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Detecting the Dynamics of Urban Growth in Africa Using DMSP/OLS Nighttime Light Data

Author

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  • Shengnan Jiang

    (School of Geographic & Oceanographic Sciences, Nanjing University, Nanjing 210023, China)

  • Guoen Wei

    (School of Geographic & Oceanographic Sciences, Nanjing University, Nanjing 210023, China)

  • Zhenke Zhang

    (School of Geographic & Oceanographic Sciences, Nanjing University, Nanjing 210023, China
    The Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing 210093, China)

  • Yue Wang

    (School of Geographic & Oceanographic Sciences, Nanjing University, Nanjing 210023, China
    The Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing 210093, China)

  • Minghui Xu

    (School of Geographic & Oceanographic Sciences, Nanjing University, Nanjing 210023, China)

  • Qing Wang

    (School of Geographic & Oceanographic Sciences, Nanjing University, Nanjing 210023, China
    The Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing 210093, China)

  • Priyanko Das

    (School of Geographic & Oceanographic Sciences, Nanjing University, Nanjing 210023, China)

  • Binglin Liu

    (School of Geographic & Oceanographic Sciences, Nanjing University, Nanjing 210023, China
    The Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing 210093, China)

Abstract

Africa has been experiencing a rapid urbanization process, which may lead to an increase in unsustainable land use and urban poverty. Assessing the spatiotemporal characteristics of urbanization dynamics is especially important and needed for the sustainable development of Africa. Satellite-based nighttime light (NTL) data are widely used to monitor the dynamics of urban growth from global to local scales. In this study, urban growth patterns across Africa were analyzed and discussed using stable nighttime light datasets obtained from DMSP/OLS (the Defense Meteorological Satellite Program’s Operational Line-scan System) spanning from 1992 to 2013. We partitioned the nighttime lighting areas into three types (low, medium, and high) using thresholds derived from the Brightness Gradient (BG) method. Our results indicated that built-up areas in Africa have increased rapidly, particularly those areas with low nighttime lighting types. Countries with higher urbanization levels in Africa, like South Africa, Algeria, Egypt, Nigeria, and Libya, were leading the brightening trend. The distribution of nighttime lighting types was consistent with the characteristics of urban development, with high nighttime lighting types showed up at the urban center, whereas medium and low nighttime lighting types appeared in the urban-rural transition zone and rural areas respectively. The impacts of these findings on the future of African cities will be further proposed.

Suggested Citation

  • Shengnan Jiang & Guoen Wei & Zhenke Zhang & Yue Wang & Minghui Xu & Qing Wang & Priyanko Das & Binglin Liu, 2020. "Detecting the Dynamics of Urban Growth in Africa Using DMSP/OLS Nighttime Light Data," Land, MDPI, vol. 10(1), pages 1-19, December.
    • Handle: RePEc:gam:jlands:v:10:y:2020:i:1:p:13-:d:468620
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    References listed on IDEAS

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    1. Kukkonen, Markus O. & Muhammad, Muhammad J. & Käyhkö, Niina & Luoto, Miska, 2018. "Urban expansion in Zanzibar City, Tanzania: Analyzing quantity, spatial patterns and effects of alternative planning approaches," Land Use Policy, Elsevier, vol. 71(C), pages 554-565.
    2. Shi, Kaifang & Chen, Yun & Yu, Bailang & Xu, Tingbao & Yang, Chengshu & Li, Linyi & Huang, Chang & Chen, Zuoqi & Liu, Rui & Wu, Jianping, 2016. "Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data," Applied Energy, Elsevier, vol. 184(C), pages 450-463.
    3. Jean-François Pekel & Andrew Cottam & Noel Gorelick & Alan S. Belward, 2016. "High-resolution mapping of global surface water and its long-term changes," Nature, Nature, vol. 540(7633), pages 418-422, December.
    4. Tilottama Ghosh & Sharolyn J. Anderson & Christopher D. Elvidge & Paul C. Sutton, 2013. "Using Nighttime Satellite Imagery as a Proxy Measure of Human Well-Being," Sustainability, MDPI, vol. 5(12), pages 1-32, November.
    5. Hu, Ting & Huang, Xin, 2019. "A novel locally adaptive method for modeling the spatiotemporal dynamics of global electric power consumption based on DMSP-OLS nighttime stable light data," Applied Energy, Elsevier, vol. 240(C), pages 778-792.
    6. Ziyang Cao & Zhifeng Wu & Yaoqiu Kuang & Ningsheng Huang & Meng Wang, 2016. "Coupling an Intercalibration of Radiance-Calibrated Nighttime Light Images and Land Use/Cover Data for Modeling and Analyzing the Distribution of GDP in Guangdong, China," Sustainability, MDPI, vol. 8(2), pages 1-18, January.
    7. Doll, Christopher N.H. & Muller, Jan-Peter & Morley, Jeremy G., 2006. "Mapping regional economic activity from night-time light satellite imagery," Ecological Economics, Elsevier, vol. 57(1), pages 75-92, April.
    8. Cohen, Barney, 2006. "Urbanization in developing countries: Current trends, future projections, and key challenges for sustainability," Technology in Society, Elsevier, vol. 28(1), pages 63-80.
    9. Hang Ren & Wei Guo & Zhenke Zhang & Leonard Musyoka Kisovi & Priyanko Das, 2020. "Population Density and Spatial Patterns of Informal Settlements in Nairobi, Kenya," Sustainability, MDPI, vol. 12(18), pages 1-14, September.
    10. Mingxing Chen & Hua Zhang & Weidong Liu & Wenzhong Zhang, 2014. "The Global Pattern of Urbanization and Economic Growth: Evidence from the Last Three Decades," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-15, August.
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    2. Li Li & Lianqi Zhu & Nan Xu & Ying Liang & Zhengyu Zhang & Junjie Liu & Xin Li, 2022. "Climate Change and Diurnal Warming: Impacts on the Growth of Different Vegetation Types in the North–South Transition Zone of China," Land, MDPI, vol. 12(1), pages 1-16, December.

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