Landscape and vegetation traits of urban green space can predict local surface temperature

Daosheng CHEN, Fei ZHANG, Mengru ZHANG, Qingyan MENG, Chi Yung JIM, Jingchao SHI, Mou Leong TAN, Xu MA

Research output: Contribution to journalArticlespeer-review

27 Citations (Scopus)


Societal and technological advances have triggered demands to improve urban environmental quality. Urban green space (UGS) can provide effective cooling service and thermal comfort to alleviate warming impacts. We investigated the relative influence of a comprehensive spectrum of UGS landscape and vegetation factors on surface temperature in arid Urumqi city in northwest China. Built-up area range was extracted from Luojia 1-01 (LJ1-01) satellite data, and within this range, the landscape metric information and vegetation index information of UGS were obtained based on PlanetScope data, and a total of 439 sampling grids (1 km × 1 km) were generated. The urban surface temperature of built-up areas was extracted from Landsat8-TIRS images. The 12 landscape metrics and 14 vegetation indexes were assigned as independent variables, and surface temperature the dependent variable. Support Vector Machine (SVM), Gradient BoostRegression Tree (GBRT) and Random Forest (RF) were enlisted to establish numerical models to predict surface temperature. The results showed that: (1) It was feasible to predict local surface temperature using a combination of landscape metrics and vegetation indexes. Among the three models, RF demonstrated the best accuracy. (2) Collectively, all the factors play a role in the surface-temperature prediction. The most influential factor was Difference Vegetation Index (DVI), followed by Green Normalized Difference Vegetation Index (GNDVI), Class Area (CA) and AREA. This study developed remote sensing techniques to extract a basket of UGS factors to predict the surface temperature at local urban sites. The methods could be applied to other cities to evaluate the cooling impacts of green infrastructures. The findings could provide a scientific basis for ecological spatial planning of UGS to optimize cooling benefits in the arid region. Copyright © 2022 Elsevier B.V. All rights reserved.
Original languageEnglish
Article number154006
JournalScience of the Total Environment
Early online date19 Feb 2022
Publication statusPublished - 15 Jun 2022


Chen, D., Zhang, F., Zhang, M., Meng, Q., Jim, C. Y., Shi, J., . . . Ma, X. (2022). Landscape and vegetation traits of urban green space can predict local surface temperature. Science of the Total Environment, 825. Retrieved from


  • Urban green space
  • Landscape metric
  • Vegetation index
  • Land surface temperature (LST) prediction
  • LJ1-01 satellite
  • PlanetScope satellite


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