaid after classification. Affected by various factors from topography, climate and vegetation conditions, there are different ecological regions in the study area. The result of ANN classification shows that there are five main ecoregions, which is displayed in Fig. 8.8. (1) Valley agricultural region. This ecoregion includes the valleys of Lhasa River and Nyemo River and their tributaries, covering 3163.24 km2 or accounting for 10.75% of total study area (Table 8.10). In these regions, water and heat resources are relatively sufficient, with lower elevation and flat terrain suited to agricultural cultivating. Most of these regions have been developed for cultivated land. Many wetlands are also distributed in lower locations. This region is an important crop production area and one of agricultural production bases in TAR. Because of better water resources and fertile soils, crop production yield is high.

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Table 8.10 Result of ecoregion classification in the Lhasa area Ecoregion type Valley agriculture region Mountain grassland Alpine meadow Glacier, ice, and alpine bare land Lake Total

No. 1 2 3 4 5

Number of pixels 79081 266165 269213 104155 16719 735333

Area (km2) 3163.24 10646.6 10768.52 4166.20 668.76 29413.32

Area percentage (%) 10.75 36.20 36.61 14.16 2.27 100.00

These river valleys have a long history of agricultural development. For thousands of years, human activities are mainly concentrated on land use and their impact on environment, particularly on agricultural environment is prominent. Moreover, with population increasing and human activity enhancing in present era, human impact on regional environment is accelerating in terms of intensity and extent. Soil erosion, grassland degradation, and relevant conflicts between livestock and grassland are increasing. Concerning about importance of this ecoregion, the integrated agriculture development and construction aiming at improving agriculture production condition in the central TP was implemented in the 1990s and the environment of agriculture production in the valleys has been improved to a certain degree. Particularly, the effectiveness of bioengineering measures such as large-scale artificial afforestation and shelterbelt construction has appeared through improving local surface vegetation condition and land cover, and preventing soil erosion and water loss. However, because of energy shortage in these regions, there is basically no organic matter to be returned to the field, so that soil organic content and soil structure cannot be improved, which in turn affects agriculture production and development. In addition, soil erosion on some foothills is severe as a result of human activities such as overcutting shrubs. (2) Mountain grassland. The mountain slope in both sides of valleys and the middle mountains belongs to this ecoregion. The average elevation is around 4000 m, with 10646.6 km2 in extent or accounting for 36.20% of the total area. The main characteristics present that in these regions the moisture and waterholding capability are declining and environment is becoming dry due to slope increasing with elevation rising. Although temperature remains high, vegetation growth to some extent is restricted by lower holding capability of moisture and water resources. The dominant vegetation is mountain grassland and shrubs while alpine meadow can be seen in some high altitudes, especially in shaded slope of high mountains. Damshung valley, located in the north, also falls into this ecoregion, where practicing pure grassland husbandry. Grassland in Damshung valley is primarily used for grazing in winter and spring seasons. Since grazing time is long, coupled with the increasing number of livestock in recent years, grassland degradation and grassland-yield decreasing is apparent in the valley.

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Mountain grassland ecoregion is environmentally most vulnerable region in the study area because of lower water-holding capability, high slope gradient, thinner soil layer, and dryer climate condition as well as impacts from human activities. Soil erosion and grassland degradation are the most severe environmental problems in this region. (3) Alpine meadow. The elevation ranges from 4200 to 5000 m, with covering 10768.52 km2 or occupying 36.61% of the total study area. It has the largest area among five ecoregions. Main characteristic is that environmental condition is better than mountain grassland due to higher moisture since precipitation is increasing with altitude on the one hand; on the other hand, snow and glacier melting are an important supplement for water resources for vegetation growth. Furthermore, lower temperature leads to lower plant and surface evapotranspiration. As a result, vegetation grows well in this region compared to mountain grassland ecoregion. As the dominant vegetation, alpine meadow has high coverage, very developed roots, and strong soil-fixing capability, which can restrain occurrence of soil erosion. In alpine meadow ecoregion, because of large diurnal temperature differences, freeze-thaw erosion is particularly obvious. Although vegetation such as alpine meadow grows well due to sufficient water condition in higher altitude, grassland degradation caused by overgrazing and damage of plateau pika is main environmental problem and is severe in some regions. (4) Glacier, ice, and alpine bare land. This ecoregion is principally distributed over 5000 m, with 4166.20 km2 in area or accounting for 14.16% of total study area. Although it is affluent in water resources from natural rainfall and snow and glacier melting, due to low temperature, high altitude, and thin soil layer, the vegetation growth is to a great extent restricted or barely grows. In addition, because of very high altitude and steep mountain slope the environment in this region is extremely fragile. In this ecoregion, erosions induced by glacier and snow are severe. At present, human activities have not reached to this altitude and it still remains a natural ecosystem. However, since this ecoregion is extremely fragile, once it is intervened and destroyed by human activities, it is difficult to be restored for a long period of time. (5) Lake. Considering lake is a special ecosystem and landscape in the study area, it was classified as separate ecoregion type. It consists of Namtso Lake, the largest lake in TAR and second largest lake on the TP next to Lake Qinghai, located in the northern flank of the Gangdise-Nyainqentanglha mountains. Administratively, it belongs to Damshung County in Lhasa area and Bangoin County in Nakchu Prefecture, featuring temperate monsoonal semi-arid region of plateau subfrigid zone. Lake area was 1920 km2 and elevation was 4718 m in the 1970s (Guan et al. 1984). Recent study shows lake area expanded from 1920 km2 in 1971 to 2015 km2 in 2004, with mean annual increasing rate of 2.81 km2 year1, and the lake volume augmented from 783.23  108 m3 in 1971 to 863.77  108m3 in 2004, with mean annual increasing rate of 2.37  108 m3 (Zhu et al. 2010). Study also shows that annual increasing rate of lake area and

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volume is more obvious recently. More water supply from glacier melting due to continued rising of air temperature, along with precipitation increasing and evaporation decreasing, contributed to increased water area and volume of Namtso Lake.

8.6

Conclusion and Discussion

(1) The spatial interpolation is an important component of GIS spatial analysis. For a region with meteorological stations sparsely or irregularly distributed, the spatial interpolation is the basic method to study changes of any spatially distributed variables from either climate conditions or other environmental aspects and the prerequisite to build any spatial models. However, as no single method is optimal for all regions, it must be chosen based on the repeated test, result comparison, internal characteristics of data, and data examination and analysis. (2) The spatial interpolation methods for temperature and precipitation were developed based on longitude, latitude, and elevation as independent variables, which can meet needs for accuracy required in this study. It has been shown that the accuracy of spatial interpolation for temperature is higher than that for precipitation. However, this algorithm only allows for zonal factors of climate variables and non-zonal factors that affect distributions of temperature and precipitation such as “urban heat island effect,” and snow covers are not involved in this algorithm. Thus, based on specific surface features, further correction and improvement for the algorithm should be carried out in case needed. (3) After selecting seven key factors influencing ecological region, the ecoregion classification is implemented using PCA and ANN techniques. There are five main ecoregion types for the Lhasa area, namely, valley agriculture region, mountain grassland, alpine meadow, glacier and ice and alpine bare land, and lake. Alpine meadow and mountain grassland among these are dominant, and grassland degradation and soil erosion are the main environmental problems. Valley agriculture region covers over 10% of total area, and human activities are mainly concentrated in this region due to long history of agricultural development and because it has been developed as one of agricultural production bases in TAR. In addition, there is a large area covered by glacier and ice and alpine bare land, remaining a relatively undisturbed natural ecosystem at present. Lake is a special ecoregion in the study area with accounting for nearly half area of Namtso Lake, showing increase both in water area and volume since beginning of satellite observation era in1970s with more prominent increase recently under currently warm and moist climate trends on the TP.

References

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Chapter 9

Land-Cover Classification

Abstract Land-cover classification is an important application area of satellite remote sensing. However, deriving thematic map from satellite imagery through classification approaches is not a straightforward task, especially from highresolution satellite imagery. In this study, Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) multispectral composite image is successfully used to land-cover classification for the Lhasa area located at central Tibetan Plateau (TP) using maximum likelihood classifier. Accuracy assessment for final results is also made using quantitative approaches. Study shows that there is a good agreement between classification results and reference data for defined land-cover classes in central TP. The overall classification accuracy is 87.68%. Reference and ancillary data are increasingly available and are very useful for refining accuracy of classification results during postclassification process. The integration of digital elevation model (DEM) into land-cover classification is particularly important in mountain region since land-cover distribution in mountain region is spatially topographydependent. Study also suggests that with increase of spatial resolution, how to effectively use the spatial information inherent in satellite remote sensing images to extract thematic maps for various applications remains a challenge and is an important task to be fulfilled in the future. Keywords Land-cover classification · Maximum likelihood classifier · MODIS · Central Tibetan Plateau

9.1

Introduction

Land-cover classification from remotely sensed data has long attracted the attention of the remote sensing community because classification results are fundamental sources for many environmental and socioeconomic applications (Gopal et al. 1999; Lu and Weng 2007). Scientists and practitioners have made great efforts in developing advanced classification approaches and techniques for improving classification accuracy (Han et al. 2015; Lu et al. 2011; Fritz et al. 2010). However, © Springer Nature Singapore Pte Ltd. 2020 D. Chu, Remote Sensing of Land Use and Land Cover in Mountain Region, https://doi.org/10.1007/978-981-13-7580-4_9

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classifying remotely sensed data into a thematic map remains a challenge because many factors, such as the complex of the landscape under investigation, the availability of reference data, the selected remotely sensed data, image-processing and image classification approaches, and the analyst’s experiences, may affect classification accuracy. Remotely sensed data have different spatial, temporal, spectral, and radiometric resolutions. Understanding the strengths and weaknesses of different types of sensor data is essential for selecting suitable remotely sensed data for land-cover classification (Lu et al. 2011). In general, at a local level, a fine-scale classification system is needed; thus high spatial resolution data such as IKONOS and QuickBird data are helpful. At a regional scale, medium spatial resolution data such as those from Landsat Thematic Mapper (TM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) are the most frequently used data. At a continental or global scale, coarse spatial resolution data such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and SPOT vegetation data are preferable. Recent concern over global land cover has resulted from global change. Many studies on land-cover mapping from regional to global scales have been conducted. The first land-cover mapping at continental and global scales has made possible by using remote sensing imagery from the AVHRR satellites (Tucker et al. 1985; DeFries and Townshend 1994). With advancement in remote sensing sensors and mapping technologies, the availability of land-cover datasets is rapidly increasing. A number of land-cover datasets at a global scale have been developed from different remotely sensed sources with resolution ranging from 300 m to 1 km (Loveland and Belward 1997; Bartholomé and Belward 2005; Friedl et al. 2002; Giri 2012), including 1 km global land-cover map of International Geosphere-Biosphere Program Data and Information System Cover (IGBP-DISCover), 1 km University of Maryland (UMD) land-cover map, 1 km Boston University land-cover map, 1 km Global Land Cover 2000 (GLC2000) sponsored by the European Space Agency (ESA), 500 m MODIS land-cover map, and 300 m ESA GlobCover land-cover map. However, the quality of these datasets is far from satisfactory for many applications. A new global land-cover dataset at 30 m resolution has been released for open access at the end of September 2014 (Chen et al. 2015). This dataset, named GlobeLand30, refers to the years 2000 and 2010. It was produced by the Chinese government and then donated to the United Nations with the aim of contributing to research on sustainable development and climate change. According to preliminary tests, GlobeLand30 achieves overall accuracy of over 80% (Chen et al. 2015; Brovelli et al. 2015). All these global land cover datasets are now freely available to users and have been widely used for many applications, representing highly valuable information to monitor the extension and status of land resources. The fundamental goal of these datasets is to improve our understanding of extent and distribution of major land cover types of the world and give an important contribution to understanding the balance between global land cover pattern, climate, and biochemistry of the earth system (Grekousis et al. 2015; Yan and Roy 2015).

