Análise de uso da terra e cobertura florestal na Amazônia central, a partir de dado polarimétrico PALSAR/ALOS-1 e coerência interferométrica TanDEM-X

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.