Abstract: In order to explore the advantages and complementarity of optical data and synthetic aperture radar（SAR） data in forest type classification, this study focused on the overlapping area of Landsat8 data and ALOS2 data from one scene SAR image in Simao District, Puer City, Yunnan Province, China, and used hierarchical classification technology for forest type classification research. Three feature sets were constructed: optical feature set（spectral + vegetation + texture + terrain features）, SAR feature set（backscattering + polarization decomposition features）, and optical-SAR fusion feature set（spectral + vegetation + texture + terrain +backscattering + polarization decomposition features）. Recursive Feature Elimination（RFE） was employed to perform stratified feature selection on the three feature sets, and random forest（RF） and support vector machine（SVM） were used for forest type classification. The SVM classification with the fusion of optical images and SAR data achieved the best results. The results showed, 1) In the first layer（vegetation/non-vegetation） classification, the overall accuracy was 98. 57%, the Kappa coefficient was 0. 971. 2) In the second layer（forest/non-forest） classification, the overall accuracy was 92. 14%, the Kappa coefficient was 0. 826. 3) In the third layer（coniferous/broad-leaved/mixed forest） classification, the overall accuracy was 83. 47%, and the Kappa coefficient was 0. 743.The fusion data showed an improvement of 6. 74% in accuracy compared to optical data feature set classification and 29. 24% compared to SAR classification. 4) In the classification of the third layer using fusion data, the influence of different window sizes（3×3, 5×5, 7×7, 9×9） of texture features in optical images was compared, and the highest accuracy was achieved with a 7×7 texture window. Results shows that, the accuracy of forest type classification using multi-source data is higher than that using a single data source.