A tree detection method based on trunk point cloud section in dense plantation forest using drone LiDAR data

Single-tree detection is one of the main research topics in quantifying the structural properties of forests. Drone LiDAR systems and terrestrial laser scanning systems produce high-density point clouds that offer great promise for forest inventories in limited areas. However, most studies have focused on the upper canopy layer and neglected the lower forest structure. This paper describes an innovative tree detection method using drone LiDAR data from a new perspective of the under-canopy structure. This method relies on trunk point clouds, with under -canopy sections split into heights ranging from 1 to 7 m, which were processed and compared, to determine a suitable height threshold to detect trees. The method was tested in a dense cedar plantation forest in the Aichi Prefecture, Japan, which has a stem density of 1140 stems center dot ha(-1) and an average tree age of 42 years. Dense point cloud data were generated from the drone LiDAR system and terrestrial laser scanning with an average point density of 5000 and 6500 points center dot m(-2), respectively. Tree detection was achieved by drawing point-cloud section projections of tree trunks at different heights and calculating the center coordinates. The results show that this trunk-section-based method significantly reduces the difficulty of tree detection in dense plantation forests with high accuracy (F1 -Score = 0.9395). This method can be extended to different forest scenarios or conditions by changing section parameters.