Advertisement

Landscape Ecology

, Volume 34, Issue 12, pp 2949–2961 | Cite as

Impact of urban greenspace spatial pattern on land surface temperature: a case study in Beijing metropolitan area, China

  • Jie Yin
  • Xiaoxu WuEmail author
  • Miaogen Shen
  • Xiaoli Zhang
  • Chenghao Zhu
  • Hongxu Xiang
  • Chunming ShiEmail author
  • Zhiyi Guo
  • Chenlu Li
Research Article
  • 233 Downloads

Abstract

Context

Urban greenspace can significantly decrease the land surface temperature (LST). The spatial characteristics and vegetation composition of urban greenspace have a great influence on its cooling capacity.

Objectives

We sought to distinguish the cooling effect by different spatial pattern factors of greenspace and by the interaction of these factors, which may be useful in understanding cooling effect and designing urban greenspace.

Methods

Both the greenspace derived from SPOT6 and LST retrieved from Landsat-8 images are employed to identify the dominant factors influencing LST and investigate the interaction between any two dominant factors in the Beijing metropolitan area.

Results

The results indicate that the dominant spatial factors affecting LST vary by greenspace type, i.e., for grass, the number of patches (NP) and patch density (PD) have a significant effect on LST while for coniferous forest, the landscape shape index (LSI) is the dominant spatial factor. And the NP and percentage of landscape are the dominant spatial factors for broad-leaved forest and mixed forest, respectively. The interaction of any two dominant factors is larger than their individual effects, and the interaction between the NP and LSI of greenspace is not as strong as the interaction between the NP and PD.

Conclusions

Urban greenspace design and planning need to consider the spatial pattern of different types of greenspace. On this basis, we proposed a pattern effective in cooling LST in cities climatically similar to Beijing, which could provide theoretical reference for the design and planning of urban greenspace.

Keywords

Urban greenspace Land surface temperature Spatial pattern Geo-detector Interactive effect 

Notes

Acknowledgements

This work was funded by the National Key Research and Development Program of China (Nos. 2018YFC1406906, and 2016YFA0600104) and Top-Notch Young Talents Program of China (to Shen).

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Supplementary material

10980_2019_932_MOESM1_ESM.docx (2.4 mb)
Electronic supplementary material 1 (DOCX 2413 kb)

