Modeling wildland fire burn severity in California using a spatial Super Learner approach
Modeling wildland fire burn severity in California using a spatial Super Learner approach
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DOI：
10.1007/s10651-024-00601-1
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发表时间：
2024-03-22
影响因子：
3.8
通讯作者：
Pascolini-Campbell，Madeleine A.
中科院分区：
文献类型：
--
作者：
Simafranca，Nicholas;Willoughby，Bryant;Pascolini-Campbell，Madeleine A.
鉴于美国西部的野地火灾日益猖獗，迫切需要开发工具来了解和准确预测烧伤的严重程度。我们开发了一种新的机器学习模型来利用火灾前的遥感数据来预测火灾后的烧伤严重程度。从加州四个地区收集的水文、生态和地形变量--金卡德火灾(2019年)、CZU闪电综合体火灾(2020)、大风火灾(2021)和KNP火灾(2021)--被用作不同的归一化燃烧比的预测因子。我们假设，使用Vecchia的高斯近似来解释空间自相关的超级学习器(SL)算法将准确地模拟烧伤的严重程度。我们使用交叉验证研究表明，空间SL模型能够以合理的分类精度预测烧伤严重程度，包括高烧伤严重程度事件。在拟合和验证SL模型的性能后，我们使用可解释的机器学习工具来确定严重烧伤损害的主要驱动因素，包括绿度、海拔和火灾天气变量。这些调查结果提供了可操作的见解，使社区能够制定干预措施的战略，例如早期火灾探测系统、火季前植被清除活动以及应急期间的资源分配。实施后，这一模式有可能最大限度地减少加州人的生命、财产、资源和生态系统的损失。
Given the increasing prevalence of wildland fires in the Western US, there is a critical need to develop tools to understand and accurately predict burn severity. We develop a novel machine learning model to predict post-fire burn severity using pre-fire remotely sensed data. Hydrological, ecological, and topographical variables collected from four regions of California — the site of the Kincade fire (2019), the CZU Lightning Complex fire (2020), the Windy fire (2021), and the KNP Fire (2021) — are used as predictors of the differenced normalized burn ratio. We hypothesize that a Super Learner (SL) algorithm that accounts for spatial autocorrelation using Vecchia’s Gaussian approximation will accurately model burn severity. We use a cross-validation study to show that the spatial SL model can predict burn severity with reasonable classification accuracy, including high burn severity events. After fitting and verifying the performance of the SL model, we use interpretable machine learning tools to determine the main drivers of severe burn damage, including greenness, elevation, and fire weather variables. These findings provide actionable insights that enable communities to strategize interventions, such as early fire detection systems, pre-fire season vegetation clearing activities, and resource allocation during emergency responses. When implemented, this model has the potential to minimize the loss of human life, property, resources, and ecosystems in California.