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.

Secondary broadleaf forests constitute a vital component of China’s forest resources, characterized by diverse ecological functions, strong regeneration capacity, and widespread distribution. They possess significant potential for carbon storage, yet their carbon sink capacity is influenced by multiple factors, including successional stage, tree species composition, soil conditions, and human disturbance levels. However, the response mechanism of carbon sequestration capacity in secondary broadleaf forest ecosystems to thinning intensity remains unclear. This study aims to elucidate the effects of different thinning intensities (0% (CK), 10% (LT), 25% (MT), and 35% (HT)) on soil greenhouse gas (GHG) emissions, vegetation, and soil organic carbon sinks. Results indicate that total GHG emissions increased by 1.9%, 31.86%, and 42.18% under LT, MT, and HT, respectively. Vegetation carbon sequestration decreased by 5.26% and 16.22% under LT and MT, respectively, while increasing by 13.17% under HT. Soil organic carbon sequestration increased by 37.33% under LT, but decreased by 5.89% and 61.41% under MT and HT, respectively. In summary, compared with the control, ecosystem carbon sequestration increased by 30.66% in LT, while decreasing by 32.06% and 71.73% in MT and HT, respectively. Our study indicates that light thinning intensity can enhance the carbon sequestration potential of ecosystems and effectively mitigate climate change.