Robust Individual Tree Parameter Estimation in Cold–Temperate Secondary Forests Using ULS–HLS Data and the RSQ-Tree Framework

Accurate quantification of individual-tree parameters is essential for improving the quality of natural secondary forests; however, conventional measurements remain challenging because of canopy overlap and interference from tall shrubs. Despite the broad use of Light Detection and Ranging (LiDAR) in forest inventory, achieving precise tree segmentation and parameter estimation in complex forest stands remains difficult. This study utilized unmanned aerial vehicle laser scanning (ULS) and handheld laser scanning (HLS) data to systematically evaluate the impact of single-source point clouds versus fused point clouds, different segmentation methods (CHM, treeX, and CSP), and different estimation approaches on the estimation of individual tree parameters, and proposed the RSQ-Tree framework for robust parameter extraction. Comparative analysis of seven experimental schemes across 473 sample trees in six plots showed that the “stem denoising + fused data + treeX + RSQ-Tree” scheme performed best, with R2 values of 0.96, 0.84, and 0.75 for estimates of diameter at breast height, tree height, and crown width, respectively, and substantially reduced RMSE. These results indicate that multi-source LiDAR fusion, combined with robust segmentation and parameter modelling, can effectively improve the accuracy and stability of individual-tree parameter estimation in complex secondary forests.