Hyperspectral Tree Species Classification of Japanese Complex Mixed Forest With the Aid of Lidar Data

The classification of tree species in forests is an important task for forest maintenance and management. With the increase in the spatial resolution of remote sensing imagery, individual tree classification is the next target of research area for the forest inventory. In this work, we propose a methodology involving the combination of hyperspectral and LiDAR data for individual tree classification, which can be extended to areas of shadow caused by the illumination of tree crowns with sunlight. To remove the influence of shadows in hyperspectral data, an unmixing-based correction is applied as preprocessing. Spectral features of trees are obtained by principal component analysis of the hyperspectral data. The sizes and shapes of individual trees are derived from the LiDAR data after individual tree-crown delineation. Both spectral and tree-crown features are combined and input into a support vector machine classifier pixel by pixel. This procedure is applied to data taken over Tama Forest Science Garden in Tokyo, Japan, to classify it into 16 classes of tree species. It is found that both shadow correction and tree-crown information improve the classification performance, which is further improved by postprocessing based on tree-crown information derived from the LiDAR data. Regarding the classification results in the case of 10% training data, when using the random sampling of pixels to select training samples, a classification accuracy of 82% was obtained, while the use of reference polygons as a more practical means of sample selection reduced the accuracy to 71%. These values are, respectively, 21.5% and 9% higher than those that are obtained using hyperspectral data only.