IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i2p486-d1313824.html
   My bibliography  Save this article

Comparing the Evolution of Land Surface Temperature and Driving Factors between Three Different Urban Agglomerations in China

Author

Listed:
  • Lizhi Pan

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, China)

  • Chaobin Yang

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, China
    Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518040, China)

  • Jing Han

    (School of Space Science and Physics, Shandong University, Weihai 264209, China)

  • Fengqin Yan

    (State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Anhua Ju

    (College of Earth Science, Chengdu University of Technology, Chengdu 610059, China)

  • Tong Kui

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, China)

Abstract

Increases in land surface temperature (LST) and the urban heat island effect have become major challenges in the process of urban development. However, few studies have examined variations in LST between different urban agglomerations (UAs). Based on MODIS LST data, we quantitatively analyzed the spatial and temporal evolution patterns of LST in three different UAs in China from 2000 to 2020—Beijing–Tianjin–Hebei (BTH) at the national level, the Shandong Peninsula (SP) at the regional level, and Central Shanxi (CS) at the city level—by employing urban agglomeration built-up area intensity (UABI), linear regression analyses, and geodetic detector models. The results showed the following: (1) The spatial and temporal evolution pattern of the LST in BTH was the most regularized; the spatial pattern of the LST in SP gradually evolved from “two points” to “a single branch”; and the LST of CS was easily influenced by the neighboring big cities. (2) The best-fitting coefficients for BTH, SP, and CS were R 2 BTH = 0.58, R 2 SP = 0.66, and R 2 CS = 0.58, respectively; every 10% increase in UABI warmed the LSTs in BTH, SP, and CS by 1.47 °C, 1.27 °C, and 1.83 °C, respectively. (3) The ranking of single-factor influence was DEM (digital elevation model) > UABI > NDVI > T 2m (air temperature at 2 m) > POP (population). The UABI interacting with DEM had the strongest warming effect on LST, with the maximum value q(UABI ∩ DEM) BTH = 0.951. All factor interactions showed an enhancement of the LST in CS, but factors interacting with POP showed a weaker effect in BTH and SP, for which q(NDVI ∩ POP) BTH = 0.265 and q(T 2m ∩ POP) SP = 0.261. As the development of UAs gradually matures, the interaction with POP might have a cooling effect on the environment to a certain degree.

Suggested Citation

  • Lizhi Pan & Chaobin Yang & Jing Han & Fengqin Yan & Anhua Ju & Tong Kui, 2024. "Comparing the Evolution of Land Surface Temperature and Driving Factors between Three Different Urban Agglomerations in China," Sustainability, MDPI, vol. 16(2), pages 1-17, January.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:486-:d:1313824
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/2/486/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/2/486/
    Download Restriction: no
    ---><---

    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:2:p:486-:d:1313824. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.