In the case of ongoing wildfire events, timely information on current fire parameters is crucial for informed decision making. Satellite imagery can provide valuable information in this regard, since thermal sensors can detect the exact location and intensity of an active fire at the moment the satellite passes over. This information can be derived and distributed in near-real time, allowing for a picture of current fire activity. However, the derivation of the size and shape of an already affected area is more complex and therefore most often not available within a short time frame. For urgent decision making though, it would be desirable to have this information available in near-real time, and on a large scale. The approach presented here works fully automatic and provides perimeters of burnt areas within two hours after the satellite scene acquisition. It uses the red and near-infrared bands of mid-resolution imagery to facilitate continental-scale monitoring of recently occurred burnt areas. To allow for a high detection capacity independent of the affected vegetation type, segmentation thresholds are derived dynamically from contextual information. This is done by using a Morphological Active Contour approach for perimeter determination. The results are validated against semi-automatically derived burnt areas for five wildfire incidents in Europe. Furthermore, these results are compared with three widely used burnt area datasets on a country-wide scale. It is shown that a high detection quality can be reached in near real-time. The large-scale inter-comparison shows that the results coincide with 63% to 76% of the burnt area in the reference datasets. While these established datasets are only available with a time lag of several months or are created by using manual interaction, the presented approach produces results in near-real time fully automatically. This work is therefore supposed to represent a valuable improvement in wildfire related rapid damage assessment.

中文翻译：

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自适应和可扩展的基于卫星的大规模燃烧区域近实时监测系统

在持续的野火事件中，及时了解当前火情参数对于做出明智的决策至关重要。在这方面，卫星图像可以提供有价值的信息，因为热传感器可以在卫星经过时检测到主动火的确切位置和强度。该信息可以近实时地导出和分发，从而可以了解当前的火灾情况。但是，已经受影响区域的大小和形状的推导更加复杂，因此通常在短时间内无法使用。但是，对于紧急决策，希望能以近实时，大规模的方式获得此信息。此处介绍的方法是全自动工作的，并在获取卫星场景后两个小时内提供烧伤区域的边界。它使用中分辨率图像的红色和近红外波段来促进对最近发生的烧伤区域进行大陆规模的监视。为了允许独立于受影响的植被类型的高检测能力，从阈值信息中动态得出分割阈值。这可以通过使用形态学主动轮廓法进行周长确定来完成。该结果针对欧洲五起野火事件的半自动燃烧区进行了验证。此外，将这些结果与全国范围内三个广泛使用的烧毁面积数据集进行了比较。结果表明，可以近实时地获得高检测质量。大规模的相互比较显示，结果与参考数据集中的燃烧面积的63％至76％相符。虽然这些建立的数据集仅在数月的时间间隔内可用，或者是通过手动交互创建的，但本文介绍的方法可以完全实时地近实时生成结果。因此，这项工作被认为是与野火有关的快速损害评估中的一项宝贵改进。