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

Nan Xu, Jiancheng Luo, Jin Zuo, Xiaodong Hu, Jing Dong, Tianjun Wu, Songliang Wu and Hao Liu
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Nan Xu: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Jiancheng Luo: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, 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

Sustainability, 2020, vol. 12, issue 11, 1-23

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.

Keywords: roof greening; suitability evaluation; remote sensing; GIS; sensitivity analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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