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O objetivo do presente trabalho foi avaliar o potencial das imagens de radar do satelite Sentinel 1A (banda C) para discriminar classes representativas de uso e cobertura da terra do Distrito Federal, Brasil. As imagens de coerencia interferometrica utilizadas nesse estudo foram obtidas a partir de pares de imagens S ingle Look Complex (SLC) de junho e julho de 2018. Foi gerada uma composicao colorida RGB a partir de imagens de coerencia, intensidade de retroespalhamento e razao de retroespalhamento. Essa imagen foi classificada pelos metodos supervisionados Support Vector Machine (SVM) e Random Forest (RF). A validacao tematica foi realizada por matrizes de confusao, indice Kappa e exatidao global. Nesse contexto, foram investigadas cinco classes tematicas (agua, area urbana, vegetacao nativa, pastagem e agricultura). O classificador RF obteve melhor desempenho classificatorio (Kappa= 0,68 e exatidao global = 79,1%) que o classificador SVM (Kappa= 0,64 e exatidao global = 75,7%). A coerencia mostrou-se eficiente principalmente na identificacao de corpos d’agua e da area urbana. Os resultados foram satisfatorios para a classificacao de uso e cobertura da terra do Distrito Federal, no entanto, houve confusao entre algumas classes e erros de comissao na classe area urbana em ambas as classificacoes. A b s t r a c t The aim of this study was to evaluate the potential of Sentinel 1A (C band) satellite radar images to discriminate representative classes of land use and land cover in the Federal District, Brazil. The interferometric coherence images used in this study were obtained from pairs of Single Look Complex images (SLC) of June and July of 2018. An RGB color composition was generated from images of coherence, backscattering intensity and backscattering ratio. This image was classified by the Supervised Methods Support Vector Machine (SVM) and Random Forest (RF). Thematic validation was performed by matrices of confusion, Kappa index and overall accuracy. In this context, five thematic classes (water, urban area, native vegetation, pasture and agriculture) were investigated. The RF classifier obtained a better classificatory performance (Kappa= 0.68 and overall accuracy = 79.1%) than the SVM classifier (Kappa= 0.64 and overall accuracy = 75.7%). The coherence was shown to be efficient mainly in the identification of water and the urban area. The results were satisfactory for the classification of use and land cover of the Federal District, however, there was confusion between some classes and errors of commission in the urban area class in both classifications. Keywords: use and land cover, Interferometry, interferometric coherence, Sentinel 1A, radar, Remote Sensing

Classificação do uso e cobertura da terra do Distrito Federal, Brasil a partir de dado de coerência interferométrica