SATVeg: A web-based tool for visualization of MODIS vegetation indices in T South America

Table of contents :
SATVeg: A web-based tool for visualization of MODIS vegetation indices in South America
Introduction
Material and methods
Remote sensing data
Time series smoothing methods available by SATVeg
Flat-bottom smoother filter
Wavelet Coiflet 4 filter
Savitzky-Golay filter
Architecture and implementation of SATVeg
Back-end processing
Download and processing of MODIS images
Geospatial database design
Importing images into the database
Web application
Results and discussion
Geospatial database development
Time series reconstruction
SATVeg web interface
Application of VI time series
Conclusions
CRediT authorship contribution statement
Declaration of Competing Interest
Acknowledgments
References

Citation preview

Computers and Electronics in Agriculture 175 (2020) 105516

Contents lists available at ScienceDirect

Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag

Original papers

SATVeg: A web-based tool for visualization of MODIS vegetation indices in South America

T

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Júlio César Dalla Mora Esquerdoa, , João Francisco Gonçalves Antunesa, Alexandre Camargo Coutinhoa, Eduardo Antonio Speranzaa, Andréia Akemi Kondob, João Luis dos Santosb a b

Embrapa Agricultural Informatics, Av. André Tosello 209, Campinas, SP, Brazil Computer Analyst, Brazil

A R T I C LE I N FO

A B S T R A C T

Keywords: Remote sensing Land-use monitoring Geospatial database Time series

Time series of satellite images have been widely used in a range of applications, specially involving the green biomass monitoring in the Earth surface. A set of land surface products, including images of vegetation indices (VI), is derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor since 2000. This paper reports the development of the Temporal Vegetation Analysis System (SATVeg), a free web-based tool designed to provide instantaneous access to temporal profiles of MODIS VI in South America. The system’s architecture is based on a geospatial database specially modeled to store the time series of images and ensure instantaneous queries and efficient updating. The results of the geospatial database modelling and respective response time for queries and updates, as well as the main functionalities of the system, are presented and discussed. Instantaneous reconstruction of time series provided by a set of filtering procedures is also related. Application examples of VI temporal profiles generated by SATVeg are presented and the system’s limitations are also discussed. Our results show the potential of the web-system for supporting a set of land use and land cover monitoring activities in South America through a simple and friendly user interface.

1. Introduction

1983). VI present high correlation with the green biomass and leaf area index (LAI) and the most used are the Normalized Difference Vegetation Index (NDVI) (Rouse et al., 1974) and the Enhanced Vegetation Index (EVI) (Huete et al., 1994). When organized and observed chronologically, these indices can be used to generate long term curves, representing the green biomass variations over time, which can be related to LULC patterns and their dynamics, such as deforestations, burnings, floods, productive system changes, among others. One of the most important sensors used in multi-temporal studies is the Moderate Resolution Imaging Spectroradiometer (MODIS), the main instrument aboard the Terra and Aqua orbital platforms of the Earth Observing System (EOS) program, led by the National Aeronautics and Space Administration (NASA). MODIS time series comprises 20 years of good radiometric and spatial quality data and is available by US Government repositories, like the Land Processes Distributed Active Archive Center (LP DAAC). In general, time series analysis requires handling a large volume of data derived from the satellite images and involves robust computer processing. Programming languages and other computational tools for

In recent years, time series of satellite images have been increasingly used in a wide range of applications, especially involving the Earth's surface monitoring. Examples of using time series include the mapping of agricultural crops (Arvor et al., 2011; Picoli et al., 2018), the detection of land use and land cover (LULC) changes (Klein et al., 2012; Usman et al., 2015), the agricultural intensification studies (Kastens et al., 2017; Oliveira et al., 2014), the vegetation seasonality and phenology identification (Martínez and Gilabert, 2009; Sakamoto et al., 2005), among others. The spectral-temporal approach explores the short time of revisiting by some orbital sensors in order to obtain more often spectral information from the Earth's surface, bringing advantages over the traditional approach, based on a limited set of images. Multi-temporal analyses of land cover monitoring are usually based on vegetation indices (VI), derived from mathematical combinations between spectral bands, which seek to enhance the response of the vegetation and decrease the soil and atmospheric influences (Jackson,

