Trend Analysis and Simulation of Human Vulnerability Based on Physical Factors of Riverbank Erosion Using RS and GIS

Abstract

This study aims to analyze the pattern of bank erosion and simulate the physical aspects of vulnerability in the lower Meghna River, Bangladesh using remote sensing (RS) and geographic information systems (GIS). The physical factors of vulnerability were analyzed using GIS-based Structured Query Language (SQL). A questionnaire survey, GPS survey and field observation survey were conducted for collecting the primary data in the study area. The secondary data were mainly satellite image collected from the United States Geological Survey (USGS) website. Using time series Landsat images (MSS, TM and OLI-TIRS), this study analyzed 36 years of erosion and accretion process in the Mehendiganj Upazila region from 1980 to 2016. The result indicates that an enormous amount of land (4470.47 ha) was submerged by the river and average land loss rate was 124.18 ha/year. The study quantifies the number of vulnerable households beneath the present condition and how much it will be altered after a positive/negative change with the factors of vulnerability related to the households. Simulation data reveals that under the present physical condition, 43.88% of households were identified as severely vulnerable. The output of this study can be used in the classification of vulnerable households and for the improvement of the physical infrastructure development process near the erosion prone areas, also helps to mitigate environmental disaster in the developing countries.

Fluvio-Geomorphic Change of the Padma-Meghna River Course Using the NDWI and MNDWI Techniques

“…(Mondal et al, 2020). The Padma is a very large alluvial river that has a 5 cm/km average slope and the Meghna River is a low-energy, multi-channel fluvial system with a system of linking channels and inter-channel zones (Alam, 1991;Chowdhury et al, 2021). Furthermore, the Upper Meghna has a 2 cm/km average slope, and the Lower Meghna has a 5 cm/km average slope (Mondal et al, 2020;Gazi et al, 2020).…”