Flood Extent Mapping during Hurricane Florence with Repeat-Pass L-Band UAVSAR Images

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Citation

Wang, Chao; Pavelsky, Tamlin M; Yao, Fangfang; Yang, Xiao; Zhang, Shuai; Chapman, Bruce; Song, Conghe H.; Sebastian, Antonia; Frizzelle, Brian; & Frankenberg, Elizabeth, et al. (2022). Flood Extent Mapping during Hurricane Florence with Repeat-Pass L-Band UAVSAR Images. Water Resources Research, 58(3), e2021WR030606.

Abstract

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 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. The 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.

URL

https://doi.org/10.1029/2021WR030606

Reference Type

Journal Article

Year Published

2022

Journal Title

Water Resources Research

Author(s)

Wang, Chao
Pavelsky, Tamlin M
Yao, Fangfang
Yang, Xiao
Zhang, Shuai
Chapman, Bruce
Song, Conghe H.
Sebastian, Antonia
Frizzelle, Brian
Frankenberg, Elizabeth
Clinton, Nicholas

Article Type

Regular

Data Set/Study

Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR)

Continent/Country

United States of America

State

North Carolina

ORCiD

Song, C - 0000-0002-4099-4906
Frizzelle - 0000-0002-7705-0204
Frankenberg - 0000-0003-0671-9684