从野火严重程度到积雪持续：2020年克里克大火的多源地理空间人工智能研究
From Wildfire Severity to Snow Persistence: A Multisource GeoAI Study of the 2020 Creek Fire
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中文总结 AI 辅助
本研究利用可解释地理空间人工智能框架，结合多源遥感和匹配对比，分析2020年克里克大火对积雪持续的影响，发现高严重度区域积雪增加2.6个百分点，并区分了火灾响应与地形气候主导的预测关系。
中文摘要 AI 辅助
准确预测火灾后积雪状况并不一定能确定野火如何改变了这些状况。我们开发了一个可解释的地理空间人工智能框架，结合多源地球观测、气象信息以及匹配的前后对比，来研究2020年加利福尼亚州内华达山脉克里克大火后的季节性积雪持续情况。我们使用协调的Landsat Sentinel-2观测数据，在500米分析网格上估算了2016至2026年共11个水文年10月至7月期间晴空观测中包含积雪的比例。我们将火场边界内的3,778个积雪区单元与使用地形和火灾前积雪条件匹配的未燃烧对照单元进行了配对；匹配后绝对标准化平均差异不超过0.047。HLS积雪持续数据与MODIS高度一致，年平均空间相关性为0.947。景观平均的前后对照影响对比为+0.0017，年份级95%置信区间为-0.023至+0.027。在最高燃烧严重度等级中出现了更强的响应，观测到的积雪持续相对于匹配对照增加了0.026，相当于2.6个百分点。在5公里空间交叉验证下，XGBoost预测原始火灾后积雪持续的折外决定系数为0.811，而对火灾调整异常值的预测技能达到0.046。海拔和温度共同占平均绝对模型归因的65.7%。这些发现将严重度相关的光学积雪响应与主导预测技能的地形-气候关系区分开来。将匹配比较与可解释的地理空间人工智能相结合，为森林监测提供了一个实用框架，能够将准确的环境制图与干扰效应的推断分开。
英文摘要
Accurate prediction of post-fire snow conditions does not necessarily establish how wildfire changed those conditions. We developed an explainable geospatial artificial intelligence framework combining multisource Earth observations, meteorological information, and matched before-after comparisons to examine seasonal snow persistence following the 2020 Creek Fire in California's Sierra Nevada. Harmonized Landsat Sentinel-2 observations were used to estimate the fraction of clear-sky observations containing snow during October-July for eleven water years, 2016-2026, on a 500 m analysis grid. We matched 3,778 snow-zone cells inside the fire perimeter to comparable unburned controls using terrain and pre-fire snow conditions; absolute standardized mean differences after matching were no greater than 0.047. HLS persistence agreed closely with MODIS, with a mean annual spatial correlation of 0.947. The landscape-average before-after control-impact contrast was +0.0017, with a year-level 95% confidence interval of -0.023 to +0.027. A stronger response emerged in the highest burn-severity class, where observed persistence increased by 0.026 relative to matched controls, equivalent to 2.6 percentage points. Under 5 km spatial cross-validation, XGBoost predicted raw post-fire persistence with an out-of-fold coefficient of determination of 0.811, whereas predictive skill for the fire-adjusted anomaly reached 0.046. Elevation and temperature together accounted for 65.7% of mean absolute model attribution. These findings distinguish a severity-associated optical snow response from the terrain-climate relationships that dominate predictive skill. Combining matched comparisons with explainable GeoAI provides a practical framework for forest monitoring that separates accurate environmental mapping from inference about disturbance effects.
发表机构
- University of California, Davis（加州大学戴维斯分校）
机构由 AI 辅助整理，请以论文原文为准。