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Corresponding author at: Embrapa Agricultural Informatics, Av. André Tosello, Campinas, SP, Brazil. E-mail address: [email protected] (J.C.D.M. Esquerdo).

https://doi.org/10.1016/j.compag.2020.105516 Received 19 June 2019; Received in revised form 30 April 2020; Accepted 16 May 2020 Available online 04 June 2020 0168-1699/ © 2020 Elsevier B.V. All rights reserved.

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2.2. Time series smoothing methods available by SATVeg

batch downloading and automatic processing are typically employed to deal with this issue. LP DAAC Data Pool repository allows batch downloading of MODIS products using specific software or programming scripts. Some applications and programming codes developed for automatic processing of MODIS products are also available and allow automatizing basic image processing steps, like format conversions, reprojection, spatial resampling and layer stacking. However, in the last few years a sort of websites has been developed to provide the visualization and analysis of geospatial data, without needing to download, process or handle a large amount of data, such as satellites images. New geospatial database models, map servers and data visualization libraries have been developed and used to build the back-end and front-end infrastructures of websites, resulting in an increasing number of web-tools specially designed to provide fast and easy access to geoinformation. Some web-tools were available to provide visualization and analysis of MODIS VI times series (Eberle et al., 2013; Freitas et al., 2011), although issues related to response time, database updating or even the lack of continuity of these services have not been completely resolved. On the other hand, on-demand services, such as AppEEARS – Application for Extracting and Exploring Analysis Ready Samples (https://lpdaac.usgs.gov/tools/appeears/) – have also been available to provide users with a variety of remote sensing data products, including MODIS VI time series, by submitting requests to subset data spatially and temporally, which are later available for download. Considering the increasing interest in this theme, the demands for more efficient and faster computational tools and solutions for data storage, organization, processing, publication and interoperability have also grown. The lack of available web-based systems for fast data querying on historical times series of MODIS NDVI and EVI images, including a set of specific features for data visualization, stimulated the seek for new alternatives. Thus, this article aims to report the development of the Temporal Vegetation Analysis System (SATVeg), a free online tool designed to provide instantaneous access to temporal profiles of MODIS VI in South America through a simple and friendly user interface.

Orbital remote sensing data is susceptible to several factors that affect the values collected by the instruments. Atmospheric interference, sun-sensor-surface geometry, instrument calibration and transmission failures are examples of disturbances sources while collecting spectral information by remote sensing. In general, these inconsistencies are mainly due to the presence of clouds and aerosols in the atmosphere, which mainly decrease the nearinfrared reflectance, affecting the quality of satellite-derived VI. Despite the use of maximum value compositions (Holben, 1986), these interferences may not be fully eliminated from the images, especially in regions with frequent cloud cover, leading to inconsistent drops in the time series values and affecting the results of several types of analyzes. Thus, a set of time series smoothing and reconstruction techniques has been proposed to eliminate or reduce such inconsistences, from the simplest methods to the most complex ones (Bradley et al., 2007; Chen et al., 2004; Gu et al., 2009; Jönsson and Eklundh, 2004; Roerink et al., 2000). In SATVeg, time series reconstruction is carried out through a two-step procedure, the pre-filtering and the filtering. Pre-filtering is an on-the-fly time series reconstruction method based on the bilinear interpolation to replace missing data or cloud and snow pixels flagged by the Pixel Reliability (PR) product. Filtering is also an on-the-fly time series reconstruction, but it is not dependent on information from PR product and can be applied to the pre-filtered or to the original time series. SATVeg offers three filtering methods with different complexity levels: Flat-bottom smoother, Wavelet Coiflet 4 and Savitzky-Golay filters. 2.2.1. Flat-bottom smoother filter Flat-bottom is a simple and conservative filter adapted from the method proposed by Wardlow et al. (2006), which runs from two steps. In the first one, the local minimum values, or negative oscillations of the VI, are identified. That is, given a value Xt (in which t refers to time), the local minimum is identified when Xt-1 > Xt < Xt+1. In the second step, the local minimum values (Xt) are replaced by the smallest adjacent value (Xt-1 or Xt+1). In order to avoid the elimination of small negative oscillations of vegetation indices, not necessarily inconsistent, the user has the option of selecting a factor to identify the local minimums, that is, a percentage value that defines the minimal difference so that a local minimum is changed (0, 10, 20 and 30%).

