IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i23p10498-d1533169.html
   My bibliography  Save this article

Spatial–Temporal Differentiation and Driving Factors of Vegetation Landscape Pattern in Beijing–Tianjin–Hebei Region Based on the ESTARFM Model

Author

Listed:
  • Yilin Wang

    (College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China)

  • Ao Zhang

    (College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China)

  • Xintong Gao

    (College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China)

  • Wei Zhang

    (Department of Water Conservancy Engineering, Hebei University of Water Resources and Electric Engineering, Cangzhou 061016, China)

  • Xiaohong Wang

    (College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China
    Tangshan Key Laboratory of Remote Sensing for Resources and Environment, Tangshan 063210, China
    Hebei Industrial Technology Institute of Mine Ecological Remediation, Tangshan 063210, China)

  • Linlin Jiao

    (College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China
    Tangshan Key Laboratory of Remote Sensing for Resources and Environment, Tangshan 063210, China
    Hebei Industrial Technology Institute of Mine Ecological Remediation, Tangshan 063210, China)

Abstract

Urbanization and industrialization have led to obvious changes in the ecological environment and landscape pattern in the Beijing–Tianjin–Hebei region. Therefore, it is crucial to clarify the spatial–temporal changes in vegetation cover and its landscape pattern and conduct its analysis with the driving factors for ecological preservation in the Beijing–Tianjin–Hebei region. This study combined AVHRR GIMMS NDVI and MODIS NDVI data based on the ESTARFM model to obtain a high spatial–temporal resolution for vegetation cover; it then analyzed the vegetation cover changes at the type and landscape scales using a landscape index and explored the driving factors of the landscape pattern through principal component analysis. The results show that (1) the vegetation is mainly of medium and higher coverage and is distributed in the northeast, the western part of the Taihang Mountains and the central plains in the study area. From 1985 to 2022, there was no statistically significant difference in the overall change in its coverage. (2) From 1985 to 2022, at the landscape level, the vegetation cover landscape exhibited the following characteristics: increased fragmentation, an increase in the complexity of the landscape shape, a decrease in connectivity, a discrete landscape and a decrease in species diversity. At the type level, the medium vegetation demonstrated the most significant degree of fragmentation. The high-vegetation-cover areas exhibited a more concentrated distribution. Additionally, the low, lower and higher vegetation types displayed an increase in complexity, shape, discreteness and heterogeneity within the landscape. (3) Meanwhile, the principal component analysis showed that the changes in the landscape pattern of vegetation cover were mainly the result of the combined effects of climatic and anthropogenic factors in the Beijing–Tianjin–Hebei region. The human factor played the dominant role; this was followed by larger contributions from climatic factors. In addition to offering pertinent scientific insights for the maximization of the ecological environment and the fostering of regional ecological and sustainable development in the Beijing–Tianjin–Hebei region, the aforementioned analysis and research could serve as the foundation for the sustainable management and planning of vegetation cover.

Suggested Citation

  • Yilin Wang & Ao Zhang & Xintong Gao & Wei Zhang & Xiaohong Wang & Linlin Jiao, 2024. "Spatial–Temporal Differentiation and Driving Factors of Vegetation Landscape Pattern in Beijing–Tianjin–Hebei Region Based on the ESTARFM Model," Sustainability, MDPI, vol. 16(23), pages 1-19, November.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10498-:d:1533169
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/23/10498/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/23/10498/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mirosław Krzyśko & Peter Nijkamp & Waldemar Ratajczak & Waldemar Wołyński & Beata Wenerska, 2024. "Spatio-temporal principal component analysis," Spatial Economic Analysis, Taylor & Francis Journals, vol. 19(1), pages 8-29, January.
    2. Yafei Wang & Xiaoli Zhao & Lijun Zuo & Zengxiang Zhang & Xiao Wang & Ling Yi & Fang Liu & Jinyong Xu, 2020. "Spatial Differentiation of Land Use and Landscape Pattern Changes in the Beijing–Tianjin–Hebei Area," Sustainability, MDPI, vol. 12(7), pages 1-15, April.
    3. Mengyao Fan & Dawei Ma & Xianglin Huang & Ru An, 2023. "Adaptability Evaluation of the Spatiotemporal Fusion Model of Sentinel-2 and MODIS Data in a Typical Area of the Three-River Headwater Region," Sustainability, MDPI, vol. 15(11), pages 1-17, May.
    4. Zhang Zhang & Huimin Zhou & Shuxian Li & Zhibin Zhao & Junbo Xu & Yuansuo Zhang, 2024. "Study on the Spatiotemporal Evolution of Urban Land Use Efficiency in the Beijing–Tianjin–Hebei Region," Sustainability, MDPI, vol. 16(7), pages 1-27, April.
    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. Shuangfei Zhao & Wei Zeng & Da Feng, 2024. "Coupling Coordination of Urban Resilience and Urban Land Use Efficiency in Hunan Province, China," Sustainability, MDPI, vol. 16(24), pages 1-33, December.
    2. Carsten Juergens & Fabian M. Meyer-Heß & Marcus Goebel & Torsten Schmidt, 2021. "Remote Sensing for Short-Term Economic Forecasts," Sustainability, MDPI, vol. 13(17), pages 1-23, August.
    3. Jia Zhao & Yuluan Zhao & Xiaopiao Yang, 2022. "Evolution Characteristics and Driving Mechanism of the Territorial Space Pattern in the Yangtze River Economic Belt, China," Land, MDPI, vol. 11(9), pages 1-29, September.
    4. Huang Yu & Shanshan Du & Jingqiu Zhang & Jinglei Chen, 2023. "Spatial Evolution and Multi-Scenario Simulation of Rural “Production–Ecological–Living” Space: A Case Study for Beijing, China," Sustainability, MDPI, vol. 15(3), pages 1-19, January.
    5. Lijia Zhang & Zihan Zhai & Yan Zhou & Shihan Liu & Liwei Wang, 2022. "The Landscape Pattern Evolution of Typical Open-Pit Coal Mines Based on Land Use in Inner Mongolia of China during 20 Years," Sustainability, MDPI, vol. 14(15), pages 1-14, August.

    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:16:y:2024:i:23:p:10498-:d:1533169. 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.