Understanding the effects of the impervious surfaces pattern on land surface temperature in an urban area

Qin NIE, Jianhua XU

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PDF(958 KB)
Front. Earth Sci. ›› 2015, Vol. 9 ›› Issue (2) : 276-285. DOI: 10.1007/s11707-014-0459-2
RESEARCH ARTICLE
RESEARCH ARTICLE

Understanding the effects of the impervious surfaces pattern on land surface temperature in an urban area

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Abstract

It is well known that urban impervious surface (IS) has a warming effect on urban land surface temperature (LST). However, the influence of an IS’s structure, components, and spatial distribution on LST has rarely been quantitatively studied within strictly urban areas. Using ETM+ remote sensing images from the downtown area of Shanghai, China in 2010, this study characterized and quantified the influence of the IS spatial pattern on LST by selecting the percent cover of each IS cover feature and ten configuration metrics. The IS fraction was estimated by linear spectral mixture analysis (LSMA), and LST was retrieved using a mono-window algorithm. The results indicate that high fraction IS cover features account for the majority of the study area. The high fraction IS cover features are widely distributed and concentrated in groups, which is similar with that of high temperature zones. Both the percent composition and the configuration of IS cover features greatly affect the magnitude of LST, but the percent composition is a more important factor in determining LST than the configuration of those features. The significances and effects of the given configuration variables on LST vary greatly among IS cover features.

Keywords

urban impervious surfaces / land surface temperature / spatial pattern / Shanghai city

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Qin NIE, Jianhua XU. Understanding the effects of the impervious surfaces pattern on land surface temperature in an urban area. Front. Earth Sci., 2015, 9(2): 276‒285 https://doi.org/10.1007/s11707-014-0459-2

References

[1]
Artis D A, Carnahan W H (1982). Survey of emissivity variability in thermography of urban areas. Remote Sens Environ, 12(4): 313–329
CrossRef Google scholar
[2]
Ding J C, Zhang Z K, Xi H, Zhou H M (2002). A study of the high temperature distribution and the heat island effect in the summer of the Shanghai area. Chin J Atmos Sci, 26(3): 412–420 (in Chinese)
[3]
Gong A D, Jiang Z X, Li J, Chen Y H, Hu H L (2005). Urban land surface temperature retrieval based on landsat TM remote sensing images in Beijing. Remote Sensing Information, (3): 18–20
[4]
Hope A, Engstrom R, Stow D (2005). Relationship between AVHRR surface temperature and NDVI in Arctic tundra ecosystems. Int J Remote Sens, 26(8): 1771–1776
CrossRef Google scholar
[5]
Lu D, Weng Q (2006). Use of impervious surface in urban land-use classification. Remote Sens Environ, 102(1–2): 146–160
CrossRef Google scholar
[6]
McGarigal K, Cushman S A, Neel M C, Ene E (2002). FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at the following web site: http://www.umass.edu/landeco/research/fragstats/fragstats.html
[7]
Meiyappan P, Jain A K (2012). Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years. Front Earth Sci, 6(2): 122–139
CrossRef Google scholar
[8]
Meng X L (2010). Multi-scale Relationships Between Impervious Surface, Vegetation, Water and Urban HeatIsland. East China Normal University
[9]
Ridd M K (1995). Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: Comparative anatomy for cities. Int J Remote Sens, 16(12): 2165–2185
CrossRef Google scholar
[10]
Slonecker E T, Jennings D B, Garofalo D (2001). Remote sensing of impervious surfaces: a review. Remote Sens Rev, 20(3): 227–255
CrossRef Google scholar
[11]
Small C (2001). Estimation of urban vegetation abundance by spectral mixture analysis. Int J Remote Sens, 22(7): 1305–1334
CrossRef Google scholar
[12]
Van De Gfiend A, Owe M (1993). On the relationship between thermal emissivity and the normalized difference vegetation index for nature surfaces. In ternational Journal of Remote Sensing, 14(6): 1119–1131
CrossRef Google scholar
[13]
Weng Q (2012). Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sens Environ, 117: 34–49
CrossRef Google scholar
[14]
Weng Q, Liu H, Lu D (2007). Assessing the effects of land use and land cover patterns on thermal conditions using land scape metrics in city of Indianapolis, United States. Urban Ecosyst, 10(2): 203–219
CrossRef Google scholar
[15]
Weng Q, Lu D, Schubring J (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens Environ, 89(4): 467–483
CrossRef Google scholar
[16]
Wong N H, Yu C (2005). Study of green areas and urban heat island in a tropical city. Habitat Int, 29(3): 547–558
CrossRef Google scholar
[17]
Xian G, Crane M, Su J (2007). An analysis of urban development and its environmental impact on the Tampa Bay watershed. J Environ Manage, 85(4): 965–976
CrossRef Google scholar
[18]
Xiao R B, Ouyang Z Y, Zheng H, Li E F, Schienke E W Wang X K (2007). Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China. J Environ Sci (China), 19(2): 250–256
CrossRef Google scholar
[19]
Xu J H, Ai N S, Chen Y, Mei A X, Liao H J (2003). Quantitative analysis and fractal modeling on the mosaic structure of landscape in the central area of Shanghai metropolis. Chin Geogr Sci, 13(3): 199–206
CrossRef Google scholar
[20]
Xu J H, Lu Y, Ai N S, Yue W Z (2001). A study on landscape mosaic structure in urban-rural area in Northwest of China with RS and GIS. Chin Geogr Sci, 11(4): 366–376
CrossRef Google scholar
[21]
Yang X, Liu Z (2005). Use of satellite-derived landscape imperviousness index to characterize urban spatial growth. Computers, Environment and Urban Systems, 29(5): 524–540.
[22]
Yue W (2005). Study on Urban Landscape Pattern and Its Thermal Environment Eeffect Based on Remote Sensing Image. East China Normal University
[23]
Yuan F, Bauer M E (2005). Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing of Environment, 106(3): 375–386.
[24]
Yue W (2009). Improvement of urban impervious surface estimation in Shanghai using Landsat7 ETM+ data. Chin Geogr Sci, 19(3): 283–290
CrossRef Google scholar
[25]
Yue W, Liu Y, Fan P, Ye X, Wu C (2012). Assessing spatial pattern of urban thermal environment in Shanghai, China. Stochastic Environ Res Risk Assess, 26(7): 899–911
CrossRef Google scholar
[26]
Zhang Y, Odeh I OA, Han C (2009a). Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. International Journal of Applied Earth Observation and Geoinformation, 11(4): 256–264
[27]
Zhang X, Zhong T, Feng X, Wang K (2009b). Estimation of the relationship between vegetation patches and urban land surface temperature with remote sensing. Int J Remote Sens, 30(8): 2105–2118
CrossRef Google scholar
[28]
Zhou S Z, Zhang C (1982). On the Shanghai urban heat island effect. Acta Geographica Sinica, 37(4): 372–381 (in Chinese)
[29]
Zhou W, Huang G, Cadenasso M L (2011). Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landsc Urban Plan, 102(1): 54–63
CrossRef Google scholar

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 41130525 and 41102224).

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
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