Classification of Tree Species Based on LiDAR Point Cloud Data

This study involved the Metasequoia glyptostroboides, Salix babylonica, Ligustrum lucidum, bamboo, and Malus pumila Mill. from the Qianjiang new town forest park of the Hangzhou city and the Hongqipo farm of the Aksu city in the Xinjiang Uygur Autonomous Region. The structural, textural, and crown features were proposed based on high-resolution point cloud data acquired by the airborne LiDAR and a support vector machine classifier. The experimental results demonstrate that the overall accuracy of the classification is 85%, with a Kappa coefficient of 0. 81. The proposed method derives promising features for a tree based on the LiDAR data and demonstrates an effective framework for improving the classification performance of the tree species.