2. Material and methods 2.1. Remote sensing data The MODIS images used in the development of SATVeg were obtained from the LP DAAC Data Pool repository (http://lpdaac.usgs.gov). We acquired the complete time series of MOD13Q1 and MYD13Q1 products (version 6), in HDF (Hierarchical Data Format) format and sinusoidal projection, covering the South America territory. These products include 16-days image composites with spatial resolution of 250 m and geometric, radiometric and atmospheric correction levels. MODIS composites are built from the best radiometric and geometric quality pixels in the 16-days period, that is, those with lower cloud presence and lower angle of view. MOD13Q1 product is derived from the Terra satellite (starting in February 2000) and MYD13Q1 product is derived from the Aqua satellite (starting in July 2002). Terra has a sun-synchronous, near-polar descending orbit timed to cross the equator at approximately 10:30 A.M. local time while Aqua has a nearpolar ascending orbit timed to cross the equator at approximately 1:30P.M. local time. These products are generated and distributed by LP DAAC in adjacent non-overlapping 1200 × 1200 km tiles, covering most of the world, 30 of them over the South America territory. The complete time series of both products were downloaded and the NDVI, EVI and Pixel Reliability (PR) layers were extracted for each date. PR layer provides quality reliability information about MODIS VI pixels in four levels (good, marginal, snow/ice and cloudy).

2.2.2. Wavelet Coiflet 4 filter Wavelets are defined as small waves with properties that make them suitable to serve as the basis for representing and describing other functions. The Wavelet Transform allows the decomposition of a time series at different scales to obtain information in the frequency and time domain, in which each scale is represented by a specific frequency. Its application in time series of satellite images has the purpose of eliminating sudden variations, resulting in filtered curves that make easier the understanding of the temporal dynamics. The filter available by SATVeg uses the Wavelet Transform with Coiflet (order = 4) as proposed by Sakamoto et al. (2005), which achieved the best results for crop’s phenological stages detection. 2.2.3. Savitzky-Golay filter The digital filter proposed by Savitzky and Golay (1964) is based on a moving window that uses a linear least square fitting by means of successive polynomial equations. In SATVeg, the user has the option to set the moving window as 2, 3, 4, 5 or 6. The larger the window, the greater the smoothing effect. Several authors have proposed this filter for VI times series reconstruction (Chen et al., 2004; Jönsson and Eklundh, 2004; Ren et al., 2008) with satisfactory results in several applications. 2

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Fig. 1. SATVeg development architecture.

a script extracted the NDVI, EVI and PR images in GeoTIFF format and performed the mosaicking process. Due to limitations of the MODIS processing software, two mosaics of images covering the South America territory were generated for each date, which were later reprojected from the original sinusoidal projection to the geographic projection, WGS-84 datum (Fig. 2).

2.3. Architecture and implementation of SATVeg The SATVeg’s development architecture is shown by Fig. 1. The front-end is composed by the web interface by which the user interacts with a map display, a time series viewer and some query tools. The back-end is composed by a PostGIS database and a set of procedures of data acquisition and processing. The development tools are mainly based on free software. Basically, the user accesses the application through a friendly interface and uses the map viewer to select any location of South America in order to make queries to the geographic database and generate a chart with the NDVI or the EVI time series profile. The most important feature of this system is the database modeling, which provides optimized performance of time series queries on large databases of satellite images.

