22 Advancing Geosciences through Geospatial Innovations (Posters)

Tuesday, 11 March 2025
8:30 AM - 5:00 PM
Lower Level Lobby

OPERATIONAL FLOOD HAZARD MAPPING AND IMPACT ASSESSMENT IN BANGLADESH USING SENTINEL-1, MODIS, AND GOOGLE EARTH ENGINE (2020-2024)

Paper number: 22-3
Program: Theme Sessions
Time: 8:30 AM – 5:00 PM

Authors

Abstract

Bangladesh, one of the world’s most flood-prone countries, faces severe flooding due to its geographic location, extensive river systems, and monsoon climate. Identifying floodplain areas, along with the associated croplands and populations, is crucial for effective flood response and management. This study aims to develop an operational approach for mapping potential flood extents and assessing flood hazards from the 2020 to 2024 flood events. The methodology involves monitoring flood dynamics by comparing pre- and post-flood conditions using a threshold value of 1.3. The slope threshold for the HydroSHEDS DEM was set at 5%. Sentinel-1 images, collected from January to March during the study years, were used to analyze pre-flood conditions, while flood data from May to August (2020–2024) were analyzed to assess inundation extents for the corresponding periods. The MODIS land cover product was employed to track land cover changes due to flooding. The study highlights the northeastern part of Bangladesh – specifically the districts of Tahirpur, Habiganj, Kishoreganj, and Sunamganj – as the most flood-prone region. The 2020 flood had the most severe impact, affecting 499,820 hectares, including 145,495 hectares of cropland, and displacing over 4 million people. In contrast, the 2022 flood was the least severe, inundating 336,723 hectares and affecting 3 million people. Flood data for 2024 shows an inundated area of 471,781 hectares; however, cropland data for this year is still being processed. As the MODIS Land Cover product is updated annually, cropland data for 2024 will be available soon. Finally, this study plans to compare the flood mapping results with outputs from a deep learning platform in future analyses. By leveraging Sentinel-1 imagery and Google Earth Engine (GEE), the study proposes a robust flood mapping approach that overcomes the limitations of traditional flood hazard assessment methods, enhancing flood-related disaster management and better protecting vulnerable communities.

Geological Society of America Abstracts with Programs. Vol. 57, No. 1

doi: 10.1130/abs/2025SC-408867

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