Improving Forest Above-Ground Biomass Estimation Using UAV LiDAR and RGB with Machine Learning Algorithm

Abstract

Accurate estimation of individual tree Above-Ground Biomass (AGB) is essential for assessing forest carbon sequestration. This study integrates multi-source Unmanned Aerial Vehicle (UAV) remote sensing (LiDAR and RGB) with machine learning to estimate the AGB of Larix principis-rupprechtii in a natural secondary forest. We applied an instance segmentation approach to identify individual trees and extract structural and spectral features, which were subsequently optimized before model training. Our results demonstrate that models utilizing combined multi-source features significantly outperformed those relying on a single data source. The Extreme Gradient Boosting (XGBoost) algorithm achieved the best performance, with an R2 of 0.770 using the combined feature set. SHapley Additive exPlanations (SHAP) interpretation revealed that structural attributes—particularly tree height and crown volume—were the most influential predictors, underscoring their greater importance over spectral information. This study presents an effective and interpretable framework for accurate tree-level AGB estimation, supporting scalable monitoring of regional forest carbon dynamics.