2.3.1.2. Geospatial database design. The SATVeg geospatial database was built using PostgreSQL 9.5 with the PostGIS 2.2 spatial extension, which supports geographic objects with functions for spatial queries and provides storage of raster data efficiently. The database was divided into four schemas, three for raster data storage (NDVI, EVI and PR) and one for textual and vector data storage, including administrative information, user registration, personal user configurations, vector files, among others. In order to improve the management of raster data and optimize the database queries performance, the image storage was carried out thought partitioned tables, allowing data retrieval at finer granularity levels. These partitions define spatial coverage areas that contain the NDVI, EVI and PR data constrained to a given piece of the image (quadrant), optimizing the response time of queries. This happens because the system’s data access layer uses a spatial index to identify quickly the location of the table partition where the time series required by a query is located. Thus, using the partitioning tables approach and the spatial indexing allowed fast retrieving of the time series from the user request. The quadrant size adopted to store the images can affect the time to import them to the database, as well as the response time of queries. Empirical tests were performed to define the size of image quadrant by measuring the PostGIS database importation time, as well as the

2.3.1. Back-end processing The back-end is composed mainly by a geospatial database specially modeled and structured to store a robust volume of time series of satellite images and allow optimized queries. Automated procedures were developed to build the geographic database as well as to update it with new images. 2.3.1.1. Download and processing of MODIS images. The download and processing of MODIS images were automated using c-shell scripts and the MODIS Reprojection Tool (MRT) software, a free package designed to process the MODIS HDF-EOS image format (Dwyer and Schmidt, 2006). Batch downloading was carried out from the LP DAAC repository in order to obtain the 30 image tiles covering the South America territory for each available date of the MOD13Q1 and MYD13Q1 products. Then, 3

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Fig. 2. Example of a set of original MODIS sinusoidal tiles of one date covering the South America (left) and the processed images (NDVI, EVI and Pixel Reliability) after mosaicking and cartographic reprojection (right) procedures.

response time of queries considering three dimensions: 100x100, 150x150 and 200x200 pixels. So, 700 pairs of NDVI mosaics were used to measure the average importation time into the PostGIS databse and 3,000 random sampling points were used to measure the response time of database queries. The points were sampled in different locations of South American territory, but in non-coincident tables in order to avoid colleting data allocated in the PostGIS cache.

municipalities. In this case, the municipalities are located from a vector layer made available from a GeoServer map server (http://geoserver. org) via Tomcat, which is accessed by the application using a Web Feature Services (WFS) protocol, considering the OGC (Open Geospatial Consortium) standards.

2.3.1.3. Importing images into the database. After creating the partition tables according to the defined quadrant size, the complete time series of NDVI, EVI and PR images were imported into the geospatial database. In this process, pixels covering the ocean were put way considering a 1.5 km buffer from the continental and islands boundaries, saving storage space and optimizing the queries.

3.1. Geospatial database development

3. Results and discussion

Building the SATVeg database involved choosing the optimal quadrant size for storing the time series in partitioned tables. Table 1 presents the results of the performance analysis of three different database setups, taking into account the importation of 700 pairs of partitioned NDVI images in quadrants of 100x100, 150x150 and 200x200 pixels. The smaller the quadrants size the greater the amount of partitions in the database table and, consequently, the longer the image import time. According to Table 1, there was a significant difference in the average time when importing images using 100x100 pixels quadrant compared to the others, due to the high number of partitions of this setup and the high I/O in the disk. Considering the 150x150 and 200x200 pixels quadrants, the average import time was very close. The decrease in processing time using both quadrant size was around 67%

