Monitoring Metropolitan Growth Dynamics for Achieving Sustainable Urbanization (SDG 11.3) in Kolkata Metropolitan Area, India
Monitoring Metropolitan Growth Dynamics for Achieving Sustainable Urbanization (SDG 11.3) in Kolkata Metropolitan Area, India
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DOI：
10.3390/rs13214423
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
S. Mithun;M. Sahana;S. Chattopadhyay;B. Johnson;K. M. Khedher;R. Avtar
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文献类型：
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作者：
S. Mithun;M. Sahana;S. Chattopadhyay;B. Johnson;K. M. Khedher;R. Avtar
在过去的几十年里，印度大城市人口的大规模积累导致了周边城市加速和史无前例的扩张。这种快速的周边发展的特点是不受控制的、低密度、零散和随意的发展拼凑，通常被称为城市蔓延。加尔各答大都会地区(KMA)一直是印度发展最快的大都市地区之一，正在经历猖獗的郊区化和外围扩张。因此，在这些快速变化的环境中了解城市增长及其动态对城市规划者和资源管理者来说至关重要。此外，了解城市扩展和城市增长模式对于实现联合国在可持续发展目标(例如，可持续发展目标11.3)中界定的包容性和可持续城市化至关重要。本研究试图以KMA为例，用一种独特的方法对大而多样的大都市地区的城市增长动态进行量化和建模。在这项研究中，通过使用支持向量机分类方法对陆地卫星图像进行分类，编制了三个不同年份(即1996年、2006年和2016年)的土地利用和土地覆盖(LULC)地图。然后，应用变化检测分析、景观度量、同心带方法和香农的熵方法对KMA的城市增长进行了时空评价和量化。分类正确率分别为89.75%、92.00%和92.75%，对应的Kappa值分别为0.879、0.904和0.912。由此得出的结论是，KMA一直在经历典型的城市扩张。与近些年变得更加紧凑的中心城市地区(即城市地区)相比，城市周边地区(即农村地区)正在迅速发展，其特点是建成区发展跨越式和碎片化。
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.