9.2 Methods and Data

183

In China, in order to protect and develop land resources, a series of land management strategies have been implemented over the years. In 2010, the State Council issued the major function-oriented zoning plan. In this plan, national land is divided into four main function zones (optimal development, key development, restricted development, and forbidden development) to realize land resources sustainability and optimal use, particularly to effectively protect basic cultivated land (Fan 2015). At the same time, the ecological civilization construction plan has been implemented, which focuses on the protection and management of national key ecological function zones, biodiversity conservation priority zones, nature reserves, and other important ecological regions. The implementation of these national strategic policies on land resources as well as China’s fast industrialization and urbanization since 1980s have a profound impact on land use. To timely reveal and monitor spatiotemporal patterns and dynamic process of land use in China, landuse database at national scale was built based on satellite remote sensing image data for the late 1980s and has been updated every 5 years (Liu et al. 2017; Zhang et al. 2014), which was updated recently and 2010–2015 land-use change database has been released for open assess (Ning et al. 2018). The main studies related to land-use and land-cover classification and mapping in Tibet Autonomous Region (TAR) include the first land resources surveys in the 1980s (Chu and Liu 1992), the integrated environmental assessment and monitoring using remote sensing and geographic information system (GIS) in the 1990s (Zhang and Chu 1998), and vegetation map of TAR as part of 1:1,000,000 vegetation atlas of China compiled by the Chinese Academy of Sciences (CAS) (Editorial Board of Vegetation Map of China of CAS 2001), which was digitized and used for landcover classification as reference data in this study. This chapter presents a brief overview of the major land-cover datasets at regional to global scales first, followed by main classification approaches, and their strengths and weaknesses. Subsequently, a single-date Terra/MODIS image is used to landcover classification using maximum likelihood classifier to investigate spatial distribution of land cover in the study area, along with accuracy assessment for final results. A summary and future outlook is discussed in the final section.

9.2 9.2.1

Methods and Data Methods

There are many classification methods available, such as neural networks, decision trees, fuzzy sets, pixel and subpixel-based methods, support vector machines, and expert systems, and have been widely applied in land-cover classification. Traditional land-cover mapping is based on pixel-based classification. New digital image analysis algorithm, such as object-oriented classification, is based on semantic information to interpret an image. This information is not represented by a single pixel but by meaningful image objects and the mutual relationship between them

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(Blaschke 2010). The main difference between object-oriented classification and pixel-based classification is that the algorithm does not classify each single pixel but classifies image objects extracted through an image segmentation step. The pixelbased image analysis methods have limitations in processing high-resolution data acquired over complex and highly variable environment. Image objects provide a more appropriate scale for mapping environmental features at multiple spatial scales and more relevant information than individual pixels (Hussain et al. 2013; McDermid et al. 2008). Classification approaches generally can be grouped into different categories, such as supervised versus unsupervised, parametric versus nonparametric, per-pixel, subpixel, and per-field (Lu and Weng 2007; Ban and Jacob 2013). In practical applications, many factors, such as spatial resolution of remote sensing data, different data sources, classification systems, and availability of classification software, must be taken into account when selecting a suitable classification method for use. If classification is based on spectral signatures, parametric classification algorithms such as maximum likelihood are often used; otherwise, if multisource data are used, nonparametric classification algorithms such as the decision tree and neural network are commonly used. Spatial resolution is an important factor affecting the selection of a suitable classification method. In high spatial resolution data such as images from IKONOS and QuickBird, the high spectral variation within the same landcover class poses a challenge. A combination of spectral and textural information and the use of per-field or object-oriented classification algorithms can reduce this problem (Thomas et al. 2003; Mallinis et al. 2008; Zhou et al. 2008; Ke et al. 2010). For medium and coarse spatial resolution data, however, spectral information is a more important attribute than spatial information because of the loss of spatial information. Since mixed pixels create a problem in medium- and coarse-resolution imagery, per-pixel classifiers have repeated difficulties in dealing with them. Subpixel-based classification methods can provide better area estimation than perpixel-based methods (Ke et al. 2010; Lu and Weng 2006; Agarwal et al. 2013; Weng 2011). Among various classification approaches, maximum likelihood classifier has been the most common method used for supervised classification of remotely sensed data (McIver and Friedl 2002), since it provides more consistent approach to data variability. Its decision rule is based on the probability that a pixel belongs to a particular class. The basic equation assumes that these probabilities are equal for all classes and that the input bands have normal distributions (Giri 2012; Strahler 1980; Ediriwickrema and Khorram 1997). In addition, postclassification processing is an important step in improving the quality and performance of classifications. More and more ancillary data, such as digital elevation models (DEM), soil, road, population, and socioeconomic data, increasingly become available and have been used to improve classification results based on the prior knowledge or relationships between land cover and these ancillary data. In this process, GIS techniques play an important role in managing and handling ancillary data. The study area, a typical mountainous region, is situated at central TP. Landcover distribution is closely related to topography as one of the important aspects of

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physical environment affecting land-cover classification accuracy. Therefore, based on elevation, slope, and aspect data derived from DEM, topographic correction must be taken into account for classification results in this mountain region.

9.2.2

MODIS Data

MODIS is one of the key instruments designed as part of NASA’s Earth-Observing System (EOS) to provide long-term global observation of land, ocean, and atmospheric properties. The instrument was developed based on experiences with AVHRR and Landsat TM. MODIS instrumental characteristics represent a new generation of sensor systems for global observation with spatial resolutions ranging from 250 m to 1 km. Global coverage of MODIS data are obtained every 1–2 days. The spectral, spatial, and radiometric resolutions are also substantially improved in MODIS compared to previous global sensor systems. In addition to spectral products commonly provided for all remote sensing platforms, MODIS science team devoted tremendous efforts in developing a wide range of MODIS-derived scientific datasets that are readily available for the scientific community (Justice et al. 1998; Huete et al. 1994; Weng 2011). MODIS data represent not only a continuous remote sensing data record that extends previous sensor systems, but also a substantial improvement by integrating the most advanced remote-sensing theory algorithm development, data processing, validation, and distribution. Two MODIS instruments were onboard EOS-AM and EOS-PM platforms and were launched December 18, 1999 (EOS-Terra), and May 4, 2002 (EOS-Aqua), respectively. Terra/MODIS data have been available since February 2000. Both EOS-Terra and EOS-Aqua are polar-orbiting sun-synchronous platforms. The orbit height of EOS platforms is 705 km at the equator. Terra’s equatorial crossing time (descending) is 10: 30 A.M. local time. Aqua crosses (ascending) the equator at approximately 1: 30 P.M. A total of 36 spectral bands are carefully positioned across the 0.412–14.235 μm spectral region. Unlike the AVHRR that mainly designed for monitoring the atmosphere, MODIS sensor is well suited for a wide range of research applications intended to improve our understanding of land, ocean, and atmosphere processes, domain interactions, and the impacts of human activity on the global environment. A wide range of validation efforts suggest a high level of data quality for the MODIS products. This can be attributed to the improvement of spectral, spatial, temporal, and radiometric resolutions, as well as improvements made in algorithm development by the MODIS science team. One of the great merits of MODIS is that satellite data can be freely received at any ground station on the earth to be able to realize rapid monitoring and response to natural hazards as well as to meet the requirements of various large-scale applications. The first-ever MODIS ground receiving station in the TP was founded in October 2002 at the Tibet Institute of Plateau Atmospheric and Environmental Sciences (TIPAES) in Lhasa. Satellite images received at this station from AVHRR, MODIS to Chinese FengYun (FY) satellite series have been extensively

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Fig. 9.1 Terra/MODIS band 6-2-1 composite image received on September 13, 2004

applied in monitoring and characterizing snow cover, vegetation growth, water body, and forest fire as well as in environmental evaluations and studies in the TP (Chu 2018). Terra/MODIS band 6-2-1 composite image received on September 13th at TIPAES was cloud-free found during vegetation growing season in summer of 2004 and used for land-cover classification in the present study, which is shown in Fig. 9.1.

9.2.3

Reference and Ancillary Data

Since the classes of interest are generally known a priori, supervised classification techniques are frequently used to land-cover classification. With these techniques, the aim is to allocate each pixel to the land cover class with which it has the greatest similarity from the set defined in the training stage of the classification (Campbell 1996). Training data is a critical component of this classification process and must be sufficiently representative and homogeneous examples of each category (Chuvieco and Huete 2009). Its quality and scale directly affect the accuracy of classification results. Thus, selection of training samples must take into account the spatial resolution of remote sensing data, availability of ground reference and other important ancillary data, and the landscape complexity of the study region. Auxiliary fieldwork and existing maps derived from various natural resources surveys, such as

9.3 Results

187

Fig. 9.2 Vegetation map of the study area clipped from scanned vegetation atlas of China

vegetation and grassland maps, land-use map, and land resources map, can be of great help for a more precise location of training areas. In this study, Tibetan part of vegetation atlas of China was used as main reference data and ground truth. It was scanned and geometrically corrected using geographical coordinates. The final digitized and geo-referenced was shown in Fig. 9.2. Main ancillary data and documents include land-use status classification system developed for the first land-use status survey of TAR described in Chap. 3 and the high-resolution DEM that has same scales with land-use map in TAR introduced in Chap. 4. In addition, in 2004 the field investigations were conducted two times a month in central TAR, which provided important understanding and insight to landscape complexity and physiographical characteristics of the study area.

