Detection of Flood Extent Using Sentinel-1A/B Synthetic Aperture Radar: An Application for Hurricane Harvey, Houston, TX release_jvyluf36eve5lgp3if2kbr3jnm

by Kristy Tiampo, 黄灵操, Conor Simmons, Clay Woods, Margaret T. Glasscoe

Published in Remote Sensing by MDPI AG.

2022   Volume 14, p2261

Abstract

The increasing number of flood events combined with coastal urbanization has contributed to significant economic losses and damage to buildings and infrastructure. Development of higher resolution SAR flood mapping that accurately identifies flood features at all scales can be incorporated into operational flood forecasting tools, improving response and resilience to large flood events. Here, we present a comparison of several methods for characterizing flood inundation using a combination of synthetic aperture radar (SAR) remote sensing data and machine learning methods. We implement two applications with SAR GRD data, an amplitude thresholding technique applied, for the first time, to Sentinel-1A/B SAR data, and a machine learning technique, DeepLabv3+. We also apply DeepLabv3+ to a false color RGB characterization of dual polarization SAR data. Analyses at 10 m pixel spacing are performed for the major flood event associated with Hurricane Harvey and associated inundation in Houston, TX in August of 2017. We compare these results with high-resolution aerial optical images over this time period, acquired by the NOAA Remote Sensing Division. We compare the results with NDWI produced from Sentinel-2 images, also at 10 m pixel spacing, and statistical testing suggests that the amplitude thresholding technique is the most effective, although the machine learning analysis is successful at reproducing the inundation shape and extent. These results demonstrate the effectiveness of flood inundation mapping at unprecedented resolutions and its potential for use in operational emergency hazard response to large flood events.
In application/xml+jats format

Archived Files and Locations

application/pdf  15.0 MB
file_nmejsy5dzfczhbihhybsqtjol4
mdpi-res.com (publisher)
web.archive.org (webarchive)

Web Captures

https://www.mdpi.com/2072-4292/14/9/2261/htm
2022-09-09 22:29:52 | 62 resources
webcapture_i4tznh3y2rgenewwftebq6i3by
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2022-05-08
Language   en ?
Container Metadata
Open Access Publication
In DOAJ
In ISSN ROAD
In Keepers Registry
ISSN-L:  2072-4292
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 9932015a-688e-4f67-8931-d74b1912c39e
API URL: JSON