Abstract:The main issues in extracting forest parameters and estimating stand volume using UAV laser scanning (UAV-LS) are insufficient accuracy in individual tree segmentation and the inability to directly obtain diameter at breast height (DBH) parameters. To address this limitation, the UAV-LS was utilized to collect high-density point cloud data from Eucalyptus and Chinese fir plantations. By improving the mean shift algorithm (IMSA), a method capable of accurately obtaining the diameter at any height of standing trees was proposed, thereby calculating tree volume and achieving accurate estimation of stand volume from the perspective of individual tree segmentation. The results showed that the improved mean shift algorithm effectively handled dense noise near the tree trunk, significantly enhancing detection accuracy. The accuracy of determining and fitting the edge points of the trunk was optimal, with coefficients of determination R2>0.93 for estimating diameters at heights of 1.3m and 2m, and average relative errors of 2.41% (Eucalyptus) and -4.05% (Chinese fir). The model can effectively estimate the diameter and volume of individual trees in Eucalyptus and Chinese fir plantations, with performance for Chinese fir being optimized compared with Eucalyptus. Point cloud density significantly affected the estimation performance of the model. When using point cloud densities of 50% or less of the original density for individual tree measurements, the omission rate was increased significantly;when using only 10% of the original density, the maximum absolute error exceeded 86%. The research result can provide technical support and theoretical basis for the timely, accurate, and efficient estimation of stand volume at the individual tree scale by using UAV-LS, while also offering a reference for high-precision forest resource assessment under resource-limited conditions.
