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

doi:10.1016/j.scitotenv.2022.154006

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

Sci Total Environ. 2022 Jun 15:825:154006. doi: 10.1016/j.scitotenv.2022.154006. Epub 2022 Feb 19.

Authors

Daosheng Chen¹, Fei Zhang², Mengru Zhang¹, Qingyan Meng³, Chi Yung Jim⁴, Jingchao Shi⁵, Mou Leong Tan⁶, Xu Ma¹

Affiliations

¹ Key Laboratory of Wisdom City and Environment Modeling of Higher Education Institute, College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China; Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China.
² Key Laboratory of Wisdom City and Environment Modeling of Higher Education Institute, College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China; Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China. Electronic address: zhangfei3s@xju.edu.cn.
³ Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China.
⁴ Department of Social Sciences, Education University of Hong Kong, Lo Ping Road, Tai Po, Hong Kong, China.
⁵ Departments of Earth Sciences, the University of Memphis, Memphis, TN 38152, USA.
⁶ GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, Penang 11800, Malaysia.

PMID: 35192831
DOI: 10.1016/j.scitotenv.2022.154006

Abstract

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 Boost Regression 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.

Keywords: LJ1-01 satellite; Land surface temperature (LST) prediction; Landscape metric; PlanetScope satellite; Urban green space; Vegetation index.

MeSH terms

Cities
Environmental Monitoring / methods
Hot Temperature*
Parks, Recreational*
Temperature
Urbanization