9.3 9.3.1

Results Land-Cover Classification

According to vegetation map of TAR as key reference data, in combination with other ancillary data, unsupervised classification results, and the spatial resolution of MODIS data used, 11 classes for land-cover classification were defined. Training samples were then selected for each category in relatively homogeneous sites using

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Fig. 9.3 Land-cover classification result base on the Terra/MODIS band 6-2-1 composite image Table 9.1 Results of land-cover classification in central TP Code 1 2 3 4 5 6 7 8 9 10 11

Cover type Glacier and snow cover Water body Alpine steppe Alpine meadow Bare soil Alpine sparse vegetation Shrub meadow Forest Crop land Mountain steppe Mountain meadow

Pixel 13020 11157 59577 268179 7457 33906 18936 3 578 27487 5787

Area (km2) 813.75 697.31 3723.56 16761.19 466.06 2119.13 1183.50 0.19 36.13 1717.94 361.69

Percentage (%) 2.92 2.50 13.35 60.09 1.67 7.60 4.24 0.00 0.13 6.16 1.30

polygons and the classification was implemented with ENVI 4.0 image processing software using maximum likelihood classifier. It was found that some categories, such as mountain steppe and sparse vegetation, cannot be properly discriminated from the input data by supervised classification method. Therefore, in postclassification process, the topographic correction was made using DEM since spatial distribution of defined land-cover classes is strongly elevation-dependent in this mountain region. The final results are shown in Fig. 9.3 and Table 9.1. It is clear that alpine meadow is predominant land cover type with covering 60.09% and is widely distributed in most upper parts of mountains, where elevation is generally

9.3 Results

189

above 4000 m above sea level (masl) and relatively more moisture are available. Compared to other cover types, alpine meadow has high vegetation coverage in central TP, which is above 50%, and its growing condition in the east is better than in the west due to more rainfall amount. Alpine meadow is primary rangeland resources for Yak in high-altitude region in the TP. Alpine steppe is the second largest cover type with accounting for 13.35% of total land area, mainly distributed in the northern region of Mt. Nyainqentanglha and some dry land in Damshung valley. Sparse vegetation is the third largest cover type with 7.60%, located between permanent snow and alpine vegetation (alpine meadow and alpine steppe) in high mountains with above 5000 masl, and vegetation growth in these areas is strongly constrained by frigid climate and poor soil conditions. Nearly 3% of study area is covered by glacier and perennial snow, distributed on high mountains above 6000 masl and most of them lies on upper part of Nyainqentanglha mountain range, the highest mountain in the study area. Mountain steppe, covering 6.16%, is principally distributed in Lhasa and Nyemo valleys, where is dry with higher temperature compared to the rest of regions. Mountain meadow, occupying 1.30%, has similar spatial distribution patterns with mountain steppe but mainly grow in shaded slopes, where it is relatively humid and has lower evaporation. Shrub meadow type is 4.24% and is distributed in eastern study area due to better vegetation growing condition in the east than in the west. Crop land is less than 1% of total land area and is primarily disturbed in Lhasa river valley.

9.3.2

Accuracy Assessment

The degree of accuracy is a key measure to assess classification results and understand their suitability in many different applications. Therefore, accuracy assessment is an important process in land-cover classification, which is further used to help refine or improve the classification results. Different approaches can be employed for accuracy assessment, ranging from a qualitative evaluation based on expert knowledge to a quantitative accuracy assessment based on sampling strategies. Essentially, classification accuracy is taken to mean the degree to which the derived image classification agrees with reality or conforms to the “truth” (Campbell 1996; Chuvieco and Huete 2009; Janssen and van der Wel 1994). Congalton (1994) identified four major historical stages in accuracy assessment. In the first, accuracy assessment was based on a basic visual appraisal of the derived map. The second was characterized by an attempt to quantify accuracy more objectively. The third stage involved the derivation of accuracy metrics that were based on a comparison of the class labels in the thematic map and ground data for a set of specific locations. Accuracy assessment in the fourth stage is a refinement of the third in which greater use of the information on the correspondence of the predicted thematic map labels to those observed on the ground is made. Presently, the confusion matrix is at the core of accuracy assessment. As a simple cross-tabulation of the mapped class label against that observed in the ground or

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Table 9.2 Confusion matrix of land-cover classification (%) Code 1 3 7 9 10 5 11 2 6 4 8

1 100 0 0 0 0 0 0 0 0 0 0

3 0 81.64 0 0 0.28 0 0 0 8.12 0 0

7 0 0 95.42 0 0 0 0 0 0 4.58 0

9 0 0 0 100 0 0 0 0 0 0 0

10 0 2.46 0 0 92.62 0 0.41 0 4.1 0.41 0

5 0 0 0 0 0 100 0 0 0 0 0

11 0 0 0 0 0 0 96.03 0 0 3.97 0

2 0.07 0 0 0 0 0.39 0 99.54 0 0 0

6 0 0.56 0 0 19.66 0 0 0 79.78 0 0

4 0 0 9.06 0 0.11 0 0 0 0 90.82 0

8 0 0 0 0 0 0.23 0 0 0 0 99.77

reference data for a sample of cases at specified locations, it provides an obvious foundation for accuracy assessment (Congalton 1994; Congalton and Green 1999; Foody 2002) and the basis on which to both describe classification accuracy and characterize errors, showing not only the general accuracy of the process but also the accuracy of each category and the conflicts between categories (Campbell 1996; Chuvieco and Huete 2009). To evaluate accuracy of land-cover classification results, two quantitative approaches are used in the study. The first is the confusion matrix of training samples. For this method, an estimate of the proximity of error associated with the classifier is obtained by dividing the number of training sample points erroneously classified by the total number of points classified. Final confusion matrix is shown in Table 9.2. We can find that classification accuracies are significantly high. Accuracy for glacier and snow cover, crop land, and bare soil reaches 100%, which means that all pixels of training samples from the area of interest are correctly assigned to corresponding classes. For shrub meadow, mountain steppe, mountain meadow, water body, alpine meadow and forest, the accuracy is greater than 90%. The least accuracy occurs at alpine sparse vegetation, which also reaches nearly 80%. The overall accuracy of land-cover classification for whole study area based on the training samples is 87.68%. The second measure used for assessing classification accuracy is to compare areal extent of each category derived from classification results and reference data digitized from vegetation map, which is illustrated in Fig. 9.4. Digitized areas of water body, glacier, and snow cover are very close to results from MODIS classification. The former is 2.26% and 2.59% of total area, while the latter is 2.50% and 2.92% of total area, respectively. Areal extent of alpine meadow from reference data and MODIS classification results are 61.94% and 60.09% of total area, respectively. Total area of alpine steppe and alpine sparse vegetation is 20.82% in reference data, whereas total areas of these two cover types acquired from MODIS classification is 20.95%. There is a certain difference in areas of crop land between the ground truth and classification, which is mainly attributed to that digitized area is cultivated land

9.4 Conclusion and Outlook

191

70 Reference data Classification result

Area percentage(%)

60 50 40 30 20 10 0 1

2

3

4

5

6

Fig. 9.4 Comparison of area percentage between classification result and reference data (1 Water body, 2 Glacier and snow cover, 3 Alpine steppe + alpine sparse vegetation, 4 Alpine meadow + shrub meadow, 5 Crop land, 6 Mountain steppe)

while classified area is portion of cultivated land that can be identified by MODIS image in the mid-September in 2004. Statistical analysis shows that relationships between the digitized and classified are significant (P < 0.01). It is concluded that a very good agreement exists between classification results and reference data for main land cover types in central TP. The land-cover classification based on the MODIS remote sensing data is reasonable and acceptable.

9.4

Conclusion and Outlook

Land-cover classification is one of the important applications of satellite remote sensing. However, extracting thematic map from satellite data through visual interpretation or computer-added classification methods is not an easy task, especially from the high-resolution satellite imagery. In this study, Terra/MODIS band 6-2-1 composite image received at TIPAES in Lhasa is used to land-cover classification for central TP, and accuracy assessment for final results are also made using two quantitative approaches. Some conclusion and future outlook are summarized as follows. 1. New distribution-free algorithms, such as decision trees, neural networks, nearest neighbor, and support vector machine algorithms, are high-performance alternatives to traditional classifiers. However, supervised maximum likelihood algorithm has advantage over other methods and is widely used in remote sensing applications for medium to coarse spatial resolution satellite data. In addition, as

192

2.

3.

4.

5.

9 Land-Cover Classification

one of the important steps in land-cover classification, accuracy assessment provides a valuable tool in evaluating the fitness of data for a particular application and further refining classification results. A good agreement exists between classification results and reference data for main land cover types in central TP. The overall accuracy of land-cover classification for whole study area is 87.68%. Reference and ancillary data are very useful to improve accuracy of classification results. The integration of DEM into classification is particularly important for land-cover classification in mountain region since land-cover distribution in mountain region is spatially topographydependent. Spatially mixed pixel is main factors that impact accuracy of land-cover classification for medium to coarse spatial resolution data. The study shows that the most serious mixed pixel occurs near the junction between land and water bodies (rivers and lakes). Shallow rivers also are not easy to be distinguished from other cover types due to mixed pixels. Approaches such as linear unmixing models have been developed and can be used to estimate subpixel fractional cover. Affected by the complex landscape, topography, and climate in the TP, making accurate, comprehensive, and geospatially consistent land-cover map still remains an arduous task. Nevertheless, with the availability of improved spatial, spectral, and radiometric resolution satellite data, new advanced classification algorithms, more detailed ground reference and ancillary data, classification accuracy will steadily increase to use for various applications and to meet end-user needs. Multispectral MODIS datasets are main data sources for land-cover classification over regional to global scales. It is also an effective approach to mapping land cover and extracting information of surface features in a spatially broad and remote mountain region like the TP. As spatial resolution increases, particularly very high-resolution satellite data increasingly available, how to effectively use the spatial information inherent in these satellite remote sensing images to extract thematic maps for various applications becomes an important task to be fulfilled in the future.

