Ramesh Sivanpillai, Maria Oreshkina, Paden Bear, Isaac Boettcher, Tyler Bradshaw, Isaac Coleman, Jessica Gifford
{"title":"使用阈值技术绘制洪水后陆地卫星图像中的新淹没区域","authors":"Ramesh Sivanpillai, Maria Oreshkina, Paden Bear, Isaac Boettcher, Tyler Bradshaw, Isaac Coleman, Jessica Gifford","doi":"10.5194/isprs-archives-xlviii-m-3-2023-235-2023","DOIUrl":null,"url":null,"abstract":"Abstract. Identifying newly inundated areas following flood events is essential for planning rescue missions. These maps must be generated quickly as the spatial extent of the inundated areas might change during a single flood event. Several methods exist for generating such maps and several rely on one or more geospatial data to exclude existing waterbodies in an affected area. In this study, we tested a rapid flood mapping method that uses a pair of pre- and post-flood satellite images on seven sites throughout the US. We derived Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) images from pre- and post-flood Landsat images and identified the optimal threshold values that highlighted newly inundated areas at these sites. The accuracy of the inundation maps was determined using manually interpreted verification data from the pairs of satellite images. Image analysts have identified the optimal threshold values between 25 and 40 minutes. Maps of newly inundated areas derived from differencing MNDWI and NDWI images had higher overall accuracy > 93%. Results obtained in this study confirms the utility of this rapid flood mapping technique to identify inundated areas using pre- and post-flood satellite images.\n","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MAPPING NEWLY INUNDATED AREAS IN POST-FLOOD LANDSAT IMAGES USING THRESHOLDING TECHNIQUES\",\"authors\":\"Ramesh Sivanpillai, Maria Oreshkina, Paden Bear, Isaac Boettcher, Tyler Bradshaw, Isaac Coleman, Jessica Gifford\",\"doi\":\"10.5194/isprs-archives-xlviii-m-3-2023-235-2023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract. Identifying newly inundated areas following flood events is essential for planning rescue missions. These maps must be generated quickly as the spatial extent of the inundated areas might change during a single flood event. Several methods exist for generating such maps and several rely on one or more geospatial data to exclude existing waterbodies in an affected area. In this study, we tested a rapid flood mapping method that uses a pair of pre- and post-flood satellite images on seven sites throughout the US. We derived Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) images from pre- and post-flood Landsat images and identified the optimal threshold values that highlighted newly inundated areas at these sites. The accuracy of the inundation maps was determined using manually interpreted verification data from the pairs of satellite images. Image analysts have identified the optimal threshold values between 25 and 40 minutes. Maps of newly inundated areas derived from differencing MNDWI and NDWI images had higher overall accuracy > 93%. Results obtained in this study confirms the utility of this rapid flood mapping technique to identify inundated areas using pre- and post-flood satellite images.\\n\",\"PeriodicalId\":30634,\"journal\":{\"name\":\"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-235-2023\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-235-2023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Social Sciences","Score":null,"Total":0}
MAPPING NEWLY INUNDATED AREAS IN POST-FLOOD LANDSAT IMAGES USING THRESHOLDING TECHNIQUES
Abstract. Identifying newly inundated areas following flood events is essential for planning rescue missions. These maps must be generated quickly as the spatial extent of the inundated areas might change during a single flood event. Several methods exist for generating such maps and several rely on one or more geospatial data to exclude existing waterbodies in an affected area. In this study, we tested a rapid flood mapping method that uses a pair of pre- and post-flood satellite images on seven sites throughout the US. We derived Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) images from pre- and post-flood Landsat images and identified the optimal threshold values that highlighted newly inundated areas at these sites. The accuracy of the inundation maps was determined using manually interpreted verification data from the pairs of satellite images. Image analysts have identified the optimal threshold values between 25 and 40 minutes. Maps of newly inundated areas derived from differencing MNDWI and NDWI images had higher overall accuracy > 93%. Results obtained in this study confirms the utility of this rapid flood mapping technique to identify inundated areas using pre- and post-flood satellite images.