Abstract
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.
Highlights
Detecting and quantifying urban expansion patterns and processes are standard practices in urban sprawl studies [1,2,3,4]
This study focused on the Kolkata Metropolitan Area (KMA) as a case study for spatiotemporal assessment and quantification of urban growth dynamics for the achievement of sustainable urbanization (SDG 11.3) in developing countries
A dilemma in urban growth seems to be evident in the metropolitan area, where, on the one hand, the non-built-up land covers were being converted into mixed built-up along the periphery at a large scale as a result of urban sprawl; on the other hand, because of the processes of urban growth such as infill, expansion, and edge growth, the existing mixed built-up areas were being converted into pure urban built-up areas over time [4,55]
Summary
Detecting and quantifying urban expansion patterns and processes are standard practices in urban sprawl studies [1,2,3,4]. Researchers have developed various indices and models coupled with RS-GIS to quantify patterns and processes of urban growth in cities. Change detection using multispectral and temporal RS images is a popular method for mapping the spatiotemporal dynamics of land cover in an area. Based on these land-cover-change maps, Shannon’s entropy (Hn ) has proven its usefulness and reliability in quantifying the degree of compactness and dispersion of urban growth in absolute scale [4,9,11,12,13]. Researchers often use Hn in combination with other landscape metrics to establish and explain the results with greater certainty [4]
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