https://doi.org/10.3390/rs14061450

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Faculty Publications

Document Type

Article

Abstract

In August 2017, Hurricane Harvey was one of the most destructive storms to make landfall in the Houston area, causing loss of life and property. Temporal and spatial changes in the depth of floodwater and the extent of inundation form an essential part of flood studies. This work estimates the flood extent and depth from LiDAR DEM (light detection and ranging digital elevation model) using data from the Synthetic Aperture Radar (SAR)–Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) and satellite sensor—Sentinel-1. The flood extent showed a decrease between 29–30 August and 5 September 2017. The flood depths estimated using the DEM were compared with the USGS gauge data and showed a correlation (R2) greater than 0.88. The use of Sentinel-1 and UAVSAR resulted in a daily temporal repeat, which helped to document the changes in the flood area and the water depth. These observations are significant for efficient disaster management and to assist relief organizations by providing spatially precise information for the affected areas.

Digital Object Identifier (DOI)

https://doi.org/10.3390/rs14061450

APA Citation

Kundu, S., Lakshmi, V., & Torres, R. (2022). Flood Depth Estimation during Hurricane Harvey Using Sentinel-1 and UAVSAR Data. Remote Sensing, 14(6), 1450. https://doi.org/10.3390/rs14061450

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