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Abstract – The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classifi cation method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classifi cation 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 fi eldwork data, TM/Landsat-5 and CCD/ CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classifi cation by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classifi cation overestimated in 21.31 % of the reference values. In regions where soybean fi elds were less prevalent, the classifi er overestimated 132.37 % in the acreage of the reference. The overall classifi cation accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classifi cation of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less effi cient, overestimating soybean areas

Topics: Index terms, Glycine max, accuracy, agricultural statistics, classifi cation, remote sensing, thematic map
Year: 2016
OAI identifier: oai:CiteSeerX.psu:
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