IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i12p9729-d1173811.html
   My bibliography  Save this article

Exploring Relationships between Spatial Pattern Change in Steel Plants and Land Cover Change in Tangshan City

Author

Listed:
  • Mingyan Ni

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Yindi Zhao

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Caihong Ma

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Xiaolin Hou

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Yanmei Xie

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

Abstract

It is of great significance for the sustainable development of steel cities to explore the relationship between the spatial pattern change in steel plants and land cover change during the transformation of steel cities. To address the issue of unsatisfactory results for segmenting steel plants based on high-resolution remote sensing images, due to insufficient sample datasets and task complexity, we proposed a steel plant segmentation strategy that combines high-resolution remote sensing images, POI data, and OSM data. Additionally, we discussed the effect of POI data and OSM data on steel plant segmentation, analyzing the spatial pattern change in steel plants in Tangshan City during 2017–2022 and its relationship with land cover change. The results demonstrate that: (1) The proposed strategy can significantly improve the accuracy of steel plant segmentation. The introduction of POI data can significantly improve the precision of steel plant segmentation, however, it will to some extent reduce the recall of steel plant segmentation, and this phenomenon weakens as the distance threshold increases. The introduction of OSM data can effectively improve the effectiveness of steel plant segmentation, however, it has significant limitations. (2) During 2017–2022, the spatial distribution center of steel plants in Tangshan City moved obviously to the southeast, and the positive change in steel plants was mainly concentrated in the coastal regions of southern Tangshan City, while the negative change in steel plants was mainly concentrated in central Tangshan City. (3) There is a relatively strong spatial correlation between the positive change in steel plants and the transition from vegetation to built area, as well as the transition from cropland to built area.

