利用Sentinel-1数据绘制马拉维恩桑杰地区洪水风险图

S. Gondwe, Shital H. Shukla
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引用次数: 2

摘要

可穿透云层的SAR系统的全天候成像能力和哨兵c波段数据的免费提供,在为马拉维等热带地区的洪水灾害管理提供关键空间信息方面非常有用。本研究展示了Sentinel-1数据如何利用VH和VV极化来绘制Nsanje地区2015年至2020年的洪水范围,并加强了风险绘图。获取多日期Sentinel-1图像,通过变化检测和阈值技术进行预处理和分析,以解释后向散射辐射,从而区分淹水和非淹水区域。VV极化结果显示,2015年、2019年和2020年,Nsanje地区未被洪水淹没的土地比例分别为3.97%、5.91%和2.05%。另一方面,VH极化结果显示,2019年和2020年分别约有7.71%和2.02%的土地未被洪水淹没。Sentinel-2洪水前图像用于确定该地区的主要土地覆盖类别。MNDWI来自与2019年Sentinel-1危机图像相同日期的Landsat 8图像,用于洪水范围验证。VH和VV极化的洪水范围圈定总体精度分别为96%和90%。观察特定地区的洪水频率,以确定物理脆弱性,从而计算可能再次发生洪水灾害的风险。此外,还利用社会数据来帮助更好地了解研究区域应对可能发生的洪水灾害的能力。
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Mapping Flood Risk of Nsanje District in Malawi Using Sentinel-1 Data
The all-time imaging ability of SAR systems which can penetrate cloud cover and free availability of Sentinel C-Band data are very useful in deriving critical spatial information for flood disaster management in tropical areas such as Malawi. This study shows how Sentinel-1 data has been used to map flood extents by utilizing VH and VV polarizations and enhance risk mapping during the years 2015 to 2020 in Nsanje district. Multi-dated Sentinel-1 images were acquired, pre-processed, and analyzed through Change Detection and Thresholding technique to interpret backscattered radiation thereby distinguishing flooded and non-flooded areas. The VV Polarization result shows that percentage of land that remained under flood water in Nsanje district was 3.97% in 2015, 5.91% in 2019 and 2.05% in 2020. On the other hand, VH Polarization result shows about 7.71% and 2.02% of land remained under flood water in 2019 and 2020 respectively.Sentinel-2 pre-flood image was used to determine major land cover classes in the district. MNDWI derived from Landsat 8 imagery of the same date as 2019 Sentinel-1 crisis imagery was used for flood extent validation. Overall Accuracy of flood extent delineation was 96% and 90% for VH and VV polarization respectively. Flooding frequency in specific areas was observed to determine physical vulnerability and hence figure out the risk to a possible repetition of flood disaster. Further, social data were used to aid a better understanding of the capacity to cope with a possible flood hazard in the study area.
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