首次量化野火蔓延的时空不确定性，助力气候适应型应急规划。
Spatial Uncertainty Quantification in Wildfire Forecasting for Climate-Resilient Emergency Planning
- 基于多源遥感数据，构建野火蔓延的不确定性空间分析方法。
- 发现不确定性集中在火线周边20-60米缓冲区，具明显空间规律。
- 揭示植被健康与火活动是主要不确定因素，适合应急决策者使用。
气候变化正加剧全球野火风险，可靠预测对适应策略至关重要。尽管机器学习在利用地球观测数据进行野火预测方面展现出潜力，但当前方法缺乏对不确定性进行量化，难以支持风险敏感型决策。本文首次系统分析了基于多模态地球观测输入的野火蔓延预测中的空间不确定性。结果表明，预测不确定性在火线附近呈现连贯的空间结构。我们提出的新型距离度量显示，高不确定性区域形成围绕预测火线的稳定20-60米缓冲带，可直接用于应急规划。特征归因分析表明，植被健康状况和火活动是主要不确定性驱动因素。本研究为构建更鲁棒的野火管理系统提供了支持，有助于社区应对气候变化带来的日益增长的火灾风险。
原文摘要 · Abstract (English)
Climate change is intensifying wildfire risks globally, making reliable forecasting critical for adaptation strategies. While machine learning shows promise for wildfire prediction from Earth observation data, current approaches lack uncertainty quantification essential for risk-aware decision making. We present the first systematic analysis of spatial uncertainty in wildfire spread forecasting using multimodal Earth observation inputs. We demonstrate that predictive uncertainty exhibits coherent spatial structure concentrated near fire perimeters. Our novel distance metric reveals high-uncertainty regions form consistent 20-60 meter buffer zones around predicted firelines - directly applicable for emergency planning. Feature attribution identifies vegetation health and fire activity as primary uncertainty drivers. This work enables more robust wildfire management systems supporting communities adapting to increasing fire risk under climate change.
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