Objective The dynamic evolution of forest structure with stand age leads to heterogeneity in the accuracy of LiDAR parameter extraction. Currently, both airborne (ALS) and ground-based (TLS) LiDAR systems face physical viewing angle limitations when acquiring vertical 3D information of forest stands. While multi-source point cloud fusion can improve accuracy, the extent to which this enhances accuracy is systematically constrained by the forest stand growth process, and the underlying physical mechanisms remain unclear. Therefore, this study investigated Chinese fir plantations at different age stages to systematically evaluate the age-dependent patterns of accuracy in extracting individual tree parameters (tree height and diameter at breast height) from fused ALS and TLS point clouds, and to quantitatively analyze the underlying physical driving mechanisms. The aim is to provide theoretical and methodological support for the development of a high-precision, adaptive forest monitoring system applicable to the entire growth cycle.

Method In nine standard plots of Chinese fir of different ages (young, middle-aged, and mature), ALS and TLS point clouds as well as manually measured individual tree data were collected simultaneously. After performing high-precision registration, denoising, and normalization of the multi-source point clouds using a target-free strategy, the fused point cloud was segmented into individual trees, and tree height and diameter at breast height (DBH) were extracted using the comparative shortest path algorithm. Extraction accuracy was comprehensively evaluated using metrics such as root mean square error (RMSE) and coefficient of determination (R²) through comparison with field measurements and results from a single-platform approach. Building on this, a generalized additive model was further employed to quantitatively examine the nonlinear relationship between the core physical characteristics of the point clouds and extraction errors, thereby elucidating the underlying physical mechanisms driving error evolution.

Result (1) The fused point cloud significantly improved the accuracy of individual tree geometric characterization; its F₁ score for individual tree segmentation (≥0.961) was comparable to that of high-density TLS and far superior to that of ALS alone (≥0.726). (2) Regarding tree height estimation, the fused point cloud performed best across all age classes. Compared to standalone ALS and TLS, its R² values increased by an average of 0.11 and 0.10, respectively, while the RMSE was reduced by 0.197 m compared to TLS, effectively correcting the systematic bias of ground-based scanning that tends to underestimate tree height. (3) The improvement in DBH extraction accuracy exhibits significant age-dependence: in young and middle-aged forests, multi-source fusion effectively corrects underlying geometric errors (RMSE reduced to 0.69 cm and 1.73 cm, respectively); however, in mature forests with high canopy closure, the accuracy-compensating effect of fusion largely disappears due to the lack of effective signals beneath the canopy. (4) Analysis of error mechanisms indicates that tree height extraction residuals exhibit a highly significant nonlinear correlation (p ≤ 0.001) with maximum point cloud reflectance in middle-aged and mature forests, confirming that beam scattering and physical degradation caused by high canopy closure are the primary sources of systematic measurement bias; in contrast, diameter at breast height (DBH) extraction errors do not show a significant correlation with macrostructural features and tend to follow a random distribution.

Conclusion The integration of airborne and terrestrial LiDAR effectively combines the advantages of canopy and understory detection, significantly improving the overall accuracy of individual tree parameter extraction for Chinese fir. However, this improvement in accuracy is strictly constrained by stand structure evolution; simple point cloud fusion that does not distinguish between differences in canopy closure struggles to leverage the synergistic advantages of the data in dense stands due to limited laser penetration. The physical structural evolution driven by stand development constitutes the underlying mechanism of systematic errors in parameter extraction. Future research should explore “age/density-adaptive” dynamic fusion strategies and introduce signal attenuation compensation models based on point cloud intensity to achieve accurate inversion in complex forest habitats.