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Urban Growth and Rural Transition in China Based on DMSP/OLS Nighttime Light Data

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  • Minghong Tan

    (Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Beijing 100101, China)

Abstract

Nighttime light (NTL) images provide uniform, consistent, and valuable data sources. Based on four reference regions, the NTL imagery of China was fully intercalibrated during the period 1992–2012. Using lit areas and the intensity of NTL imagery, this study synthetically analyzed the urbanization process and rural transition in China. The results showed that, over the whole country, the pixel numbers of urban areas increased by 173% from 1992 to 2012. During the 2000s, urban areas expanded much more quickly than during the 1990s. Urban growth varied greatly across the four regions, which could be pictured using the flying-geese paradigm. In the 1990s, East China experienced the most rapid rate of growth, while a decade later Middle China had the highest growth rate. NTL imagery can also be used to describe changes in rural human activities if the imagery is corrected using completely dark rural pixels. In China, because of the massive rural-urban migration, some marginal regions experienced a decrease in nighttime light intensity (NTLI) and rural areas went through a period of transition.

Suggested Citation

  • Minghong Tan, 2015. "Urban Growth and Rural Transition in China Based on DMSP/OLS Nighttime Light Data," Sustainability, MDPI, vol. 7(7), pages 1-14, July.
    • Handle: RePEc:gam:jsusta:v:7:y:2015:i:7:p:8768-8781:d:52160
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    5. Yu Cao & Yucen Wang & Guoyu Li & Xiaoqian Fang, 2019. "Vegetation Response to Urban Landscape Spatial Pattern Change in the Yangtze River Delta, China," Sustainability, MDPI, vol. 12(1), pages 1-18, December.
    6. Xie, Yanhua & Weng, Qihao, 2016. "Detecting urban-scale dynamics of electricity consumption at Chinese cities using time-series DMSP-OLS (Defense Meteorological Satellite Program-Operational Linescan System) nighttime light imageries," Energy, Elsevier, vol. 100(C), pages 177-189.
    7. Xiao, Hongwei & Ma, Zhongyu & Mi, Zhifu & Kelsey, John & Zheng, Jiali & Yin, Weihua & Yan, Min, 2018. "Spatio-temporal simulation of energy consumption in China's provinces based on satellite night-time light data," Applied Energy, Elsevier, vol. 231(C), pages 1070-1078.
    8. Shi, Kaifang & Chen, Yun & Li, Linyi & Huang, Chang, 2018. "Spatiotemporal variations of urban CO2 emissions in China: A multiscale perspective," Applied Energy, Elsevier, vol. 211(C), pages 218-229.
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