Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/90445
Type: Artigo de periódico
Title: Soybean Crop Area Estimation Through Image Classification Normalized By The Error Matrix [estimativa De área De Soja Por Classificação De Imagens Normalizada Pela Matriz De Erros]
Author: Antunes J.F.G.
Mercante E.
Esquerdo J.C.D.M.
Lamparelli R.A.D.C.
Rocha J.V.
Abstract: The objective of this work was to estimate soybean crop area by the normalization of the error matrix generated from the supervised classification of TM/Landsat-5 images. Eight municipalities of the state of Paraná, Brazil, were evaluated using data from the 2003/2004 crop season. Classifications were carried out using the parallelepiped and maximum likelihood methods, resulting in a "soybean mask". Kappa index values for the eight municipalities were above 0.6. Estimated soybean areas, corrected by the error matrix, were highly correlated with official estimates of the state and with estimates generated from an alternative method called "direct expansion". Soybean crop area estimation by the normalization of the error matrix is less costly and can aid conventional methods in estimating harvests in a less subjective manner.
Editor: 
Rights: aberto
Identifier DOI: 10.1590/S0100-204X2012000900014
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-84871301666&partnerID=40&md5=a0048a3756d7025ef92889f452655fdf
Date Issue: 2012
Appears in Collections:Unicamp - Artigos e Outros Documentos

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