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  • Influence of landscape features on urban land surface temperature_R1_accepted

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  • Yi Shi
    Central South University of Forestry and Technology, Changsha
  • Shuguang Liu
    Central South University of Forestry and Technology
  • Wende Yan
    Central South University of Forestry and Technology
  • Shuqing Zhao
    Peking University
  • Ying Ning
    Central South University of Forestry and Technology
  • Xi Peng
    Central South University of Forestry and Technology
  • Wei Chen
    Central South University of Forestry and Technology
  • Liding Chen
    Central South University of Forestry and Technology
  • Xijun Hu
    Chinese Academy of Sciences, Beijing
  • Bojie Fu
    Chinese Academy of Sciences, Beijing
  • Robert Kennedy
    Oregon State University
  • Yihe Lv
    Chinese Academy of Sciences, Beijing
  • Juyang Liao
    Hunan Forest Botanical Garden
  • Chungliang Peng
    Hunan Forest Botanical Garden
  • Isabel Rosa
  • David Roy
    Michigan State University
  • Shouyun Shen
    Chinese Academy of Sciences, Beijing
  • Andy Smith
  • Chen Wang
    Chinese Academy of Forestry, Beijing
  • Zhao Wang
    Central South University of Forestry and Technology
  • Li Xiao
    Central South University of Forestry and Technology
  • Lu Yang
    Peking University
  • Wenping Yuan
    Sun Yat-sen University, China
  • Min Yi
    Ecology and Environment Department of Hunan Province
  • Hankui Zhang
    South Dakota State University
  • Meifang Zhao
    Central South University of Forestry and Technology
  • Yu Zhu
    Central South University of Forestry and Technology
  • Jingfeng Xiao
    University of New Hampshire
Higher land surface temperature (LST) in cities than its surrounding areas presents a major sustainability challenge for cities. Adaptation and mitigation of the increased LST require in-depth understanding of the impacts of landscape features on LST. We studied the influences of different landscape features on LST in five large cities across China to investigate how the features of a specific urban landscape (endogenous features), and neighboring environments (exogenous features) impact its LST across a continuum of spatial scales. Surprisingly, results show that the influence of endogenous landscape features (Eendo) on LST can be described consistently across all cities as a nonlinear function of grain size (gs) and neighbor size (ns) (Eendo = βnsgs-0.5, where β is a city-specific constant) while the influence of exogenous features (Eexo) depends only on neighbor size (ns) (Eexo = γ-εns0.5, where γ and ε are city-specific constants). In addition, a simple relationship describing the relative strength of endogenous and exogenous impacts of landscape features on LST was found (Eendo > Eexo if ns > kgs2/5, where k is a city-specific parameter; otherwise, Eendo < Eexo). Overall, vegetation alleviates 40%-60% of the warming effect of built-up while surface wetness intensifies or reduces it depending on climate conditions. This study reveals a set of unifying quantitative relationships that effectively describes landscape impacts on LST across cities, grain and neighbor sizes, which can be instrumental towards the design of sustainable cities to deal with increasing temperature.

Keywords

  • Urban heat island, Neighbor landscape features, Scale dependence, Landscape composition, Ridge regression
Original languageEnglish
Article number145381
JournalScience of the Total Environment
Volume771
Early online date27 Jan 2021
DOIs
Publication statusPublished - 1 Jun 2021
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