2.3.2. Web application The web application was implemented in Primefaces, a suite of components that meets the JSF specification of Java EE technology and is available on a JBoss application server. The web interface is basically divided into two main parts: the map viewer and the time series chart viewer. The map viewer uses Google Maps layer to support the search for points or areas of interest, making possible the navigation in an intuitive way. The implementation of these features was carried out through OpenLayers 2 (http://openlayers.org/two/), which is a library of JavaScript maps for spatial data rendering. Once the point or area of interest is selected, the application retrieves the data stored in the database and displays the time series in the chart viewer, which uses the Dygraphs (http://www.dygraphs.com) JavaScript graphical library. A control panel and a side menu located on the map viewer offer the user a set of functionalities with effects in the map or in the chart visualization, as well as options to save favorite places and upload vector files. Another feature of the control panel is the search form where the user inserts the geographic coordinates of any location or the name of

Table 1 Average image import time and number of partitions created in the database using three sizes of quadrants.

Number of partitions Average import time

4

Size of partitions (pixels) 100 × 100 150 × 150

200 × 200

38,046 27′38″

9765 8′41″

17,117 9′26″

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agriculture lands present a more compatible temporal behavior with their natural pattern. Instantaneous reconstruction of time series using specific procedures represents an innovation of SATVeg. One of the system’s basic assumption is to organize and store the original time series data without any kind of filtering application, but providing the user with a set of reconstruction methods. This feature allows to avoid storing smoothed time series, reducing the size of the geospatial database and, on the other hand, lets the user to decide to view the original VI data or to reconstruct the time series using the available algorithms. The decision to view the original or the filtered data is an important issue when analyzing the VI curve patterns, once, depending on the smoothing algorithm, time series reconstruction procedures can significantly change the original signal, masking some specific features of the plant phenology.

Table 2 Average response time (in seconds) of database queries considering three sizes of partitions with no filter and with pre-filter and Savitzky-Golay filter applied. Size of partitions

100 × 100 150 × 150 200 × 200

Average response time (in seconds) No filters

Pre-filter

Savitzky-Golay filter

1.49 1.83 1.85

1.75 2.26 2.32

1.61 1.96 2.02

when compared to the 100x100 pixels quadrant. Table 2 shows the average response time of 3,000 points according to each partitioning size. The tests also considered the use of the prefiltering and the Savitzky-Golay filter to reconstruct the time series. Table 2 shows a lower response time when using smaller size of partitions, once data is located more quickly when stored in smaller volumes. Table 2 shows that the response time is lower when using smaller quadrants. Smaller partitions have smaller volumes of data stored, which are more quickly located during the search process. Table 2 also shows that pre-filtering and Savitzky-Golay filtering increase the response time for spatial queries. When pre-filtering is activated, the response time increases by an average of 19.4%, while the Savitzky-Golay filter increases the response time by 7.7%. Using pre-filtering causes the response time to be increased, once this method uses data from the pixel reliability layer and therefore the database is accessed twice. The results indicate that the quadrant size adopted to partition the database tables has a direct influence on the response time of spatial queries, as well as the time required to import new images for the database updating. The smaller the region stored per partition, the faster the response time and the slower the uploading of a new image into the database. On the other hand, the larger the stored region, the longer database queries become and the faster the new imports become. In a hypothetical situation in which the number of partitions of the database table were exactly the same as the number of pixels in the image, the time to retrieve the time series would be minimal. On the other hand, the process of importing a new image would be extremely costly as it would involve inserting data into 385 million partitions, the approximate number of pixels within a MODIS image covering South America. However, if a single partition were to be considered to store all 385 million pixels of an image, the time spent on import would be minimal, while the data retrieval from the database would be nearly unfeasible, taking into account an instantaneous system, such as SATVeg. Considering these two extreme cases, the size of the regions stored by table partitioning can be adjusted, in order to seek a better relation between importing and querying response times. This setting mainly depends on the image resolution and the time series size. From the results, the SATVeg geospatial database was built using quadrants of 150x150 pixels.

