The increasing knowledge in the capabilities of satellite imagery to hazard applications is especially useful in emergency situations where timing and ability to cover large areas are of the essence. For optical imagery, cloud coverage can corrupt an image rendering it unusable for intended emergency analyses. This study proposes the use of Synthetic Aperture Radar (SAR) imagery for burn severity analysis for western United States sites, as an alternative to its optical based counterpart, differenced normalized burn ratio (dNBR). Unlike optical sensors, the radar sensor is an active sensor that is able to penetrate clouds and smoke, an attribute that is crucial in emergency situations where immediate burn severity data are needed to assess the vulnerability of fire affected areas to post-fire hazards. Using C5 decision tree algorithm we developed a SAR-based metric that attempts to classify burn severities of fire affected locations in the western USA. We then compared the performance of this developed metric to that obtained by the existing dNBR metric, to determine if there is any merit to its adoption as an alternative for the western USA landscape. The results showed the SAR approach to produce higher validation metrics in comparison to the dNBR. It had an overall accuracy and kappa of 60% and 0.35, respectively, in comparison to the 35% and 0.1 of the dNBR approach. This shows an improved ability to quickly obtain burn severity data and make better informed decisions in emergency situations.

在紧急情况下至关重要的是紧急情况，在这种情况下，增加对卫星图像进行灾害应用的能力的了解尤其有用。对于光学图像，云覆盖会破坏图像，使其无法用于预期的紧急情况分析。这项研究建议使用合成孔径雷达（SAR）图像进行美国西部站点的烧伤严重性分析，作为基于光学的对应物的不同归一化烧伤率（dNBR）的替代方法。与光学传感器不同，雷达传感器是一种有源传感器，能够穿透云层和烟雾，这是紧急情况下的关键属性，在紧急情况下，需要立即烧伤严重性数据来评估火灾影响区域对火灾后危害的脆弱性。使用C5决策树算法，我们开发了一种基于SAR的度量标准，试图对美国西部受火灾影响的地点的烧伤严重程度进行分类。然后，我们将该开发指标的性能与现有dNBR指标获得的性能进行了比较，以确定采用该指标作为美国西部景观的替代方案是否有价值。结果表明，与dNBR相比，SAR方法可产生更高的验证指标。与dNBR方法的35％和0.1相比，它的总体准确度和kappa分别为60％和0.35。这显示了在紧急情况下快速获得烧伤严重性数据和做出更明智的决策的能力。然后，我们将该开发指标的性能与现有dNBR指标获得的性能进行了比较，以确定采用该指标作为美国西部景观的替代方案是否有价值。结果表明，与dNBR相比，SAR方法可产生更高的验证指标。与dNBR方法的35％和0.1相比，它的总体准确度和kappa分别为60％和0.35。这显示了在紧急情况下快速获得烧伤严重性数据和做出更明智的决策的能力。然后，我们将该开发指标的性能与现有dNBR指标获得的性能进行了比较，以确定采用该指标作为美国西部景观的替代方案是否有价值。结果表明，与dNBR相比，SAR方法可产生更高的验证指标。与dNBR方法的35％和0.1相比，它的总体准确度和kappa分别为60％和0.35。这显示了在紧急情况下快速获得烧伤严重性数据和做出更明智的决策的能力。与dNBR方法的35％和0.1相比，它的总体准确度和kappa分别为60％和0.35。这显示了在紧急情况下快速获得烧伤严重性数据和做出更明智的决策的能力。与dNBR方法的35％和0.1相比，它的总体准确度和kappa分别为60％和0.35。这显示了在紧急情况下快速获得烧伤严重性数据和做出更明智的决策的能力。