IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i10p3782-d176916.html
   My bibliography  Save this article

Spatial Pattern Evolution and Optimization of Urban System in the Yangtze River Economic Belt, China, Based on DMSP-OLS Night Light Data

Author

Listed:
  • Yang Zhong

    (School of Resources and Environmental Sciences, Wuhan University, Wuhan 430072, China
    Key Laboratory of Geographic Information System, Wuhan University, Wuhan 430079, China)

  • Aiwen Lin

    (School of Resources and Environmental Sciences, Wuhan University, Wuhan 430072, China
    Key Laboratory of Geographic Information System, Wuhan University, Wuhan 430079, China)

  • Zhigao Zhou

    (School of Resources and Environmental Sciences, Wuhan University, Wuhan 430072, China
    Key Laboratory of Geographic Information System, Wuhan University, Wuhan 430079, China)

  • Feiyan Chen

    (School of Resources and Environmental Sciences, Wuhan University, Wuhan 430072, China
    Key Laboratory of Geographic Information System, Wuhan University, Wuhan 430079, China)

Abstract

It is of great significance to research the spatial pattern of the urban system of the Yangtze River economic belt to analyze the characteristics and laws of the spatial structure of the Yangtze River economic belt and to promote the optimal development of the urban system of the Yangtze River economic zone. In this paper, the time data of the Yangtze River economic zone are corrected using Landsat satellite data and the clustering analysis method. The threshold of the urban built area is obtained by comparing the auxiliary data with other auxiliary data. Based on this threshold, a total of eight typical landscape pattern indicators—including the total area of the landscape, the total patch number, and the aggregation index—are used, and then FRAG-STATS 4.2 software is used to analyze the spatial pattern of urban development in the Yangtze River economic zone from 1992 to 2013. The results show the following: (1) During the period from 1992 to 2013, the urbanization of the Yangtze River economic zone expanded rapidly; the area of urban built-up area increased by a factor of 9.68, the number of patches increased by a factor of 2.39, and the patch density increased greatly, indicating that the Yangtze River economic zone, with an increasing number of towns and urban areas, continues to expand. (2) The complexity of the landscape patch shape gradually increased, the small and medium-sized cities continued to grow, more small towns emerged, and the total length of the border and the average density had average annual growth rates of 21.56% and 21.58%; the degree of aggregation and the mutual influence are increasing. (3) The maximum plaque index and the aggregation index show an overall declining trend. However, there are some fluctuations and disorder in the process of evolution, such as the total area of the landscape, the total patch number and the total patch density, which reflects that the Yangtze River economic zone is in the process of urbanization and has irregular and disordered characteristics.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3782-:d:176916
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/10/3782/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/10/10/3782/
    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. Gu, Qiwei & Wang, Hongqi & Zheng, Yinan & Zhu, Jingwen & Li, Xiaoke, 2015. "Ecological footprint analysis for urban agglomeration sustainability in the middle stream of the Yangtze River," Ecological Modelling, Elsevier, vol. 318(C), pages 86-99.
    3. Ying Cheng & Wei Liu & Jian Lu, 2017. "Financing Innovation in the Yangtze River Economic Belt: Rationale and Impact on Firm Growth and Foreign Trade," Canadian Public Policy, University of Toronto Press, vol. 43(s2), pages 122-135, April.
    4. Long, Fenjie & Zheng, Longfei & Song, Zhida, 2018. "High-speed rail and urban expansion: An empirical study using a time series of nighttime light satellite data in China," Journal of Transport Geography, Elsevier, vol. 72(C), pages 106-118.
    5. 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.
    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.
    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. 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.
    2. 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.
    3. Vera Shanshan Lin & Yuan Qin & Tianyu Ying & Shujie Shen & Guangming Lyu, 2022. "Night-time economy vitality index: Framework and evidence," Tourism Economics, , vol. 28(3), pages 665-691, May.
    4. Huimin Xu & Shougeng Hu & Xi Li, 2023. "Urban Distribution and Evolution of the Yangtze River Economic Belt from the Perspectives of Urban Area and Night-Time Light," Land, MDPI, vol. 12(2), pages 1-21, January.

    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. 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).
    3. Wang, Jiaxin & Lu, Feng, 2021. "Modeling the electricity consumption by combining land use types and landscape patterns with nighttime light imagery," Energy, Elsevier, vol. 234(C).
    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. Jasiński, Tomasz, 2019. "Modeling electricity consumption using nighttime light images and artificial neural networks," Energy, Elsevier, vol. 179(C), pages 831-842.
    6. Guibor Camargo & Andrés Miguel Sampayo & Andrés Peña Galindo & Francisco J Escobedo & Fernando Carriazo & Alejandro Feged-Rivadeneira, 2020. "Exploring the dynamics of migration, armed conflict, urbanization, and anthropogenic change in Colombia," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-18, November.
    7. Naeher,Dominik & Narayanan,Raghavan & Ziulu,Virginia, 2021. "Impacts of Energy Efficiency Projects in Developing Countries : Evidence from a SpatialDifference-in-Differences Analysis in Malawi," Policy Research Working Paper Series 9842, The World Bank.
    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 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.
    10. Sun, Yeran & Wang, Shaohua & Zhang, Xucai & Chan, Ting On & Wu, Wenjie, 2021. "Estimating local-scale domestic electricity energy consumption using demographic, nighttime light imagery and Twitter data," Energy, Elsevier, vol. 226(C).
    11. Gao, Ming & Ma, Ke & Yu, Jie, 2023. "The characteristics and drivers of China’s city-level urban-rural activity sectors’ carbon intensity gap during urban land expansion," Energy Policy, Elsevier, vol. 181(C).
    12. 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.
    13. Du, Mengbing & Ruan, Jianhui & Zhang, Li & Niu, Muchuan & Zhang, Zhe & Xia, Lang & Qian, Shuangyue & Chen, Chuchu, 2024. "China's local-level monthly residential electricity power consumption monitoring," Applied Energy, Elsevier, vol. 359(C).
    14. 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.
    15. Li, Shuyi & Cheng, Liang & Liu, Xiaoqiang & Mao, Junya & Wu, Jie & Li, Manchun, 2019. "City type-oriented modeling electric power consumption in China using NPP-VIIRS nighttime stable light data," Energy, Elsevier, vol. 189(C).
    16. Shi, Kaifang & Yang, Qingyuan & Fang, Guangliang & Yu, Bailang & Chen, Zuoqi & Yang, Chengshu & Wu, Jianping, 2019. "Evaluating spatiotemporal patterns of urban electricity consumption within different spatial boundaries: A case study of Chongqing, China," Energy, Elsevier, vol. 167(C), pages 641-653.
    17. Yan, Sen & Sun, Xinyu & Zhang, Yurong, 2024. "High-speed railway ripples on the greenness: Insight from urban green vegetation cover," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    18. Fan, Xiaomin & Xu, Yingzhi, 2023. "Does high-speed railway promote urban innovation? Evidence from China," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    19. Thushyanthan Baskaran & Sonia Bhalotra & Brian Min & Yogesh Uppal, 2024. "Women legislators and economic performance," Journal of Economic Growth, Springer, vol. 29(2), pages 151-214, June.
    20. Jin, Peizhen & Mangla, Sachin Kumar & Song, Malin, 2021. "Moving towards a sustainable and innovative city: Internal urban traffic accessibility and high-level innovation based on platform monitoring data," International Journal of Production Economics, Elsevier, vol. 235(C).

    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:10:y:2018:i:10:p:3782-:d:176916. 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.