Modelling cyclone-induced flood impact assessment and quantifying the effect on biophysical dynamics employing geospatial techniques in South–West of Bangladesh

Md Tanvir Miah (), Md. Rakibul Hasan (), Jannatun Nahar Fariha (), Jarin Jannati Tammi (), Raiyan Raiyan (), Pankaj Kanti Jodder (), Remon Ahmed Mishu (), Salima Ahamed Usha (), Md Zakir Hossain () and Khan Rubayet Rahaman ()
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Md Tanvir Miah: Khulna University
Md. Rakibul Hasan: Khulna University
Jannatun Nahar Fariha: Khulna University
Jarin Jannati Tammi: RMIT University
Raiyan Raiyan: Khulna University
Pankaj Kanti Jodder: University of North Carolina, Charlotte
Remon Ahmed Mishu: Khulna University
Salima Ahamed Usha: Khulna University
Md Zakir Hossain: Khulna University
Khan Rubayet Rahaman: St. Mary’s University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 10, No 18, 11771 pages

Abstract: Abstract This article investigates the influence of climatic and geographical characteristics in south-western region of Bangladesh on the temporal dynamics of post-cyclone impacts, with a critical focus on biophysical contexts. By quantitatively assessing the environmental consequences of cyclones Amphan (2020), Yaas (2021), Mocha (2023) and Remal (2024), the study offers a nuanced understanding of flood damage extent and vegetation health, measured through advanced remote sensing and geospatial techniques. Using Sentinel-1 (GRD) and Sentinel-2 (MSI) satellite imageries from 2020 to 2024, the study has examined post-cyclone changes of vegetation health and flood damage extent using available indices such as Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI). The results exhibit substantial spatial disparities occurred due to the cyclone events, with NDVI variations ranging from − 0.124 to 0.546 (Amphan), − 0.033 to 0.498 (Mocha), − 0.086 to 0.458 (Yaas), and − 0.061 to 0.362 (Remal), indicating significant ecological stress. Corresponding SAVI changes ranged from − 0.001 to 0.396 (Amphan), − 0.029 to 0.338 (Mocha), − 0.002 to 0.345 (Yaas), and − 0.0524 to 0.269 (Remal). Negative indices underscore potential vegetation degradation, while positive values indicate resilience or post-cyclone recovery. Furthermore, flood damage analysis indicates to a more severe and unevenly distributed impact than previously recognized, particularly in areas with pre-existing vulnerabilities with the damage extent variations between − 35.918 to − 2.0093 (Amphan), − 35.334 to − 4.4059 (Mocha), − 34.806 to − 0.94921 (Yaas), and − 48.469 to 0.00255 (Remal). The Geographically Weighted Regression (GWR), model demonstrates a robust relationship, with r2 values of 0.894, 0.889, 0.899, and 0.95, indicating that approximately 85% of the ecological changes are driven by fluctuations of vegetation due to flood. The insight from this research provides a foundation of flood damage assessment technique occurred by cyclones in a short span of time to aid immediate policy recommendations to enhance resilience in remote areas of the coastal regions of Bangladesh.

Keywords: Flood Damage Assessment; Costal resilience; Vegetation dynamics; Remote sensing; Spatial model (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07259-3

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