India is emerging as a major influence of socioeconomic change connecting diverse cultures, people with improved mobility across geographic boundaries. Rapid urbanizations owing to rural to urban migration have made the existing infrastructure and resources inadequate. A case study on Varanasi city has been conducted to demonstrate the urban growth pattern for the years 2000, 2013 and 2014. Satellite imagery of Cartosat-1 (2.5m) and LISS-IV (5m) is used to create the high-resolution multi-spectral data through image processing techniques. In the year 2000, the urban built-up covered an area of 42.75 sq km to 109.03 sq km and the cultivated land area has decreased by 16%. Every year cultivated land decreases for mounting urban sprawl with another 2% decrease in the year 2014 and a 9% increase in built-up. This paper demonstrates the utilization of open source GIS to generate results.
Unplanned urbanization has drastically altered the natural topography of the cities and the surrounding areas. Migration from rural areas to Cities has led the transformation of the nearby rural areas into the extended city. The increase in an urban environment as an outcome of socio-economic activities has resulted in the urban sprawl of major cities globally including India. Urban sprawl has raised concerns over unplanned land use but also day-to-day weather conditions of the city. Concentrated development in the major cities of Bangalore, Karnataka and Capital city of New Delhi has given rise to serious implications in form of an increase in local temperatures during the last decade. The study aims at representing the change with an increase in urbanization with the formation of UHI. Sophisticated techniques of remote sensing and GIS are used to perform the analysis. Land Surface Temperature (LST) extracted from the thermal band (band 10) of the Landsat-8 OLI data from the DN values is evaluated for detecting spatial-temporal variations in the formation of heat island of the two cities. The LST calculated for each city is analyzed with the NDBI and NDVI to study the relation of surface temperature and respective indices. It was found that the surface temperature follows a positive co-relation with NDBI and a negative co-relation with NDVI. The indices are used as a basis to delineate pervious and impervious areas in the two cities. Landsat and Sentinel 2a satellite imagery temporal data are used to study the area.