Impact of urban greenspace spatial pattern on land surface temperature: a case study in Beijing metropolitan area, China
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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.
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.
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.
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.
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.
KeywordsUrban greenspace Land surface temperature Spatial pattern Geo-detector Interactive effect
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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Ji HC (2015) Analysis on temperature change characteristics in Beijing city from 1982 to 2012. Mod Agric Sci Technol 7:259–261Google Scholar
- Jonsson P (2004) Vegetation as an urban climate control in the subtropical city of Gaborone, Botswana. Int J Climatol 24:1307–1322Google Scholar
- 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
- 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
- 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
- Li H, Wu JG (2004) Use and misuse of landscape indices. Landsc Ecol 19(4):389–399Google Scholar
- 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
- 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
- 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
- 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
- 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
- McGarigal KS, Cushman S, Neel M, Ene E (2002) FRAGSTATS: Spatial pattern analysis program for categorical mapsGoogle Scholar
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Shih W (2017) Greenspace patterns and the mitigation of land surface temperature in Taipei metropolis. Habitat Int 60:69–80Google Scholar
- Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86(3):370–384Google Scholar
- Wang JF, Hu Y (2012) Environmental health risk detection with GeogDetector. Environ Modell Softw 33:114–115Google Scholar
- 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
- 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
- Wang JF, Xu CD (2017) Geodetector: principle and prospective. Acta Geogr Sin 72(1):116–134Google Scholar
- Watkins R (2002) The impact of the urban environment on the energy used for cooling buildings. In: International conference on nuclear engineeringGoogle Scholar
- 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
- 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
- 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
- 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
- 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