Forest Disturbance Monitoring and Terrain Control Effects Based on LandTrendr Algorithm

By integrating the Google Earth Engine (GEE) platform with the LandTrendr time-series segmentation algorithm, this study developed a multi-dimensional median synthesis method to enhance time-series continuity. A three-tiered disturbance type classification system based on disturbance duration, recovery rate, and trajectory morphology was constructed, and the driving mechanisms of terrain factors were quantitatively analyzed. Key results were: High temporal accuracy of disturbance year detection (R² = 0.95, MAE = 1.15 years) and satisfactory spatial consistency of extracted disturbance patches (Mean Spatial Consistency, MSC = 85.7%). Cumulative disturbance area from 1993 to 2024 reached (872.7 ± 15.2) km², peaking historically at 228.7 km² in 2023. The evolution exhibited three distinct phases: fluctuating decline (1993–2010), oscillating rise (2011–2020), and extreme disturbance (2021–2024). (3) Spatially, disturbances were significantly concentrated in the mid-altitude zone (1600–1800 m; 58.6%) and gentle slopes (≤10°; 69.2%). (4) Anthropogenic disturbance dominated (63.1%), followed by fire disturbance (27.0%, primarily human-ignited) and drought stress disturbance (9.9%), with terrain accessibility identified as the key regulatory factor. This study establishes a forest disturbance monitoring framework suitable for complex karst terrain, revealing a four-dimensional pattern encompassing temporal dynamics, spatial distribution, type composition, and terrain regulation mechanisms. It provides a technical foundation for precise identification of regional forest disturbance risks, dynamic monitoring, and adaptive management.