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Mapeamento de semeaduras de soja (Glycine max (L.)Merr.) mediante dados MODIS/Terra E TM/Landsat 5: um comparativo Mapping of soybean (Glycine max (L.) Merr.) culture by MODIS/Terra and TM/Landsat 5: a comparative

By Rubens A. C. Lamparelli, Waste M. O. de Carvalho and Erivelto Mercante

Abstract

O objetivo deste trabalho foi comparar mapeamentos de semeadura da cultura da soja na região oeste do Paraná, realizados com imagens MODIS/Terra e TM/Landsat 5. Primeiramente, construiu-se máscara de referência, considerando seis imagens TM ao longo do ciclo da cultura, utilizando-se dos algoritmos Paralelepípedo e MaxVer com posterior análise visual. As imagens MODIS foram classificadas com o algorítimo Paralelepípedo, em quatro passagens referentes ao pico vegetativo. O desempenho das classificações foi avaliado por meio de Matrizes de Erros, calculadas pela análise de 100 pontos amostrais (soja ou não-soja), aleatoriamente distribuídos em cada um dos oito municípios da área de estudo. Os principais resultados mostraram que a Exatidão Global (EG) e o Índice Kappa (IK), que variaram entre 0,55 e 0,80, em ambos os sensores, são considerados bons a muito bons. Quando EG e IK dos sensores TM e MODIS foram comparados, não se encontrou diferença significativa. O mapeamento da soja utilizando o sensor MODIS produziu 70% de confiabilidade sob o ponto de vista do usuário. A principal conclusão é a viabilidade de mapear a soja pelo sensor MODIS com as vantagens de que as imagens MODIS têm melhor resolução temporal e são disponibilizadas gratuitamente na Internet.<br>The objective of this work was to compare the soybean crop mapping in the western of Parana State by MODIS/Terra and TM/Landsat 5 images. Firstly, it was generated a soybean crop mask using six TM images covering the crop season, which was used as a reference. The images were submitted to Parallelepiped and Maximum Likelihood digital classification algorithms, followed by visual inspection. Four MODIS images, covering the vegetative peak, were classified using the Parallelepiped method. The quality assessment of MODIS and TM classification was carried out through an Error Matrix, considering 100 sample points between soybean or not soybean, randomly allocated in each of the eight municipalities within the study area. The results showed that both the Overall Classification (OC) and the Kappa Index (KI) have produced values ranging from 0.55 to 0.80, considered good to very good performances, either in TM or MODIS images. When OC and KI, from both sensors were compared, it wasn't found no statistical difference between them. The soybean mapping, using MODIS, has produced 70% of reliance in terms of users. The main conclusion is that the mapping of soybean by MODIS is feasible, with the advantage to have better temporal resolution than Landsat, and to be available on the internet, free of charge

Topics: monitoramento, matriz de erros, previsão de safras, imagens orbitais, culturas agrícolas, monitoring, error matrix, yield estimate, orbital images, crops, Agriculture (General), S1-972, Agriculture, S, DOAJ:Agriculture (General), DOAJ:Agriculture and Food Sciences
Publisher: Sociedade Brasileira de Engenharia Agrícola
Year: 2008
DOI identifier: 10.1590/S0100-69162008000200014
OAI identifier: oai:doaj.org/article:3c5d1995cfba43f6b444d419e0eb094d
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