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A Simulation Study on the Urban Population of China Based on Nighttime Light Data Acquired from DMSP/OLS

Author

Listed:
  • Qingxu Huang

    (Center for Human-Environment System Sustainability, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, 19 Xinjiekouwai Street, Beijing 100875, China)

  • Yang Yang

    (Teaching and Research Section of Land Resources Management, Department of Public Administration, Law & Politics School, Ocean University of China, 238 Songling Road, Qingdao 266100, China)

  • Yajing Li

    (Teaching and Research Section of Land Resources Management, Department of Public Administration, Law & Politics School, Ocean University of China, 238 Songling Road, Qingdao 266100, China)

  • Bin Gao

    (Center for Human-Environment System Sustainability, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, 19 Xinjiekouwai Street, Beijing 100875, China
    College of Resources Science & Technology, Beijing Normal University, 19 Xinjiekouwai Street, Beijing 100875, China)

Abstract

The urban population (UP) measure is one of the most direct indicators that reflect the urbanization process and the impacts of human activities. The dynamics of UP is of great importance to studying urban economic, social development, and resource utilization. Currently, China lacks long time series UP data with consistent standards and comparability over time. The nighttime light images from the Defense Meteorological Satellite Program’s (DMSP) Operational Linescan System (OLS) allow the acquisition of continuous and highly comparable long time series UP information. However, existing studies mainly focus on simulating the total population or population density level based on the nighttime light data. Few studies have focused on simulating the UP in China. Based on three regression models ( i.e. , linear, power function, and exponential), the present study discusses the relationship between DMSP/OLS nighttime light data and the UP and establishes optimal regression models for simulating the UPs of 339 major cities in China from 1990 to 2010. In addition, the present study evaluated the accuracy of UP and non-agricultural population (NAP) simulations conducted using the same method. The simulation results show that, at the national level, the power function model is the optimal regression model between DMSP/OLS nighttime light data and UP data for 1990–2010. At the provincial scale, the optimal regression model varies among different provinces. The linear regression model is the optimal regression model for more than 60% of the provinces. In addition, the comparison results show that at the national, provincial, and city levels, the fitting results of the UP based on DMSP/OLS nighttime light data are better than those of the NAP. Therefore, DMSP/OLS nighttime light data can be used to effectively retrieve the UP of a large-scale region. In the context of frequent population flows between urban and rural areas in China and difficulty in obtaining accurate UP data, this study provides a timely and effective method for solving this problem.

Suggested Citation

  • Qingxu Huang & Yang Yang & Yajing Li & Bin Gao, 2016. "A Simulation Study on the Urban Population of China Based on Nighttime Light Data Acquired from DMSP/OLS," Sustainability, MDPI, vol. 8(6), pages 1-13, May.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:6:p:521-:d:71028
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    References listed on IDEAS

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    Cited by:

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    3. Hoyong Kim & Donghyun Kim, 2022. "Changes in Urban Growth Patterns in Busan Metropolitan City, Korea: Population and Urbanized Areas," Land, MDPI, vol. 11(8), pages 1-18, August.
    4. Jingtao Wang & Haibin Liu & Di Peng & Qian Lv & Yu Sun & Hui Huang & Hao Liu, 2021. "The County-Scale Economic Spatial Pattern and Influencing Factors of Seven Urban Agglomerations in the Yellow River Basin—A Study Based on the Integrated Nighttime Light Data," Sustainability, MDPI, vol. 13(8), pages 1-22, April.
    5. Yue Li & Chengmeng Zhang & Yan Tong & Yalu Zhang & Gong Chen, 2022. "Prediction of the Old-Age Dependency Ratio in Chinese Cities Using DMSP/OLS Nighttime Light Data," IJERPH, MDPI, vol. 19(12), pages 1-23, June.
    6. Jie Liu & Qingshan Yang & Jian Liu & Yu Zhang & Xiaojun Jiang & Yangmeina Yang, 2020. "Study on the Spatial Differentiation of the Populations on Both Sides of the “Qinling-Huaihe Line” in China," Sustainability, MDPI, vol. 12(11), pages 1-25, June.
    7. Yang Zhong & Aiwen Lin & Zhigao Zhou, 2019. "Evolution of the Pattern of Spatial Expansion of Urban Land Use in the Poyang Lake Ecological Economic Zone," IJERPH, MDPI, vol. 16(1), pages 1-14, January.
    8. Ge Shi & Nan Jiang & Yang Li & Bin He, 2018. "Analysis of the Dynamic Urban Expansion Based on Multi-Sourced Data from 1998 to 2013: A Case Study of Jiangsu Province," Sustainability, MDPI, vol. 10(10), pages 1-18, September.

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