Urban Sprawl Change Detection Using Geographic Information System And Remote Sensing

Abstract

Urban growth monitoring is essential to enhance city functions and elevate economic growth while also improving the people’s life style. Tirunelveli is becoming one of the smart cities in India, needs to be focused on regularising its rapid growth expansion. It is important to sort out the current spatial growth and to predict the model for spatial dynamic growth for future urban development. In this research, Three Landsat series satellite images were used to detect the urban sprawl of Tirunelveli taluk for the years 2010, 2015, and 2020 with using advanced GIS and RS techniques. Maximum likelihood algorithms are used for the supervised classifications of the satellite data and using Principal components analysis, the change detection is carried out for the satellite data to analyze the urban sprawl effects that have taken place in these 10 years. Post-classification comparison and image-to-image comparisons were applied for important features like land, water, vegetation, and build-up areas. This study can be further extended by applying Spatial dynamic growth models like the Cellular automata model and ANN techniques for predicting the development of the city. The statistical computational software that was used for this detection of urban sprawl are ArcGIS 10.4, QGIS 2.18