Avaliação de dados de radar do sensor SAR-R99B no mapeamento do uso e cobertura da terra na Amazônia Central, município de Manaus, AM

Abstract

In recent decades the areas of rainforest in the Amazon region has been heavily impacted by a rapid process of conversion of vegetation cover in other types of use due to human action. In the context of global change, the use of mapping and monitoring land cover and provide information for the analysis and evaluation of environmental impacts due to accelerated changes in the landscape. Therefore, this study evaluated the potential of data from synthetic aperture radar for discriminating use and land cover in the region of Manaus, Amazonas state. We used a multipolarized image from sensor airborne SAR-R99B (L band), with 3 m spatial resolution. Were evaluated the MAXVER-ICM and SVM (Support Vector Machine) classifiers, where in all cases we used the images individually multipolarized amplitude (HH, HV and VV), in pairs (HH and HV), (HV and VV) and (HH and VV) and together (HH, HV and VV). The results were compared using as parameter the Kappa coefficient. The SVM classifier had higher accuracy compared to MAXVER-ICM classifier. The best classifications were obtained for the dual polarization (HH and VV) with MARVER-ICM classifier and (HH, HV and VV) with the SVM classifier both using the images with the filter. The accuracy was highest with SVM for classification and filter images (kappa = 0.7736). Were analyzed the influence of using GAMMA filter performance on the classifiers where it showed that filtered images have provided an increase in the results, on average, about 8%. Thus there was the analysis of the classification results, which found that the best result was provided by the dataset multipolarized (HH, HV and VV) classified by the SVM method. Thus, we concluded that the use of radar imagery in mapping thematic classes use and land cover in tropical regions, can be considered as a viable proposal.