Fusion of hyperspectral and LiDAR data for tree species classification

Abstract

Classification of tree species is one of the most important applications in remote sensing. In this study, the authors propose a methodology to classify tree species using hyperspectral and LiDAR data. The method consists of shadow correction, individual tree crown delineation and classification by support vector machine (SVM). Shadows in hyperspectral data are modified by unmixing. Individual tree crown delineation is achieved by a local maxima and region growing method for a LiDAR derived canopy height model (CHM). The input variables of SVM classifiers are principal components of hyperspectral data and the canopy form (height and size). The authors applied this method to the hyperspectral and LiDAR dataset taken over Tama Forest Science Garden in Tokyo and classified the data into 19 classes. As a result, we achived classification accuracy of 68 %, which is 20 % higher than what is obtained by using hyperspectral data only.