揭示城市野火中建筑密度与植被湿度对房屋损毁的影响
The spatial anatomy of urban wildfire vulnerability: a spatially validated GeoAI framework reveals the roles of building density and vegetation moisture in structure loss during the 2025 Palisades Fire
- 用空间验证的GeoAI框架整合多源数据建模
- 100米内建筑数量每增一标准差，损毁风险升4.12倍
- 植被湿度在100-300米范围有保护作用，绿度则相反
城市野火韧性取决于建筑形态、植被状况与极端火情天气的交互作用，但现有城市尺度风险模型常忽视预测能力在不同社区间的可迁移性。本文针对2025年1月帕利塞德大火，构建了空间验证的GeoAI工作流，将12,081次加州消防局灾后检查结果与灾前哨兵2号植被指数、陆地卫星地表温度、LANDFIRE燃料数据、地形及开放街图建筑与道路信息关联。在9,883栋被检住宅中，5,566栋被毁。随机交叉验证下集成XGBoost模型的ROC-AUC达0.92，但1公里空间块验证后降至0.75；逻辑回归表现相近且校准更优。100米内建筑数量是最强预测因子，每增加一个标准差，损毁几率提升4.12倍。植被湿度（NDMI）在100-300米范围具保护作用（OR 0.52），而绿度（NDVI）在30-100米范围在控制湿度后与损毁正相关（OR 1.74）。预测信息集中于100-300米社区尺度。另通过灾后轨迹分析映射烧毁严重性与植被恢复，无信息泄露。结果支持以社区尺度开展脆弱性筛查、重视湿度的植被管理，并建议空间块验证作为单事件城市野火建模的最低标准。
原文摘要 · Abstract (English)
Urban wildfire resilience depends on interactions among built form, vegetation condition, and extreme fire weather, yet city-scale risk models often overlook whether predictive skill transfers across neighborhoods. We developed a spatially validated GeoAI workflow for the January 2025 Palisades Fire, linking 12,081 CAL FIRE damage inspections to pre-fire Sentinel-2 vegetation indices, Landsat surface temperature, LANDFIRE fuels, terrain, and OpenStreetMap buildings and roads. Among 9,883 inspected residential structures, 5,566 were destroyed. Random cross-validation yielded ROC-AUC 0.92 for the integrated XGBoost model, but 1 km spatial block validation reduced performance to 0.75; logistic regression performed similarly and was better calibrated. Building count within 100 m was the strongest predictor, with destruction odds increasing 4.12-fold per standard deviation. Vegetation moisture and greenness showed opposing conditional associations: NDMI at 100-300 m was protective (OR 0.52), whereas NDVI at 30-100 m was positively associated with destruction after accounting for moisture (OR 1.74). Predictive information was concentrated at the 100-300 m neighborhood scale. A separate post-fire track mapped burn severity and vegetation recovery without leakage. The results support neighborhood-scale susceptibility screening, moisture-aware vegetation management, and spatial block validation as a minimum standard for single-event urban wildfire modeling.
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