{"title":"利用Sentinel-1数据绘制马拉维恩桑杰地区洪水风险图","authors":"S. Gondwe, Shital H. Shukla","doi":"10.37591/.V11I3.1066","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":427440,"journal":{"name":"Journal of Remote Sensing & GIS","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Mapping Flood Risk of Nsanje District in Malawi Using Sentinel-1 Data\",\"authors\":\"S. Gondwe, Shital H. Shukla\",\"doi\":\"10.37591/.V11I3.1066\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":427440,\"journal\":{\"name\":\"Journal of Remote Sensing & GIS\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Remote Sensing & GIS\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37591/.V11I3.1066\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Remote Sensing & GIS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37591/.V11I3.1066","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.