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Urban Expansion and Agricultural Land Loss in China: A Multiscale Perspective

Author

Listed:
  • Kaifang Shi

    (Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
    CSIRO Land and Water, Canberra 2601, Australia)

  • Yun Chen

    (CSIRO Land and Water, Canberra 2601, Australia)

  • Bailang Yu

    (Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China)

  • Tingbao Xu

    (Fenner School of Environment and Society, The Australian National University, Linnaeus Way, Canberra 2601, Australia)

  • Linyi Li

    (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China)

  • Chang Huang

    (College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China)

  • Rui Liu

    (Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China)

  • Zuoqi Chen

    (Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China)

  • Jianping Wu

    (Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China)

Abstract

China’s rapid urbanization has contributed to a massive agricultural land loss that could threaten its food security. Timely and accurate mapping of urban expansion and urbanization-related agricultural land loss can provide viable measures to be taken for urban planning and agricultural land protection. In this study, urban expansion in China from 2001 to 2013 was mapped using the nighttime stable light (NSL), normalized difference vegetation index (NDVI), and water body data. Urbanization-related agricultural land loss during this time period was then evaluated at national, regional, and metropolitan scales by integrating multiple sources of geographic data. The results revealed that China’s total urban area increased from 31,076 km 2 in 2001 to 80,887 km 2 in 2013, with an average annual growth rate of 13.36%. This widespread urban expansion consumed 33,080 km 2 of agricultural land during this period. At a regional scale, the eastern region lost 18,542 km 2 or 1.2% of its total agricultural land area. At a metropolitan scale, the Shanghai–Nanjing–Hangzhou (SNH) and Pearl River Delta (PRD) areas underwent high levels of agricultural land loss with a decrease of 6.12% (4728 km 2 ) and 6.05% (2702 km 2 ) of their total agricultural land areas, respectively. Special attention should be paid to the PRD, with a decline of 13.30% (1843 km 2 ) of its cropland. Effective policies and strategies should be implemented to mitigate urbanization-related agricultural land loss in the context of China’s rapid urbanization.

Suggested Citation

  • Kaifang Shi & Yun Chen & Bailang Yu & Tingbao Xu & Linyi Li & Chang Huang & Rui Liu & Zuoqi Chen & Jianping Wu, 2016. "Urban Expansion and Agricultural Land Loss in China: A Multiscale Perspective," Sustainability, MDPI, vol. 8(8), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:8:p:790-:d:75780
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    References listed on IDEAS

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    1. Yi, Fujin & Sun, Dingqiang & Zhou, Yingheng, 2015. "Grain subsidy, liquidity constraints and food security—Impact of the grain subsidy program on the grain-sown areas in China," Food Policy, Elsevier, vol. 50(C), pages 114-124.
    2. Kraemer, Roland & Prishchepov, Alexander V & Müller, Daniel & Kuemmerle, Tobias & Radeloff, Volker C & Dara, Andrey & Terekhov, Alexey & Frühauf, Manfred, 2015. "Long-term agricultural land-cover change and potential for cropland expansion in the former Virgin Lands area of Kazakhstan," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 10(5), pages 1-17.
    3. Yan, Tingting & Wang, Jinxia & Huang, Jikun, 2015. "Urbanization, agricultural water use, and regional and national crop production in China," Ecological Modelling, Elsevier, vol. 318(C), pages 226-235.
    4. Shi, Kaifang & Chen, Yun & Yu, Bailang & Xu, Tingbao & Chen, Zuoqi & Liu, Rui & Li, Linyi & Wu, Jianping, 2016. "Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis," Applied Energy, Elsevier, vol. 168(C), pages 523-533.
    5. 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.
    6. Christopher D. Elvidge & Daniel Ziskin & Kimberly E. Baugh & Benjamin T. Tuttle & Tilottama Ghosh & Dee W. Pack & Edward H. Erwin & Mikhail Zhizhin, 2009. "A Fifteen Year Record of Global Natural Gas Flaring Derived from Satellite Data," Energies, MDPI, vol. 2(3), pages 1-28, August.
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