The rapid and precise acquisition of forest stand parameters is a key challenge in forest resource assessment. Terrestrial laser scanning (TLS) provides a fast and accurate method, but its accuracy is influenced by factors like tree segmentation parameters. This study focuses on Pinus tabuliformis plantations in the Caijiachuan watershed, Jixian, Shanxi, on the Loess Plateau. Based on field survey data, including tree number, height (H), diameter at breast height (DBH), and biomass, high-precision point cloud data were acquired using TLS. A comparative shortest path (CSP) algorithm was used for individual tree segmentation to investigate the effect of parameter selection on measurement accuracy. The results show that minimum tree height has a significant impact on segmentation accuracy. As the minimum tree height increased from 3.0 to 5.5 m, the recall rate (R) decreased while the precision (P) increased. The highest precision (F-score = 0.9470) and biomass estimation accuracy (0.9066) were obtained with a minimum tree height of 4.5 m, and the best extraction accuracies for H and DBH (0.9677 and 0.9518) were obtained at 5.0 m. Optimizing the minimum tree height parameter improves segmentation accuracy, thereby enhancing the use of TLS for soil and water conservation on the Loess Plateau.

Forest ecosystems play a pivotal role in the global carbon cycle and climate change mitigation. Forest aboveground biomass (AGB), a critical indicator of carbon storage and sequestration capacity, has garnered significant attention in ecological research. Recently, uncrewed aerial vehicle-borne laser scanning (ULS) technology has emerged as a promising tool for rapidly acquiring three-dimensional spatial information on AGB and vegetation carbon storage. This study evaluates the applicability and accuracy of UAV-LiDAR technology in estimating the spatiotemporal dynamics of AGB and vegetation carbon storage in Robinia pseudoacacia (R. pseudoacacia) plantations in the gully regions of the Loess Plateau, China. At the sample plot scale, optimal parameters for individual tree segmentation (ITS) based on the canopy height model (CHM) were determined, and segmentation accuracy was validated. The results showed root mean square error (RMSE) values of 13.17 trees (25.16%) for tree count, 0.40 m (3.57%) for average tree height (AH), and 320.88 kg (16.94%) for AGB. The regression model, which links sample plot AGB with AH and tree count, generated AGB estimates that closely matched the observed AGB values. At the watershed scale, ULS data were used to estimate the AGB and vegetation carbon storage of R. pseudoacacia plantations in the Caijiachuan watershed. The analysis revealed a total of 68,992 trees, with a total carbon storage of 2890.34 Mg and a carbon density of 62.46 Mg ha⁻¹. Low-density forest areas (<1500 trees ha⁻¹) dominated the landscape, accounting for 94.38% of the tree count, 82.62% of the area, and 92.46% of the carbon storage. Analysis of tree-ring data revealed significant variation in the onset of growth decline across different density classes of plantations aged 0–30 years, with higher-density stands exhibiting delayed growth decline compared to lower-density stands. Compared to traditional methods based on diameter at breast height (DBH), carbon storage assessments demonstrated superior accuracy and scientific validity. This study underscores the feasibility and potential of ULS technology for AGB and carbon storage estimation in regions with complex terrain, such as the Loess Plateau. It highlights the importance of accounting for topographic factors to enhance estimation accuracy. The findings provide valuable data support for density management and high-quality development of R. pseudoacacia plantations in the Caijiachuan watershed and present an efficient approach for precise forest carbon sink accounting.