Classifying original bands and/or image components may cause unsatisfactory results in fields that have heterogeneous reflectance. In such cases, the demand for accurate land-use, land-cover, vegetation, and forestry information may require more specific components. The components should represent peculiar information collected from several inputs for target land covers. In this study, a new technique of land-cover classification was explored to prepare an input which increases the success of landslide susceptibility mapping in a subtropical region, Asarsuyu Catchment Area (Duzce). Land-cover mapping is a difficult issue in this area by only carrying out field studies and aerial-photo interpretations. Moreover, applying different classifications of Landsat Thematic Mapper bands and/or their secondary products does not produce acceptable results. For this reason, vegetation indices, soil/surface moisture indices, topographic wetness index and drainage density were calculated to produce feature representative components for the land-cover classification process. Results obtained from the proposed technique show that feature representative components significantly improve the conventional classification accuracy from 77% to 89% and the resultant land-cover map is such a valuable input for landslide susceptibility mapping that it increases the success of the landslide susceptibility map from 63% to 88%.