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Simulating the Relationship between Land Use/Cover Change and Urban Thermal Environment Using Machine Learning Algorithms in Wuhan City, China

Author

Listed:
  • Maomao Zhang

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430079, China
    These authors contributed equally to this work.)

  • Cheng Zhang

    (College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
    These authors contributed equally to this work.)

  • Abdulla-Al Kafy

    (Department of Urban & Regional Planning, Rajshahi University of Engineering & Technology, Rajshahi 6203, Bangladesh
    ICLEI South Asia, Rajshahi City Corporation, Rajshahi 6200, Bangladesh)

  • Shukui Tan

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430079, China)

Abstract

The changes of land use/land cover (LULC) are important factor affecting the intensity of the urban heat island (UHI) effect. Based on Landsat image data of Wuhan, this paper uses cellular automata (CA) and artificial neural network (ANN) to predict future changes in LULC and LST. The results show that the built-up area of Wuhan has expanded, reaching 511.51 and 545.28 km 2 , while the area of vegetation, water bodies and bare land will decrease to varying degrees in 2030 and 2040. If the built-up area continues to expand rapidly, the proportion of 30~35 °C will rise to 52.925% and 55.219%, and the affected area with the temperature >35 °C will expand to 15.264 and 33.612 km 2 , respectively. The direction of the expansion range of the LST temperature range is obviously similar to the expansion of the built-up area. In order to control and alleviate UHI, the rapid expansion of impervious layers (built-up areas) should be avoided to the greatest extent, and the city’s “green development” strategy should be implemented.

Suggested Citation

  • Maomao Zhang & Cheng Zhang & Abdulla-Al Kafy & Shukui Tan, 2021. "Simulating the Relationship between Land Use/Cover Change and Urban Thermal Environment Using Machine Learning Algorithms in Wuhan City, China," Land, MDPI, vol. 11(1), pages 1-17, December.
    • Handle: RePEc:gam:jlands:v:11:y:2021:i:1:p:14-:d:708448
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    Citations

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    Cited by:

    1. Lu Zhang & Xuehan Lin & Bingkui Qiu & Maomao Zhang & Qingsong He, 2022. "The Industrial Sprawl in China from 2010 to 2019: A Multi-Level Spatial Analysis Based on Urban Scaling Law," IJERPH, MDPI, vol. 19(23), pages 1-14, December.
    2. Yi Cheng, 2023. "Analysis of Development Strategy for Ecological Agriculture Based on a Neural Network in the Environmental Economy," Sustainability, MDPI, vol. 15(8), pages 1-17, April.
    3. Qin Wang & Qin Ju & Yueyang Wang & Quanxi Shao & Rongrong Zhang & Yanli Liu & Zhenchun Hao, 2022. "Vegetation Changing Patterns and Its Sensitivity to Climate Variability across Seven Major Watersheds in China," IJERPH, MDPI, vol. 19(21), pages 1-19, October.

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