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Chapter 10

Fractional Vegetation Cover

Abstract Fractional vegetation cover (FVC) is an important parameter in the study of ecosystem balance, soil erosion, and climate change and is often used to evaluate and monitor vegetation degradation and desertification. Remote sensing provides the only feasible way to estimate FVC at regional and global scales. In the present study, an empirical model of FVC estimation is developed for central Tibetan Plateau (TP) based on the relationships between vegetation indices from Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) and corresponding field measurements derived from digital camera, which is followed by in-depth analysis on the spatial distribution of vegetation coverage using proposed method. Study shows that a linear relationship exists between vegetation coverage from the field observation and MODIS NDVI with coefficient of determination of R2 ¼ 0.90, which is slightly better than MODIS SAVI performance with R2 ¼ 0.89 and is an optimal regression model for FVC estimation. Vegetation coverage ranges 20–90% in the most part of central TP, presenting moderate to high as a whole, and generally decreases from east to west with strong regional differences due to discrepancies in land-cover types, plant species, topography and water resources availability, and so on. Keywords Fractional vegetation cover · Ground remote sensing · Field measurement · MODIS · Central Tibetan Plateau

10.1

Introduction

Vegetation is a general term for the plant community on the ground surface, such as forests, shrubs, grassland, and agricultural crops, and it can intercept rainfall, alleviate runoff, prevent desertification, and conserve soil and water (Zhang et al. 2012). It is the basic component of the terrestrial ecosystem and the natural belt to link soil, water, and atmosphere. Vegetation plays a pivotal role in energy exchange process, climate change, and hydrological and biogeochemical circles on Earth’s surface (Zhang et al. 2012; Jia et al. 2015). Changes in vegetation cover impact surface water and energy budgets through plant transpiration, surface albedo, © Springer Nature Singapore Pte Ltd. 2020 D. Chu, Remote Sensing of Land Use and Land Cover in Mountain Region, https://doi.org/10.1007/978-981-13-7580-4_10

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emissivity, and roughness (Aman et al. 1992; Nemani and Running 1996; Liu et al. 2012; Chen et al. 2016). Vegetation also is a sensitive indicator of climate change and human activities and thus influences climate by affecting the energy, water, and carbon cycle (Bonan 2008; Jiang et al. 2015; Duan et al. 2011). To promote understanding of environmental process, we must conduct observation and measurement for biophysical parameters of the terrestrial vegetation to characterize surface biophysical process. Several biophysical parameters are commonly used to describe the structural characteristics of land surface vegetation (Purevdorj et al. 1998; Lu et al. 2003; Li et al. 2013; Morsdorf et al. 2006), including crown cover (CC), foliage projective cover (FPC), leaf area index (LAI), net primary productivity (NPP), biomass, fractional vegetation cover (FVC), and absorbed photosynthetically active radiation (APAR). CC is defined as the percentage of ground area covered by the vertical projection of crowns. FPC is the percentage of ground area covered by the vertical projection of foliage. LAI is defined as the maximum projected leaf area per unit ground surface area. NPP is defined as the accumulation of dry matter by green plants per unit time and space. Biomass refers to the total dry weight of all parts that make up a live plant, including those above and below ground. FVC is the ratio of the vertical projected area of vegetation to the total ground area. FPAR is the fraction of incident photosynthetically active radiation (PAR) absorbed by green vegetation. Among these, FVC or vegetation coverage is an important parameter in the study of ecosystem balance, soil erosion, and climate change (Jiang et al. 2006; Coy et al. 2016; Xiao et al. 2016; Liu et al. 2018; Zhang et al. 2018), and it is often used to evaluate and monitor vegetation degradation and desertification (Jiapaer et al. 2011; Chen et al. 2016). Accurate estimation of FVC on the regional and global scales is required for land surface processes and climate change studies as well as for its extensive applications in agriculture, forestry, environment management, disaster risk monitoring, and drought monitoring (Gutman and Ignatov 1998; Godinez-Alvarez et al. 2009). Currently, there are two main methods to estimate FVC, viz., field measurement and remote sensing. Field measurement is a conventional method for monitoring FVC and is the most reliable method to obtain accurate FVC and only approach to provide ground truth for validation of remote sensing retrieval (Okin et al. 2013). However, due to being timeconsuming and labor-intensive over large areas, along with significant spatial and temporal variations, it is difficult to estimate FVC over large spatial scale (Peng et al. 2012). Remote sensing method provides a unique opportunity for estimating FVC at regional to global scales, but it needs to be validated against ground truth from field measurement for various applications. The common methods to retrieve FVC from remotely sensed data include empirical methods, pixel unmixing models, and machine learning methods (Zhou and Robson 2001; Xiao and Moody 2005; Chen et al. 2016). As a relatively new remote sensing technology, unmanned aerial vehicle (UAV) is increasingly employed to investigate ground FVC, and the study on alpine grassland in the Qinghai-Tibetan Plateau shows that the use of UAV to estimate FVC at the satellite image pixel scale provides more accurate results and is more efficient than conventional ground-based survey methods, especially in areas with a heterogeneous underlying surface (Chen et al. 2016).

10.2

Data and Methods

197

The main methods for field measurements of FVC include ocular estimation (Li et al. 2005) and field sampling (Jiapaer et al. 2011). As a new method for collecting field-observed FVC, the digital camera has been widely used in measuring ground FVC, which is characterized by low-cost, high-efficiency, more accuracy, and fast acquirement. White et al. (2000) used an agricultural digital camera (ADC) to measure the FVC of the arid ecological system in the United States, and their results showed that ADC is effective and accurate for long-term monitoring of the arid ecosystem and is considered a relatively easy and reliable ground survey technique for verifying the FVC information retrieved from remotely sensed data. Zhou et al. (1998) estimated vegetation coverage in semiarid grassland using various field measurement approaches, such as visible estimation, sampling belt and digital camera, etc., showing that in comparison with other field observation methods, the digital camera approach can get higher accuracy. Li et al. (2003) used the field sample, digital camera, Landsat ETM (Enhanced Thematic Mapper), and the National Oceanic and Atmospheric Administration/the Advanced Very-High-Resolution Radiometer (NOAA/AVHRR) data to detect the vegetation coverage of typical steppe in Northern China and results showed that vegetation coverage measured by digital camera is reliable and more precise than results measured by other methods and can be used to validate other measuring results. Li et al. (2005) used the digital camera to measure vegetation coverage in the temperate steppe in Northern China and evaluated different field measurement methods used to calculate the FVC of images captured by the digital camera. This study aims to develop FVC estimation method for central Tibetan Plateau (TP) based on the field measurement of vegetation coverage captured through digital camera approach and corresponding Terra/MODIS remote sensing data and to further characterize the spatial distribution patterns of vegetation cover in the study area using proposed approach.

10.2

Data and Methods

10.2.1 Data The field investigation was conducted on September 13 and 14, 2004, in central Tibet, and nine observation sites were set up in relatively homogenous regions in typical land-cover types. In each observation site, three quadrats with 0.5 m  0.5 m iron frames to confine the quadrat size were randomly selected, and digital photograph for each quadrat was taken vertically from 1.0 m height above surface using a conventional camera. The positions of observation sites were recorded using the GPS (Global Positioning System). Other surface variables observed or collected during the fieldwork included visual vegetation coverage, aboveground biomass, and soil moisture content. Ocular estimation is the most common method used to calculate the vegetation coverage using digital camera images (Li et al. 2005). Therefore, in this study, by using ocular estimation, the arithmetic average of three

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Fig. 10.1 Field observation sites for FVC estimation in central Tibet

images of quadrats was taken as the FVC for particular observation site. In addition, on September 15, 2004, Tibet Institute of Animal Husbandry Sciences carried out field measurements for vegetation coverage and rangeland biomass near the yak breeding farm in Lhundup County within the study area, and the digital photo on sample sites was taken using same methods. These observation data were also used in this study. All field observation sites are shown in Fig. 10.1. MODIS is one of the key instruments onboard Terra satellite and is complemented by another MODIS on the Aqua satellite. MODIS sensors are substantially improved in spectral, spatial, and radiometric resolutions compared to previous global sensor system such as NOAA/AVHRR. Its global coverage is obtained every 1–2 days. With 36 spectral bands and 12-bit radiometric resolution, MODIS has the highest number of spectral bands of any global-coverage moderateresolution imager (Townshend and Justice 2002). Terra/MODIS remote sensing data on September 13, 2004, received at Tibet Institute of Plateau Atmospheric and Environmental Sciences (TIPAES) were cloud-free and used in this study. All MODIS images were processed using ENVI image-processing software.

10.2.2 Methods Remote sensing is the most efficient way to derive FVC at regional and global scales. There are mainly three types of methods for estimating FVC from remotely sensed

10.3

Results

199

data: the empirical methods, pixel unmixing model, and physical methods. The empirical methods are based on relationships between the field-observed FVC and vegetation indices or specific spectral reflectance from remote sensing sensor and are widely used in FVC estimation on large scales (Yang et al. 2017; Carlson and Ripley 1997; North 2002). Empirical methods require sufficiently representative and reliable field samples as ground truth for developing empirical equations. Vegetation indices derived from satellite remote sensing data are one of the primary sources of information for monitoring of surface vegetation cover. Since information contained in a single spectral channel is insufficient to characterize vegetation status, vegetation indices are usually developed to extract vegetation information from two or more spectral bands (Jensen 1996). Until now, a great number of vegetation indices have been proposed to characterize surface vegetation, biophysical process, and terrestrial ecosystem. Among these, the normalized difference vegetation index (NDVI) is the most commonly used to develop regression models for FVC estimation as it retains the ability to minimize topographic effects while producing a linear measurement scale (Liu and Yang 2001). NDVI also partially compensates changes in the illumination conditions, surface slope, aspect, and other illumination factors (Lillesand and Kiefer 1987). NDVI is calculated as NDVI ¼ ðNIR  RÞ=ðNIR þ RÞ

ð10:1Þ

where NIR is the surface reflectance of MODIS band 2 (near-infrared band) and R is the surface reflectance of MODIS band 1 (red band). NDVI value ranges from 1.0 to 1.0. Soil background condition exerts considerable influence on partial canopy spectra and the calculated vegetation indices. In order to reduce the soil background effect, Huete (1988) proposed soil-adjusted vegetation index (SAVI), and it is expressed as follows: SAVI ¼

ðNIR  RÞ  ð1 þ LÞ ðNIR þ R þ LÞ

ð10:2Þ

where NIR and R correspond to the reflectance in the near-infrared and red wavelengths of MODIS, respectively. L is the correction factor, ranging from 0.0 for very high vegetation cover to 1.0 for very low vegetation cover. Here, L ¼ 0.5 and is the accommodation coefficient of the soil background.

