The frequency of extreme storm events has significantly increased in the past decades, causing significant damage to European forests. To mitigate the impacts of extreme events, a rapid assessment of forest damage is crucial, and satellite data are an optimal candidate for this task. The integration of satellite data in the operational phase of monitoring forest damage can exploit the complementarity of optical and Synthetic Aperture Radar (SAR) open datasets from the Copernicus programme. This study illustrates the testing of Sentinel 1 and Sentinel 2 data for the detection of areas impacted by the Vaia storm in Northern Italy. The use of multispectral Sentinel 2 provided the best performance, with classification overall accuracy (OA) values up to 86 percent; however, optical data use is seriously hampered by cloud cover that can persist for months after the event and in most cases cannot be considered an appropriate tool if a fast response is required. The results obtained using SAR Sentinel 1 were slightly less accurate (OA up to 68 percent), but the method was able to provide valuable information rapidly, mainly because the acquisition of this dataset is weather independent. Overall, for a fast assessment Sentinel 1 is the better of the two methods where multispectral and ground data are able to further refine the initial SAR-based assessment.

中文翻译：

---

卫星开放数据来监测极端气候引起的事件对森林的破坏：以意大利北部的Vaia风暴为例

在过去的几十年中，极端风暴事件的发生频率显着增加，对欧洲森林造成了严重破坏。为了减轻极端事件的影响，对森林破坏的快速评估至关重要，而卫星数据是完成此任务的最佳人选。在监测森林破坏的操作阶段将卫星数据整合可以利用哥白尼计划中光学和合成孔径雷达（SAR）开放数据集的互补性。这项研究说明了对Sentinel 1和Sentinel 2数据的测试，以检测受意大利北部Vaia风暴影响的区域。多光谱Sentinel 2的使用提供了最佳性能，分类总精度（OA）值高达86％。然而，云覆盖严重阻碍了光学数据的使用，云覆盖可能在事件发生后持续数月，在大多数情况下，如果需要快速响应，则不能认为是合适的工具。使用SAR Sentinel 1获得的结果准确性稍差（OA高达68％），但是该方法能够迅速提供有价值的信息，这主要是因为该数据集的获取与天气无关。总体而言，对于快速评估，“哨兵1”是两种方法中较好的一种，在这种方法中，多光谱和地面数据能够进一步完善基于SAR的初始评估。主要是因为此数据集的获取与天气无关。总体而言，对于快速评估，“哨兵1”是两种方法中较好的一种，在这种方法中，多光谱和地面数据能够进一步完善基于SAR的初始评估。主要是因为此数据集的获取与天气无关。总体而言，对于快速评估，“哨兵1”是两种方法中较好的一种，在这种方法中，多光谱和地面数据能够进一步完善基于SAR的初始评估。