Estimating Regional Forest Carbon Density Using Remote Sensing and Geographically Weighted Random Forest Models: A Case Study of Mid- to High-Latitude Forests in China

In the realm of global climate change and environmental protection, the precise estimation of forest ecosystem carbon density is essential for devising effective carbon management and emission reduction strategies. This study employed forest inventory, soil carbon, and remote sensing data combined with three models—Random Forest (RF), Geographically Weighted Regression (GWR), and the innovative Geographically Weighted Random Forest (GWRF) model—integrated with remote sensing technology to develop a framework for assessing the regional spatial distribution of the forest vegetation carbon density (FVC) and forest soil carbon density (FSC). The findings revealed that the GWRF model outperformed the other models in estimating both the FVC and FSC. The data indicated that the FVC in Heilongjiang Province ranged from 4.91 t/ha to 72.39 t/ha, with an average of 40.88 t/ha. In contrast, the average FSC was 182.29 t/ha, with a range of 96.01 t/ha to 255.09 t/ha. Additionally, the forest ecosystem carbon density (FEC) varied from 124.36 t/ha to 302.18 t/ha, averaging 223.17 t/ha. Spatially, the FVC, FSC, and FEC exhibited a consistent growth trend from north to south. The results of this study demonstrate that machine learning models that consider spatial relationships can improve predictive accuracy, providing valuable insights for the future spatial modeling of forest carbon storage.