10.3

Results

10.3.1 Empirical Model Development The procedure to develop FVC estimation model is as follows: first, NDVI and SAVI were separately calculated based on Eqs. (10.1) and (10.2) using Terra/

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100 y = 205.03x - 18.665 2 R = 0.8974

90

FVC (%)

80 70 60 50 40 30 20 0.20

0.25

0.30

0.35

0.40 0.45 MODIS NDVI

0.50

0.55

0.60

Fig. 10.2 Relationships between observed FVC and MODIS NDVI

MODIS band-1 and band-2 reflectances; second, the field-observed value of each observation site was obtained after averaging vegetation coverage of three quadrats based on the ocular estimation of digital camera images that took vertically to three quadrats from 1.0 m height above surface; third, NDVI and SAVI values of each observation sites were extracted from NDVI and SAVI images based on GPS points of ten observation sites; and finally, the relationships between vegetation coverage observed and NDVI and SAVI were established, respectively. The result shows that there are linear relationships between the field-observed FVC from digital photograph and NDVI and SAVI derived from MODIS remote sensing data, respectively. As shown in Figs. 10.2 and 10.3, for the NDVI the linear regression equation is FVC_NDVI ¼ 205.03 NDVI 18.665 (R2 ¼ 0.90), and for the SAVI, it is FVC_SAVI ¼ 136.97  SAVI 19.034 (R2 ¼ 0.89). NDVI is correlated with certain biophysical properties of the vegetation canopy, such as LAI, FVC, vegetation condition, and biomass. NDVI increases near linearly with increasing LAI and then enters an asymptotic phase in which NDVI increases very slowly with increasing LAI. Several studies have found this asymptotic region pertains to a surface almost completely covered by leaves (Carlson and Ripley 1997; Lillesand and Kiefer 1987). Over densely vegetated surfaces, the NDVI responds primarily to red reflectance and is relatively insensitive to variations of the nearinfrared reflectance (Huete et al. 1997). According to experimental measurements with different soil backgrounds, NDVI approach their maximum values at fractional vegetation cover between 80% and 90% (Huete et al. 1985; Díaz and Blackburn 2003). NDVI can easily reach saturation in an area where vegetation is particularly lush (Li et al. 2014). However, the NDVI values in area with low vegetation cover are severely affected by soil variations (Heute 1988).

10.3

Results

201

100 y = 136.97x - 19.034 R2 = 0.8869

90

FVC (%)

80 70 60 50 40 30 20 0.3

0.4

0.5

0.6 MODIS SAVI

0.7

0.8

0.9

Fig. 10.3 Relationships between observed FVC and MODIS SAVI

According to results of the first land resources survey in the Lhasa area in the 1980s (Agricultural and Pastoral Bureau of Lhasa Municipality 1991), alpine meadow is a predominant vegetation type in the study area covering 73.6% of the total area, and general FVC ranges 40–90%, while it is higher in alpine shrub meadow than pure alpine meadow, reaching 50–90%. As the second largest vegetation cover in the study area, alpine steppe covers 14.5% of the total area and general vegetation coverage is 30–70%, and its productivity is low compared to other vegetation cover, followed by temperate steppe accounting for 11.2% of the total area and reaching 20–60% of vegetation coverage in general. Lowland meadow and wetland as the rest of cover types have higher livestock carrying capacity and productivity but only cover 0.5% and 0.1%, respectively, while their vegetation coverage is above 80%. All these imply that vegetation coverage in the study area is generally intermediate, ranging from 20% to 90%. Therefore, for FVC estimation model, saturation effect caused by high-density canopy and soil background effect are very limited. SAVI has no its advantage over NDVI. Instead, the effectiveness to estimate vegetation coverage by NDIV is slightly better than that by SAVI and is the optimal model since the linear correlation coefficient between NDVI and in situ vegetation coverage is higher than that between SAVI and in situ data. The study also shows that there were no significant differences in estimating FVC with different vegetation indices in the TP area since the overlap of the vegetation in the vertical projection is extremely small, and vegetation growth is constrained due to hard climate conditions and high altitudes (Chen et al. 2016). Thus, the following analysis on vegetation coverage is based on the NDVI-derived optimal estimation model.

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10.3.2 Comparison with Carlson and Ripley Algorithm Apart from empirical models for FVC estimation based on the relationships between vegetation indices from satellite remote sensing and in situ measurements, many scientists came up with direct methods to estimate vegetation coverage based on the vegetation index alone instead of in combination with field investigation. Carlson and Ripley algorithm is one of these models (Carlson and Ripley 1997) and is expressed as follows:  Coverage ðx; yÞ ¼

I NDVI ðx; yÞ  I NDVI, min I NDVI, max  I NDVI, min

2 ð10:3Þ

where coverage (x, y) is the fractional vegetation cover of a pixel in the study region, INDVI is the NDVI value of a pixel, INDVI, min is the minimum NDVI value, INDVI, max is the maximum NDVI value, and INDVI, mean is the mean NDVI value. In Terra/ MODIS cloud-free image acquired on September 13, 2004, covering the study area, INDVI, min ¼ 0.67, INDVI, max ¼ 0.77, and INDVI, mean ¼ 0.32. By using Eq. (10.3), FVC for the study area was calculated and compared with field measurements from ten observation sites to validate model suitability for the central TP. The result shows that when the vegetation coverage is 30–80%, the values derived from Eq. (10.3) and measured in the field are very close and a significant linear correlation exists with r ¼ 0.94. However, when it is above 90%, the values derived from this algorithm and observed presented apparent differences, as shown in Fig. 10.4. In brief, Carlson and Ripley algorithm which is based on sole vegetation index of satellite data is applicable for estimating vegetation coverage in the study area since

120

Field measured

NDVI estimated

100

FVC (%)

80

60

40

20

0 RiduoA

RiduoB

Lamuxiang

Lhasa

DamshungA DamshungB

DamshungC DamshungD

Yangbajian

Lhundup

Fig. 10.4 FVC in observation sites from Carlson and Ripley algorithm and from field measurements

10.3

Results

203

moderate coverage is predominant in central TP. However, a considerable difference between field measurement and the algorithm inversion is found when FVC is higher than 90%. Because of vegetation coverage ranging from 20% to 90% in most part of the study area, this algorithm can be used to study on vegetation cover dynamics, and relevant grassland degradation and desertification in the center TP area and reasonable accuracy will be achieved.

10.3.3 Spatial Distribution of Vegetation Coverage The comparison between NDVI and SAVI for estimating vegetation coverage in the Lhasa area located at central TP shows that the linear correlation coefficient between MODIS NDVI and the field measurement is higher than that between SAVI and in situ vegetation coverage. Thus, as a relatively optimal regression model for FVC estimation, the equation FVC_NDVI ¼ 205.03  NDVI 18.665 (R2 ¼ 0.90) was used to analyze the spatial distribution of vegetation cover with 250 m spatial resolution of MODIS NDVI data for the study area. The final result is shown in Fig. 10.5, and its statistic summary is illustrated in Fig. 10.6 with 10% FVC interval, showing that vegetation cover in the study area presents moderate to high as a whole and generally decreases from east to west, with pronounced regional differences caused by various factors, such as surface cover types, plant species, topography,

Fig. 10.5 Spatial distribution of fractional vegetation cover at the central TP

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Area percentage (%)

12 10 8 6 4 2 0 No

1-10

10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-99 FVC range (%)

100

Fig. 10.6 Statistic summary of fractional vegetation cover with 10% FVC interval

climate, water resources availability, and so on. The area with FVC above 50% is 61.2% of the total area, while the area with FVC less than 10% is 8.1% of the total area. FVC ranging from 20% to 90% accounts for 70% of the total area, which demonstrates the evidence that general FVC in the TP is moderate to high.

10.4

Conclusion and Discussion

As one of the important vegetation parameters to describe the surface vegetation and characterize ecosystems, FVC is often difficult to measure accurately among various parameters of plant communities. As a new method in recent years, digital camera has been widely used to measure vegetation cover due to its low-cost, high-efficiency, high-quality, and quick acquirement. In the present study, FVC estimation method is developed for central TP based on the relationships between Terra/ MODIS vegetation indices and corresponding field measurements captured through digital camera approach. The spatial distribution of vegetation cover is further analyzed using the proposed method. A linear relationship exists between FVC from field measurement through digital photography and NDVI from MODIS visible images with the coefficient of determination of R2 ¼ 0.90, while the coefficient of determination with SAVI is 0.89, which means that soil background effect is very limited during vegetation growing season in the study area. NDVI-based FVC estimation method is an optimal regression model. Carlson and Ripley algorithm, which is based on sole vegetation index, is applicable for estimating and monitoring vegetation cover in central TP since FVC below 90% is predominant in this region covering 86.5%.

References

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Alpine meadow is a predominant vegetation type in central TP covering 73.6% of the total area, and FVC ranges 40–90% in general, while alpine steppe as the second largest vegetation cover type covers 14.5% of the total area with vegetation coverage of 30–70%, indicating that vegetation cover in central TP presents moderate to high as a whole. These facts are further proved by results from proposed FVC algorithm. According to proposed algorithm, FVC in most part of central TP is from 20 to 90% accounting for 70% of the total area. FVC above 90% covers 13.5% and below 10% covers 8.1%, while FVC above 50% occupies 61.2% of the total study area. Vegetation coverage generally decreases from east to west with considerable regional differences caused by surface cover types, plant species, elevation and water resources availability, and so on. Remote sensing method provides a unique opportunity for estimating FVC at regional to global scales, but it needs to be validated against sufficiently representative and reliable ground truth from field measurement for various applications (Song et al. 2017; Mu et al. 2015). Newly developed ground and near-ground remote sensing techniques, such as digital photography and unmanned aerial vehicle (UAV), provide new approaches to measuring ground FVC with high-efficiency, more accuracy, finer spatial and temporal resolution, low-cost, and fast acquirement. It is appropriate and feasible to measure ground FVC in central TP using the digital camera as the field apparatus. However, the methods to derive FVC from digital photo more accurately are particularly important, and currently the most common methods include ocular estimation, visual interpretation, image classification, and machine learning approaches. Among these, ocular estimation is the most commonly used FVC measurement method in the field and used in this study. How to reduce subjective randomness of this method and the use of more objective and quantitative methods, such as object-based image classification and machine learning approaches, to more accurately calculate the FVC of images captured by the digital camera is crucial to make use of advantage of ground-based remote sensing techniques.

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Chapter 11

Aboveground Biomass of Grassland

Abstract Biomass is an important component of grassland ecosystems and plays a critical role in the sustainable use of grassland resources and the global carbon cycle. Satellite remote sensing provides an important approach for estimating aboveground biomass (AGB) at large spatial scales while biomass harvesting offers reliable and site-specific biomass magnitude and is only way to give indispensable ground truth for satellite remote sensing. In this study, estimate models for grassland AGB for the Lhasa area located at the central Tibetan Plateau (TP) are developed based on the relationships between the field measurements and Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices (NDVI, EVI), and the models are validated against independent field measurements. The result shows that exponential relationships exist between AGB and MODIS vegetation indices. MODIS NDVI is more effective to estimate grassland AGB in the study area with R2 ¼ 0.63 than EVI with R2 ¼ 0.50 and is an optimal regression model for AGB estimation. For green AGB estimation, the performance of NDVI (R2 ¼ 0.69) is also better than EVI (R2 ¼ 0.59). In the study area, AGB spatially presents decreases from east to west, with great regional differences due to inhomogeneous grassland types and impact of various environmental and climatic factors. AGB is above 100 g/m2 in some eastern regions whereas it is lower than 20 g/m2 in the west. Keywords Aboveground biomass · Remote sensing · Field measurement · MODIS · Central Tibetan Plateau

11.1

Introduction

Grassland ecosystem is one of the most important terrestrial ecosystems on the Earth, covering around one fourth of the Earth’s terrestrial surface and providing ecosystem functions of soil and water conservation, wind erosion prevention, sand fixation, and air purification (White and Murray 2000; Lobell and Field 2007; Dusseux et al. 2015; Han et al. 2018).

