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Chinese Journal of Applied Ecology ›› 2022, Vol. 33 ›› Issue (8): 2213-2220.doi: 10.13287/j.1001-9332.202208.024

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Urban thermal environment effects based on visual indices: A case study in Xuzhou City, China

ZHOU Hong-xuan*, PU Hong-tong, CUI Lu-lu, ZHOU Feng-lin, SUN Jing   

  1. School of Architecture and Design, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China
  • Received:2021-10-20 Accepted:2022-05-31 Online:2022-08-15 Published:2023-02-15

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Abstract: Urban thermal environments are closely related to habitats, citizens' health, and sustainable development. Based on green view index (GVI), we proposed two new visual indices, construction view index (CVI) and imperious surface view index (R&PVI). Mobile observation was used to obtain urban thermal environment data, images and coordinates synchronously in Xuzhou City in late summer, including urban area (U), scenic area (S), exterior of university campus (E), and university campus inside (CUMT). We analyzed the impacts of the urban composition represented by the visual index on the urban thermal environment. The results showed that, along the sampling line, mean air temperature (Ta) was highest (30.42 ℃) and mean relative humidity (RH) was lowest (40.7%) in urban area, while mean Ta was lowest (29.35 ℃) and mean RH was highest (48.4%) in scenic area. The situation of mean wind-chill temperature (TaW) was the highest (32.95 ℃) in the urban area and the lowest (31.93 ℃) in the scenic area. As for CVI, urban area, university campus inside, exterior of university campus and scenic area ranked in descending order, while GVI showed an opposite pattern. CVI was significantly positively correlated to Ta and TaW, but negatively to RH. GVI was significantly negatively correlated to Ta and TaW, but positively to RH. R&PVI was significantly positively correlated to Ta and TaW, but not correlated to RH. CVI and GVI influenced Ta significantly, with the independent effects being 10.4% and 18.9%, and joint effects being 7.8% and 11.3%, respectively. As for RH, CVI and GVI contributed significantly as well, independent effects were 37.5% and 15.7%, and joint effects were 51.4% and 30.2%, respectively. As for TaW, the three visual indices contributed significantly, but independent and joint effects were lower than those on Ta. Moreover, visual indices contributed more on RH than Ta or TaW. The results could provide ideas for optimizing urban thermal environments and mitigating urban heat island effects, and have practical implications for urban renewal and improvement of the quality of human living environment.

Key words: urban heat island effect, urban microclimate, deep learning, green view index, construction view index