Data/hora: 12/06/2024 02:11:00

Biblioteca(s): 	Embrapa Meio Ambiente; Embrapa Unidades Centrais.	
Data corrente: 	13/05/2010	
Data da última atualização: 	05/01/2023	
Autoria: 	EPIPHANIO, R. D. V.; FORMAGGIO, A. R.; RUDORFF, B. F. T.; MAEDA, E. E.; LUIZ, A. J. B.	
Afiliação: 	Rui Dalla Valle Epiphanio, Louis Dreyfus Commodities Brasil S.A.; Antonio Roberto Formaggio, INPE; Bernardo Friedrich Theodor Rudorff, INPE; Eduardo Eiji Maeda, University of Helsinki; ALFREDO JOSE BARRETO LUIZ, CNPMA.	
Título: 	Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil.	
Ano de publicação: 	2010	
Fonte/Imprenta: 	Pesquisa Agropecuária Brasileira, Brasília, DF v. 45, n. 1, p. 72-80, 2010.	
Idioma: 	Inglês
Português	
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.	
Palavras-Chave: 	Mapa temático; Thematic map.	
Thesagro: 	Estatística Agrícola; Glycine Max; Sensoriamento Remoto.	
Thesaurus Nal: 	Accuracy; Agricultural statistics; Classification; Remote sensing.	
Categoria do assunto: 	--	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/AI-SEDE-2010/47610/1/45n01a10.pdf	
Registro original: 	Embrapa Meio Ambiente (CNPMA)	
Exemplares: 	AI-SEDE - (47610-1) - UPE - AP - PP - 630.72081 - P474 -  - DIS
CNPMA - (9513-1) - UPC - AP - DD - AP - PAT 2010 - 2010.00063 - DIS