Estimation of forest above-ground biomass based on stacked ensemble model in Chongqing, China.

ABSTRACT: Accurate regional-scale estimation of forest aboveground biomass (AGB) is critical for effective forest management and terrestrial carbon cycle research. However, applications integrating multiple machine learning models (MLs) for forest AGB estimation in mountainous forests remain limited. In this study, we introduced a practical method to estimate diameter at breast height (DBH < 5 cm) for under-threshold trees using National Forest Inventory (NFI) data. By combining Sentinel-2 remote sensing imagery and DEM data, we employed individual MLs (RF, XgBost, CatBoost and SVM) and a stacking approach to estimate forest AGB in Chongqing under two scenarios: with and without under-threshold trees. The DBH estimation method achieved high accuracy (R²=0.93, RMSE=1.46 cm). Feature importance analys

SUBMITTER: Liu J 

PROVIDER: S-EPMC12634669 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature