Integrating stepwise residual refinement and explainable AI for interpretable forest volume modeling in Hokkaido, Japan

This is the authors' abstract. We don't add key points for this paper.

Accurate estimation of forest stem volume is essential for effective forest resource management and carbon accounting. However, spatial heterogeneity in forest conditions often leads to systematic modeling errors, especially across ecological and operational gradients. This study proposes an integrated framework that combines XGBoost-based modeling with a novel Stepwise Residual Refinement (SRR) approach and explainable AI techniques utilizing SHapley Additive exPlanations (SHAP) to enhance both prediction accuracy and model interpretability. The framework was applied to forest inventory and remote sensing data across Hokkaido, Japan, incorporating topographic, climatic, structural, and socioeconomic variables. The initial XGBoost model achieved a root mean square error (RMSE) of 170.21 m 3 /ha and a percentage RMSE (%RMSE) of 36.90%. Following the application of SRR corrections, the final model improved significantly, yielding an RMSE of 105.75 m 3 /ha and a %RMSE of 22.93%. KernelSHAP analysis revealed region-specific patterns of variable influence, highlighting how environmental and human factors differentially shape forest volume across regions. SHAP-derived zoning delineated clusters of forest quality that aligned with workforce presence and ecological conditions, particularly in conifer-dominated areas. These results demonstrate the importance of integrating explainable AI and spatial refinement to uncover nuanced forest dynamics and support adaptive, data-driven forest management. This study highlights how interpretable machine learning can simultaneously improve predictive accuracy and reveal latent socio-ecological processes that drive forest conditions. • Proposed SSR framework significantly improves forest volume prediction accuracy (R 2 from 0.47 to 0.80) • Spatially explicit SHAP analysis reveals key variable influences on volume • Novel SHAP-based zoning delineates forest quality zones aligned with ecology • SHAP-derived zones reflect structural heterogeneity & management intensity