Tree-Species Classification in Subtropical Forests Using Airborne Hyperspectral and LiDAR Data

Accurate classification of tree-species is essential for sustainably managing forest resources and effectively monitoring species diversity. In this study, we used simultaneously acquired hyperspectral and LiDAR data from LiCHy (Hyperspectral, LiDAR and CCD) airborne system to classify tree-species in subtropical forests of southeast China. First, each individual tree crown was extracted using the LiDAR data by a point cloud segmentation algorithm (PCS) and the sunlit portion of each crown was selected using the hyperspectral data. Second, different suites of hyperspectral and LiDAR metrics were extracted and selected by the indices of Principal Component Analysis (PCA) and the mean decrease in Gini index (MDG) from Random Forest (RF). Finally, both hyperspectral metrics (based on whole crown and sunlit crown) and LiDAR metrics were assessed and used as inputs to Random Forest classifier to discriminate five tree-species at two levels of classification. The results showed that the tree delineation approach (point cloud segmentation algorithm) was suitable for detecting individual tree in this study (overall accuracy = 82.9%). The classification approach provided a relatively high accuracy (overall accuracy > 85.4%) for classifying five tree-species in the study site. The classification using both hyperspectral and LiDAR metrics resulted in higher accuracies than only hyperspectral metrics (the improvement of overall accuracies = 0.4-5.6%). In addition, compared with the classification using whole crown metrics (overall accuracies = 85.4-89.3%), using sunlit crown metrics (overall accuracies = 87.1-91.5%) improved the overall accuracies of 2.3%. The results also suggested that fewer of the most important metrics can be used to classify tree-species effectively (overall accuracies = 85.8-91.0%).