The safe operation of power transmission lines is critical for ensuring the stability of the power supply, especially given the increasing frequency of extreme weather events and the risks posed by tree growth. This study proposes a novel method for detecting and predicting the tree barrier risks on transmission lines using Unmanned Aerial Vehicle–Light Detection and Ranging (UAV-LiDAR) technology. The method employs point cloud classification to effectively separate ground, conductor, tower, and vegetation points, followed by 3D reconstruction of the power lines using the catenary equation. Tree growth models are integrated with measured data to predict future tree barrier risks. The experimental results demonstrate that the point-cloud-based method detects 31 tree barriers, with an RMSE of 0.08 m, while the 3D-reconstruction-based method detects 32 tree barriers, with an RMSE of 0.04 m, indicating its higher accuracy. The Cloth Simulation Filter (CSF) ground point classification method achieved the lowest roughness (1.5%), mean error (0.147 m), and RMSE (0.174 m), proving its effectiveness for flat terrain. Additionally, the assisted seed point individual tree segmentation method extracted tree height with high accuracy (R² = 0.84, RMSE = 1.01 m). This study predicts an average tree growth rate of 0.248 m/year over the next five years, identifying a new tree barrier at the coordinates 30°15′16.64″ N, 119°43′16.01″ E. This method enhances the efficiency and accuracy of transmission line inspections, supporting both power line safety and sustainable forest management. Its findings provide a robust technical approach to improving power line operations and forest resource utilization.

Subtropical forest ecosystems, especially evergreen broad-leaved forests in the East Asian monsoon region, are a crucial component of the global terrestrial carbon cycle and make a key contribution to the “missing carbon sequestration” in the Northern Hemisphere. This review systematically integrates recent research progress on the carbon pool patterns, aboveground-subsurface biogeochemical processes, and global change responses of subtropical forests, summarizing the potential mechanisms of their sustainable carbon sequestration capacity and identifying current cognitive bottlenecks. Studies have shown that subtropical mature forests have carbon sequestration potential that exceeds traditional theoretical expectations, but there are still significant shortcomings in terms of carbon stability in deep soil (>1 m), quantitative constraints on rhizosphere activating effects, and assessment of ecosystem resilience under extreme climate events. Furthermore, the nonlinear interactions between factors such as climate warming, precipitation changes, and nitrogen deposition may trigger a critical turning point in carbon sink functions, and the water-carbon-geological coupling processes in special habitats such as karst and mangrove forests are often underestimated. We further propose that future research should focus on developing coupled models of “plant–soil–microbe hydrology”, combining molecular and isotopic techniques to elucidate microbial carbon pump mechanisms and strengthening long-term in situ experiments under combined extreme events to provide scientific support for subtropical forest carbon sink management and prediction.