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Country-wide flood exposure analysis using Sentinel-1synthetic aperture radar data: Case study of 2019 Iran flood

dataset
posted on 2022-12-22, 16:42 authored by Sonam Futi SherpaSonam Futi Sherpa, Manoochehr ShirzaeiManoochehr Shirzaei

We provide county and state-level flood exposure data, precipitation data, and individual flood maps for each SAR frames to understand flood exposure from the 2019 Flood of Iran at the country level utilizing 673 Sentinel-1 Synthetic Aperture Radar intensity images spanning January to February. A complete description of the method used to obtain probabilistic flood maps and exposure can be found in Sherpa and Shirzaei (2020) but is briefly stated below.

  • We applied a Bayesian framework to SAR intensity images to calculate the probability of a SAR pixel being flooded (Giustarini et al., 2016; Sherpa et al., 2020), for which a likelihood probability density function was estimated, thereby providing a continuous value between 0 and 1 as a probabilistic flood map.
  • To obtain an estimate of likelihood, an image segmentation scheme using the fast marching algorithm (FMA) is implemented (Sethian, 1999).
  • The percent area exposed to flooding is estimated as the pixel area's multiplication with its flooding probability for pixels located within each county or state divided by the county or state area. 
  • The population exposure is calculated by multiplying each county or state's percent area exposure values with their population, assuming a uniform population distribution. 

Anyone wishing to use this dataset should cite Sherpa and Shrizaei (2022) and this dataset. Please also contact and contact Sonam Futi Sherpa at sfsherpa@vt.edu for any questions with details of their work, so that we may offer guidance in regard to the best usage of our produced dataset. 


Sherpa, S. F., & Shirzaei, M. (2021). Country‐wide flood exposure analysis using Sentinel‐1 synthetic aperture radar data: Case study of 2019 Iran flood. Journal of Flood Risk Management, 15(1), e12770. 

https://doi.org/10.1111/jfr3.12770


Additional references: 

  • Sherpa, S. F., Shirzaei, M., Ojha, C., Werth, S., & Hostache, R. (2020). Probabilistic mapping of august 2018 flood of Kerala, India, using space-borne synthetic aperture radar. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 896-913. 10.1109/JSTARS.2020.2970337 
  • Giustarini, Laura, Renaud Hostache, Dmitri Kavetski, Marco Chini, Giovanni Corato, Stefan Schlaffer, and Patrick Matgen. "Probabilistic flood mapping using synthetic aperture radar data." IEEE Transactions on Geoscience and Remote Sensing 54, no. 12 (2016): 6958-6969.
  • Sethian, J. A. (1999). Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science (Vol. 3). Cambridge university press.

Funding

National Aeronautics and Space Administration, 80NSSC170567

History

Publisher

University Libraries, Virginia Tech

Location

Blacksburg, Virginia

Corresponding Author Name

Sonam Futi Sherpa

Corresponding Author E-mail Address

sfsherpa@vt.edu

Files/Folders in Dataset and Description

The dataset contains: 1. Flood_Maps.zip: Probabilistic flood maps for 673 Sentinel-1 SAR frames over the entire county of Iran. Some of these maps are found in the Supporting Information section of the associated publication. 2. State_Monthly_FloodAreaPer.csv: Monthly flooding area exposure percentile at the state-level flooding months January, February, and March 2019. 3. County_Monthly_FloodAreaPer.csv: Monthly flooding area exposure percentile at the county level for the flooding months, January, February, and March 2019. 4. State_Monthly_Floodexposed_Population.csv : Monthly flooding population exposure map at the state level for flooding months, January, February, and March 2019 5. County_Monthly_Floodexposed_Population.csv: Monthly flooding population exposure map at the county level for flooding months, January, February, and March 2019. 6. State_Monthly_Precipitation.csv: Monthly total precipitation aggregated over states for flooding months, January, February, and March 2019. 7. County_Monthly_Precipitation.csv: Monthly total precipitation aggregated over counties for flooding months, January, February, and March 2019.