干旱区生态环境知识资源中心(Arid): Landscape and vegetation traits of urban green space can predict local surface temperature

Arid

DOI	10.1016/j.scitotenv.2022.154006
	Landscape and vegetation traits of urban green space can predict local surface temperature
	Chen, Daosheng; Zhang, Fei; Zhang, Mengru; Meng, Qingyan; Jim, Chi Yung; Shi, Jingchao; Tan, Mou Leong; Ma, Xu
通讯作者	Zhang, F
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
ISSN	0048-9697
EISSN	1879-1026
出版年	2022
卷号	825
英文摘要	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 x 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.
英文关键词	Urban green space Landscape metric Vegetation index Land surface temperature (LST) prediction PlanetScope satellite
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000766791900006
WOS关键词	HEAT-ISLAND ; THERMAL ENVIRONMENT ; GLOBAL VEGETATION ; SPECIES RICHNESS ; HOT WEATHER ; INDEX ; PERFORMANCE ; IMPACT ; CITY ; MITIGATION
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394360
推荐引用方式 GB/T 7714	Chen, Daosheng,Zhang, Fei,Zhang, Mengru,et al. Landscape and vegetation traits of urban green space can predict local surface temperature[J],2022,825.
APA	Chen, Daosheng.,Zhang, Fei.,Zhang, Mengru.,Meng, Qingyan.,Jim, Chi Yung.,...&Ma, Xu.(2022).Landscape and vegetation traits of urban green space can predict local surface temperature.SCIENCE OF THE TOTAL ENVIRONMENT,825.
MLA	Chen, Daosheng,et al."Landscape and vegetation traits of urban green space can predict local surface temperature".SCIENCE OF THE TOTAL ENVIRONMENT 825(2022).