Wildfires are increasing in risk and prevalence. The most destructive wildfires in decades in Australia occurred in 2019–2020. However, there is still a challenge in developing effective models to understand the likelihood of wildfire spread (susceptibility) and pre‐fire vegetation conditions. The recent launch of NASA's ECOSTRESS presents an opportunity to monitor fire dynamics with a high resolution of 70 m by measuring ecosystem stress and drought conditions preceding wildfires. We incorporated ECOSTRESS data, vegetation indices, rainfall, and topographic data as independent variables and fire events as dependent variables into machine learning algorithms applied to the historic Australian wildfires of 2019–2020. With these data, we predicted over 90% of all wildfire occurrences 1 week ahead of these wildfire events. Our models identified vegetation conditions with a 3‐week time lag before wildfire events in the fourth week and predicted the probability of wildfire occurrences in the subsequent week (fifth week). ECOSTRESS water use efficiency (WUE) consistently emerged as the leading factor in all models predicting wildfires. Results suggest that the pre‐fire vegetation was affected by wildfires in areas with WUE above 2 g C kg−1 H₂O at 95% probability level. Additionally, the ECOSTRESS evaporative stress index and topographic slope were identified as significant contributors in predicting wildfire susceptibility. These results indicate a significant potential for ECOSTRESS data to predict and analyze wildfires and emphasize the crucial role of drought conditions in wildfire events, as evident from ECOSTRESS data. Our approaches developed in this study and outcome can help policymakers, fire managers, and city planners assess, manage, prepare, and mitigate wildfires in the future.

中文翻译：

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使用 ECOSTRESS 数据和机器学习方法检查野火动态：澳大利亚东南部黑色夏季的案例


野火的风险和患病率都在增加。澳大利亚几十年来最具破坏性的野火发生在 2019 年至 2020 年。然而，开发有效的模型来了解野火蔓延的可能性（易感性）和火灾前的植被状况仍然存在挑战。美国宇航局最近推出的 ECOSTRESS 提供了一个机会，可以通过测量野火之前的生态系统压力和干旱条件来监测 70 m 的高分辨率火灾动态。我们将 ECOSTRESS 数据、植被指数、降雨量和地形数据作为自变量，将火灾事件作为因变量纳入应用于 2019-2020 年澳大利亚历史性野火的机器学习算法中。凭借这些数据，我们在这些野火事件发生前 1 周预测了超过 90% 的野火事件。我们的模型确定了在第四周野火事件发生前有 3 周时间滞后的植被条件，并预测了下周（第五周）野火发生的概率。ECOSTRESS 水分利用效率 （WUE） 一直是预测野火的所有模型的主要因素。结果表明，在 WUE 高于 2 g C kg-1 H₂O 的地区，火前植被受到野火的影响，概率为 95%。此外，ECOSTRESS 蒸发应力指数和地形坡度被确定为预测野火易感性的重要贡献者。这些结果表明，ECOSTRESS 数据在预测和分析野火方面具有巨大潜力，并强调了干旱条件在野火事件中的关键作用，这从 ECOSTRESS 数据中可以明显看出。 我们在本研究和结果中开发的方法可以帮助政策制定者、消防管理人员和城市规划者评估、管理、准备和减轻未来的野火。