The mass accumulation of population in the larger cities of India has led to accelerated and unprecedented peripheral urban expansion over the last few decades. This rapid peripheral growth is characterized by an uncontrolled, low density, fragmented and haphazard patchwork of development popularly known as urban sprawl. The Kolkata Metropolitan Area (KMA) has been one of the fastest-growing metropolitan areas in India and is experiencing rampant suburbanization and peripheral expansion. Hence, understanding urban growth and its dynamics in these rapidly changing environments is critical for city planners and resource managers. Furthermore, understanding urban expansion and urban growth patterns are essential for achieving inclusive and sustainable urbanization as defined by the United Nations in the Sustainable Development Goals (e.g., SDGs, 11.3). The present research attempts to quantify and model the urban growth dynamics of large and diverse metropolitan areas with a distinct methodology considering the case of KMA. In the study, land use and land cover (LULC) maps of KMA were prepared for three different years (i.e., for 1996, 2006, and 2016) through the classification of Landsat imagery using a support vector machine (SVM) classification approach. Then, change detection analysis, landscape metrics, a concentric zone approach, and Shannon’s entropy approach were applied for spatiotemporal assessment and quantification of urban growth in KMA. The achieved classification accuracies were found to be 89.75%, 92.00%, and 92.75%, with corresponding Kappa values of 0.879, 0.904, and 0.912 for 1996, 2006, and 2016, respectively. It is concluded that KMA has been experiencing typical urban sprawl. The peri-urban areas (i.e., KMA-rural) are growing rapidly, and are characterized by leapfrogging and fragmented built-up area development, compared to the central KMA (i.e., KMA-urban), which has become more compact in recent years.

In India, urban settlements, especially metropolitan cities, are expanding at a rapid pace, transforming non-built-up areas into built-up landscapes inducing various irreversible environmental consequences. A metropolis generally is composed of statutory urban areas at the core and rural or periurban areas at the periphery, with various differences to the core. Therefore, simultaneous study of growth dynamics of different parts in the same metropolis, namely metropolitan urban (core), and metropolitan rural (periphery) seems to be important for effective urban planning. This study attempts to assess the built-up dynamics of the Kolkata Metropolitan Area (KMA) in India using a proposed zoning approach; that is, three different spatial levels of KMA—KMA-Total (the entire KMA), KMA-Urban (metropolitan urban), and KMA-Panchayet (metropolitan rural)—for the time period between 1980 and 2010 employing Shannon's entropy and some other landscape metrics integrated with Remote Sensing and Geographical Information System. The results show that the built-up expansion of KMA-Total is gradually becoming compact over time. But, within KMA, built-up growth of KMA-Urban has declined sharply, becoming more compact, whereas, KMA-Panchayet is experiencing a higher degree of built-up growth featured by dispersed and fragmented growth. Importantly, the zoning approach proposed in this study appears to be an effective one to analyze spatiotemporal built-up dynamics in large cities.

The present study attempts to model the spatiotemporal urban growth of the Kolkata metropolitan area (KMA), India, in a comparative modeling framework using three (remote sensing and geographic information system) RS-GIS integrated techniques, namely stochastic-choice Markov-chain (STCHOICE), cellular automata-Markov (CA-MARKOV), and multi-layer perceptron neural network (MLPNN) coupled with Markov-chain approaches intending to monitor land-use efficiency defined by United Nations for sustainable urban development in KMA. In order to find out the best modeling approach, each of the three techniques is engaged in modeling KMA’s growth, and the model thus obtained is employed to predict future growth. The MLPNN (Kappa = 0.9025) appears to be a considerably better approach as compared to the other two approaches: CA-MARKOV (Kappa = 0.6941) and STCHOICE (Kappa = 0.5392). The MLPNN simulated urban growth for 2036 reveals that the urban built-up cover is expected to be about 55% from that of 31% in 2016 due to the significant conversion of other land covers. The study reveals that urban and peri-urban areas do not have a similar pattern of land consumption and land-use efficiency in KMA. The central KMA reflects better land-use efficiency due to the compact built-up growth compared to the periphery reflecting leapfrog growth as a result of rapid urban sprawling.

The continual urban growth alters the urban ecological landscape pattern and urban ecosystem functions, posing significant challenges to urban environmental and ecological management. The primary goal of this research is to analyze the dynamic character of urbanization and its impact on urban ecosystem services because of the alteration of land use and land cover in Asansol Municipal Corporation. This study employs various analytical tools, including Shannon's entropy and loss and gain analysis, to assess and quantify the urban growth of the study area over the last two decades (2001, 2011, and 2021). The ecosystem service values (ESVs) have been examined using remote sensing and GIS techniques, which correlate with the global value coefficient to estimate total ecosystem service values and individual ecosystem service functions. The present study finds ESVs have declined over time in the study region. The loss in total and some individual ESV in this study landscape necessitates immediate action to improve urban ecosystem sustainability through proper planning and policy implementation.