Estimating aboveground biomass using environmental covariates and a machine-learning approach in the Lower Brazos River Basin, Texas, USA

Forest ecosystems play a pivotal role in global carbon sequestration, serving as essential carbon sinks for climate change mitigation, while also providing a range of ecosystem services such as seed dispersal, pollination, pest control, and habitat provisioning. This study aimed to estimate aboveground biomass density (AGBD) using environmental covariates and a machine learning approach from the Global Ecosystem Dynamics Investigation Light Detection And Ranging (GEDI-LiDAR) in the Lower Brazos River Watershed, Texas, USA. Specifically, GEDI Level 4A data from the National Aeronautics and Space Administration (NASA) Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) was integrated with Landsat-9 Operational Land Imagery (OLI) and Shuttle Radar Topographic Mission (SRTM) data to enhance predictive accuracy for AGBD. Spectral indices, including the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were derived from Landsat 9 to support AGBD prediction. Three machine learning models, such as Multivariate Adaptive Regression Splines (MARS), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), were deployed, with performance assessed using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). Among the models, XGBoost achieved the highest predictive accuracy (R² = 0.43, RMSE = 31.03, MAE = 22.49). The modelling indicated that longitude, latitude, moisture stress indices (MSI), and digital elevation model (DEM) are among the critical predictors for AGBD. The mean AGBD across the watershed was estimated at 72.3 Mg ha^-1, corresponding to a total biomass of approximately 66.6 million tons. Evergreen forests showed the highest AGBD values at 110 Mg ha^-1, while cultivated lands averaged 33 Mg ha^-1. These findings highlight the effectiveness of integrating environmental covariates with machine learning to estimate AGBD from GEDI LiDAR across diverse ecosystems. This approach provides a robust tool for advancing carbon management and climate change mitigation efforts, while also supporting data-driven conservation planning in both forested and agricultural landscapes.