References

  1. Akbari H, Pomerantz M, Taha H (2001) Cool surfaces and shade trees to reduce energy use and improve air quality in urban areas. Sol Energy 70(3):295–310Google Scholar
  2. Arnfield AJ (2003) Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. Int J Climatol 23(1):1–26Google Scholar
  3. Asgarian A, Amiri BJ, Sakieh Y (2014) Assessing the effect of green cover spatial patterns on urban land surface temperature using landscape metrics approach. Urban Ecosyst 18(1):209–222Google Scholar
  4. Bao TLG, Li XM, Zhang J, Zhang YJ, Tian SZ (2016) Assessing the distribution of urban green spaces and its anisotropic cooling distance on urban heat island pattern in Baotou, China. ISPRS Int J Geo-Inf 5(12):13Google Scholar
  5. Baris M, Sahin S, Yazgan ME (2009) The contribution of trees and green spaces to the urban climate: the case of Ankara. Afr J Agr Res 4(9):791–800Google Scholar
  6. Cao X, Onishi A, Chen J, Imura H (2010) Quantifying the cool island intensity of urban parks using ASTER and IKONOS data. Landsc Urban Plan 96(4):224–231Google Scholar
  7. Chang CR, Li MH, Chang SD (2007) A preliminary study on the cool-island intensity of Taipei city parks. Landsc Urban Plan 80:386–395Google Scholar
  8. Chen ZX, Su XH, Liu SZ, Gu RZ (1998) Study on ecological benefits of urban landscaping in Beijing (6). Chin Landsc Arch 14(60):53–56 (in Chinese) Google Scholar
  9. Du C, Ren H, Qin Q, Meng J, Zhao S (2015) A practical split-window algorithm for estimating land surface temperature from Landsat 8 Data. Remote Sens 7(1):647–665Google Scholar
  10. Estoque RC, Murayama Y, Myint SW (2017) Effects of landscape composition and pattern on land surface temperature: an urban heat island study in the megacities of Southeast Asia. Sci Total Environ 577:349–359PubMedGoogle Scholar
  11. Feyisa GL, Dons K, Meilby H (2014) Efficiency of parks in mitigating urban heat island effect: an example from Addis Ababa. Landsc Urban Plan 123:87–95Google Scholar
  12. Gao JX, Song T, Zhang B, Han YW, Gao XT, Feng CY (2016) The relationship between urban green space community structure and air temperature reduction and humidity increase in Beijing. Resour Sci 38(6):1028–1038Google Scholar
  13. Ge WQ, Zhou HM, Tu DJ (2005) The surveying on thermal influence area of Shanghai urban greenbelt. Remote Sens Technol Appl 20(5):496–500 (in Chinese) Google Scholar
  14. Guo L, Liu R, Men C, Wang Q, Miao Y, Zhang Y (2019a) Quantifying and simulating landscape composition and pattern impacts on land surface temperature: a decadal study of the rapidly urbanizing city of Beijing, China. Sci Total Environ 654:430–440PubMedGoogle Scholar
  15. Guo G, Wu Z, Chen Y (2019b) Complex mechanisms linking land surface temperature to greenspace spatial patterns: evidence from four southeastern Chinese cities. Sci Total Environ 674:77–87PubMedGoogle Scholar
  16. Huang L, Chen H, Ren H, Wang J, Guo Q (2013) Effect of urbanization on the structure and functional traits of remnant subtropical evergreen broad-leaved forests in South China. Environ Monit Assess 185(6):5003–5018PubMedGoogle Scholar
  17. Jaganmohan M, Knapp S, Buchmann CM, Schwarz N (2016) The bigger, the better? The influence of urban green space design on cooling effects for residential areas. J Environ Qual 45(1):134–145PubMedGoogle Scholar
  18. Ji HC (2015) Analysis on temperature change characteristics in Beijing city from 1982 to 2012. Mod Agric Sci Technol 7:259–261Google Scholar
  19. Jonsson P (2004) Vegetation as an urban climate control in the subtropical city of Gaborone, Botswana. Int J Climatol 24:1307–1322Google Scholar
  20. Kong FH, Sun CF, Liu FF et al (2016) Energy saving potential of fragmented green spaces due to their temperature regulating ecosystem services in the summer. Appl Energy 183:1428–1440Google Scholar
  21. Kong FH, Yin HW, James P, Hutyra LR, He HS (2014) Effects of spatial pattern of greenspace on urban cooling in a large metropolitan area of eastern China. Landsc Urban Plan 128:35–47Google Scholar
  22. Li JX, Song CH, Cao L, Zhu FG, Meng XL, Wu JG (2011) Impacts of landscape structure on surface urban heat islands: a case study of Shanghai, China. Remote Sens Environ 115(12):3249–3263Google Scholar
  23. Li H, Wu JG (2004) Use and misuse of landscape indices. Landsc Ecol 19(4):389–399Google Scholar
  24. Li XM, Zhou WQ, Ouyang ZY (2013) Relationship between land surface temperature and spatial pattern of greenspace: what are the effects of spatial resolution? Landsc Urban Plan 114:1–8Google Scholar
  25. Li XM, Zhou WQ, Ouyang ZY, Xu WH, Zheng H (2012) Spatial pattern of greenspace affects land surface temperature: evidence from the heavily urbanized Beijing metropolitan area, China. Landsc Ecol 27(6):887–898Google Scholar
  26. Liu FF, Yan WJ, Kong FH, Yin HW, Ban YL, Xu WB (2017) A review on the urban green space cooling effect based on field measurement of air temperature. Chin J Appl Ecol 28(4):1387–1396Google Scholar