3.3. SATVeg web interface The SATVeg web interface is available for on-line access at www.satveg.cnptia.embrapa.br, with versions in Portuguese, English and Spanish. The access is free of charge through a registration process, where the user provides basic information such as name, email address, institution and password. The SATVeg interface (Fig. 4) is divided into two parts and presents a set of tools available on a control panel (on the left) and a side menu (on the right). In the map area, the user can select a point of interest anywhere in South America by clicking on the Google Maps layer. Then the system identifies the MODIS pixel for the clicked position and instantaneously generate the time series chart with the chosen VI. The control panel is divided into two menus with map and chart functionalities. In the map menu, the user has a set of functions for searching points by geographical coordinates or by municipalities name, drawing of polygons, layer activation control, overlap of WMS layers, among others. In the chart menu, the user can select the vegetation indice (NDVI or EVI) and the satellites (Terra, Aqua or both) to be plot in the chart, as well as to active pre-filtering and filtering functionalities or to enable the display of pixel reliability flags over the time series curve. When activating Terra and Aqua satellites together, the VI data is plotted chronologically, according to the passing date of each platform. The possibility of using data from both satellites is an important feature when analyzing regions with very frequent cloud coverage, increasing the chances to obtain good VI values over the time. The chart area shows the time series profile of the selected location considering the user-defined options in the control panel. By default, the chart is drawn considering all the available dates in the database; however, a slider tool in the bottom of the chart area allows the user to reduce the temporal window of visualization. Features for exporting the chart in PNG (Portable Network Graphics) or XLSX (Microsoft Excel) formats are also available. The high spatial resolution images of Google Maps work only as a reference for the user's location since the VI data are retrieved from the MODIS database, whose pixel has spatial resolution of 250 by 250 m (6.25 ha). Thus, when interacting with the system, the user must consider the spatial dimensions of the selected MODIS pixel, which can be composed by several (Fig. 5a) or single (Fig. 5b) LULC types when compared to the Google’s high spatial resolution images. Time series profiles extracted from homogeneous areas represent faithfully the green biomass variation over time, while pixels composed by heterogeneous areas present high spectral mixing, affecting the quality of the information presented by the VI time series. Additionally, the system also allows the generation of time series profiles from polygons drawn on the screen (Fig. 5c) and, in this case, the chart returns an average VI curve and the corresponding standard deviations of each date. In addition to the screen-drawing functionality, the user can import polygons in shapefile or Keyhole Markup Language (KML) vector formats. In this type of query, the response time to generate an average VI

3.2. Time series reconstruction Fig. 3 shows examples of pre-filtering effect on the Savitzky-Golay filter results applied to NDVI time series obtained from three sites with different land cover patterns (forest, pasture and agriculture) between February 2016 and February 2018. The areas are located in a tropical region, with rainy seasons predominantly between December and March. The original NDVI data show some negative oscillations along the time series, mainly during the raining season. When pre-filtering is not activated, the Savitzky-Golay smoothing method is ineffective, since the VI drops are not fully eliminated. On the other hand, when applying the bilinear interpolation method (pre-filtering) in the pixels flagged as cloud by the PR band, the results of the Savitzky-Golay filter are more effective and the smoothed NDVI curves of forest, pasture and 5

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Fig. 3. Effects of pre-filtering on the NDVI time series reconstruction of Forest (A), Pasture (B) and Agriculture (C) lands using the Savitzky-Golay filter.

3.4. Application of VI time series

curve is greater and depends on the number of MODIS pixels contained within the polygon, whose maximum area should not exceed 3,000 ha. Another issue affecting the response time of polygon queries is the intersection of the vector boundaries with the database partitions. Fig. 6 illustrates three square polygons with the maximum acceptable area intercepting one, two, and four database partition boundaries and the respective response time of the spatial queries. The measured response time considered the query of approximately 470 pixels in each of the polygons and indicated a significant increase when the intersection happens in more than one quadrant of the database partitions. When the polygons intercept two or four partitions, the system’s response time is 1.7 and 3.4 times greater, respectively, when compared to the query restricted to one partition.

