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首页> 外文期刊>International journal of applied earth observation and geoinformation >Utilizing satellite radar remote sensing for burn severity estimation
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Utilizing satellite radar remote sensing for burn severity estimation

机译：利用卫星雷达遥感烧伤严重性估计

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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.
机译：在卫星图像的能力增加到危险应用的知识越来越多的知识在紧急情况下特别有用，其中覆盖大面积的时序和能力是本质的。对于光学图像，云覆盖可能会破坏图像呈现它无法使用的图像，以便预期的紧急分析。本研究提出了使用合成孔径雷达（SAR）图像用于西部美国站点的烧伤严重性分析，作为其基于光学对应的替代方案，差异标准化烧伤比（DNBR）。与光学传感器不同，雷达传感器是能够穿透云和烟雾的有源传感器，该属性在需要立即烧伤严重性数据的紧急情况下至关重要，以评估火灾影响地区的漏洞到火灾后危险。使用C5决策树算法，我们开发了一种基于SAR的公制，该公制试图在美国西部的火灾受影响位置进行分类。然后，将此发达度量标准的性能与现有的DNBR公制获得的性能进行了比较，以确定是否存在其采用的任何优点作为美国西部景观的替代方案。结果表明，与DNBR相比，SAR方法产生更高的验证度量。与DNBR方法的35％和0.1相比，它分别具有60％和0.35的整体准确性和κ30％和0.35。这表明快速获得烧伤严重性数据的能力提高，并在紧急情况下做出更好的明智决策。

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