Determining the Mechanisms that Influence the Surface Temperature of Urban Forest Canopies by Combining Remote Sensing Methods, Ground Observations, and Spatial Statistical Models

Year: 2018

Type: article

Abstract: The spatiotemporal distribution pattern of the surface temperatures of urban forest canopies (STUFC) is influenced by many environmental factors, and the identification of interactions between these factors can improve simulations and predictions of spatial patterns of urban cool islands. This quantitative research uses an integrated method that combines remote sensing, ground surveys, and spatial statistical models to elucidate the mechanisms that influence the STUFC and considers the interaction of multiple environmental factors. This case study uses Jinjiang, China as a representative of a city experiencing rapid urbanization. We build up a multisource database (forest inventory, digital elevation models, population, and remote sensing imagery) on a uniform coordinate system to support research into the interactions that influence the STUFC. Landsat-5/8 Thermal Mapper images and meteorological data were used to retrieve the temporal and spatial distributions of land surface temperature. Ground observations, which included the forest management planning inventory and population density data, provided the factors that determine the STUFC spatial distribution on an urban scale. The use of a spatial statistical model (GeogDetector model) reveals the interaction mechanisms of STUFC. Although different environmental factors exert different influences on STUFC, in two periods with different hot spots and cold spots, the patch area and dominant tree species proved to be the main factors contributing to STUFC. The interaction between multiple environmental factors increased the STUFC, both linearly and nonlinearly. Strong interactions tended to occur between elevation and dominant species and were prevalent in either hot or cold spots in different years. In conclusion, the combining of multidisciplinary methods (e.g., remote sensing images, ground observations, and spatial statistical models) helps reveal the mechanism of STUFC on an urban scale.

Source: Remote Sensing

DOI: 10.3390/rs10111814

Authors: Shudi Zuo, Shaoqing Dai, Xiaodong Song, Chengdong Xu, Yilan Liao, Weiyin Chang, Qi Chen, Yaying Li, Jianfeng Tang, Wang Man, Yin Ren

Institutions: Chinese Academy of Sciences, Institute of Urban Environment, University of Chinese Academy of Sciences, Zhejiang University of Water Resource and Electric Power, Institute of Geographic Sciences and Natural Resources Research, State Key Laboratory of Resources and Environmental Information System, Fujian Agriculture and Forestry University, University of Hawaiʻi at Mānoa, Xiamen University of Technology

Language: English

Topic: Urban Heat Island Mitigation

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Climate action, Life on land

Open Access status: gold

Estimated APC paid: $3,258

Funders: National Natural Science Foundation of China, Chinese Academy of Sciences, Youth Innovation Promotion Association of the Chinese Academy of Sciences, National Office for Philosophy and Social Sciences, Youth Innovation Promotion Association

OpenAlex ID: W2900653557