© Springer Nature Singapore Pte Ltd. 2020 D. Chu, Remote Sensing of Land Use and Land Cover in Mountain Region, https://doi.org/10.1007/978-981-13-7580-4_11

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Biomass is an important component of grassland ecosystems and plays a critical role in the sustainable use of grassland resources and the global carbon cycle as the most direct indicator of grassland’s ecological status (Zhao et al. 2014; Eisfelder et al. 2017). Biomass also is a sensitive indicator of environmental change and ecological functioning and largely influences biodiversity and environmental processes such as grassland degradation, hydrological cycle, and soil erosion (Lu 2006). It is a determining factor for ecosystem variability and resilience (Segoli et al. 2008) and provides pivotal information for understanding the responses of vegetation to the climate change (Costanza et al. 1997; Liang et al. 2016). Biomass has been widely used to evaluate productivity and is an important indicator in carbon cycle studies related to vegetation growth (Houghton et al. 2000). The aboveground biomass (AGB) of grassland provides the basis for estimating the net primary productivity of grassland (Lauenroth et al. 1986) and can be used to directly estimate grassland productivity (Jobbagy and Sala 2000; Meng et al. 2017). Particularly, estimation of grassland AGB provides information useful for sustainable grassland management and grazing activities. Quantification of AGB is particularly essential to identify and monitor those areas threatened by degradation and desertification (Eisfelder et al. 2017). This is especially important in arid and semiarid environments. Monitoring and estimating grassland AGB in degraded and desertified areas can not only show the status of the growth of local vegetation but also provide evidence that ecosystem managers and scientists can use to evaluate the effects of ecological restoration in these areas, as well as to study the carbon cycle and to realize sustainable development of grassland ecosystems (Tsalyuk et al. 2015; Yan et al. 2015). Satellite remote sensing can provide large-scale, frequent, low-cost, and updated information on the surface and has been widely applied for the estimation of grassland biomass. Biomass harvesting is a common and reliable method of estimating AGB. Although applicability of traditional biomass harvesting is constrained by the intense field sampling, sample processing requirements, labor cost, and so on, it is the only way to offer indispensable ground truth for satellite remote sensing and site-specific biomass magnitude (Shen et al. 2008). How to combine these two methods more effectively have become main approaches to more accurately estimate AGB at regional to global scales. Grassland is the largest terrestrial ecosystem in China with accounting for a third of the total land area (Ministry of Agriculture of China 1996), mainly distributed in the Northeast, Inner Mongolia, Loess Plateau, Qinghai-Tibet Plateau, and Xinjiang Province, of which the temperate steppe in the arid and semiarid climate zone is an important part of Eurasian steppe (Bai et al. 2008;). Alpine grassland primarily grows in the Qinghai-Tibet Plateau (Chu et al. 2007). Tibet, referring to Tibet Autonomous Region (TAR) here, is the main body of Qinghai-Tibet Plateau and one of China’s five major pastoral regions and key livestock raising provinces. Grassland is the most extensive vegetation type in Tibet and plays a vital role in animal husbandry development and environmental conservation. Given that geographical location, complex terrain, vast spatial extent, various environmental and climatic conditions, Tibet has diverse grassland types. In

11.2

Data and Methods

211

terms of temperature conditions, there are tropical, subtropical, temperate, alpine, and alpine desert grassland types. Based on water availability, moisture, sub-moisture, semiarid, arid, and extremely arid grassland types exist in Tibet. In 18 national grassland types in China, 17 grassland types can be found in TAR except for savanna (Tibet Land Management Bureau and Tibet Animal Husbandry Bureau 1994), making it one of the most diverse and complex grassland types in China. Under impacts of climate warming, human activities, and natural variability, in combination with fast growing grazing pressure and rodent damage, Tibet has experienced grassland degradation and desertification (Harris 2010; Chen et al. 2014; Wang et al. 2013). Grassland productivity has obviously declined since the 1980s, which has become major social concerns and restricting factors for sustainable social and economic development and environmental protection in Tibet (Liu et al. 1999; Gao et al. 2006). As the most direct indicator of grassland’s ecological status and grassland degradation, grassland biomass will directly cause grassland productivity decreasing and change in process of biomass accumulation (Tsalyuk et al. 2015). At present, most of studies on grassland biomass and degradation in Tibet are based on the vegetation indices obtained from various remote sensing satellite platforms from space to retrieve biomass change and degree of grassland degradation. The first grassland resources survey in TAR was carried out in the 1980s (Tibet Land Management Bureau and Tibet Animal Husbandry Bureau 1994). However, due to lack of continuous ground grassland observation, the spatial distribution and temporal variations of grassland biomass in Tibet area largely remain unknown. Particularly, there is no appropriate approach available to accurately estimate AGB of these heterogeneous grasslands at lager spatial scales based on the remote sensing data, which can be used for routinely monitoring grassland growth status and understanding degree of grassland degradation and desertification. Therefore, the aims of this study are (1) to develop a general method for estimating AGB from MODIS vegetation indices, (2) to validate estimating models using independent field measurements, (3) to analyze AGB of different grassland types in the study area based on in situ measurements and to compare with previous research results, and (4) to apply the developed models to investigate the spatial distribution pattern of AGB in the study area. The study focuses on developing useful tools for monitoring grassland degradation, seasonal variations of grassland productivity, and growth for effective management of grassland resources to realize sustainable development of grassland ecosystems.

11.2

Data and Methods

11.2.1 Study Area As the study area, Lhasa area is located at the middle and lower reaches of Lhasa River basin in central Tibetan Plateau (TP). Grassland is main land cover type and

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covers most of regions except for lakes, perennial snow, glaciers, bare soil, and small patches of forest and shrub on the eastern edge. According to the first land resources survey in TAR in the 1980s (Agricultural and Pastoral Bureau of Lhasa Municipality 1991), alpine meadow is predominant in the study area covering 73.6% of the total area and is distributed in the middle to upper parts of mountains and broad regions in riverheads ranging from 4400 to 5000 meters above sea level (masl). As the second largest vegetation cover, alpine steppe accounts for 14.5% of the total area, primarily distributed in Namtso Lake basin and surroundings in the north of Mt. Nyainqentanglha, which is followed by the temperate steppe covering 11.2% of the total area and is mainly distributed in valley terraces, river beaches, and mountains on both sides of river valleys with elevation below 4200 masl. In the study area, precipitation decreases from east to west and temperature decreases from south to north in general.

11.2.2 Remote Sensing Data Vegetation indices calculated from the reflectances detected by satellite sensors can reflect the photosynthetic activity of the vegetation and are extensively used to monitor surface biomass (Jin et al. 2014; Gaitán et al. 2013; Gao et al. 2013) and have become the most effective approach to biomass estimation. The most commonly used MODIS vegetation indices are the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), which are calculated through combination of reflectance in the red band, near-infrared band, and blue band. NDVI is designed to reflect vegetation activity by using information on chlorophyll radiation absorption in the red band and radiation scattering by mesophyll in the nearinfrared band (Shen et al. 2014). EVI is a modification of NDVI designed to minimize impacts of soil background and atmospheric noise (Huete et al. 1994). Both NDVI and EVI have been proven to be direct indicators of vegetation activity. MODIS sensor on board Terra and Aqua satellites is a multispectral instrument with 36 bands and spatial resolution of 250 m in 2 bands, 500 m in 5 bands, and 1 km in 29 bands (Salomonson and Appel 2004). It is operational on two NASA’s Earth Observing System (EOS) spacecrafts designed to satisfy diverse need for terrestrial, atmospheric, and oceanographic observations. Due to high signal-to-noise ratio, improved spectral and spatial characteristics, measurement precision, comprehensive and frequent global earth imaging as compared to previous satellites, MODIS vegetation index can better response the temporal and spatial variation characteristics of vegetation and has been successfully used to characterize vegetation structure and density and has become the main remote sensing data for monitoring grassland biomass and other variables on the surface from regional to global scales. In this study, two clear-day Terra/MODIS scenes on September 13 and 27, 2004, received at Lhasa MODIS Receiving Station of Tibet Institute of Plateau Atmospheric and Environmental Sciences (TIPAES) were used. August is rainy season in Tibet and it is mostly cloudy. It is difficult to find cloud-free MODIS images.

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Data and Methods

213

Therefore, MODIS vegetation index product (MOD13Q1) downloaded from NASA’s Land Processes Distributed Active Archive Center (LP DAAC) were used. MOD13Q1 is provided every 16 days at 250 m spatial resolution as a gridded level-3 product in the sinusoidal projection, including Terra/MODIS NDVI and EVI data, which are computed from atmospherically corrected bidirectional surface reflectances that have been masked for water, clouds, heavy aerosols, and cloud shadows. The field AGB measurements conducted on August 12–17 and 28–30 in 2004 correspond to the first 16-day and second 16-day MODIS vegetation index data in August of 2004, respectively. The field measurements in June and July of 2004 were used to validate accuracy assessment of the models.

11.2.3 Field Measurements The field measurement of AGB is the most reliable method to obtain accurate and site-specific grassland biomass and provides valuable ground truth for validation of satellite-based AGB estimation methods. In this study, ten observation sites were set up in relatively homogenous and spatially representative region with typical grassland types. AGB samples in these observation sites were collected two times within 3 days every 15th and 30th day of the month during the vegetation growing season from June to September. In each observation site, three quadrats with 0.5 m  0.5 m iron frames were randomly selected, and all aboveground biomass were clipped to the ground level using sharp shears. GPS (global positioning system) points, land cover types, dominant species, vegetation coverage, and digital photograph for each quadrat were also recorded. The procedure of AGB sampling and processing is as follows. First, three 0.5 m  0.5 m iron frames were randomly thrown at the grassland observation site, and all grassland AGB in three 0.5 m  0.5 m quadrats in each site were clipped to the ground level using sharp shears. Second, after removing soil adhered, gravel, litter, and other non-plant materials, all samples were packed in paper bags and brought to the grassland experimental laboratory of Tibet Institute of Animal Husbandry Sciences. Third, at the laboratory, AGB samples were separated into green and dry materials and were dried at 85 until the weight remained constant. Finally, as a general rule, biomass is expressed in dry weight per unit area, since fresh weight varies according to environmental conditions (Barrachina et al. 2015). Thus, green and dry materials were weighted separately, and the biomass unit was converted into dry matter content per square meter (gm2). The weight of green grass material is green dry matter (GDM) content, while the weight of dry grass material is residual dry matter (RDM) content. The sum of GDM and RDM is total AGB and here is referred to as AGB or total AGB. The residual dry matter (RDM) here refers to the dry weight of dry and dead grass material left on the ground after the last growing season. Green dry matter (GDM) refers to the dry weight of green plant material of newly grown grass during the growing season and here is also named green AGB or GDM.

