ASTER/Terra Imagery and a Multilevel Semantic Network for Semi-automated Classification of Landforms in a Subtropical Area

This research is committed to develop a semi-automated landforms classification method for a subtropical area located in the southeast of Brazil, using optical medium-resolution imagery from ASTER/Terra. A four-level semantic network driven by a set of spectral, textural, and geomorphometric variables was used. The textural and geomorphometric variables were extracted from an ASTER/Terra DEM. The semantic network was initially conceived to classify macro morphogenetic landforms and was then further detailed to allow a finer classification, which amounted to eleven classes of morphographic landforms. In order to assess the classification accuracy, statistical indices were derived from a contingency table obtained by means of a comparison between the classified scene and a reference map. The final agreement indices for the macro and detailed landforms classifications were 76 percent and 80 percent, respectively. The employed object-based image analysis has proved to be a suitable method for semi-automated procedures in the classification of landforms.