Automatic individual tree detection and canopy segmentation from three-dimensional point cloud images obtained from ground-based lidar

Lidar (light detection and ranging) has been widely utilized for estimating the structural parameters of plants, such as tree height, leaf inclination angle, and biomass. However, individual trees have been primarily manually extracted from three-dimensional (3D) point cloud images. Automatically detecting each tree and analyzing its structural parameters is desirable. In this study, we propose a method to (1) detect each tree from 3D point cloud images obtained from ground-based lidar, (2) estimate the number of trees and diameter at breast height (DBH) from the detected 3D point cloud images of trees, and (3) segment each tree canopy. First, we focused on point clouds whose height ranged from 0.5 to 1.5 m and detected each cluster of tree trunks. Then, the clusters were expanded by classifying other points to the clusters that are located near the points and then repeating this process. The process assigns the points in the 3D point cloud image to each tree in the upward direction and separates not only tree trunks but also tree canopies. As a result, the trees in 3D point cloud images were detected with high accuracy, and the number of trees and DBII was estimated. Moreover, each tree canopy was segmented.