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Fig. 11.1 Geographical location of grassland AGB sample sites

In the study area, the grass generally reaches mature stage from August to September, and AGB reaches maximum value within the year and relatively remains stable for a period of time. In order to monitor this key period of grassland productivity using MODIS vegetation index, biomass sample data collected between August and September were used to develop general AGB estimate model. Remaining sample data from June to July were employed for validating model accuracy. In addition, Tibet Institute of Animal Husbandry Sciences investigated natural grassland near the yak breeding farm in Lhundup County within the study area and collected AGB and vegetation coverage. These data also were used in the study and there were 11 observation sites in total. The geographical location of all sampling sites is shown in Fig. 11.1. The observation site Damshung F is additional sampling point for AGB and other sites are same with observation sites used for vegetation coverage estimation shown in Fig. 10.1. The grassland and vegetation types of 11 observation sites are listed in Table 11.1 as referenced the first grassland resources survey in TAR implemented in the 1980s. Riduo A, Damshung D, and Lhundup belong to alpine meadow. Damshung B and Yangbajian are typical alpine steppe, located in Damshung valley. Lhasa and Lamuxiang observation sites are located in the southern Lhasa river valley with temperate steppe. Damshung A and Damshung F are alpine swamp meadow grassland, located in relatively lower land in the Damshung valley, where grassland is fenced for winter and spring grazing. Riduo B is in Medro Gongkar County of eastern region and belongs to alpine swamp meadow without fencing. Damshung C is alpine meadow grassland, located at the west side of Qinghai-Tibet highway in the

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Data and Methods

215

Table 11.1 AGB observation site and auxiliary information Site Riduo A Riduo B

Lamuxiang Lhasa

Grassland Longitude/ Latitude/ Altitude/m type 92.2927 29.6908 4418 Alpine meadow 92.0968 29.7099 4150 Alpine swamp meadow 91.5444 29.8043 3720 Temperate steppe 91.1452 29.6251 3693 Temperate steppe

Damshung A 91.1257

30.4975

4233

Damshung B 91.0959

30.4948

4249

Damshung C 90.9724

30.4127

4216

Damshung D 90.6275

30.2000

4590

Damshung F 90.8933

30.3574

4236

Lhundup

91.2363

30.0919

4546

Yangbajian

90.4720

30.0761

4300

Alpine swamp meadow Alpine steppe Alpine meadow Alpine meadow Alpine swamp meadow Alpine meadow Alpine steppe

Main vegetation type Source Kobresia pygmaea [a] Dasiphora parvifolia, Kobresia pygmaea Artemisia younghusbandii Artemisia younghusbandii, Pennisetum flaccidum Kobresia littledalei

[a]

[b]

Stipa purpurea

[b]

Kobresia pygmaea Polygonum macrophyllum Kobresia pygmaea

[b]

[b]

Kobresia pygmaea

[b]

Kobresia pygmaea

[a]

Stipa purpurea

[b]

[a] [a]

[a] Tibet Animal Husbandry Bureau and Tibet Land Management Bureau. 1:200 000 Map of Grassland Types in the Central Tibet Autonomous Region. 1991.8 [b] Tibet Land Management Bureau and Animal Husbandry Bureau. 1:2 000 000 Map of Grassland Types in Tibet Autonomous Region.1991

Damshung valley, where Qinghai-Tibet railways are passing through the nearby. Compared to Riduo A and Damshung D both as alpine meadow, in Damshung C the impact of human activities is more intensive and its representative is poorer than Riduo A and Damshung D.

11.2.4 Methods Vegetation indices derived from satellite remote sensing data are primary information sources for monitoring of surface vegetation cover and can be efficiently correlated to plant biophysical parameter and biomass (Liang et al. 2016; Barrachina

216

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et al. 2015). A great number of vegetation indices have been proposed to characterize surface vegetation, biophysical process, and terrestrial ecosystem. Among these, NDVI is the most frequently used index for regression model development of biomass estimation. NDVI exploits the difference between the maximum absorption of radiation in the red spectral wavelength and the maximum reflection of radiation in the nearinfrared spectral wavelength. NDVI is expressed as follows: NDVI ¼ ðNIR  RÞ=ðNIR þ RÞ

ð11:1Þ

where NIR is the surface reflectance in the near-infrared region (MODIS band 2) from 841 to 876 nm and R is the surface reflectance in the red region (MODIS band 1) from 620 to 670 nm. NDVI values range from –1.0 to 1.0. However, it is well known that NDVI has several limitations, including saturation in a multilayer closed canopy and sensitivity to both atmospheric aerosols and the soil background (Heute 1988; North 2002). To overcome these limitations of NDVI, as a new MODIS product, EVI was developed and offers improvements over the NDVI, reducing saturation of the signal at high vegetation coverage and soil background effects (Heute 1988). EVI is expressed as follows:  EVI ¼ G 

ðNIR  RÞ ðNIR þ C1  R  C2  B þ LÞ

 ð11:2Þ

where B is the surface reflectance in blue band (MODIS band 3) from 459 to 479 nm. C1 and C2 are coefficients designed to correct for aerosol scattering and absorption, which uses the blue band to correct for aerosol influences in the red band. C1 and C2 have been set at 6.0 and 7.5, while G is a gain factor (set at 2.5) and L is a canopy background adjustment (set at 1.0 here).

11.3

Results

11.3.1 AGB Magnitude The study area is just located around 30 N latitude and the grassland tends to be mature from August to September and its productivity is generally at the highest stage in a year. However, due to constraints of alpine climate, soil, and water conditions in this elevated plateau, AGB is low compared to plain regions of same latitudes on the earth. The average AGB sampled of all grassland types is 96.88 g/ m2, among which average GDM and RDM is 77.66 and 19.22 g/m2, respectively, with the proportion of GDM above 80%. During the August to September, some sampling site has no residual material appeared and total AGB consists of only green plants (Table 11.2).

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217

Table 11.2 Statistic summary of AGB sampled in the field (g/m2) Grassland type Alpine meadow

Alpine steppe

Temperate steppe

Alpine swamp meadow

Average

Statistic item GDM RDM AGB GDM RDM AGB GDM RDM AGB GDM RDM AGB GDM RDM AGB

Maximum 70.14 39.62 91.84 45.08 24.92 58.24 77.14 26.04 87.22 541.24 81.34 589.12 541.24 81.34 589.12

Minimum 18.90 0.00 37.10 26.18 0.00 41.58 23.66 0.00 49.70 190.05 22.68 212.73 18.90 0.00 37.10

Mean 44.43 17.18 61.61 38.97 9.90 48.87 52.11 12.38 64.48 303.26 53.59 356.84 77.66 19.22 96.88

Affected by grassland type, geographical location, water and temperature conditions, and so forth, there is a great difference in magnitude of AGB samples in 11 observation sites, ranging from 37.10 to 589.12 g/m2. Among these, AGB sample of alpine swamp meadow located in relatively lower land near the Damshung County town is highest with 589.12 g/m2, followed by Riduo B ranging from 212.73 to 327.04 g/m2 as alpine swamp meadow also. Sampled AGB in the rest of observation sites is lower than 100 g/m2, with a minimum of 37.10 g/m2 occurred in Riduo A as typical alpine meadow grassland. On average, AGB of alpine swamp meadow is highest with reaching to 356.84 g/m2, followed by temperate steppe (64.48 g/m2) and alpine meadow (61.61 g/m2), whereas AGB of alpine steppe is lowest with 48.87 g/m2 on average. Similarly, the difference of sampled GDM in different grassland types is great as well with a maximum of 541.24 g/m2 and a minimum of 18.90 g/m2 and an average of 77.66 g/m2, accounting for 80.16% of total aboveground biomass of grassland. On average, GDM of alpine steppe is lowest with 38.97 g/m2, followed by alpine meadow (44.43 g/m2) and temperate steppe (52.11 g/m2), while alpine swamp meadow also has the highest GDM of 303.26 g/m2. Over the period from August to September, the sampled value of RDM ranges 0–81.34 g/m2. Alpine swamp meadow has the highest RDM of 81.34 g/m2 and its lowest RDM reaches 22.68 g/ m2, which means residual or dry plants always exists during the sampling for alpine swamp meadow. In other three grassland types, the minimum of RDM is 0, which means there is no residual or dry plant existed for some samples during August to September. On average, RDM of alpine swamp meadow is highest with 53.59 g/m2, followed by alpine meadow (17.18 g/m2) and temperate steppe (12.38 g/m2), while RDM is lowest in alpine steppe of 9.9 g/m2. Average RDM of all grassland types is 19.22 g/m2 with accounting for 19.84% of total AGB on average.

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600 500

y = 19.421e3.1775x R2 = 0.63

AGB(g/m2)

400 300 200 100 0 0.05

0.15

0.25

0.35

0.45

0.55

0.65

0.75

0.85

MODIS NDVI

Fig. 11.2 Relationships between AGB and MODIS NDVI

11.3.2 AGB Estimate Model Vegetation index is an important indicator to represent the vegetation characteristics of land surface from remotely sensed data and has been widely applied for the estimation of grassland biomass. The essence of satellite-based AGB estimate method is to build regression models between biomass measurements in the field and remote sensing-based vegetation index to estimate biomass for larger scales (Liang et al. 2016; Yuan et al. 2016; Barrachina et al. 2015; Liu et al. 2017; Eisfelder et al. 2017). In this study, taking sampled grassland AGB as a dependent variable and MODIS vegetation indices (NDVI, EVI) as independent variables, the regression models were established, respectively. In order to find out the optimal AGB monitoring and estimating models suitable for central TP based on the MODIS vegetation indices, a linear and four commonly used nonlinear regression models (logarithmic, inverse, power, and exponent models) were tested. The result shows that the relationships between AGB and MODIS vegetation indices can be best quantitatively expressed by exponent models. According to the principle of regression modeling with higher coefficient of determination and value of F-test, the NDVI-based exponent model (AGB ¼ 19.421e(3.178NDVI)) simulated best with coefficient of determination (R2 ¼ 0.63) and F-test value (F ¼ 49.2) at a significance level of 0.001 as show in Fig. 11.2. The fitting performance of EVI (R2 ¼ 0.50, F ¼ 28.9) is slightly lower than NDVI for estimating AGB as shown in Fig. 11.3, reflecting that saturation effect caused by high-density canopy and soil background effect is very limited in the study area since moderate vegetation coverage is dominant in the central TP. EVI has no its advantage over the NDVI. Instead, the effectiveness to estimate AGB by

