IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i1p117-d194871.html
   My bibliography  Save this article

Evolution of the Pattern of Spatial Expansion of Urban Land Use in the Poyang Lake Ecological Economic Zone

Author

Listed:
  • Yang Zhong

    (School of Resources and Environmental Sciences, Wuhan University, Wuhan 430079, China
    Key Laboratory of Geographic Information System, Wuhan University, Wuhan 430079, China)

  • Aiwen Lin

    (School of Resources and Environmental Sciences, Wuhan University, Wuhan 430079, China
    Key Laboratory of Geographic Information System, Wuhan University, Wuhan 430079, China)

  • Zhigao Zhou

    (School of Resources and Environmental Sciences, Wuhan University, Wuhan 430079, China
    Key Laboratory of Geographic Information System, Wuhan University, Wuhan 430079, China)

Abstract

To grasp the evolutionary characteristics and regularity of urban land expansion patterns in the Poyang Lake Ecological Economic Zone, this study, based on nighttime lighting data, uses the Landsat series satellite simultaneous data and cluster analysis to correct the Defense Meteorological Satellite Program–Operational Linescan System (DMSP-OLS) nighttime lighting data and then uses the auxiliary data-based comparison method to determine the threshold for extracting the urban built-up area. Based on this threshold, a total of eight typical landscape pattern indicators, including landscape total area, total patches number, patches density, maximum patches index, and agglomeration index, etc., are selected. Next, the landscape spatial pattern analysis method and standard deviation ellipse method are used. The results show the following: (1) In 1992–2013, urbanization in the Poyang Lake Ecological Economic Zone expanded rapidly. The urban built-up area increased by 8.13 times, the number of plaques increased by 1.5 times, and the shape complexity of landscape plaques gradually increased. There is a large correlation between the changes in the total boundary length, and the average boundary density, the average annual growth rate of the two is 21.33% and 17.45%. (2) The two indicators of maximum plaque index and aggregation index show a downward trend year by year. However, there are some fluctuations and irregularities in the evolution of the total landscape area, total plaque number and plaque density. (3) The long axis and the short axis of the standard deviation ellipse of the Poyang Lake Ecological Economic Zone show small variation during the inspection period and generally have an elliptical shape. The movement of the center of gravity is mainly from the southwest to the northeast, but the migration of the center of gravity is relatively small. Based on this, this paper proposes three countermeasures and suggestions as a guide to promote the optimization and development of the spatial expansion pattern of the Poyang Lake eco-economic zone.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:1:p:117-:d:194871
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/1/117/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/16/1/117/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Hualin Xie & Peng Wang & Hongsheng Huang, 2013. "Ecological Risk Assessment of Land Use Change in the Poyang Lake Eco-economic Zone, China," IJERPH, MDPI, vol. 10(1), pages 1-19, January.
    3. Yang Zhong & Aiwen Lin & Zhigao Zhou & Feiyan Chen, 2018. "Spatial Pattern Evolution and Optimization of Urban System in the Yangtze River Economic Belt, China, Based on DMSP-OLS Night Light Data," Sustainability, MDPI, vol. 10(10), pages 1-14, October.
    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. Shisong Cao & Deyong Hu & Wenji Zhao & You Mo & Shanshan Chen, 2017. "Monitoring Spatial Patterns and Changes of Ecology, Production, and Living Land in Chinese Urban Agglomerations: 35 Years after Reform and Opening Up, Where, How and Why?," Sustainability, MDPI, vol. 9(5), pages 1-18, May.
    6. Si-hua Chen, 2017. "An Evolutionary Game Study of an Ecological Industry Chain Based on Multi-Agent Simulation: A Case Study of the Poyang Lake Eco-Economic Zone," Sustainability, MDPI, vol. 9(7), pages 1-27, July.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yali Wei & Ying Li & Siying Wang & Junyi Wang & Yu Zhu, 2023. "Research on the Spatial Expansion Characteristics and Industrial and Policy Driving Forces of Chengdu–Chongqing Urban Agglomeration Based on NPP-VIIRS Night Light Remote Sensing Data," Sustainability, MDPI, vol. 15(3), pages 1-22, January.
    2. Yudan Zhang & Yuanqing Li & Yanan Chen & Shirao Liu & Qingyuan Yang, 2022. "Spatiotemporal Heterogeneity of Urban Land Expansion and Urban Population Growth under New Urbanization: A Case Study of Chongqing," IJERPH, MDPI, vol. 19(13), pages 1-25, June.
    3. Tianzhu Zhang & Yang Gao & Chao Li & Zhen Xie & Yuyang Chang & Bailin Zhang, 2020. "How Human Activity Has Changed the Regional Habitat Quality in an Eco-Economic Zone: Evidence from Poyang Lake Eco-Economic Zone, China," IJERPH, MDPI, vol. 17(17), pages 1-21, August.
    4. Hui Wang, 2021. "Regional assessment of human-caused ecological risk in the Poyang Lake Eco-economic Zone using production–living–ecology analysis," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-22, February.
    5. Pengcheng Lv & Xiaodong Li & Haoyu Zhang & Xiang Liu & Lingzhang Kong, 2022. "Research on the Spatial and Temporal Distribution of Logistics Enterprises in Xinjiang and the Influencing Factors Based on POI Data," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
    6. Xinyang Li & Marek Kozlowski & Sumarni Binti Ismail & Sarah Abdulkareem Salih, 2024. "Spatial Distribution Characteristics of Leisure Urban Spaces and the Correlation with Population Activity Intensity: A Case Study of Nanjing, China," Sustainability, MDPI, vol. 16(16), pages 1-21, August.
    7. Hualin Xie & Zhe Li & Yu Xu, 2022. "Study on the Coupling and Coordination Relationship between Gross Ecosystem Product (GEP) and Regional Economic System: A Case Study of Jiangxi Province," Land, MDPI, vol. 11(9), pages 1-20, September.

