Estimation of Forest Stock Volume in Complex Terrain Using Spaceborne Lidar

In forest remote sensing monitoring of complex terrain, spaceborne lidar data has become a key technology for obtaining large-scale forest structure parameters due to its uniquethree-dimensional observation capabilities. However, in complex terrain conditions, there are still many challenges for spaceborne lidar. Particularly in mountainous forest areas with significant topographic relief, overcoming the limitations imposed by complex terrain conditions to achieve high-precision forest stock volume estimation has emerged as one of the most challenging and cutting-edge research areas in vegetation remote sensing. Objective: This study aims to explore the feasibility and methods of forest stock volume estimation using spaceborne lidar data ICESat-2/ATL08 in complex terrain and to compare the effectiveness of three machine learning regression models for this purpose. Method: Based on the ATL08 product from ICESat-2/ATLAS data, a sequential Gaussian conditional simulation was used for spatial interpolation of forest areas in Jingdong Yi Autonomous County, Pu’er City, Yunnan Province. XGBoost, LightGBM, and Random Forest methods were then employed to develop stock volume models, and their estimation capabilities were analyzed and compared. Results: (1) Among the 57 ICESat-2/ATLAS footprint parameters extracted, 13 were retained for interpolation after analysis and screening. (2) Based on sequential Gaussian conditional simulation, three parameters demonstrating lower interpolation accuracy were eliminated, with the remaining ten parameters allocated for inversion model development. (3) In terms of inversion model accuracy, XGBoost outperformed LightGBM and Random Forest, achieving an R2 of 0.89 and an rRMSE of 10.5912. The average forest stock volume derived from the inversion was 141.00 m3/hm2. Conclusions: Overall, large-area forest stock volume estimation through spaceborne Lidar inversion using ICESat-2/ATLAS photon-counting footprints proved feasible for mountainous environments with complex terrain. The XGBoost method demonstrates strong forest stock volume inversion capabilities. This study provides a case study for investigating forest structure parameters in complex mountainous terrain using spaceborne lidar ICESat-2/ATLAS data.