Accuracy Assessment of Remote Sensing Forest Height Retrieval for Sustainable Forest Management: A Case Study of Shangri-La

Forest height is a critical parameter for understanding ecosystem functions, assessing carbon stocks, and supporting sustainable forest management. Its accurate measurement is essential for climate change mitigation and understanding the global carbon cycle. While traditional methods like field surveys and airborne LiDAR provide accurate measurements, their high costs and limited spatial coverage make them impractical for the large-scale, dynamic monitoring required for effective sustainability initiatives. This research presents a multi-source remote sensing fusion approach to tackle this problem. For regional forest height inversion, it includes Sentinel-1 SAR, Sentinel-2 multispectral images, ICESat-2 lidar, and SRTM DEM data. Sentinel-1 + ICESat-2 + SRTM, Sentinel-2 + ICESat-2 + SRTM, and Sentinel-1 + Sentinel-2 + ICESat-2 + SRTM were the three data combination methods built using Shangri-La Second-class Category Resource Survey data as ground truth. An accuracy assessment was performed using three machine learning models: Light Gradient Boosting (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Based on the results, the ideal configuration using the LightGBM model and the following sensors: Sentinel-1, Sentinel-2, ICESat-2, and SRTM yields a correlation coefficient of 0.72, an R M S E of 5.52 m, and an M A E of 4.08 m. The XGBoost model obtained r = 0.716, R M S E = 5.55 m, and M A E = 4.10 m using the same data combination as the Random Forest model, which produced r = 0.706, R M S E = 5.63 m, and M A E = 4.16 m. The multi-source comprehensive fusion technique produced the greatest results; however, including either Sentinel-1 or Sentinel-2 enhances model performance, according to comparisons across multiple data combinations. This work presents an efficient technological strategy for monitoring forest height in complex terrains, thereby providing a scalable and robust methodological reference for supporting sustainable forest management and large-scale ecological assessment. The proposed multi-source spatiotemporal fusion framework, coupled with systematic model evaluation, demonstrates significant potential for operational applications, especially in regions with limited LiDAR coverage.