  27. Liu L, Zhang YZ (2011) Urban heat island analysis using the Landsat TM data and ASTER data: a case study in Hong Kong. Remote Sens 3(7):1535–1552Google Scholar
  28. Maimaitiyiming M, Ghulam A, Tiyip T et al (2014) Effects of green space spatial pattern on land surface temperature: implications for sustainable urban planning and climate change adaptation. ISPRS J Photogramm 89:59–66Google Scholar
  29. McGarigal KS, Cushman S, Neel M, Ene E (2002) FRAGSTATS: Spatial pattern analysis program for categorical mapsGoogle Scholar
  30. Oliveira S, Andrade H, Vaz T (2011) The cooling effect of green spaces as a contribution to the mitigation of urban heat: a case study in Lisbon. Build Environ 46(11):2186–2194Google Scholar
  31. Park J, Kim J-H, Lee DK, Park CY, Jeong SG (2017) The influence of small green space type and structure at the street level on urban heat island mitigation. Urban For Urban Gree 21:203–212Google Scholar
  32. Potchter O, Cohen P, Bitan A (2006) Climatic behavior of various urban parks during hot and humid summer in the mediterranean city of Tel Aviv, Israel. Int J Climatol 26(12):1695–1711Google Scholar
  33. Ren HZ, Chen D, Liu RY et al (2015) Atmospheric water vapor retrieval from Landsat 8 thermal infrared images. J Geophys Res Atmos 120:1723–1738Google Scholar
  34. Riitters KH, O’Neill RV, Hunsaker CT et al (1995) A factor analysis of landscape pattern and structure metrics. Landsc Ecol 10(1):23–39Google Scholar
  35. Riva-Murray K, Riemann R, Murdoch P, Fischer JM, Brightbill R (2010) Landscape characteristics affecting streams in urbanizing regions of the Delaware River Basin (New Jersey, New York, and Pennsylvania, US). Landsc Ecol 25(10):1489–1503Google Scholar
  36. Shashua-Bar L, Hoffman ME (2000) Vegetation as a climatic component in the design of an urban street: an empirical model for predicting the cooling effect of urban green areas with trees. Energy Build 31(3):221–235Google Scholar
  37. Sheikhi A, Kanniah KD, Ho CH (2015) Effect of land cover and green space on land surface temperature of a fast growing economic region in Malaysia. Spie Remote Sens 9644:9644131–9644138Google Scholar
  38. Shih W (2017) Greenspace patterns and the mitigation of land surface temperature in Taipei metropolis. Habitat Int 60:69–80Google Scholar
  39. Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86(3):370–384Google Scholar
  40. Wang JF, Hu Y (2012) Environmental health risk detection with GeogDetector. Environ Modell Softw 33:114–115Google Scholar
  41. Wang YQ, Li B (2016) On influence of various plants groups on changes of temperature and moisture of green land in Beijing. Shanxi Archit 42(29):196–197 (in Chinese) Google Scholar
  42. Wang JF, Li XH, Christakos G et al (2010) Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China. Int J Geogr Inf Sci 24(1):107–127Google Scholar
  43. Wang JF, Xu CD (2017) Geodetector: principle and prospective. Acta Geogr Sin 72(1):116–134Google Scholar
  44. Watkins R (2002) The impact of the urban environment on the energy used for cooling buildings. In: International conference on nuclear engineeringGoogle Scholar
  45. Xiang Y, Yu HY, Luo YY, Yang M (2010) Exploration on the edge effect and its influence of urban public green space. Nor Hortic 4:109–112 (in Chinese) Google Scholar
  46. Yang J, Sun J, Ge QS, Li XM (2017) Assessing the impacts of urbanization-associated green space on urban land surface temperature: a case study of Dalian, China. Urban For Urban Gree 22:1–10Google Scholar
  47. Zhang XY, Zhong TY, Feng XZ, Wang K (2009) Estimation of the relationship between vegetation patches and urban land surface temperature with remote sensing. Int J Remote Sens 30(8):2105–2118Google Scholar
  48. Zhou WQ, Huang GL, Cadenasso ML (2011) Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landsc Urban Plan 102(1):54–63Google Scholar
  49. Zoran M (2008) Satellite observation of biophysical indicators related to urban heat island effect. In: 37th COSPAR scientific assembly, Montréal, Canada, p A31-0086-08Google Scholar
  50. Zoulia I, Santamouris M, Dimoudi A (2009) Monitoring the effect of urban green areas on the heat island in Athens. Environ Monit Assess 156(1–4):275–292PubMedGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Jie Yin
    • 1
    • 2
  • Xiaoxu Wu
    • 1
    Email author
  • Miaogen Shen
    • 3
  • Xiaoli Zhang
    • 2
  • Chenghao Zhu
    • 4
  • Hongxu Xiang
    • 1
  • Chunming Shi
    • 1
    Email author
  • Zhiyi Guo
    • 1
  • Chenlu Li
    • 1
  1. 1.State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System ScienceBeijing Normal UniversityBeijingChina
  2. 2.Beijing Key Laboratory of Precision Forestry, Forestry CollegeBeijing Forestry UniversityBeijingChina
  3. 3.Key Laboratory of Alpine Ecology and Biodiversity, CAS Center for Excellence in Tibetan Plateau Earth Sciences, Institute of Tibetan Plateau ResearchChinese Academy of SciencesBeijingChina
  4. 4.Satellite Environment CenterMinistry of Environmental ProtectionBeijingChina

Personalised recommendations