Over the past half-century, environmental degradation and human disturbances have threatened the aboveground biomass carbon (AGC) in China’s karst ecosystems. However, recent ecological programs have led to environmental improvements, leaving it unclear whether China’s karst ecosystems act as an AGC sink or AGC source. In this study, we utilized L-band vegetation optical depth to quantify the dynamics of AGC across the karst regions of China from 2015 to 2021. We observed an increase in AGC density of 0.73 Mg C ha⁻¹ yr⁻¹, suggesting that karst ecosystems in China functioned as an AGC sink throughout the research period. The largest increase in AGC density, 1.29 Mg C ha⁻¹ yr⁻¹, was observed in Central China, indicating an AGC sink capacity stronger than that of other regions. Among the different land-use types, forests played a dominant role, exhibiting the largest net change in AGC density at 1.03 Mg C ha⁻¹ yr⁻¹. Furthermore, using the random forest model, temperature, soil clay content, and altitude were identified as the primary factors driving AGC changes. Our results enhance the understanding of the role of China’s karst terrestrial ecosystem in the global carbon cycle, emphasizing its contribution to the global carbon sink.

Accurately quantifying fine-scale forest canopy-absorbed photosynthetically active radiation (APAR) is essential for monitoring forest growth and understanding ecological processes. The development of 3D radiative transfer models (3D RTMs) enables the precise simulation of canopy–light interactions, facilitating better quantification of forest canopy radiation dynamics. However, the complex parameters of 3D RTMs, particularly detailed 3D scene structures, pose challenges to the simulation of radiative information. While high-resolution LiDAR offers precise 3D structural data, the effectiveness of different tree crown reconstruction methods for APAR quantification using airborne laser scanning (ALS) data has not been fully investigated. In this study, we employed three ALS-based tree crown reconstruction methods: alphashape, ellipsoid, and voxel-based combined with the 3D RTM LESS to assess their effectiveness in simulating and quantifying 3D APAR distribution. Specifically, we used two distinct 3D forest scenes from the RAMI-V dataset to simulate ALS data, reconstruct virtual forest scenes, and compare their simulated 3D APAR distributions with the benchmark reference scenes using the 3D RTM LESS. Furthermore, we simulated branchless scenes to evaluate the impact of branches on APAR distribution across different reconstruction methods. Our findings indicate that the alphashape-based tree crown reconstruction method depicts 3D APAR distributions that closely align with those of the benchmark scenes. Specifically, in scenarios with sparse (HET09) and dense (HET51) canopy distributions, the APAR values from scenes reconstructed using this method exhibit the smallest discrepancies when compared to the benchmark scenes. For HET09, the branched scenario yields RMSE, MAE, and MAPE values of 33.58 kW, 33.18 kW, and 40.19%, respectively, while for HET51, these metrics are 12.74 kW, 12.97 kW, and 10.27%. In the branchless scenario, HET09′s metrics are 10.65 kW, 10.22 kW, and 9.79%, and for HET51, they are 2.99 kW, 2.65 kW, and 2.11%. However, differences remain between the branched and branchless scenarios, with the extent of these differences being dependent on the canopy structure. Our conclusion demonstrated that among the three tree crown reconstruction methods tested, the alphashape-based method has the potential for simulating and quantifying fine-scale APAR at a regional scale. It provides a convenient technical support for obtaining fine-scale 3D APAR distributions in complex forest environments at a regional scale. However, the impact of branches in quantifying APAR using ALS-reconstructed scenes also needs to be further considered.