Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/783629
Unidade da Embrapa/Coleção:: Área de Informação da Sede - Artigo em periódico indexado (ALICE)
Data do documento: 13-Mai-2010
Tipo do Material: Artigo em periódico indexado (ALICE)
Autoria: EPIPHANIO, R. D. V.
FORMAGGIO, A. R.
RUDORFF, B. F. T.
MAEDA, E. E.
LUIZ, A. J. B.
Informaçães Adicionais: Louis Dreyfus Commodities Brasil S.A.; Instituto Nacional de Pesquisas Espaciais; Instituto Nacional de Pesquisas Espaciais; University of Helsinki; Embrapa Meio Ambiente.
Título: Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil.
Edição: 2010
Fonte/Imprenta: Pesquisa Agropecuária Brasileira, Brasília, DF, v. 45, n. 1, p. 72-80, jan. 2010.
Idioma: en
Conteúdo: Abstract ? The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating soybean areas.
NAL Thesaurus: Glycine max
Accuracy
Agricultural statistics
Classification
Remote sensing
Thematic map
Ano de Publicação: 2010
Aparece nas coleções:Artigo em periódico indexado (AI-SEDE) / Embrapa Informação Tecnológica (SCT)

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