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		<secondarykey>INPE-15937-PRE/10547</secondarykey>
		<isbn>978-85-17-00044-7</isbn>
		<citationkey>MelloVieiAguiRudo:2009:ClCoCa</citationkey>
		<title>Classificação da colheita da cana-de-açúcar por meio de imagens de satélite utilizando superfícies de resposta espectro-temporais</title>
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		<year>2009</year>
		<secondarytype>PRE CN</secondarytype>
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		<author>Mello, Márcio Pupin de,</author>
		<author>Vieira, Carlos Antônio Oliveira,</author>
		<author>Aguiar, Daniel Alves de,</author>
		<author>Rudorff, Bernardo Friedrich Theodor,</author>
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		<group>DSR-OBT-INPE-MCT-BR</group>
		<group></group>
		<group>DSR-OBT-INPE-MCT-BR</group>
		<affiliation>Instituto Nacional de Pesquisas Espaciais - INPE</affiliation>
		<affiliation>Universidade Federal de Viçosa - UFV</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais - INPE</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais - INPE</affiliation>
		<electronicmailaddress>pupin@dsr.inpe.br</electronicmailaddress>
		<electronicmailaddress>carlos.vieira@ufv.br</electronicmailaddress>
		<electronicmailaddress>daniel@dsr.inpe.br</electronicmailaddress>
		<electronicmailaddress>bernardo@dsr.inpe.br</electronicmailaddress>
		<editor>Epiphanio, José Carlos Neves,</editor>
		<editor>Galvão, Lênio Soares,</editor>
		<e-mailaddress>lise@dpi.inpe.br</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Sensoriamento Remoto, 14 (SBSR)</conferencename>
		<conferencelocation>Natal</conferencelocation>
		<date>25-30 abr. 2009</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>279-286</pages>
		<booktitle>Anais</booktitle>
		<tertiarytype>Artigo</tertiarytype>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<keywords>remote sensing, multitemporal image classification, accuracy assessment, automatization, sensoriamento remoto, classificação multitemporal de imagens, avaliação da exatidão, automatização.</keywords>
		<abstract>Environmental impacts related to sugarcane crop cultivation are becoming a worldwide issue due to the great potential that ethanol has to mitigate the emission of green house gases. However, the sugarcane straw burning prior to harvest is still a critical environmental problem that needs special attention. São Paulo State represents more than 60% of the Brazilian sugarcane production with 4.9 millions ha of cultivated area. The State government together with the private sugarcane production sector established in 2007 a protocol to gradually stop the sugarcane straw burning up to 2014. Remote sensing images have the potential to monitor the harvest management procedure identifying the fields that were harvested with and without straw burning prior to harvest. Currently, this identification and classification is carried out using visual interpretation which provides high quality results but is extremely tedious and time consuming. The present work has the objective to propose an automated classification procedure based on Spectral Temporal Response Surfaces (STRS) to classify the recent harvested sugarcane into burned and non-burned fields. This procedure is based on a pixel-by-pixel classification considering the spectral-temporal reflectance of each image pixel generating a thematic map. A visual interpreted reference map was used to assess the automated classification map accuracy which showed an overall index of 87.3%. The STRS classification procedure showed to be a promising alternative to automate the generation of thematic maps of harvested sugarcane with and without straw burning based on spectral-temporal remote sensing images.</abstract>
		<area>SRE</area>
		<subject>Agricultura</subject>
		<session>Agricultura</session>
		<type>Agricultura</type>
		<language>pt</language>
		<targetfile>279-286.pdf</targetfile>
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