Monitoring Land Use/Cover Change and Urban Sprawl Using Remote Sensing Data: A Study of Siliguri and Raiganj Urban Agglomerations, India

Abstract

AbstractSpatial assimilation and the dynamicity of urban land use are significant issues in the study of modern towns and cities. Many studies have been conducted to monitor urban land use and sprawl of metropolitan cities or other big cities in India and other countries. But the same kinds of studies conducted for small and medium towns/cities are lesser in number. In this chapter, supervised image classification technique with maximum likelihood classifier algorithm has been applied to estimate the land use/land cover (LULC) change over two time periods using ERDAS imagine (v.14). For assessing the supervised classification technique’s accuracy, confusion or error matrix and kappa coefficient (K) have been applied. A conversion map has been generated from the classified image pairs to measure the quantitative characteristic of changes. Shannon entropy method has been used to find out the urban sprawls. The result of this analysis indicates that the built-up increased significantly from 32.86 km2 in 1990 to 61.16 km2 in 2019 in Siliguri (UA), and for Raiganj (UA), it increased from 4.76 km2 in 1991 to 22.41 km2 in 2019, resulting in a loss in prime agricultural land, fallow land, and vegetation. Shannon entropy has provided excellent assistance for quantifying the sprawling mechanism in both areas to obtain the result. The findings of this chapter may help planners and policymakers guiding urban land management in the context of rapid conversion, as seen in the recent past.KeywordsLand use/coverChange detectionUrban sprawlRemote sensing and GIS