Quantifying the influences of various ecological factors on land surface temperature of urban forests.
Identifying factors that influence the land surface temperature (LST) of urban forests can help improve simulations and predictions of spatial patterns of urban cool islands. This requires a quantitative analytical method that combines spatial statistical analysis with multi-source observational data. The purpose of this study was to reveal how human activities and ecological factors jointly influence LST in clustering regions (hot or cool spots) of urban forests. Using Xiamen City, China from 1996 to 2006 as a case study, we explored the interactions between human activities and ecological factors, as well as their influences on urban forest LST. Population density was selected as a proxy for human activity. We integrated multi-source data (forest inventory, digital elevation models (DEM), population, and remote sensing imagery) to develop a database on a unified urban scale. The driving mechanism of urban forest LST was revealed through a combination of multi-source spatial data and spatial statistical analysis of clustering regions. The results showed that the main factors contributing to urban forest LST were dominant tree species and elevation. The interactions between human activity and specific ecological factors linearly or nonlinearly increased LST in urban forests. Strong interactions between elevation and dominant species were generally observed and were prevalent in either hot or cold spots areas in different years. In conclusion, quantitative studies based on spatial statistics and GeogDetector models should be conducted in urban areas to reveal interactions between human activities, ecological factors, and LST.
Key Laboratory of Urban Environment and Health, Key Laboratory of Urban Metabolism of Xiamen, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China; Ningbo Urban Environment Observation and Research Station-NUEORS, Chinese Academy of Sciences, Ningbo, 315800, China. Electronic address: yren@iue.ac.cn.
Yin
Y
Ren
Key Laboratory of Urban Environment and Health, Key Laboratory of Urban Metabolism of Xiamen, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
Lu-Ying
LY
Deng
Key Laboratory of Urban Environment and Health, Key Laboratory of Urban Metabolism of Xiamen, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China; Ningbo Urban Environment Observation and Research Station-NUEORS, Chinese Academy of Sciences, Ningbo, 315800, China.
Shu-Di
SD
Zuo
College of Environment and Natural Resources, Zhejiang University, Hangzhou, 310058, China.
Xiao-Dong
XD
Song
State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Nature Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Yi-Lan
YL
Liao
State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Nature Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Cheng-Dong
CD
Xu
Department of Geography, University of Hawai'i at Manoa, Honolulu, 96822, USA.
Qi
Q
Chen
Department of Spatial Information Science and Engineering, Xiamen University of Technology, Xiamen, 361024, China.
Li-Zhong
LZ
Hua
China United Network Communications Group Co. Ltd, Nanjing, 210000, China.
Zheng-Wei
ZW
Li
D002681:China; D002947:Cities; D004784:Environmental Monitoring; D065928:Forests; D006801:Humans; D013696:Temperature; D014197:Trees / Q000502:physiology*
Driving mechanism; GeogDetector model; Integrated quantitative analysis; Land surface temperature (LST); Multiple ecological factors; Spatial statistical analysis; Urban forest