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Using multisource remote sensing data to predict the maximum flood extent in poorly gauged basins

文献类型：期刊论文

作者Gao, Long1,2,3; Liu, Kai2; Yan, Fuli3
刊名JOURNAL OF APPLIED REMOTE SENSING
出版日期2022
卷号16期号:1页码:19
关键词flood early warning peak stage flood early warning maximum flood extent peak stage maximum flood extent remote sensing
DOI10.1117/1.JRS.16.014522
通讯作者Yan, Fuli(yanfl@radi.ac.cn)
英文摘要For small- or medium-sized river basins that suffer from floods, the lack of river gauging data is an important reason why flood warning at the appropriate time is difficult for local stakeholders. As a valuable dataset observed from spaceborne or airborne platforms, remote sensing images can capture slices of time-series information to cover the whole river basin. We consider the Nilwara Ganga tributaries in southern Sri Lanka for our research. The maximum flood extents and the corresponding elevations of flooded areas were determined in the postflood Sentinel-1 images. Relying only on commonly available remote sensing datasets, the rainfall-triggered flood inundation models were proposed to simulate the maximum inundation extent of a potential flood. The results show that the models proposed here can be well applied in the determination of peak stage, especially for the non-tributary river reach (root mean square error = 0.59, R-square = 0.76). The average of Intersection over Union (IoU) accuracy of the maximum flood extent predictions was 0.75. Validations of two flood events had the IoU accuracy of 0.79 and 0.69, respectively. For gauging data-scarce areas, this approach has great potential to provide a different perspective for flood early warning and will benefit the subsequent risk assessment and disaster mitigation. (C) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)
WOS关键词MODELS ; UNCERTAINTY ; CALIBRATION ; LEVEL ; AREAS
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19080101] ; National Key Research and Development Program of China[2016YFB0501505] ; National Natural Science Foundation of China[41971315]
WOS研究方向Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000777198700040
出版者SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; National Key Research and Development Program of China ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/174215]  
专题中国科学院地理科学与资源研究所
通讯作者Yan, Fuli
作者单位1.Univ Chinese Acad Sci, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
3.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Gao, Long,Liu, Kai,Yan, Fuli. Using multisource remote sensing data to predict the maximum flood extent in poorly gauged basins[J]. JOURNAL OF APPLIED REMOTE SENSING,2022,16(1):19.
APA Gao, Long,Liu, Kai,&Yan, Fuli.(2022).Using multisource remote sensing data to predict the maximum flood extent in poorly gauged basins.JOURNAL OF APPLIED REMOTE SENSING,16(1),19.
MLA Gao, Long,et al."Using multisource remote sensing data to predict the maximum flood extent in poorly gauged basins".JOURNAL OF APPLIED REMOTE SENSING 16.1(2022):19.

入库方式： OAI收割

来源：地理科学与资源研究所

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