Forest Carbon Storage Dynamics and Influencing Factors in Southeastern Tibet: GEE and Machine Learning Analysis

As an important ecological security barrier on the Tibetan Plateau, southeastern Tibet is crucial to maintaining regional carbon balance under climate change. This study innovatively integrates multi-source remote sensing data (Landsat 8, Sentinel-1, and GEDI) on the Google Earth Engine (GEE) platform, and uses machine learning to model forest carbon storage dynamics from 2019 to 2023. The fusion of multi-source data improves forest vertical structure characterization and makes up for the shortage of single optical data. By comparing machine learning algorithms, the Gradient Boosting model performs excellently (validation set R2 = 0.909, RMSE = 26.608 Mg/Ha), achieving high-resolution spatiotemporal mapping. The results show significant spatial heterogeneity; the increase in carbon storage in the central and southern regions is mainly in contrast to the scattered decreases in the eastern and western regions, reflecting vegetation restoration and topographic influence. High-altitude areas are subject to climate restrictions and small changes, while low-altitude areas show significant fluctuations due to human activities. Key drivers were elevation (importance score 22.06), slope (17.00), and temperature (22.04). Land use transformation (such as forest expansion) promotes net carbon accumulation and highlights the effectiveness of regional protection policies. This study provides a scientific basis for targeted ecological management of high-altitude ecosystems.