SATVeg has supported several modalities of land surface monitoring by the productive, academic and governmental sectors. Usually, the system has been used to identify the LULC at a particular moment, as well as its changes over time. Fig. 7 shows a set of NDVI curves generated by the SATVeg illustrating the temporal behavior of the green biomass obtained from different LULC types in Brazil along the complete time series available by the system. The curves comprise the period between February 2000 and February 2019 and show variations due to the plant’s phenological cycle or LULC changes. These examples show the pre-filtered NDVI curve (green line, based on the Terra and Aqua satellites) and the smoothed NDVI curve using the Savitzky-Golay filter (red line), adopting a moving window of size 4.

Fig. 4. SATVeg web interface, consisting mainly of map and chart areas, control panel and side menu. 6

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Fig. 5. MODIS pixels with different (A) or single (B) land use and land cover type(s); and a set of pixels selected from a polygon drawn on the screen (C).

exploring the window of the rainy season. This process is quite typical in several regions of Brazil and has been responsible for the increase of agricultural production in the country in the last decades, without increasing the area of croplands in the same proportion. Fig. 7C shows the NDVI evolution obtained from a eucalyptus production area, whose productive cycle extends for years. During the observed period it is possible to identify two complete growing cycles, separated by NDVI “valleys”, when the harvest is done and another planting is started. Finally, Fig. 7D presents the NDVI profile from a native natural cover area in the municipality of Mariana, in the state of Minas Gerais, Brazil. In the end of 2015, the NDVI values drop significantly and began to oscillate in levels lower than those observed during the previous years. In this case, the native vegetation was covered by mud after a dam collapse, one of the worst environmental disasters in Brazil's history (Phillips, 2016), when a liquid mix of water, sands and clays has spread over vast areas, destroying villages, killing people and affecting seriously the local fauna and flora. The analysis performed from the charts of Fig. 7 was carried out by visual interpretation of the curves and depends on some basic technical knowledge about how the vegetative index works. A set of further examples of LULC patterns and transitions can be found in the Pattern Library page, available in SATVeg. SATVeg has successfully contributed as a tool for fast generation of vegetation indices profiles through a friendly and intuitive web interface, free of charge. The system has been used in a set of activities, such as the crop biomass assessment, inspection of agricultural losses for insurance support, deforestation detection, monitoring of native protected areas, forest recovery evaluation, among others. Large-scale LULC mapping projects have also used SATVeg as a supporting tool or even as a source of information to validate the results achieved by traditional methods of image classifications. In Brazil, initiatives like the TerraClass Amazonia Project (Almeida et al., 2016) and TerraClass Cerrado Project (Scaramuzza et al., 2017), which are Governmental actions responsible for mapping the LULC in the Amazon and in the Cerrado Brazilian biomes, respectively, have used SATVeg as a supporting tool. The maps produced by these projects are based on Landsat-like images obtained during the dry season, when the cloud coverage is lower; however, traditional mapping classification based on few or unique images may led to misclassification, mainly when different targets have similar spectral behavior. In these cases, the spectral-temporal approach, represented here by VI time series curves, may be an effective source of information to assess the accuracy of the traditional spectral-based methods. The novel solutions presented in the development of SATVeg allow the fast visualization of MODIS VI time series. The geospatial database was conceived to deal with queries based on points or small polygons. Regional analyzes based on larger areas, such as municipalities or even states boundaries, include a large number of pixels to be retrieved during the queries and are not feasible considering the strategy adopted to model the SATVeg’s geospatial database. The low spatial resolution

Fig. 6. Response time of polygon queries with different intersections with the database partitions boundaries.

