Forest density is a key parameter in forestry research, and its variation can significantly impact ecosystems. Saihanba, as a focal site for afforestation and restoration, offers an ideal case for monitoring these dynamics. In this study, we compared three machine learning algorithms—Random Forest, Support Vector Regression, and XGBoost—using Landsat surface reflectance data together with the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI), and reference tree densities derived from LiDAR individual tree segmentation. The best-performing algorithm, XGBoost (R2 = 0.65, RMSE = 174 trees ha−1), was then applied to generate a long-term forest density dataset for Saihanba at five-year intervals, covering the period from 1988 to 2023. Results revealed distinct differences among tree species, with larch achieving the highest accuracy (R2 = 0.65, RMSE = 161 trees ha−1), whereas spruce had larger prediction errors (RMSE = 201 trees ha−1) despite a relatively high R2 (0.70). Incorporating 30 m slope data revealed that moderate slopes (5–30°) favored faster forest recovery. From 1988 to 2023, average forest density rose from 521 to 628 trees ha−1—a 20.6% increase—demonstrating the effectiveness of restoration and providing a transferable framework for large-scale ecological monitoring.
Wang et al. (2025) studied this question.