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Spatial Cluster Characteristics of Land Surface Temperatures

Author

Listed:
  • Donghe Li

    (School of Architecture and Built Environment, Faculty of Science Engineering and Built Environment, Deakin University, Geelong, VIC 3220, Australia)

  • Xin Hu

    (School of Architecture and Built Environment, Faculty of Science Engineering and Built Environment, Deakin University, Geelong, VIC 3220, Australia)

  • John Rollo

    (School of Architecture and Built Environment, Faculty of Science Engineering and Built Environment, Deakin University, Geelong, VIC 3220, Australia)

  • Mark Luther

    (School of Architecture and Built Environment, Faculty of Science Engineering and Built Environment, Deakin University, Geelong, VIC 3220, Australia)

  • Min Lu

    (Landscape Architecture Research Centre, Shandong Jianzhu University, Jinan 250101, China)

  • Chunlu Liu

    (School of Architecture and Built Environment, Faculty of Science Engineering and Built Environment, Deakin University, Geelong, VIC 3220, Australia)

Abstract

Accurately measuring the characteristics of spatial clusters and changes in urban land surface temperature (LST) provides essential data that assist in urban heat island effect mitigation and sustainable urban development. Previous studies on the thermal environment often focused on the identification and spatial distribution of land surface temperature values and the lack of quantitative research on the LST spatial cluster characteristics, making it difficult to determine where mitigation strategies can be best applied to reduce high-temperature cluster (HH) areas and increase urban low-temperature cluster (LL) areas. Based on remote sensing (RS) images and geographic information system (GIS) technology, the cluster classification and spatial cluster characteristics analysis methods were used in this research to quantitatively assess the LST spatial cluster characteristics in Huaiyin District, Jinan City in 2000, 2005, 2010, 2015, 2020, and 2024. The results show the following: (1) The LST exhibited significant spatial cluster characteristics, with a strong correlation between the LST spatial cluster areas and their spatial locations. The spatial distributions of the HH and LL areas showed contrasts from north to south and west to east. (2) Decreasing temperature transformations were mainly located in new areas covered by water bodies and vegetation, while increasing temperature transformations were mainly located within re-developed built-up areas in the old urban area and in the newly built urban growth areas. The HH areas were larger, simpler in patch shape, and had more aggregated spatial distributions than the LL areas. Additionally, the barycentre distribution and migration trajectory of the HH areas were closely related to urban development planning. These quantitative results provide a scientific basis for understanding the urban LST spatial cluster characteristics, thus quantifying the core problem areas of urban planning and thermal environment regulation policies.

Suggested Citation

  • Donghe Li & Xin Hu & John Rollo & Mark Luther & Min Lu & Chunlu Liu, 2025. "Spatial Cluster Characteristics of Land Surface Temperatures," Sustainability, MDPI, vol. 17(6), pages 1-24, March.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:6:p:2653-:d:1614179
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    References listed on IDEAS

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    1. Li, Xiaoma & Zhou, Yuyu & Yu, Sha & Jia, Gensuo & Li, Huidong & Li, Wenliang, 2019. "Urban heat island impacts on building energy consumption: A review of approaches and findings," Energy, Elsevier, vol. 174(C), pages 407-419.
    2. Tong Wu & Lucang Wang & Haiyang Liu, 2021. "Spatiotemporal Differentiation of Land Surface Thermal Landscape in Yangtze River Delta Region, China," Sustainability, MDPI, vol. 13(16), pages 1-20, August.
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    1. Perez-Aguilar, Lidia Yadira & Plata-Rocha, Wenseslao & Monjardin-Armenta, Sergio Alberto & López-Osorio, Ramon Fernando, 2025. "Implementation of a web-based system for monitoring and simulation of arid zones in northwestern Mexico. Region of Mexico," Ecological Modelling, Elsevier, vol. 501(C).
    2. Ali Mansouri & Abdolmajid Erfani, 2025. "Machine Learning Prediction of Urban Heat Island Severity in the Midwestern United States," Sustainability, MDPI, vol. 17(13), pages 1-21, July.

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