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Assessing the Impact of Land Cover Changes on Surface Urban Heat Islands with High-Spatial-Resolution Imagery on a Local Scale: Workflow and Case Study

Author

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  • Peng Ren

    (State Key Laboratory of Subtropical Building Science, Department of Architecture, South China University of Technology, Guangzhou 510640, China)

  • Xinxin Zhang

    (State Key Laboratory of Subtropical Building Science, Department of Architecture, South China University of Technology, Guangzhou 510640, China
    Architectural Design Research Institute of SCUT, Guangzhou 510640, China)

  • Haoyan Liang

    (State Key Laboratory of Subtropical Building Science, Department of Architecture, South China University of Technology, Guangzhou 510640, China
    Institute of Geography and Tourism, Guangdong University of Finance & Economics, Guangzhou 510320, China)

  • Qinglin Meng

    (State Key Laboratory of Subtropical Building Science, Department of Architecture, South China University of Technology, Guangzhou 510640, China)

Abstract

Low-altitude remote sensing platform has been increasingly applied to observing local thermal environments due to its obvious advantage in spatial resolution and apparent flexibility in data acquisition. However, there is a general lack of systematic analysis for land cover (LC) classification, surface urban heat island (SUHI), and their spatial and temporal change patterns. In this study, a workflow is presented to assess the LC’s impact on SUHI, based on the visible and thermal infrared images with high spatial resolution captured by an unmanned airship in the central area of the Sino-Singapore Guangzhou Knowledge City in 2012 and 2015. Then, the accuracy assessment of LC classification and land surface temperature (LST) retrieval are performed. Finally, the commonly-used indexes in the field of satellites are applied to analyzing the spatial and temporal changes in the SUHI pattern on a local scale. The results show that the supervised maximum likelihood algorithm can deliver satisfactory overall accuracy and Kappa coefficient for LC classification; the root mean square error of the retrieved LST can reach 1.87 °C. Moreover, the LST demonstrates greater consistency with land cover type (LCT) and more fluctuation within an LCT on a local scale than on an urban scale. The normalized LST classified by the mean and standard deviation (STD) is suitable for the high-spatial situation; however, the thermal field level and the corresponded STD multiple need to be judiciously selected. This study exhibits an effective pathway to assess SUHI pattern and its changes using high-spatial-resolution images on a local scale. It is also indicated that proper landscape composition, spatial configuration and materials on a local scale exert greater impacts on SUHI.

Suggested Citation

  • Peng Ren & Xinxin Zhang & Haoyan Liang & Qinglin Meng, 2019. "Assessing the Impact of Land Cover Changes on Surface Urban Heat Islands with High-Spatial-Resolution Imagery on a Local Scale: Workflow and Case Study," Sustainability, MDPI, vol. 11(19), pages 1-24, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5188-:d:269598
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    2. Jamal Suliman Alawamy & Siva K. Balasundram & Ahmad Husni Mohd. Hanif & Christopher Teh Boon Sung, 2020. "Detecting and Analyzing Land Use and Land Cover Changes in the Region of Al-Jabal Al-Akhdar, Libya Using Time-Series Landsat Data from 1985 to 2017," Sustainability, MDPI, vol. 12(11), pages 1-24, June.
    3. Owais Bashir & Shabir Ahmad Bangroo & Wei Guo & Gowhar Meraj & Gebiaw T. Ayele & Nasir Bashir Naikoo & Shahid Shafai & Perminder Singh & Mohammad Muslim & Habitamu Taddese & Irfan Gani & Shafeeq Ur Ra, 2022. "Simulating Spatiotemporal Changes in Land Use and Land Cover of the North-Western Himalayan Region Using Markov Chain Analysis," Land, MDPI, vol. 11(12), pages 1-18, December.

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