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Monitoring Metropolitan Growth Dynamics for Achieving Sustainable Urbanization (SDG 11.3) in Kolkata Metropolitan Area, India

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Title: Monitoring Metropolitan Growth Dynamics for Achieving Sustainable Urbanization (SDG 11.3) in Kolkata Metropolitan Area, India
Authors: Mithun, Sk Browse this author
Sahana, Mehebub Browse this author
Chattopadhyay, Subrata Browse this author
Johnson, Brian Alan Browse this author
Khedher, Khaled Mohamed Browse this author
Avtar, Ram Browse this author →KAKEN DB
Keywords: land use and land cover
change detection
landscape metrics
Kolkata Metropolitan Area
urban growth dynamics
SDG 11.3
concentric zone approach
spatiotemporal heterogeneity
Shannon's entropy
Issue Date: Nov-2021
Publisher: MDPI
Journal Title: Remote Sensing
Volume: 13
Issue: 21
Start Page: 4423
Publisher DOI: 10.3390/rs13214423
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.
Type: article
Appears in Collections:環境科学院・地球環境科学研究院 (Graduate School of Environmental Science / Faculty of Environmental Earth Science) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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