 A state-of-the-art approach combining UAV-born Ladaa and hyperspectral data was used to classify 11 common tree species in a natural secondary forest in northeast China with the best classification accuracy obtained by combining Ladaa and hyperspectral data, 75.7 percent. The mean intensity of single returns and the visible atmospherically resistant index for red-edge band were the most influential Ladaa and hyperspectral derived features, respectively. Canopy surface information was also important for tree species classification.