Landscape Ecology

, Volume 27, Issue 6, pp 887–898 | Cite as

Spatial pattern of greenspace affects land surface temperature: evidence from the heavily urbanized Beijing metropolitan area, China

  • Xiaoma Li
  • Weiqi Zhou
  • Zhiyun Ouyang
  • Weihua Xu
  • Hua Zheng
Research Article


The urban heat island describes the phenomenon that air/surface temperatures are higher in urban areas compared to their surrounding rural areas. Numerous studies have shown that increased percent cover of greenspace (PLAND) can significantly decrease land surface temperatures (LST). Fewer studies, however, have investigated the effects of configuration of greenspace on LST. This paper aims to fill this gap using Beijing, China as a case study. PLAND along with six configuration metrics were used to measure the composition and configuration of greenspace. The metrics were calculated based on a greenspace map derived from SPOT imagery, and LST data were retrieved from Landsat TM thermal band. Ordinary least squares regression and spatial autoregression were employed to investigate the relationship between LST and spatial pattern of greenspace using the census tract as the analytical unit. The results showed that PLAND was the most important predictor of LST. A 10 % increase in PLAND resulted in approximately a 0.86 °C decrease in LST. Configuration of greenspace also significantly affected LST. Given a fixed amount of greenspace, LST increased significantly with increased patch density. In addition, the variance of LST was largely explained by both composition and configuration of greenspace. The unique variation explained by the composition was relatively small, and was close to that of the configuration. Results from this study can expand our understanding of the relationship between LST and vegetation, and provide insights for improving urban greenspace planning and management.


Urban heat island Urban greenspace Landscape metrics Configuration Spatial autocorrelation Spatial autoregression Greenspace planning Thermal infrared remote sensing 



This study was supported by the National Natural Science Foundation of China (41030744), the National Key Technology Research and Development Program in the 12th Five-year Plan of China (2012BAC13B04), and the Special Foundation of the State Key Lab of Urban and Regional Ecology. We sincerely thank the editor and three anonymous reviewers for their constructive comments and suggestions. Much gratitude is given to Christina Wong at the Arizona State University, who carefully reviewed the language of this manuscript.


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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Xiaoma Li
    • 1
  • Weiqi Zhou
    • 1
  • Zhiyun Ouyang
    • 1
  • Weihua Xu
    • 1
  • Hua Zheng
    • 1
  1. 1.State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijingChina

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