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		<identifier>8JMKD3MGP6W34M/3PSM444</identifier>
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		<isbn>978-85-17-00088-1</isbn>
		<label>59414</label>
		<citationkey>SouzaAranPareJúni:2017:AnPaIm</citationkey>
		<title>Padrões e tendências das pastagens do Brasil: uma análise a partir de imagens índice de vegetação MODIS e algoritmos de detecção de mudanças</title>
		<format>Internet</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
		<numberoffiles>1</numberoffiles>
		<size>1814 KiB</size>
		<author>Souza, Guilherme Ferreira Arantes,</author>
		<author>Arantes, Arielle Elias,</author>
		<author>Parente, Leandro Leal,</author>
		<author>Júnior, Laerte Guimarães Ferreira,</author>
		<electronicmailaddress>guifas3000@gmail.com</electronicmailaddress>
		<editor>Gherardi, Douglas Francisco Marcolino,</editor>
		<editor>Aragão, Luiz Eduardo Oliveira e Cruz de,</editor>
		<e-mailaddress>daniela.seki@inpe.br</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Sensoriamento Remoto, 18 (SBSR)</conferencename>
		<conferencelocation>Santos</conferencelocation>
		<date>28-31 maio 2017</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>4977-4984</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<abstract>A pasture undergoing degradation is characterized by a decrease in vegetative vigor through time, which culminates in different environmental impacts (e.g.soil erosion) and economic losses. As a phenomenon occurring in the temporal domain, the use of satellite vegetation index time series, associated with robust algorithms for detecting land  cover  change  and  trend  estimations,  such  as BFAST,  can be instrumental in identifying pasture degradation. Thus, the objective of this study was to evaluate the potential and performance of the BFAST algorithm to identify patterns of change (breakpoints), and loss of vegetative vigor (trend) of the Brazilian pasturelands. To  this end, MODIS NDVI time-series (2000 to 2016) were analyzed via BFAST, considering both specific  pasture points, as well as the entire area of  the Rio Vermelho Watershed (BHRV, State of Goiás).  BFAST proved capable of detecting major land cover transitions, as well as pasture trends related to the loss of vegetative vigor / degradation. At the landscape scale (i.e. BHRV),  even though the processing was done pixel by pixel, the resulting slopes and breakpoints (dates of major changes) showed a spatial consistency, indicating the potential of BFAST to identify spatial patterns for large areas.</abstract>
		<area>SRE</area>
		<type>Agricultura e pecuária</type>
		<language>pt</language>
		<targetfile>59414.pdf</targetfile>
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