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Monitoring urbanization dynamics in India using DMSP/OLS night time lights and SPOT-VGT data |
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| Institution: | 1. Department of Natural Resources, TERI University, New Delhi, India;2. Yale School of Forestry & Environmental Studies, Yale University, New Haven, CT, United States;10. Department of Civil, Construction and Environmental Engineering, University of Alabama, Tuscaloosa, AL 35487, USA;11. Center for Sustainable Infrastructure, , University of Alabama, Tuscaloosa, AL 35487, USA;12. Center for Geospatial Technology, Texas Tech University, Lubbock, TX, 79409, USA;1. School of Geographical Science, Northeast Normal University, Changchun, Jilin 130024, China;2. Department of Geography, University of Cincinnati, Cincinnati, OH 45221, USA;3. Department of Geography and Geosciences, University of Louisville, Louisville, KY 40292, USA;4. Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China;1. Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA;2. Social, Urban, Rural and Resilience Global Practice, World Bank, Washington, DC 20433, USA;1. Center for Urban and Environmental Change, Department of Earth and Environmental Systems, Indiana State University, Terre Haute, IN 47809, USA;2. Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China |
| Abstract: | India is a rapidly urbanizing country and has experienced profound changes in the spatial structure of urban areas. This study endeavours to illuminate the process of urbanization in India using Defence Meteorological Satellites Program – Operational Linescan System (DMSP-OLS) night time lights (NTLs) and SPOT vegetation (VGT) dataset for the period 1998–2008. Satellite imagery of NTLs provides an efficient way to map urban areas at global and national scales. DMSP/OLS dataset however lacks continuity and comparability; hence the dataset was first intercalibrated using second order polynomial regression equation. The intercalibrated dataset along with SPOT-VGT dataset for the year 1998 and 2008 were subjected to a support vector machine (SVM) method to extract urban areas. SVM is semi-automated technique that overcomes the problems associated with the thresholding methods for NTLs data and hence enables for regional and national scale assessment of urbanization. The extracted urban areas were validated with Google Earth images and global urban extent maps. Spatial metrics were calculated and analyzed state-wise to understand the dynamism of urban areas in India. Significant changes in urban proportion were observed in Tamil Nadu, Punjab and Kerala while other states also showed a high degree of changes in area wise urban proportion. |
| Keywords: | Urban growth Night time lights Intercalibration Support vector machine Landscape metrics |
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