XXII ISPRS Congress 2012: Technical Commission VIII

Shortis, M.; Shimoda, H.; Cho, K.

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663822514

Title:: Technical Commission VIII

Scope:: 590 Seiten

Year of publication:: 2014

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663822514

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Shortis, M.; Shimoda, H.; Cho, K.

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VIII/6: Agriculture, Ecosystems and Bio-Diversity]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: MODIS TIME SERIES FOR LAND USE CHANGE DETECTION IN FIELDS OF THE AMAZON SOY MORATORIUM J. Risso, B. F. T. Rudorff, M. Adami, A. P. D. Aguiar, R. M. Freitas

Document type:: Multivolume work

Structure type:: Chapter

published a report in 2006 entitled “Eating Up the Amazon”. This report contained evidence that soy cultivated in deforested areas of the Amazon biome was exported to Europe and used to feed chicken to produce the “McNuggets” (Greenpeace, 2006). Given the impact of these actions on public opinion and the questions of European clients about the origin of Brazilian soy, the topic of deforestation began to be part of the soy traders' agenda. In this way, NGOs and Brazilian soy traders initiated a dialogue with the aim of defining a common agenda for stopping the advance of soy into the Amazon rainforest resulting in the Soy Moratorium that was signed on July 24. 2006. This agreement requires that all soy cultivated in deforested areas (legal or illegal) after this date will not be commercialized by any company linked to the Brazilian Association of Vegetable Oil Industries (Associaçäo Brasileira das Indástrias de Óleo Vegetal - ABIOVE) and the National Association of Cereal Exporters (Associacdo Nacional dos Exportadores de Cereais - ANEC). The Soy Moratorium has been renewed annually and, since 2009, satellite images are being used for monitoring soy cultivation in deforested areas mapped by the Program for the Calculation of Deforestation in the Amazon (Programa de Cálculo do Desflorestamento da Amazónia - PRODES) (Rudorff et al., 2011). The monitoring method is based on the EVI2 time series from the MODIS sensor and the Crop Enhancement Index (CEI) approach (Rizzi et al., 2009). At the same time, images with improved spatial resolution from the AWIFS, LISS3, TM and ETM+ sensors are used to better identify and delineate the soy plantations within the deforested polygons (Rudorff et al, 2012). In some instances the deforestation date is contested by the farmer and the visualization tool for EVI2 time series developed by INPE (Freitas et al., 2011) is used, together with TM images, to ensure the correct deforestation date whenever there is a conflict between PRODES data and the farmer argument. The visualization tool has been used to determine the exact period in which deforestation occurred. In addition, it allows the identification of various LUC patterns through the seasonal analyses of terrestrial targets (Adami et al., 2012). In this sense, the objective of this work is to use the MODIS/EVI2 time series to identify the LUC patterns and trajectories of deforested areas within the Soy Moratorium context. 2. STUDY AREA The study area is comprised by the Soy Moratorium polygons selected during the fourth year of monitoring (crop year 2010/11). The selected polygons are from the PRODES years of 2007 to 2010 within municipalities that produced more than 5,000 ha of soy each in the previous crop year, and with an area higher than 25 ha (Rudorff et al., 2011). Figure 1 indicates the location of the Amazon biome and the states and municipalities monitored in crop year 2010/11. In crop year 2010/11, 3,571 polygons with a total area of 375,500 ha were monitored. Of this total, soy plantations were found in 146 polygons corresponding to an area of 11,698 ha. 3. MATERIAL AND METHODS The 3,571 deforested polygons were pre-evaluated with regards to size and compactness criteria to exclude those polygons that are too narrow or irregular. The compactness index (CI) is the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia ratio between the perimeter and the square root of the area of the polygon. Higher IC values indicate more irregular geometries, while lower IC values indicate more regular geometries. A polygons to be selected should have IC less than 9 for area greater than 40 ha, or IC less than 6 for area smaller than 40 ha. This procedure selected 1,971 polygons without soy and 91 polygons with soy. A subsample of 50 polygons was selected from each stratum: with and without soy. 80°W 70° W 60°W 50°W 40° W N 10°N- 0°7 Es 109$4 e 20?S1 B E 30984 5 = © Jn 40°S1 | | Selected Municipalities Amazon Biome 50°87 Political Boundaries [ 1 South America Figure 1. Location of the study area. Adapted from Rudorff et al. (2011). In the next step, the subsampled polygons were evaluated with respect to LUC homogeneity. Very heterogeneous polygons represented an additional difficulty in the characterization of LUC patterns and transitions after deforestation. For the polygons without soy (first stratum), the evaluation was conducted based on variations in the EVI2 temporal profile of several MODIS pixels within the polygon. Each polygon rejected in this evaluation phase was replaced by another polygon. For the polygons with soy (second stratum) the homogeneity was evaluated based on land use information acquired from the aerial survey and available at: <http://www.abtove.com.br/ss_relatoriousol0 br.asp>. After the final selection of the 50 polygons from each stratum a central pixel of each polygon was selected to perform the MODIS/EVI2 time series profile analyses using the MODIS Time Series Visualization for Land Use and Cover Analysis (<https://www.dsr.inpe.br/laf/series/>) of the Remote Sensing Time Series Virtual Laboratory developed to integrate a large eM m8 00 0 à mo mA RO m Ya

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