    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. 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.
    5. Yang Zhong & Aiwen Lin & Zhigao Zhou & Feiyan Chen, 2018. "Spatial Pattern Evolution and Optimization of Urban System in the Yangtze River Economic Belt, China, Based on DMSP-OLS Night Light Data," Sustainability, MDPI, vol. 10(10), pages 1-14, October.
    6. Gang Xu & Tianyi Zeng & Hong Jin & Cong Xu & Ziqi Zhang, 2023. "Spatio-Temporal Variations and Influencing Factors of Country-Level Carbon Emissions for Northeast China Based on VIIRS Nighttime Lighting Data," IJERPH, MDPI, vol. 20(1), pages 1-17, January.
    7. Yongxing Li & Wei Guo & Peixian Li & Xuesheng Zhao & Jinke Liu, 2023. "Exploring the Spatiotemporal Dynamics of CO 2 Emissions through a Combination of Nighttime Light and MODIS NDVI Data," Sustainability, MDPI, vol. 15(17), pages 1-17, August.
    8. Jasiński, Tomasz, 2019. "Modeling electricity consumption using nighttime light images and artificial neural networks," Energy, Elsevier, vol. 179(C), pages 831-842.
    9. Yangyang Gu & Xuning Qiao & Mengjia Xu & Changxin Zou & Dong Liu & Dan Wu & Yan Wang, 2019. "Assessing the Impacts of Urban Expansion on Bundles of Ecosystem Services by Dmsp-Ols Nighttime Light Data," Sustainability, MDPI, vol. 11(21), pages 1-17, October.
    10. Lv, Zhuoran & Guo, Huadong & Zhang, Lu & Liang, Dong & Zhu, Qi & Liu, Xuting & Zhou, Heng & Liu, Yiming & Gou, Yiting & Dou, Xinyu & Chen, Guoqiang, 2024. "Urban public lighting classification method and analysis of energy and environmental effects based on SDGSAT-1 glimmer imager data," Applied Energy, Elsevier, vol. 355(C).
    11. 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.
    12. Cui, Yuanzheng & Zhang, Weishi & Wang, Can & Streets, David G. & Xu, Ying & Du, Mingxi & Lin, Jintai, 2019. "Spatiotemporal dynamics of CO2 emissions from central heating supply in the North China Plain over 2012–2016 due to natural gas usage," Applied Energy, Elsevier, vol. 241(C), pages 245-256.
    13. 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.
    14. Shi, Kaifang & Yu, Bailang & Zhou, Yuyu & Chen, Yun & Yang, Chengshu & Chen, Zuoqi & Wu, Jianping, 2019. "Spatiotemporal variations of CO2 emissions and their impact factors in China: A comparative analysis between the provincial and prefectural levels," Applied Energy, Elsevier, vol. 233, pages 170-181.
    15. 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).
    16. 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).
    17. Hu, Ting & Wang, Ting & Yan, Qingyun & Chen, Tiexi & Jin, Shuanggen & Hu, Jun, 2022. "Modeling the spatiotemporal dynamics of global electric power consumption (1992–2019) by utilizing consistent nighttime light data from DMSP-OLS and NPP-VIIRS," Applied Energy, Elsevier, vol. 322(C).
    18. Yanjun Wang & Fei Teng & Mengjie Wang & Shaochun Li & Yunhao Lin & Hengfan Cai, 2022. "Monitoring Spatiotemporal Distribution of the GDP of Major Cities in China during the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(13), pages 1-29, June.
    19. Guo, Jinyu & Ma, Jinji & Li, Zhengqiang & Hong, Jin, 2022. "Building a top-down method based on machine learning for evaluating energy intensity at a fine scale," Energy, Elsevier, vol. 255(C).
    20. 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.

    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:jijerp:v:16:y:2019:i:1:p:117-:d:194871. 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.