ABSTRACT

Monitoring large-scale flood damage can be complicated and costly. Damages caused by floods affect also the agricultural sector. Permanence, height and quantity of stagnant water can significantly influence crop yield. Many studies exploit satellite data to map flooded areas, but only a few are focused on the timing of water persistence. This work refers to the river Sesia flooding event which occurred on 3 October 2020 in Northwest Italy with the aim of detecting damages to local crops. The analysis was based on Sentinel-1 data processed by Google Earth Engine platform. In particular, the Otsu’s method was applied to test the difference between pre- and post-event images. Areas that were mapped as flooded were successively analysed to estimate local water persistence: specifically, 1-2-6 days after the event. According to the available Corine Land Cover 2018 dataset, it was found that flood mainly affected agricultural areas (about 3288 ha). Since damage also relies on water persistence, a focus area was selected to test the effectiveness of S1 multi-temporal in mapping its distribution. Results show that only 3.5% of the agricultural fields in the focus area remained underwater for at least 6 days and 69% for only 1 day.

摘要

监视大规模洪水破坏可能既复杂又昂贵。洪水造成的破坏也影响到农业部门。积水的持久性，高度和数量会显着影响农作物的产量。许多研究利用卫星数据绘制洪水泛滥区，但只有少数研究集中在水持续时间上。这项工作是指2020年10月3日在意大利西北部发生的塞西亚河洪水事件，目的是检测对当地农作物的破坏。该分析基于由Google Earth Engine平台处理的Sentinel-1数据。尤其是，采用了Otsu的方法来测试事件前和事件后图像之间的差异。连续分析了被洪水淹没的地区，以估计当地的水持续存在：特别是在事件发生后的1-2-6天。根据可用的Corine Land Cover 2018数据集，发现洪水主要影响农业地区（约3288公顷）。由于损害还取决于水的持久性，因此选择了一个重点领域来测试S1多时相在绘制其分布方面的有效性。结果表明，重点地区只有3.5％的农田在水下至少呆了6天，而69％的农田只呆了1天。