Riverbank Landscape Change Detection Using Remote Sensing through Change Vector Analysis: Study on Dakshin Tekani Part of Jamuna River, Bangladesh

Abstract The Jamuna River (JR) is one of the richest sources of biodiversity and a highly productive ecosystem reservoir in the riverine country of Bangladesh. However, the riverbank landscape change (RLC) has undergone a radical alteration in its form over the decades, especially since the edge of the twentieth century. In this study, the RLC of Dakshin Tekani, part of JR, was assessed from 1993 to 2013 using Landsat Satellite images, following remote sensing (RS) and geographical information system (GIS) techniques. There were several methods to evaluate the RLC, but the present study followed the change vector analysis (CVA) tool in GIS, which was a unique contribution to the present study. Both pre-classification and post-classification change detection approaches were used to assess and evaluate the changes. The ArcGIS v10.5 and ERDAS Imagine v14 software were used to process satellite imagery, map the study area, and evaluate quantitative data for RLC assessment. The maximum likelihood supervised classification technique was performed to create the signature class of significant RLC categories (i.e., river, vegetation, sandy clay, and char land). According to the results of the Analysis of Variance (ANOVA) test, significant differences were observed in the river, vegetation, sandy clay, and char land across the RLC throughout the study period. This study illustrated that, following the application of the CVA tool, approximately 10.25% of the river area decreased, 31.02% of the area was converted to vegetated areas, about 1.83% of the sandy clay land increased, and about 12.10% of the char land area decreased over the last twenty years. The river area and sandy clay disappeared rapidly, and the widespread development of settlements and dominant shallow water features converted the natural wetland into permanent, low-lying agricultural land. The study suggested that the research community, urban planners, environmentalists, policymakers, and stakeholders should utilise the CVA tool to identify the RLC, thereby enhancing their understanding of the changes and the necessary precautionary measures.