Scholarlink

<p>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 r<sup>2</sup> 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 发表日期：

    2025年5月1日

查看全文
  • 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,
  • Khan Rubayet Rahaman

摘要

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.