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.

Against the backdrop of climate change, forest fires increasingly undermine ecosystem stability and reshape species distributions in Heilongjiang Province. Therefore, quantifying the drivers of fire occurrence and conducting long-term fire risk forecasting holds critical value for regional ecological security. Centered on the forested regions of Heilongjiang Province, this study systematically assessed the relative contributions of multi-source factors—including topography, vegetation, and meteorological conditions—to fire occurrence and compared the predictive performance of three models: Deep Neural Network with Residual Connections (ResDNN), Artificial Neural Network (ANN), and Support Vector Machine (SVM). Modeling results based on historical fire records indicated that the ResDNN model achieved the highest accuracy (85.6%). Owing to its robust nonlinear mapping capability, it performed better in capturing complex feature interactions than ANN and SVM. These results demonstrate its strong applicability to forest fire prediction in Heilongjiang Province. Building on these findings, the study employed the best-performing ResDNN model in conjunction with CMIP6 multi-model climate projections to simulate and map the spatiotemporal probability of forest fire occurrence from 2030 to 2070. The results provide an intuitive representation of long-term fire-risk trajectories under future climate scenarios and offer scientific support for regional fire prevention, monitoring, early-warning systems, and forest management under climate change.