A Computationally Efficient Method for Updating Fuel Inputs for Wildfire Behavior Models Using Sentinel Imagery and Random Forest Classification
A Computationally Efficient Method for Updating Fuel Inputs for Wildfire Behavior Models Using Sentinel Imagery and Random Forest Classification
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DOI：
10.3390/rs14061447
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发表时间：
2022-03-01
期刊：
影响因子：
5
通讯作者：
Balch, Jennifer K.
中科院分区：
文献类型：
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作者：
DeCastro, Amy L.;Juliano, Timothy W.;Balch, Jennifer K.
干扰事件发生的时间范围可能比荒地火灾燃料数据更新快得多。当用作荒地火灾行为模型的输入时，过时的燃料数据集可能会导致误导性的预测，这对消防、缓解和荒地火灾研究产生影响。遥感和机器学习方法可以为按需燃料估算提供解决方案。在这里，我们展示了一个概念验证，使用 C 波段合成孔径雷达和多光谱图像、土地覆盖类别和树木死亡率调查来训练随机森林分类器来估计东麻烦火灾（科罗拉多州）域的荒地火灾燃料数据。该算法对超过 80% 的测试数据集进行了正确分类，生成的荒地火灾燃料数据用于使用大气-荒地耦合火灾行为模型 WRF-Fire 来模拟东部麻烦火灾。使用修改后的燃料输入进行模拟，其中 43% 的原始燃料被代表死树的燃料取代，将燃烧面积预测提高了 38%。这项研究表明需要实时提供最新的燃料地图来准确预测荒地火灾蔓延，并概述了基于高分辨率卫星观测和机器学习的方法，可以完成这项任务。
Disturbance events can happen at a temporal scale much faster than wildland fire fuel data updates. When used as input for wildland fire behavior models, outdated fuel datasets can contribute to misleading forecasts, which have implications for operational firefighting, mitigation, and wildland fire research. Remote sensing and machine learning methods can provide a solution for on-demand fuel estimation. Here, we show a proof of concept using C-band synthetic aperture radar and multispectral imagery, land cover classes, and tree mortality surveys to train a random forest classifier to estimate wildland fire fuel data in the East Troublesome Fire (Colorado) domain. The algorithm classified over 80% of the test dataset correctly, and the resulting wildland fire fuel data was used to simulate the East Troublesome Fire using the coupled atmosphere-wildland fire behavior model, WRF-Fire. The simulation using the modified fuel inputs, where 43% of original fuels are replaced with fuels representing dead trees, improved the burn area forecast by 38%. This study demonstrates the need for up-to-date fuel maps available in real time to provide accurate prediction of wildland fire spread, and outlines the methodology based on high-resolution satellite observations and machine learning that can accomplish this task.