Analysis of impervious land-cover expansion using remote sensing and GIS: A case study of Sylhet sadar upazila. (September 2018) - Gupta, Sushanta; Islam, Sabina; Hasan, Md. Muyeed
Abstract: Unplanned urbanization practice creates pressure on the natural functioning of land and resources. In this context, it is essential to monitor its expansion. This study seeks to analyze the pattern and process of urban expansion at Sylhet Sadar Upazila from 1981 to 2016 by extracting impervious land-cover from satellite images and using Shannon's entropy. To find out a comparatively accurate method of extracting impervious land-cover from satellite images, three classification methods viz., supervised maximum likelihood algorithms, index-based classification and neural classification are experimenting with a single Landsat images of 2016. A method is selected on the basis of accuracy measures. The study finds the neural classification can extract impervious land-cover accurately (about 90% accuracy) than other classification. This classification method is adopted to classify the Landsat images of 1981, 1991, 2001, 2011 and the amount of impervious land-cover is estimated in ArcGIS platform. Finally, the entropy value is calculated using the amount of impervious land-cover for each union and city corporation. Up to 2011, the city expands mainly along north-south direction and this expansion occurs at northen and eastern direction after 2011. The urban expansion in Sylhet city and its vicinity is scattered in nature but the compactness increases slightly in 2016. Number of Population and elevation acted as the driving force of urban expansion in case of Sylhet city. Highlights: Impervious land-cover can be best extracted using neural classification. It produces better results than MLC and index-based classification. Sylhet City expands rapidly after 2011 in western and eastern direction in a scattered way. A relationship exists between the increasing population and urban development at Sylhet City.