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Comparison of multisource data support vector Machine classification for mapping of forest cover

The use of remotely sensed data for the classification of forest cover has been effectively proven by means of multi- source remotely sensed data. This study concerns on two forest sites in Southern Ecuador and Central Indonesia. Support vector machine classification is applied on both sites, elaborating texture data as additional information to improve classification accuracy. Two types of texture data, which are estimated using grey level co-occurrence matrix (GLCM) and geostatistics methods, were applied by means of moving window. The result showed that the performance of the SVM had notably improved when texture data used with spectral data for mapping of forest cover.