IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v8y2016i8p790-d75780.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/8/8/790/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/8/8/790/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Shi, Kaifang & Yu, Bailang & Huang, Chang & Wu, Jianping & Sun, Xiufeng, 2018. "Exploring spatiotemporal patterns of electric power consumption in countries along the Belt and Road," Energy, Elsevier, vol. 150(C), pages 847-859.
    3. 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.
    4. 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.
    5. Jie Zhao & Nguyen Xuan Thinh & Cheng Li, 2017. "Investigation of the Impacts of Urban Land Use Patterns on Energy Consumption in China: A Case Study of 20 Provincial Capital Cities," Sustainability, MDPI, vol. 9(8), pages 1-22, August.
    6. Yongguang Zhu & Deyi Xu & Saleem H. Ali & Ruiyang Ma & Jinhua Cheng, 2019. "Can Nighttime Light Data Be Used to Estimate Electric Power Consumption? New Evidence from Causal-Effect Inference," Energies, MDPI, vol. 12(16), pages 1-14, August.
    7. Qingwei Shi & Jingxin Gao & Xia Wang & Hong Ren & Weiguang Cai & Haifeng Wei, 2020. "Temporal and Spatial Variability of Carbon Emission Intensity of Urban Residential Buildings: Testing the Effect of Economics and Geographic Location in China," Sustainability, MDPI, vol. 12(7), pages 1-23, March.
    8. Lu, Linlin & Weng, Qihao & Xie, Yanhua & Guo, Huadong & Li, Qingting, 2019. "An assessment of global electric power consumption using the Defense Meteorological Satellite Program-Operational Linescan System nighttime light imagery," Energy, Elsevier, vol. 189(C).
    9. Yang, Di & Luan, Weixin & Qiao, Lu & Pratama, Mahardhika, 2020. "Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery," Applied Energy, Elsevier, vol. 268(C).
    10. 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.
    11. Zhao, Jincai & Ji, Guangxing & Yue, YanLin & Lai, Zhizhu & Chen, Yulong & Yang, Dongyang & Yang, Xu & Wang, Zheng, 2019. "Spatio-temporal dynamics of urban residential CO2 emissions and their driving forces in China using the integrated two nighttime light datasets," Applied Energy, Elsevier, vol. 235(C), pages 612-624.
    12. Yajing Liu & Shuai Zhou & Ge Zhang, 2023. "Spatio-Temporal Dynamics and Driving Forces of Multi-Scale Emissions Based on Nighttime Light Data: A Case Study of the Pearl River Delta Urban Agglomeration," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
    13. Wanchun Leng & Guojin He & Wei Jiang, 2019. "Investigating the Spatiotemporal Variability and Driving Factors of Artificial Lighting in the Beijing-Tianjin-Hebei Region Using Remote Sensing Imagery and Socioeconomic Data," IJERPH, MDPI, vol. 16(11), pages 1-20, June.
    14. Tilottama Ghosh & Christopher D. Elvidge & Paul C. Sutton & Kimberly E. Baugh & Daniel Ziskin & Benjamin T. Tuttle, 2010. "Creating a Global Grid of Distributed Fossil Fuel CO 2 Emissions from Nighttime Satellite Imagery," Energies, MDPI, vol. 3(12), pages 1-19, December.
    15. Thomas Akpan Harry & Ekemini John Peter & Nsidibe Akpan Udoduk, 2022. "Environmental Impact Assessment Of Oil Producing Communities In Part Of The Niger Delta. A Case Study Of Ibeno, Ikot Abasi, Onna And Esit-Eket Local Government Area In Akwa Ibom State, Nigeria," Environmental Contaminants Reviews (ECR), Zibeline International Publishing, vol. 5(2), pages 49-56, April.
    16. Boslett, Andrew & Hill, Elaine & Ma, Lala & Zhang, Lujia, 2021. "Rural light pollution from shale gas development and associated sleep and subjective well-being," Resource and Energy Economics, Elsevier, vol. 64(C).
    17. Yaxi Gong & Xiang Ji & Yuan Zhang & Shanshan Cheng, 2023. "Spatial Vitality Evaluation and Coupling Regulation Mechanism of a Complex Ecosystem in Lixiahe Plain Based on Multi-Source Data," Sustainability, MDPI, vol. 15(3), pages 1-32, January.
    18. Huang, Kaixing & Yan, Wenshou & Huang, Jikun, 2020. "Agricultural subsidies retard urbanisation in China," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 64(04), January.
    19. Xuemei Wang & Mingguo Ma, 2017. "The luminous intensity of regional ‘night-light’ output can predict the growing volume of published scientific research by ‘luminaries’ in developing countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 1005-1010, February.
    20. Andrew M. Linke & Frank D. W. Witmer & John O'Loughlin, 2012. "Space-Time Granger Analysis of the War in Iraq: A Study of Coalition and Insurgent Action-Reaction," International Interactions, Taylor & Francis Journals, vol. 38(4), pages 402-425, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:8:y:2016:i:8:p:790-:d:75780. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.