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supervised classification algorithms of remote sensing data, to develop a methodology to assist in their physical characterization, as well as subsidizing forensic reports of rural properties. We used an ASTER image of May 2nd, 2013 on demand product AST_07XT that comes with the atmospheric and crosstalk corrections, besides being a surface reflectance data. For processing adopted the comparative analysis DIP tools (Digital Image Processing) hyperspectral classifier SAM (Spectral Angle Mapper), and spectral indices EVI 2 (Enhanced Vegetation Index 2) and IFe (Ferric index) as well as the use of digital elevation model, based on SRTM image, optimized for 30-meter spatial resolution. We analyzed topography, soil, river network, climate and natural and exotic vegetation. Following four thematic information plans were produced, one for each DIP tool, to identifying wet areas, due to the variables presented. As a Revista Brasileira de Geografia Física, vol.07, n.06 (2014) 1062-1077. 1063 Borges, B.C.; Baptista, G.M.M; Meneses P.R. result, the integrated analysis demonstrated that, although it was possible to separate wet from dry areas individually, none of the processing was able to provide a result with precision. It is meant that additional work should be e ncouraged so that the forensic reports issued related to evaluation of rural properties become more technically accurate and justified.

revista brasileira de geografia física

Identification of Hydromorphic Areas by Means of Spectral Analysis of Remote Sensing Data, as Support for the Preparation of Forensic Reports Issued to Evaluation of Rural Properties