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		<isbn>978-65-89159-04-9</isbn>
		<citationkey>BritoCCSSFSQ:2023:UsSéTe</citationkey>
		<title>Uso de séries temporais para classificações de uso e cobertura da terra em Petrolina, Pernambuco</title>
		<format>Internet</format>
		<year>2023</year>
		<secondarytype>PRE CN</secondarytype>
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		<author>Brito, Pedro Vinícius da Silva,</author>
		<author>Chaves, Michel Eustáquio Dantas,</author>
		<author>Carvalho, Herica F. de Sousa,</author>
		<author>Souza, Felipe Carvalho de,</author>
		<author>Silva, Baggio Luiz de Castro e,</author>
		<author>Ferreira, Karine Reis,</author>
		<author>Santos, Rafael Duarte Coelho dos,</author>
		<author>Queiroz, Gilberto Ribeiro de,</author>
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		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Universidade Federal de Pernambuco (UFPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<electronicmailaddress>pedro.brito@inpe.br</electronicmailaddress>
		<electronicmailaddress>michel.chaves@inpe.br</electronicmailaddress>
		<electronicmailaddress>herica.carvalho@ufpe.br</electronicmailaddress>
		<electronicmailaddress>felipe.carvalho@inpe.br</electronicmailaddress>
		<electronicmailaddress>baggio.silva@inpe.br</electronicmailaddress>
		<electronicmailaddress>karine.ferreira</electronicmailaddress>
		<electronicmailaddress>rafael.santos@inpe.br</electronicmailaddress>
		<electronicmailaddress>gilberto.queiroz@inpe.br</electronicmailaddress>
		<editor>Gherardi, Douglas Francisco Marcolino,</editor>
		<editor>Aragão, Luiz Eduardo Oliveira e Cruz de,</editor>
		<editor>Sanches, Ieda DelArco,</editor>
		<conferencename>Simpósio Brasileiro de Sensoriamento Remoto, 20 (SBSR)</conferencename>
		<conferencelocation>Florianópolis</conferencelocation>
		<date>02-05 abril 2023</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>e156223</pages>
		<booktitle>Anais</booktitle>
		<tertiarytype>full paper</tertiarytype>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<keywords>Random Forest, Cubos de dados, Análise temporal,Random Forest, Data cubes, Temporal analysis.</keywords>
		<abstract>A vasta gama de imagens de satélite disponibilizadas gratuitamente e os avanços de métodos computacionais ampliaram o uso de técnicas de sensoriamento remoto para analisar a superfície terrestre, melhorando análises sobre vegetação. Em regiões como a zona semiárida do bioma Caatinga, isso tem sido essencial para avaliar as condições e a sazonalidade característica da vegetação local. O objetivo deste estudo foi gerar classificações de uso e cobertura da terra e avaliar mudanças entre 2018 e 2020 por meio de duas abordagens: (1) classificação de séries temporais de imagens de satélite e (2) classificação de cada imagem de forma individual. Esse estudo conclui que a primeira abordagem usando o método Randon Forest e séries temporais NDVI potencializou a capacidade de diferenciar classes de uso e cobertura da terra em Petrolina-PE. ABSTRACT: The wide range of freely available satellite images and advances in computational methods have promoted the use of remote sensing techniques to analyze the Earths surface, improving analysis of vegetation. In regions such as the semi-arid zone of the Caatinga biome, this has been essential to assess the conditions and seasonality characteristic of the local vegetation. The objective of this study was to generate land use and land cover classifications and assess changes between 2018 and 2020 through two approaches: (1) classification of satellite image time series and (2) classification of each satellite image individually. This study concludes that the first approach using the Randon Forest method and NDVI time series enhanced the differentiation of land use and land cover classes in Petrolina-PE.</abstract>
		<area>SRE</area>
		<type>Mudança de uso e cobertura da Terra</type>
		<language>pt</language>
		<targetfile>156223.pdf</targetfile>
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