December 5, 2025Open Access

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

Key Points

Above-Ground Biomass estimation improved when combining multiple features from UAVs and machine learning algorithms, enhancing carbon sequestration assessments.
An R2 value of 0.770 was obtained using the Extreme Gradient Boosting algorithm for biomass predictions.
The instance segmentation technique was applied to identify individual trees and extract relevant features effectively.
Findings underscore the critical role of tree height and structural attributes in biomass estimations, aiding forest carbon monitoring.

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.

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