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

文献类型：期刊论文

作者Zuo, Shudi1,2,3; Dai, Shaoqing1,2; Song, Xiaodong4; Xu, Chengdong5; Liao, Yilan5; Chang, Weiyin6; Chen, Qi7; Li, Yaying1,3; Tang, Jianfeng1,3; Man, Wang8
刊名REMOTE SENSING
出版日期2018-11-01
卷号10期号:11页码:18
关键词remote sensing ground surveys spatial statistical model STUFC environmental factors interaction mechanisms
ISSN号2072-4292
DOI10.3390/rs10111814
通讯作者Ren, Yin(yren@iue.ac.cn)
英文摘要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.
WOS关键词STOMATAL CONDUCTANCE ; LAND-COVER ; LANDSCAPE ; PARKS ; EFFICIENCY ; PHOENIX ; ECOLOGY ; SYSTEM ; TRENDS ; STRESS
资助项目National Science Foundation of China[31670645] ; National Science Foundation of China[31470578] ; National Science Foundation of China[31200363] ; National Science Foundation of China[41801182] ; National Science Foundation of China[41771462] ; National Science Foundation of China[41807502] ; National Social Science Fund[17ZDA058] ; National Key Research Program of China[2016YFC0502704] ; Fujian Provincial Department of ST Project[2016T3032] ; Fujian Provincial Department of ST Project[2016T3037] ; Fujian Provincial Department of ST Project[2016Y0083] ; Fujian Provincial Department of ST Project[2018T3018] ; Fujian Provincial Department of ST Project[2015Y0083] ; Xiamen Municipal Department of Science and Technology[3502Z20130037] ; Xiamen Municipal Department of Science and Technology[3502Z20142016] ; Key Laboratory of Urban Environment and Health of CAS[KLUEH-C-201701] ; Key Program of the Chinese Academy of Sciences[KFZDSW-324] ; Youth Innovation Promotion Association CAS[2014267]
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:000451733800145
出版者MDPI
资助机构National Science Foundation of China ; National Social Science Fund ; National Key Research Program of China ; Fujian Provincial Department of ST Project ; Xiamen Municipal Department of Science and Technology ; Key Laboratory of Urban Environment and Health of CAS ; Key Program of the Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS
源URL[http://ir.igsnrr.ac.cn/handle/311030/51440]  
专题中国科学院地理科学与资源研究所
通讯作者Ren, Yin
作者单位1.Chinese Acad Sci, Inst Urban Environm, Key Lab Urban Environm & Hlth, Xiamen 361021, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, NUEORS, Ningbo 315800, Zhejiang, Peoples R China
4.Zhejiang Univ Water Resources & Elect Power, Coll Geomat & Municipal Engn, Hangzhou 310018, Zhejiang, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
6.Fujian Agr & Forestry Univ, Coll Forestry, Fuzhou 350002, Fujian, Peoples R China
7.Univ Hawaii Manoa, Dept Geog & Environm, Honolulu, HI 96822 USA
8.Xiamen Univ Technol, Dept Spatial Informat Sci & Engn, Xiamen 361024, Peoples R China
推荐引用方式
GB/T 7714		 Zuo, Shudi,Dai, Shaoqing,Song, Xiaodong,et al. Determining the Mechanisms that Influence the Surface Temperature of Urban Forest Canopies by Combining Remote Sensing Methods, Ground Observations, and Spatial Statistical Models[J]. REMOTE SENSING,2018,10(11):18.
APA Zuo, Shudi.,Dai, Shaoqing.,Song, Xiaodong.,Xu, Chengdong.,Liao, Yilan.,...&Ren, Yin.(2018).Determining the Mechanisms that Influence the Surface Temperature of Urban Forest Canopies by Combining Remote Sensing Methods, Ground Observations, and Spatial Statistical Models.REMOTE SENSING,10(11),18.
MLA Zuo, Shudi,et al."Determining the Mechanisms that Influence the Surface Temperature of Urban Forest Canopies by Combining Remote Sensing Methods, Ground Observations, and Spatial Statistical Models".REMOTE SENSING 10.11(2018):18.

入库方式： OAI收割

来源：地理科学与资源研究所

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