Sophisticated sensors of satellites help researchers collect detailed maps of land surface in various image wavebands. These wavebands are processed to form spectral features identifying distinct land structures. However, depending on the structures subject to the research topic, only a portion of collected features might be sufficient for identification. Aim of this study is to present a scheme to pick most valuable spectral features derived from ASTER imagery in order to distinguish four types of tree ensembles: 'Sugi' (Japanese Cedar), 'Hinoki' (Japanese Cypress), 'Mixed deciduous', and 'Others'. Forward selection, a type of wrapper techniques, was employed with four types of classifiers in several train/test splits. Final rank of each feature was determined by Condorcet ranking after application of each classifier. Results showed that among four classifiers, artificial neural networks helped the selection process choose the most valuable features and a high accuracy value of 90.42% (with a true skill statistics score of 91.26%) was obtained using only top-ten features. For feature sets in smaller sizes, support vector machines classifier also performed well and provided an accuracy of 80.33% (with a true skill statistics score of 81.84%) using only top-three features. With help of these findings, landscape data can be represented in smaller forms with spectral features having most discriminative power. This will help reduce processing time and storage needs of expert systems. (C) 2018 Elsevier Ltd. All rights reserved.