Suggested Citation

  • Mingyan Ni & Yindi Zhao & Caihong Ma & Xiaolin Hou & Yanmei Xie, 2023. "Exploring Relationships between Spatial Pattern Change in Steel Plants and Land Cover Change in Tangshan City," Sustainability, MDPI, vol. 15(12), pages 1-24, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9729-:d:1173811
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/12/9729/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/12/9729/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wencang Shen & Jianjun Zhang & Xiangli Zhou & Shengnan Li & Xiaoli Geng, 2021. "How to Perceive the Trade-Off of Economic and Ecological Intensity of Land Use in a City? A Functional Zones-Based Case Study of Tangshan, China," Land, MDPI, vol. 10(6), pages 1-17, May.
    2. Zhou, Yang & Li, Xunhuan & Liu, Yansui, 2021. "Cultivated land protection and rational use in China," Land Use Policy, Elsevier, vol. 106(C).
    3. Zheng, Heyun & Ge, Liming, 2022. "Carbon emissions reduction effects of sustainable development policy in resource-based cities from the perspective of resource dependence: Theory and Chinese experience," Resources Policy, Elsevier, vol. 78(C).
    4. Zishu Wang & Jie Zhao & Sijie Lin & Yi Liu, 2019. "Identification of Industrial Land Parcels and Its Implications for Environmental Risk Management in the Beijing–Tianjin–Hebei Urban Agglomeration," Sustainability, MDPI, vol. 12(1), pages 1-14, December.
    5. Jia-Li Zhong & Wei Qi & Min Dong & Meng-Han Xu & Jia-Yu Zhang & Yi-Xiao Xu & Zi-Jie Zhou, 2022. "Land Use Carbon Emission Measurement and Risk Zoning under the Background of the Carbon Peak: A Case Study of Shandong Province, China," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
    6. Ruomu Miao & Yuxia Wang & Shuang Li, 2021. "Analyzing Urban Spatial Patterns and Functional Zones Using Sina Weibo POI Data: A Case Study of Beijing," Sustainability, MDPI, vol. 13(2), pages 1-15, January.
    7. Wang, Na & Fu, Xiaodong & Wang, Shaobin, 2022. "Spatial-temporal variation and coupling analysis of residential energy consumption and economic growth in China," Applied Energy, Elsevier, vol. 309(C).
    8. Qian Sun & Mingjie Wu & Peiyu Du & Wei Qi & Xinyang Yu, 2022. "Spatial Layout Optimization and Simulation of Cultivated Land Based on the Life Community Theory in a Mountainous and Hilly Area of China," Sustainability, MDPI, vol. 14(7), pages 1-15, March.
    9. Yuan, Huaxi & Feng, Yidai & Lee, Chien-Chiang & Cen, Yan, 2020. "How does manufacturing agglomeration affect green economic efficiency?," Energy Economics, Elsevier, vol. 92(C).
    10. Xin Janet Ge & Xiaoxia Liu, 2021. "Urban Land Use Efficiency under Resource-Based Economic Transformation—A Case Study of Shanxi Province," Land, MDPI, vol. 10(8), pages 1-20, 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. Houtian Tang & Yuanlai Wu & Jinxiu Chen & Liuxin Deng & Minjie Zeng, 2022. "How Does Change in Rural Residential Land Affect Cultivated Land Use Efficiency? An Empirical Study Based on 42 Cities in the Middle Reaches of the Yangtze River," Land, MDPI, vol. 11(12), pages 1-20, December.
    2. Dongbing Li & Yao Chang & Zibibula Simayi & Shengtian Yang, 2022. "Multi-Scenario Dynamic Simulation of Urban Agglomeration Development on the Northern Slope of the Tianshan Mountains in Xinjiang, China, with the Goal of High-Quality Urban Construction," Sustainability, MDPI, vol. 14(11), pages 1-19, June.
    3. Lingzhang Kong & Jinye Li, 2022. "Digital Economy Development and Green Economic Efficiency: Evidence from Province-Level Empirical Data in China," Sustainability, MDPI, vol. 15(1), pages 1-26, December.
    4. Chengkun Huang & Feiyang Lin & Deping Chu & Lanlan Wang & Jiawei Liao & Junqian Wu, 2021. "Coupling Relationship and Interactive Response between Intensive Land Use and Tourism Industry Development in China’s Major Tourist Cities," Land, MDPI, vol. 10(7), pages 1-17, July.
    5. Wang, Mei Ling, 2023. "Effects of the green finance policy on the green innovation efficiency of the manufacturing industry: A difference-in-difference model," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    6. Tuan Nguyen Tran, 2023. "A comparative study of urban land use efficiency of the cities of Hai Phong and Can Tho, Vietnam," Environmental & Socio-economic Studies, Sciendo, vol. 11(3), pages 43-53, September.
    7. Zhen Wang & Xupeng Zhang & Chaozheng Zhang & Qing Yang, 2022. "How Regional Integration Affects Urban Green Development Efficiency: Evidence from Urban Agglomeration in the Middle Reaches of the Yangtze River," IJERPH, MDPI, vol. 19(13), pages 1-16, June.
    8. Li, Mengxu & Liu, Jianghua & Chen, Yang & Yang, Zhijiu, 2023. "Can sustainable development strategy reduce income inequality in resource-based regions? A natural resource dependence perspective," Resources Policy, Elsevier, vol. 81(C).
    9. Xiaowei Yao & Ting Luo & Yingjun Xu & Wanxu Chen & Jie Zeng, 2022. "Prediction of Spatiotemporal Changes in Sloping Cropland in the Middle Reaches of the Yangtze River Region under Different Scenarios," IJERPH, MDPI, vol. 20(1), pages 1-22, December.
    10. Matheus Koengkan & José Alberto Fuinhas & Emad Kazemzadeh & Fariba Osmani & Nooshin Karimi Alavijeh, 2022. "Measuring the economic efficiency performance in Latin American and Caribbean countries: An empirical evidence from stochastic production frontier and data envelopment analysis," International Economics, CEPII research center, issue 169, pages 43-54.
    11. Jiaxin Zhang & Zhilin Yu & Yunqin Li & Xueqiang Wang, 2023. "Uncovering Bias in Objective Mapping and Subjective Perception of Urban Building Functionality: A Machine Learning Approach to Urban Spatial Perception," Land, MDPI, vol. 12(7), pages 1-20, June.
    12. Yuewen Yang & Dongyan Wang & Zhuoran Yan & Shuwen Zhang, 2021. "Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China," Land, MDPI, vol. 10(11), pages 1-21, November.
    13. Wang, Quan-Jing & Wang, Hai-Jie & Chang, Chun-Ping, 2022. "Environmental performance, green finance and green innovation: What's the long-run relationships among variables?," Energy Economics, Elsevier, vol. 110(C).
    14. Oliveira, Renata Lúcia Magalhães de & Dablanc, Laetitia & Schorung, Matthieu, 2022. "Changes in warehouse spatial patterns and rental prices: Are they related? Exploring the case of US metropolitan areas," Journal of Transport Geography, Elsevier, vol. 104(C).
    15. Yang Sheng & Weizhong Liu & Hailiang Xu & Xianchao Gao, 2021. "The Spatial Distribution Characteristics of the Cultivated Land Quality in the Diluvial Fan Terrain of the Arid Region: A Case Study of Jimsar County, Xinjiang, China," Land, MDPI, vol. 10(9), pages 1-29, August.
    16. Xiaoxue Liu & Fuzhen Cao & Shuangshuang Fan, 2022. "Does Human Capital Matter for China’s Green Growth?—Examination Based on Econometric Model and Machine Learning Methods," IJERPH, MDPI, vol. 19(18), pages 1-27, September.
    17. Zhang, Bangbang & Li, Xian & Chen, Haibin & Niu, Wenhao & Kong, Xiangbin & Yu, Qiang & Zhao, Minjuan & Xia, Xianli, 2022. "Identifying opportunities to close yield gaps in China by use of certificated cultivars to estimate potential productivity," Land Use Policy, Elsevier, vol. 117(C).
    18. Rendao Ye & Yue Qi & Wenyan Zhu, 2023. "Impact of Agricultural Industrial Agglomeration on Agricultural Environmental Efficiency in China: A Spatial Econometric Analysis," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    19. Yedong Chen & Jiang Chang & Zixuan Li & Li Ming & Cankun Li & Cheng Li, 2023. "Coupling Coordination and Spatiotemporal Analysis of Urban Compactness and Land-Use Efficiency in Resource-Based Areas: A Case Study of Shanxi Province, China," Land, MDPI, vol. 12(9), pages 1-23, August.
    20. Liu, Yansui & Zhou, Yang, 2021. "Reflections on China's food security and land use policy under rapid urbanization," Land Use Policy, Elsevier, vol. 109(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:15:y:2023:i:12:p:9729-:d:1173811. 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.