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Open AccessArticle

Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh

1
International Centre for Integrated Mountain Development, Kathmandu 44700, Nepal
2
Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK 757500, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1581; https://doi.org/10.3390/rs11131581
Received: 1 May 2019 / Revised: 22 June 2019 / Accepted: 23 June 2019 / Published: 3 July 2019
(This article belongs to the Special Issue Imaging Floods and Glacier Geohazards with Remote Sensing)
Bangladesh is one of the most flood-affected countries in the world. In the last few decades, flood frequency, intensity, duration, and devastation have increased in Bangladesh. Identifying flood-damaged areas is highly essential for an effective flood response. This study aimed at developing an operational methodology for rapid flood inundation and potential flood damaged area mapping to support a quick and effective event response. Sentinel-1 images from March, April, June, and August 2017 were used to generate inundation extents of the corresponding months. The 2017 pre-flood land cover maps were prepared using Landsat-8 images to identify major land cover on the ground before flooding. The overall accuracy of flood inundation mapping was 96.44% and the accuracy of the land cover map was 87.51%. The total flood inundated area corresponded to 2.01%, 4.53%, and 7.01% for the months April, June, and August 2017, respectively. Based on the Landsat-8 derived land cover information, the study determined that cropland damaged by floods was 1.51% in April, 3.46% in June, 5.30% in August, located mostly in the Sylhet and Rangpur divisions. Finally, flood inundation maps were distributed to the broader user community to aid in hazard response. The data and methodology of the study can be replicated for every year to map flooding in Bangladesh. View Full-Text
Keywords: flood mapping; damage assessment; SAR image; Sentinel-1; Landsat-8; Google Earth Engine; GEE; Bangladesh flood mapping; damage assessment; SAR image; Sentinel-1; Landsat-8; Google Earth Engine; GEE; Bangladesh
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MDPI and ACS Style

Uddin, K.; Matin, M.A.; Meyer, F.J. Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh. Remote Sens. 2019, 11, 1581.

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