Abstract: Accurate quantification of forest structural parameters, such as tree height (H), crown vertical projection area (CPA) and crown volume (CV), is essential for precise estimation of forest carbon sequestration, monitoring succession dynamics, and improving carbon cycle models. In natural forests characterized by high species diversity and complex stand structures, the capability of terrestrial laser scanning (TLS) and unmanned aerial vehicle laser scanning (UAV-LS) to measure forest structural parameters across different tree heights for coniferous and broadleaved species, remains unevaluated under the influence of canopy shading effects. This study investigated deciduous broadleaf -Korean pine forests by integrating TLS and UAV-LS point clouds using geographic coordinates and combining inventory data to identify tree species from individual tree point clouds. The fused point cloud of forest structural parameters served as a baseline dataset to evaluate TLS and UAV-LS accuracy during the period of no leaf cover. The results showed a strong correlation between TLS and UAV-LS with the fused point cloud (R² = 0.96–0.99) TLS and UAV-LS had greater accuracy in measuring H, CPA and CV for coniferous trees than for broadleaf trees, with smaller D-rRMSE differences for conifers (0.7%–3.6%) than for broadleaves (1.1%–21.1%) Across height categories, TLS maintained relatively stable rRMSE values except when height exceeded 25 m, where rRMSE increased. Conversely, UAV-LS showed a significant reduction in rRMSE and RMSE as height increased (88.0% to 1.4%) These results highlight the greater stability of TLS than UAV-LS in measuring the structural parameters of the forest during the period of no foliage.

Key words: Geographic coordinates, LiDAR, Point cloud fusion, Forest structure parameters