欢迎访问《应用生态学报》官方网站,今天是 分享到:

应用生态学报 ›› 2022, Vol. 33 ›› Issue (8): 2213-2220.doi: 10.13287/j.1001-9332.202208.024

• 研究论文 • 上一篇    下一篇

基于视觉指数的城市热环境效应——以徐州市为例

周宏轩*, 濮宏桐, 崔璐璐, 周凤林, 孙婧   

  1. 中国矿业大学建筑与设计学院, 江苏徐州 221116
  • 收稿日期:2021-10-20 接受日期:2022-05-31 出版日期:2022-08-15 发布日期:2023-02-15
  • 通讯作者: * E-mail: zhouhongxuan@live.cn
  • 作者简介:周宏轩, 男, 1984年生, 博士, 副教授。主要从事城市热环境、矿区生态恢复等研究。E-mail: zhouhongxuan@live.cn
  • 基金资助:
    国家自然科学基金项目(51908544)和徐州市科技计划项目(KC21036)资助。

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

摘要: 城市热环境与人居环境、居民健康以及城市可持续发展密切相关。本研究以绿视率(GVI)为基础,提出地上构筑物视率(CVI)和硬化地表视率(R&PVI)2种新的视觉指数,使用移动观测方法,在夏季末获取徐州市主城区、风景区、大学校园外部以及大学校园的城市热环境数据,并同步获取采样沿线的影像和坐标信息,分析以视觉指数所表示的城市构成对城市热环境的影响。结果表明: 在采样沿线上,研究区主城区平均气温(Ta,30.42 ℃)最高但相对湿度(RH,40.7%)最低,风景区的平均Ta(29.35 ℃)最低但RH(48.4%)最高;平均风冷温度(TaW)在主城区最高(32.95 ℃),在风景区最低(31.93 ℃)。CVI由高至低依次为:主城区、大学校园、大学校园外部和风景区,GVI与CVI相反。CVI分别与TaTaW呈极显著正相关,与RH呈极显著负相关;GVI分别与TaTaW呈极显著负相关,与RH呈极显著正相关;R&PVI分别与TaTaW呈显著和极显著正相关,与RH的相关性不显著。CVI和GVI对Ta的贡献显著,独立贡献率分别为10.4%和18.9%,联合贡献率分别为7.8%和11.3%;对RH而言,二者贡献同样显著,独立贡献率分别为37.5%和15.7%,联合贡献率分别为51.4%和30.2%;对TaW而言,3种参数的贡献均达到显著水平,三者的独立贡献率和联合贡献率均低于对Ta的影响;3种参数对RH的影响高于对TaTaW。研究结果可为优化城市热环境和缓解城市热岛效应提供思路,也对城市更新以及人居环境质量提升具有实践意义。

关键词: 城市热岛效应, 城市微气候, 深度学习, 绿视率, 构筑物视率

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