Fig. 7A presents the evolution of the green biomass over time obtained from an area in the north of the State of Mato Grosso, in the Brazilian Amazon. Until the middle of 2002 the vegetal cover was composed by primary natural forest, with high and stable NDVI values, due to the large amount of green biomass. A deforestation process occurred at the end of 2002 and the NDVI values dropped sharply with the shallow cut-off of the natural forest cover. Thereafter, a new green biomass variation pattern started, resulting from the practice of pasture cultivation, represented by curves with annual seasonal fluctuations, with high NDVI values in the rainy seasons and lower values during droughts. By the end of 2012, there was a third biomass variation pattern, since the area was converted to grain production of annual crops, with shorter productive cycles and higher fluctuations of NDVI values. This LULC changes sequence corresponds to a traditional process of forest occupation in the regions of the Legal Amazon’s agricultural frontier, where forest clearing gives space to pasturelands followed by croplands. Fig. 7B shows the historical NDVI evolution of a consolidated cropland, with successive cultivation cycles over the years. Each crop cycle is defined by a curve section, in which the initial NDVI values start low (after the crop sowing) and then grow until reaching the vegetative peak (moment of greatest green biomass production). Then, the values decrease (due to crop senescence) until reaching the same initial levels, when the harvest is carried out. Such behavior is characteristic of annual grain and fiber crops, such as soybean, maize and cotton. This example also shows the agricultural intensification process, once this area used to be cultivated with single crops (in this case one phenological cycle of cotton per year) until 2008 and, from then on, the area started to be used for double cropping cultivation (in this case the soybean cycle followed by the corn cycle in the same cropping season), 7

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Fig. 7. NDVI time series obtained from different land use and land cover types between February 2000 and February 2019 using SATVeg. A) Forest/pasture/ agriculture transitions; B) Agricultural intensification (single crop to double crop); D) Silviculture (eucalyptus); E) Environmental disaster in Mariana, MG (Brazil). The green lines represent the pre-filtered NDVI data and the red lines the smoothed data using the Savitzky-Golay filter.

CRediT authorship contribution statement

of the MODIS VI products can also limit the use of SATVeg in regions with a very segmented land-ownership structure, based on small fields and properties. Further works must provide new solutions to improve the geospatial database modeling in order to deal with higher spatial resolution images, such as those provided by the Copernicus Sentinel-2 mission, which combine high spatial and temporal resolutions.

Júlio César Dalla Mora Esquerdo: Conceptualization, Software, Investigation, Validation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Funding acquisition. João Francisco Gonçalves Antunes: Conceptualization, Investigation, Validation, Writing - review & editing, Supervision, Funding acquisition. Alexandre Camargo Coutinho: Conceptualization, Investigation, Validation, Resources, Writing - review & editing, Supervision, Funding acquisition. Eduardo Antonio Speranza: Methodology, Software, Writing - review & editing. Andréa Akemi Kondo: Methodology, Software, Validation, Visualization, Writing - original draft. João Luis dos Santos: Software.

4. Conclusions We presented the Temporal Vegetation Analysis System (SATVeg), a web-based system for fast access and visualization of time series of vegetation indices in South America. The back-end architecture was implemented through an open source relational geospatial database to store the complete time series of MODIS VI data. The strategy of using thousands of partition tables in a geospatial database to store the VI time series data ensured quick queries and efficient updating. The frontend was developed to provide the user with a friendly interface, including a set of data filtering procedures to improve the visualization of the time series. The novel solutions presented in the development of the geospatial database allowed fast queries for points and small polygons. Examples of application were presented and showed the potential of SATVeg for supporting a set of land use and land cover monitoring activities.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments SATVeg was partially funded by Agroicone company through a technical and financial cooperation agreement between the Brazilian Agricultural Research Corporation (Embrapa) and the Arthur Bernardes 8

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Foundation (Funarbe). The authors thank NASA’s Land Processes Distributed Active Archive Center (LP DAAC) for providing the MODIS images used by SATVeg.

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