Regional Forest Carbon Stock Estimation Based on Multi-Source Data and Machine Learning Algorithms

Abstract

Accurately assessing forest carbon stock (FCS) is essential for analyzing its spatial distribution and gauging the capacity of forests to sequester carbon. This research introduces a novel approach for estimating FCS by integrating multiple data sources, such as Sentinel-1 (S1) radar imagery, optical images from Sentinel-2 (S2) and Landsat 8 (L8), digital elevation modeling (DEM), and inventory data used in forest management and planning (FMP). Additionally, the estimation of FCS incorporates four key ecological features, including forest composition, primary tree species, humus thickness, and slope direction, to improve the accuracy of the estimation. Subsequently, insignificant features were eliminated using Lasso and recursive feature elimination (RFE) feature selection techniques. Three machine learning (ML) models were employed to estimate FCS: XGBoost, random forest (RF), and LightGBM. The results show that the inclusion of ecological information features improves the performance of the models. Among the models, LightGBM achieved superior performance (R2 = 0.78, mean squared error (MSE) = 0.85, root mean squared error (RMSE) = 0.92, mean absolute error (MAE) = 0.58, relative RMSE (rRMSE) = 41.37%, and mean absolute percentage error (MAPE) = 30.72%), outperforming RF (R2 = 0.76, MSE = 0.93, RMSE = 0.97, MAE = 0.60, rRMSE = 43.42%, and MAPE = 30.85%) and XGBoost (R2 = 0.77, MSE = 0.90, RMSE = 0.95, MAE = 0.61, rRMSE = 42.66%, and MAPE = 34.61%).