11.3

Results

219

600 500

y = 19.251e5.5071x R2 = 0.50

AGB(g/m2)

400 300 200 100 0 0.05

0.1

0.15

0.2

0.25 0.3 MODIS EVI

0.35

0.4

0.45

0.5

Fig. 11.3 Relationships between AGB and MODIS EVI

NDIV is slightly better than that by EVI and is the optimal model, indicating that NDVI is more suitable for monitoring and estimating grassland AGB in the central Tibet. Similarly, the regression models were developed using field-sampled green AGB as a dependent variable and MODIS vegetation indices (NDVI, EVI) as independent variables, respectively. A linear and four commonly used nonlinear regression models (logarithmic, inverse, power and exponent models) were also tested during the model development. For green AGB estimation, the NDVI-based exponent model, expressed by GreenAGB ¼ 10.929e(3.850NDVI), performs best with highest R2 and F-test value with a significance level of 0.001, which is shown in Fig. 11.4. Similar with estimating for AGB mentioned above, the coefficient of determination (R2 ¼ 0.69) and F-test value (F ¼ 65.8) of NDVI-based regression models is higher than that EVI-based models with R2 ¼ 0.59 and F ¼ 41.9. Obviously, for monitoring and estimating green aboveground biomass of grassland in the central Tibet, the fitting performance of NDVI is also better than EVI and is an optimal model to estimate green biomass in the study area. Regression analysis between biomass and spectral vegetation indices above indicated that the exponential relationships between green AGB and NDVI (R2 ¼ 0.69) is better than that between total AGB and NDVI (R2 ¼ 0.63), which is associated with that vegetation indices are designed to be sensitive to the green component of vegetation canopy and do not correspond well to total vegetation cover in the presence of significant senescent vegetation (Hagen et al. 2012; Butterfield and Malmstrom 2009). The regression models in Figs. 11.2, 11.3, 11.4 and 11.5 show that either using MODIS NDVI or EVI the performance of estimate models for green AGB is always

220

11

Aboveground Biomass of Grassland

600

Green AGB(g/m2)

500

y = 10.929e3.85x 2 R = 0.69

400 300 200 100 0 0.05

0.15

0.25

0.35

0.45 0.55 MODIS NDVI

0.65

0.75

0.85

Fig. 11.4 Relationships between green AGB and MODIS NDVI 600

Green AGB(g/m2)

500

y = 10.208e6.9157x R2 = 0.59

400 300 200 100 0 0.05

0.1

0.15

0.2

0.25 0.3 MODIS EVI

0.35

0.4

0.45

0.5

Fig. 11.5 Relationships between green AGB and MODIS EVI

better than that for total AGB, indicating that MODIS vegetation index is more suitable for green biomass estimation, which reflects unique spectral response of green grassland detected by satellite sensors. It is well known that vegetation index is based on differences in reflectances between visible and near-infrared bands of satellite sensors. The strong photosynthesis of green vegetation makes it has low reflectance and strong absorption in the red visible band and higher reflectance and

11.3

Results

221

weaker absorption in the near-infrared band. In this study, green AGB (viz., GDM) only consists of fresh grass that grows during the growing season, while AGB not only includes green biomass of fresh grass but also residual plants (all dry and dead grass materials) on the observation sites such as standing litter and withered parts with accounting for less than 20% of total AGB on average. There is an obvious difference in spectral responses for dry and green vegetation that primarily presents that dry vegetation has higher reflectance both in the red and near-infrared bands but has no strong absorption and low reflectance in visible band like green vegetation due to chlorophyll absorption through photosynthesis. Moreover, the reflectance of green vegetation in the near-infrared band is obviously higher than the reflectance of dry vegetation. Low chlorophyll content of dry vegetation reduces the red-to-near infrared spectral contrast (Tsalyuk et al. 2015; Butterfield and Malmstrom 2009). These factors collectively affect final fitting performance of MODIS vegetation index for estimating grassland biomass.

11.3.3 Model Validation In order to verify the accuracy of MODIS remote sensing-based biomass estimation models, AGB estimated by NDVI-based optimal exponent model is validated against independent field AGB sampled two times a month during June and July of 2004. Results of comparison for grassland AGB are presented in Fig. 11.6, showing that the correlation between the field-sampled and NDVI-estimated AGB is statistically significant with linear correlation coefficient of 0.84 at a significance

NDVI-predicted AGB(g/m2)

250 y = 1.1999x - 21.23 r = 0.84 p < 0.001 RMSE = 29.1g/m2

200

y = x line

150

100

50

0 0

50

100 150 Field-sampled AGB(g/m2)

200

Fig. 11.6 Comparison between field-sampled and MODIS NDVI-predicted AGB

250

222

11

Aboveground Biomass of Grassland

Fig. 11.7 Spatial distribution of grassland AGB in central Tibet

level ( p < 0.001). It means that there is no significant difference between sampled and estimated grassland AGB under a significance level of 0.001. Additionally, averaged AGB measured in the field and estimated by the model is 62.9 and 70.1 g/m2, respectively. The average AGB measured is 7.2 g/m2 lower than that estimated by model. It can be seen from Fig. 11.6 that the general trend is that when field-measured AGB is higher than 90 g/m2, NDVI-predicted AGB is below y ¼ x line and tends to underestimate the actual AGB, while if it is lower than 50 g/m2, NDVI will generally overestimate the actual AGB, and most of its values are above y ¼ x line, which is mainly associated with high spatial heterogeneity of land cover types and great regional differences in grassland biomass in the study area as well as model limitations. However, the correlation coefficient is 0.84 with overall RMSE (root mean square error) of 29.1 g/m2 and mean absolute error of 22.9 g/m2. According to these statistical indicators, it is concluded that estimating or monitoring the aboveground biomass of grassland in the central Tibet using MODIS NDVI is effective and reliable.

11.3.4 Spatial Distribution of AGB The spatial distribution of grassland AGB in the study area is mapped using NDVIbased optimal exponent model in Fig. 11.2 and the result is shown in Fig. 11.7. It is clear that the overall distribution characteristics of AGB present decrease from east to west. AGB is above 100 g/m2 in mountain slopes in Medro Gongkar and Lhundup

11.3

Results

223

Fig. 11.8 Spatial distribution of green AGB of grassland in central Tibet

counties in the east and eastern part of Damshung valley in the north, while it reaches above 200 g/m2 in some regions within these areas. Grassland AGB is less than 20 g/ m2 in some western regions, particularly on the high mountain ranges in the west. There is no vegetation growth for 3.47% of the total area consisting of lakes, perennial snow, glaciers, and alpine bare rock. The area with above 100 g/m2 of AGB covers 17.35% of the total area and is mainly distributed in the eastern part of the study area where the rainfall and ground water are relatively abundant; the area with AGB ranging from 50 to 60 g/m2 is 12% of the total area. Land area with AGB above 50 g/m2 approximately accounts for 70% of the total area. These are comparable with results from the first grassland resources survey in TAR in the 1980s (Agricultural and Pastoral Bureau of Lhasa Municipality 1991). Likewise, the spatial distribution of green AGB of grassland (viz., GDM) in the central Tibet is calculated based on NDVI-based optimal exponent model in Fig. 11.4 and the result is shown in Fig. 11.8. The general spatial distribution of green AGB is similar with total AGB, presenting gradually reducing from east to west. Green biomass in the eastern area and some Damshung valley in the north reach 100 g/m2, which, however, only covers 2.94% of the study area. Nonvegetation area is 3.47%. Green biomass below 20 g/m2 is primarily distributed in the western arid area and regions below alpine glacier and ice, covering 9.73 % of the study area. There is a large area with green biomass ranging from 20 to 70 g/m2, being above 60% of total study area, while the area with green biomass above 50 g/m2 is close to 50% of total land area and is broadly distributed in the whole study area.

224

11.4

11

Aboveground Biomass of Grassland

Conclusion and Discussion

Monitoring and estimating AGB of grassland cannot only show the status of vegetation growth but also provide evidence to assess the effects of ecological restoration in degraded areas to realize sustainable development of grassland ecosystems. The major objective of this study is to develop an approach for biomass estimation in central TP using MODIS remote sensing data that can be used to identify and monitor those areas under degradation and desertification and to assess grassland productivity and degradation status in the TP. (1) MODIS NDVI-based exponent models are optimum regression models for estimating and monitoring aboveground biomass in the central Tibet. The previous study reported that there is an exponential model between the field measurements of biomass and SPOT-VGT NDVI (Liu et al. 2015). It is also found that either using MODIS NDVI or EVI, the effectiveness for monitoring and estimating green biomass of grassland is obviously better than the total aboveground biomass, which reflects unique spectral response characteristics of green vegetation in red and near-infrared bands of satellite sensors. (2) AGB generally reaches the maximum from August to September within a year in the central TP. However, constrained by alpine climate, soil, and moisture conditions, the average grassland AGB is relatively low with 96.88 g/m2, of which green biomass is 77.66 g/m2, accounting for 80.16% of the total AGB, and the residual biomass covers less than 19.84% of the total AGB. AGB spatially presents decrease from east to west. Both AGB and green AGB are above 100 g/m2 in some regions of eastern study area and decrease to below 20 g/m2 in the west. These are comparable with results from the first grassland resources survey in TAR in the 1980s. (3) Based on the field measurements, there are great differences in grassland AGB due to strong spatial heterogeneity of grassland types in the central TP, ranging from 37.1 to 589.12 g/m2, of which the average ABG of alpine swamp meadow is highest with 356.84 g/m2, followed by temperate steppe (64.48 g/m2) and alpine meadow (61.61 g/m2), while alpine steppe has the least biomass with 48.87 g/m2. The study on aboveground biomass in Tibet carried out by the Tibet Institute of Animal Husbandry Sciences indicates that AGB of typical alpine steppe and alpine meadow is 50 g/m2 (Ji et al. 2008). Another study reports that the average AGB of alpine meadow is 68.80 g/m2, and the average AGB of alpine steppe is 50.10 g/m2 in Tibet (Yang et al. 2009; Gao et al. 2009). The first grassland resources survey in TAR conducted in the 1980s shows that the general aboveground biomass of grassland in Tibet ranges 30–75 g/m2 (Nakchu Bureau of Animal Husbandry 1992). These results are consistent with main findings from this study. (4) Monitoring and estimating grassland aboveground biomass (AGB) are crucial for ensuring sustainable grassland ecosystem function and maintaining its environmental conservation values. Satellite remote sensing provides only feasible approach to monitoring and estimating aboveground biomass at large spatial

References

225

scales. However, the spatial mismatch between the satellite data and field measurements may cause some uncertainties in AGB estimates. To overcome these uncertainties, more field measurements should be carried out and finer spatial resolution remote sensing data should be used in future studies.

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