Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2021WR030606 |
Flood extent mapping during Hurricane Florence with repeat-pass L-band UAVSAR images | |
Chao Wang; Tamlin M. Pavelsky; Fangfang Yao; Xiao Yang; Shuai Zhang; Bruce Chapman; Conghe Song; Antonia Sebastian; Brian Frizzelle; Elizabeth Frankenberg; Nicholas Clinton | |
2022-02-14 | |
发表期刊 | Water Resources Research |
出版年 | 2022 |
英文摘要 | Extreme precipitation events are intensifying due to a warming climate, which, in some cases, is leading to increases in flooding. Detection of flood extent is essential for flood disaster response, management, and prevention. However, it is challenging to delineate inundated areas through most publicly available optical and short-wavelength radar data, as neither can “see” through dense forest canopies. In 2018, Hurricane Florence produced heavy rainfall and subsequent record-setting riverine flooding in North Carolina, USA. NASA/JPL collected daily high-resolution full-polarized L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data between September 18th and 23rd. Here, we use UAVSAR data to construct a flood inundation detection framework through a combination of polarimetric decomposition methods and a Random Forest classifier. Validation of the established models with compiled ground references shows that the incorporation of linear polarizations with polarimetric decomposition and terrain variables significantly enhances the accuracy of inundation classification, and the Kappa statistic increases to 91.4% from 64.3% with linear polarizations alone. We show that floods receded faster near the upper reaches of the Neuse, Cape Fear, and Lumbee Rivers. Meanwhile, along the flat terrain close to the lower reaches of the Cape Fear River, the flood wave traveled downstream during the observation period, resulting in the flood extent expanding 16.1% during the observation period. In addition to revealing flood inundation changes spatially, flood maps such as those produced here have great potential for assessing flood damages, supporting disaster relief, and assisting hydrodynamic modeling to achieve flood-resilience goals. This article is protected by copyright. All rights reserved. |
领域 | 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/346691 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | Chao Wang,Tamlin M. Pavelsky,Fangfang Yao,et al. Flood extent mapping during Hurricane Florence with repeat-pass L-band UAVSAR images[J]. Water Resources Research,2022. |
APA | Chao Wang.,Tamlin M. Pavelsky.,Fangfang Yao.,Xiao Yang.,Shuai Zhang.,...&Nicholas Clinton.(2022).Flood extent mapping during Hurricane Florence with repeat-pass L-band UAVSAR images.Water Resources Research. |
MLA | Chao Wang,et al."Flood extent mapping during Hurricane Florence with repeat-pass L-band UAVSAR images".Water Resources Research (2022). |
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