Improved Forest Parameter Estimation Using Registered Airborne–Terrestrial Point Clouds via a Novel Adaptive Clustering and Vertical Layering Method

Diameter at breast height (DBH) and tree height are core parameters for forest monitoring. LiDAR enables rapid high-precision point cloud acquisition, yet single-platform data have limitations: airborne laser scanning (ALS) cannot capture sub-canopy structure, while terrestrial laser scanning (TLS) cannot fully acquire canopy information. Based on 92 plots in Guangxi, this study proposes a weighted point-to-normal iterative closest point (WPN-ICP) registration algorithm for high-precision fusion of ALS and TLS point clouds, with comparisons to trimmed iterative closest point (TrICP) and iterative closest point (ICP). An adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm was developed for diameter at breast height (DBH) estimation, and a DBH cluster-based vertical layering method (DCVL-THE) for tree height estimation. Three segmentation-based methods—canopy height model (CHM), region growing (RG), and comparative shortest path (CSP)—were selected for comparison. WPN-ICP achieved a mean MAE of 0.0359 m, with reductions of 85.87%, 69.03%, and 73.00% compared with coarse registration, TrICP, and ICP. Using the fused point cloud, DBH estimation achieved an RMSE of 0.6326 cm with an RRMSE of 5.18%, and tree height estimation achieved an RMSE of 0.7160 m with an RRMSE of 6.58%. Compared with the suboptimal CSP method, the RMSE and RRMSE for DBH were reduced by 41.79% and 41.68%, and for tree height by 25.66% and 25.68%. This method does not require full individual-tree or crown segmentation, thereby avoiding the error propagation associated with such segmentation and enabling accurate stand-scale estimation.