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

IF 4.5 Q2 ENVIRONMENTAL SCIENCES
Kotaro Iizuka , Nobuo Ishiyama , Yasutaka Nakata
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引用次数: 0

Abstract

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 m3/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 m3/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.
基于残差逐步细化和可解释人工智能的日本北海道森林可解释体积建模
准确估算森林茎体积对有效的森林资源管理和碳核算至关重要。然而,森林条件的空间异质性往往导致系统建模误差,特别是在生态和操作梯度上。本研究提出了一个集成框架,将基于xgboost的建模与新颖的逐步残差细化(SRR)方法和利用SHapley加性解释(SHAP)的可解释人工智能技术相结合,以提高预测精度和模型可解释性。将该框架应用于日本北海道的森林清查和遥感数据,其中包含地形、气候、结构和社会经济变量。初始XGBoost模型的均方根误差(RMSE)为170.21 m3/ha,百分比RMSE (%RMSE)为36.90%。应用SRR校正后,最终模型得到显著改善,RMSE为105.75 m3/ha, %RMSE为22.93%。KernelSHAP分析揭示了区域特定的可变影响模式,突出了环境和人为因素如何在不同区域差异地影响森林体积。shap衍生的分区划定了与劳动力存在和生态条件相一致的森林质量集群,特别是在针叶树为主的地区。这些结果表明,整合可解释的人工智能和空间细化对于揭示细微的森林动态和支持自适应、数据驱动的森林管理的重要性。这项研究强调了可解释的机器学习如何同时提高预测准确性并揭示驱动森林条件的潜在社会生态过程。
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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