| Cite this article: | 李健锋,张宗科,魏显虎,等.基于Google Earth Engine和Sentinel-1/2的洪水信息快速提取模型研究[J].灌溉排水学报,2020,(Supp.2):-. |
| lijianfeng,Zhang Zongke,Wei Xianhu,et al.基于Google Earth Engine和Sentinel-1/2的洪水信息快速提取模型研究[J].灌溉排水学报,2020,(Supp.2):-. |
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| Research on Quick Extraction Model of Flood Information based onGoogle Earth Engine and Sentinel-1/2 |
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lijianfeng1,2,3,4, Zhang Zongke2,5, Wei Xianhu2,5, Yang Liangyan1,4, Ye Huping6,7
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1.Institute of Land Engineering and Technology, Shaanxi Provincial Land Engineering Construction Group Co., Ltd.;2.Aerospace Information Research Institute, Chinese Academy of Sciences;3.China-Sri Lanka Joint Research and Demonstration Center for Water Technology, Chinese Academy of Sciences, Beijing 100085, China;4.Shaanxi Provincial Land Engineering Construction Group Co., Ltd.;5.China-Sri Lanka Joint Research and Demonstration Center for Water Technology, Chinese Academy of Sciences.;6.State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences;7.China-Sri Lanka Joint Research and Demonstration Center for Water Technology, Chinese Academy of Sciences
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| Abstract: |
| Flood disaster is one of the ten natural disasters threatening human survival and economic development. Obtaining flood information quickly and accurately has important practical significance for flood disaster assessment, rescue and resource allocation. Taking the east-central Sri Lanka as the study area, this paper based on the Google Earth Engine, the accuracy of the Sentinel-1 image water body extracted by OTSU-threshold model and the Sentinel-2 image water body extracted by OTSU-NDWI model were compared from visual interpretation and quantitative analysis. Through the analysis, it was found that the extraction accuracy of Sentinel-1 image was significantly higher than that in Sentinel-2 image in cloudy area, which can effectively avoid the influence of cloud and cloud shadow on the water extraction result; the extraction result of Sentinel-2 image was better in cloudless area, and the boundary of the water can be extracted relatively accurately; both images mistakenly divided some mountain shadows into water bodies. On this basis, this paper combined with the mountain shadow mask data obtained by terrain modeling, a quick flood information extraction model based on Google Earth Engine and Sentinel-1/2 images was constructed. The model was successfully applied to the analysis of severe flood in Matala District of Sri Lanka in May 2017 and Poyang Lake in July 2020. The results show that the model has high efficiency, strong feasibility, and zero cost, and effectively avoids time-consuming image download and pre-processing. It can extract the flooded area in near real time, which can be widely used in flood disaster research. |
| Key words: Google Earth Engine; flood disaster; Sentinel-1/2; quick extraction; OTSU |
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