A Comparative Analysis of Remote Sensing Estimation of Aboveground Biomass in Boreal Forests Using Machine Learning Modeling and Environmental Data

Abstract

It is crucial to have precise and current maps of aboveground biomass (AGB) in boreal forests to accurately track global carbon levels and develop effective plans for addressing climate change. Remote sensing as a cost-effective tool offers the potential to update AGB maps for boreal forests in real time. This study evaluates different machine learning algorithms, namely Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR), for predicting AGB in boreal forests. Conducted in the Qilian Mountains, northwest China, the study integrated field measurements, space-borne LiDAR, optical remote sensing, and environmental data to develop a training dataset. Among 34 variables, 22 were selected for AGB estimation modeling. Our findings revealed that the LightGBM AGB model had the highest level of accuracy (R2 = 0.84, RMSE = 15.32 Mg/ha), outperforming the XGBoost, RF, and SVR AGB models. Notably, the LightGBM AGB model effectively addressed issues of underestimation and overestimation. We also observed that the disparity in accuracy among the models widens with increasing altitude. Remarkably, the LightGBM AGB model consistently demonstrates optimal performance across all elevation gradients, with residuals generally below 25 Mg/ha for low-value overestimation and below −38 Mg/ha for high-value underestimation. The model developed in this study presents a viable and alternative approach for enhancing AGB estimation accuracy in boreal forests based on remote sensing technology.