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Accurate Suitability Evaluation of Large-Scale Roof Greening Based on RS and GIS Methods

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Listed:
  • Nan Xu

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Jiancheng Luo

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Jin Zuo

    (School of Architecture, Tianjin University, Tianjin 300072, China)

  • Xiaodong Hu

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China)

  • Jing Dong

    (School of Architecture, Tianjin University, Tianjin 300072, China)

  • Tianjun Wu

    (School of Science, Changan University, Xi’an 710064, China)

  • Songliang Wu

    (School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China)

  • Hao Liu

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

Under increasingly low urban land resources, carrying out roof greening to exploit new green space is a good strategy for sustainable development. Therefore, it is necessary to evaluate the suitability of roof greening for buildings in cities. However, most current evaluation methods are based on qualitative and conceptual research. In this paper, a methodological framework for roof greening suitability evaluation is proposed based on the basic units of building roofs extracted via deep learning technologies. The building, environmental and social criteria related to roof greening are extracted using technologies such as deep learning, machine learning, remote sensing (RS) methods and geographic information system (GIS) methods. The technique for order preference by similarity to an ideal solution (TOPSIS) method is applied to quantify the suitability of each roof, and Sobol sensitivity analysis of the score results is conducted. The experiment on Xiamen Island shows that the final evaluation results are highly sensitive to the changes in weight of the green space distance, population density and the air pollution level. This framework is helpful for the quantitative and objective development of roof greening suitability evaluation.

Suggested Citation

  • Nan Xu & Jiancheng Luo & Jin Zuo & Xiaodong Hu & Jing Dong & Tianjun Wu & Songliang Wu & Hao Liu, 2020. "Accurate Suitability Evaluation of Large-Scale Roof Greening Based on RS and GIS Methods," Sustainability, MDPI, vol. 12(11), pages 1-23, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:11:p:4375-:d:363383
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    References listed on IDEAS

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    1. Teresa Santos & José António Tenedório & José Alberto Gonçalves, 2016. "Quantifying the City’s Green Area Potential Gain Using Remote Sensing Data," Sustainability, MDPI, vol. 8(12), pages 1-16, November.
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    3. Hashemi, Sajedeh Sadat Ghazizadeh & Mahmud, Hilmi Bin & Ashraf, Muhammad Aqeel, 2015. "Performance of green roofs with respect to water quality and reduction of energy consumption in tropics: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 669-679.
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