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Spatial and Temporal Changes of Urban Built-Up Area in the Yellow River Basin from Nighttime Light Data

Author

Listed:
  • Jingxu Wang

    (Key Laboratory of Remote Sensing and Geographic Information System of Henan Province, Institute of Geography, Henan Academy of Sciences, Zhengzhou 450052, China)

  • Shike Qiu

    (Key Laboratory of Remote Sensing and Geographic Information System of Henan Province, Institute of Geography, Henan Academy of Sciences, Zhengzhou 450052, China)

  • Jun Du

    (Key Laboratory of Remote Sensing and Geographic Information System of Henan Province, Institute of Geography, Henan Academy of Sciences, Zhengzhou 450052, China)

  • Shengwang Meng

    (Qianyanzhou Ecological Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Chao Wang

    (Key Laboratory of Remote Sensing and Geographic Information System of Henan Province, Institute of Geography, Henan Academy of Sciences, Zhengzhou 450052, China)

  • Fei Teng

    (Key Laboratory of Remote Sensing and Geographic Information System of Henan Province, Institute of Geography, Henan Academy of Sciences, Zhengzhou 450052, China)

  • Yangyang Liu

    (PIESAT International Information Technology Limited, Beijing 100195, China)

Abstract

Nighttime light (NTL) images obtained by the Visible Infrared Imaging Radiometer (VIIRS) mounted on the National Polar-orbiting Partnership (NPP) could objectively represent human activities and instantly identify urban shapes on a temporal and spatial scale. From 2013 to 2020, the built-up areas of eight provincial capital cities were extracted using NPP/VIIRS NTL data to examine the dynamic changes in city expansion and socioeconomic development in the Yellow River Basin during the urbanization process. The spatial characteristics of urban built-up area expansion were generated using the eight-quadrant analysis method and combined with the statistical data of population and (gross domestic product) GDP to analyze the correlations between the light intensity of built-up areas, population and GDP; this enables an understanding of the changes in population and economy in the development of urban built-up area expansion. The findings show that: (1) unbalanced city development existed in the Yellow River Basin’s upper, middle, and lower reaches, and the expansion and light intensity of cities in the upper reaches were slower than those in the middle and lower reaches; (2) the spatial differentiation of urban expansion was significant between each of the reaches in the Yellow River Basin, and greatly influenced by natural geographical elements; and (3) positive correlation exists between light intensity, population, and GDP in the built-up areas of the middle and lower reaches, while the correlations in the upper reaches were not stable. In conclusion, light data indirectly reflects urban development and could be used as a substitute variable for socioeconomic development indicators under certain conditions.

Suggested Citation

  • Jingxu Wang & Shike Qiu & Jun Du & Shengwang Meng & Chao Wang & Fei Teng & Yangyang Liu, 2022. "Spatial and Temporal Changes of Urban Built-Up Area in the Yellow River Basin from Nighttime Light Data," Land, MDPI, vol. 11(7), pages 1-14, July.
  • Handle: RePEc:gam:jlands:v:11:y:2022:i:7:p:1067-:d:861688
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    References listed on IDEAS

    as
    1. Wen, Tianzuo & Qiang, Wei & Liu, Xingjian, 2022. "Exploring the geography of urban comprehensive development in mainland Chinese cities," Land Use Policy, Elsevier, vol. 115(C).
    2. Berliant, Marcus & Weiss, Adam, 2013. "Measuring Economic Growth from Outer Space: A Comment," MPRA Paper 51055, University Library of Munich, Germany.
    3. J. Vernon Henderson & Adam Storeygard & David N. Weil, 2012. "Measuring Economic Growth from Outer Space," American Economic Review, American Economic Association, vol. 102(2), pages 994-1028, April.
    4. Charlotta Mellander & José Lobo & Kevin Stolarick & Zara Matheson, 2015. "Night-Time Light Data: A Good Proxy Measure for Economic Activity?," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-18, October.
    5. Xiling Zhang & Yusheng Kong & Xuhui Ding, 2020. "How High-Quality Urbanization Affects Utilization Efficiency of Agricultural Water Resources in the Yellow River Basin under Double Control Action?," Sustainability, MDPI, vol. 12(7), pages 1-16, April.
    6. Luping Shi & Zhongyao Cai & Xuhui Ding & Rong Di & Qianqian Xiao, 2020. "What Factors Affect the Level of Green Urbanization in the Yellow River Basin in the Context of New-Type Urbanization?," Sustainability, MDPI, vol. 12(6), pages 1-15, March.
    7. Lyu, Rongfang & Zhang, Jianming & Xu, Mengqun & Li, Jijun, 2018. "Impacts of urbanization on ecosystem services and their temporal relations: A case study in Northern Ningxia, China," Land Use Policy, Elsevier, vol. 77(C), pages 163-173.
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