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Frontiers of Earth Science

Front. Earth Sci.    2015, Vol. 9 Issue (2) : 276-285     DOI: 10.1007/s11707-014-0459-2
Understanding the effects of the impervious surfaces pattern on land surface temperature in an urban area
Qin NIE1,*(),Jianhua XU2
1. Department of Spatial Information Science and Engineering, Xiamen University of Technology, Xiamen 361024, China
2. The Research Center for East-West Cooperation in China, The Key Laboratory of GIScience of the Ministry of Education of China, East China Normal University, Shanghai 200241, China
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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     
Corresponding Authors: Qin NIE   
Online First Date: 09 September 2014    Issue Date: 30 April 2015
 Cite this article:   
Qin NIE,Jianhua XU. Understanding the effects of the impervious surfaces pattern on land surface temperature in an urban area[J]. Front. Earth Sci., 2015, 9(2): 276-285.
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Fig.1  The map of study area
Fig.2  The RMS frequency distribution and spatial image
Fig.3  The accuracy assessment of the impervious surface estimation
Fig.4  The spatial pattern of IS cover features
Fig.5  The spatial pattern of LST
IS cover features
Composition Percent cover 0.58** 0.6** -0.77** -0.76** -0.72** -0.74** -0.24* 0.001
Configuration PD 0.504** 0.26** -0.61** -0.76** -0.7** -0.78** -0.31**
LPI 0.02 0.37** -0.51** -0.62** -0.6** -0.43** -0.09
ED 0.73** 0.7** -0.74** -0.76** -0.71** -0.76** -0.27*
MNArea 0.14 -0.03 -0.78** -0.45** -0.36** -0.29* 0.22
SDArea 0.01 0.29* -0.72** -0.52** -0.33** -0.26* -0.02
MNShape 0.37** -0.4** -0.75** -0.43** -0.32** -0.13 0.01
SDShape 0.47** 0.175 -0.72** -0.49** -0.09 -0.27* 0.01
MNEnn -0.67** -0.48** 0.69** 0.65** 0.22 0.41** 0.05
SDEnn -0.64** -0.52** 0.51** 0.74** 0.45** 0.1 0.18
CI 0.39** 0.29* -0.69** -0.36** -0.26** -0.31** 0.18
Tab.1  Pearson correlation coefficients between LST and variables of composition and configuration of IS cover features
Model 1 Model 2 Model 3 Model 4
Cover feature Coefficient Cover feature Coefficient Cover feature Coefficient Cover feature Coefficient
IS2 0.51(0.11) IS2 0.015(0.003) IS2 1.02*(0.22) IS2 0.92*(0.2)
IS3 -0.86**(-0.45) IS3 -1.03**(-0.53) IS3 7.46(3.84) IS3 -1.2**(-0.62)
IS4 1.76**(2.66) IS4 1.64**(2.47) IS4 1.19**(1.79) IS4 1.1(1.66)
IS5 -0.22*(-1.1) IS5 -0.39**(-1.89) IS5 -0.01(-0.05) IS5 -0.17(-0.84)
IS7 0.16**(1.08) IS7 -0.16**(-1.9) IS7 0.18**(1.23) IS7 0.12**(0.84)
IS8 0.096**(1.73) IS8 -0.06**(-1.11) IS8 0.12**(2.14) IS8 0.06*(0.99)
R2 0.872(0.861) PD_IS2 -0.04(-0.06) PD_IS3 -1.17**(-4.1) PD_IS4 0.13(1.06)
ED_IS2 0.03(0.07) LPI_IS3 7.93*(0.46) LPI_IS4 -28.86**(?1.8)
R2 0.871(0.86) MNArea_IS3 -32.02**(-2.53) MNArea_IS4 -8.37(-0.39)
SDArea_IS3 -14.52**(-2.21) SDArea_IS4 19.65**(1.53)
SDShape_IS3 1.66(0.16) MNShape_IS4 -7.35(-0.19)
MNEnn_IS3 -0.0001(-0.15) SDEnn_IS4 0(.01)
CI_IS3 0.1**(3.61) CI_IS4 0.01(0.2)
R2 0.92(0.903) R2 0.91(0.89)
Model 5 Model 6 Model 7 Model 8
Cover feature Coefficient Cover feature Coefficient Cover feature Coefficient Cover feature Coefficient
IS2 -0.33(-0.07) IS2 -0.39(-0.09) IS2 0.28(0.06) IS2 0.52(0.11)
IS3 -0.26(-0.13) IS3 -0.09(-0.05) IS3 -1.08*(-5.6) IS3 -1.49**(-0.77)
IS4 1.38**(2.08) IS4 1.24**(1.87) IS4 1.97**(2.97) IS4 1.33**(2)
IS5 -1.36**(-6.7) IS5 -0.08(-0.42) IS5 -0.42**(-0.83) IS5 -0.24**(-1.2)
IS6 -0.08**(-0.96) IS7 0.05**(0.37) IS6 -0.07*(-0.83) IS6 -0.16**(-1.9)
IS7 0.06**(0.38) PD_IS6 -0.03(?0.29) IS7 0.1(0.67) IS7 -0.05(?0.33)
PD_IS5 0.3**(4.72) LPI_IS6 1.22**(1.49) PD_IS7 0.01(0.23) PD_IS8 0.26**(1.66)
LPI_IS5 0.07(0.02) MNArea_IS6 -5.61(-1.16) LPI_IS7 -0.004(-0.04) ED_IS8 -0.04**(-2.87)
MNArea_IS5 4.89(0.48) SDArea_IS6 -3.76*(-2.69) ED_IS7 -0.004(-0.099) MNShape_IS8 5.58**(0.47)
SDArea_IS5 -3.03(-0.79) MNShape_IS6 7.17(0.53) SDArea_P7 0.05(0.16) SDShape_IS8 5.47**(2.06)
MNShape_IS5 5.06(0.21) SDShape_IS6 -3(-0.49) MNShape_P7 0.02(0.001) MNEnn_IS8 -0.03(-0.39)
SDShape_IS5 -4.61(-0.47) MNEnn_IS6 0.003(0.05) MNEnn_P7 0.005(0.009) SDEnn_IS8 0.02*(0.28)
MNEnn_IS5 0.018*(0.36) SDEnn_IS6 0(-0.17) SDEnn_P7 -0.05(-0.17) CI_IS8 -0.15**(-0.71)
SDEnn_IS5 0(-0.15) CI_IS6 0.03*(0.71) CI_IS7 -0.03(-0.29) R2 0.957(0.947)
CI_IS5 0.07**(1.56) R2 0.936(0.92) R2 0.899(0.874)
R2 0.938(0.922)
Tab.2  Linear regression models between LST and composition variables or the combination of composition and some configuration variables
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