URBAN SPRAWL MAPPING OF AJMER CITY (2020-2024) USING SATELLITE IMAGERY (Prakriti - The International Multidisciplinary Research Journal, 2025)
The rate of urbanization in Indian cities, like several other developing cities around  the world is increasing at an alarming rate. This unprecedented and uncontrolled  urbanization may result in urban sprawl or suburban sprawl, the rapid expansion of the  geographic extent of cities and towns, often characterized by low-density residential  housing, single-use zoning, and increased reliance on the private automobile for  transportation. Urban sprawl is caused in part by the need to accommodate a rising  urban population; however, in many metropolitan areas, it results from a desire for  increased living space and other residential amenities. Urbanization is the process  in which the population shifts from rural to cities or urban centres for an improved  standard of living due to economic growth and development. As the migration rate  increases, urban cities undergo major changes in land use and land cover which  trigger several negative effects such as overcrowding, water scarcity, air pollution, loss  of productive agricultural lands and forest cover. Remote sensing and Geographical  Information System (GIS) are used for monitoring the emerging urbanization  of the cities using satellite images. The ill effects of urban sprawl in developing  countries are a bit complicated compared to those of developed countries because  of uncontrolled population growth and haphazard urbanization.An overview of  the enormous potential of remote sensing and GIS techniques in mapping and  monitoring the Spatio-temporal patterns urban sprawl is dealt with here. The spatial  pattern and dynamics of the urban sprawl of Ajmer City (Rajasthan, India) during  the period from 2020 to 2024 using the integrated approach of remote sensing and  GIS are attempted here. Supervised classification is used for the rapid and automated  extraction of built-up features from the time series satellite imageries. The extracted  built-up areas of each year are then used for the quantification of urban sprawl and  its associated area (in km). The results and analysis highlight the fact that there was  an alarming increase in the built-up area extent from 2020 to 2024.
Keywords: Urban sprawl; Remote sensing; GIS; Supervised classification; satellite imageries