In this study, we develop a system for efficiently measuring detailed information of trees in a forest environment using a small unmanned aerial vehicle (UAV) equipped with light detection and ranging (lidar). The main purpose of forest measurement is to predict the volume of wood for harvesting and delineating forest boundaries by tree location. Herein, we pro-pose a method for extracting the position, number of trees, and vertical height of trees from a set of three-dimensional (3D) point clouds acquired by a UAV lidar system. The point cloud obtained from a UAV is dense in the tree’s crown, and the trunk 3D points are sparse because the crown of the tree obstructs the laser beam. Therefore, it is difficult to extract single-tree information from 3D point clouds because the characteristics of 3D point clouds differ significantly from those of conventional 3D point clouds using ground-based laser scanners. In this study, we segment the forest point cloud into three regions with different densities of point clouds, i.e., canopy, trunk, and ground, and process each region individually to extract the target information. By comparing a ground laser survey and the proposed method in an actual forest environment, it is discovered that the number of trees in an area measuring 100 m × 100 m is 94.6% of the total number of trees. The root mean square error of the tree position is 0.3 m, whereas that of the vertical height is 2.3 m, indicating that single-tree information can be measured with sufficient accuracy for forest manage-ment.
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