Long-range cross-correlation between urban impervious surfaces and land surface temperatures

Abstract

The thermal effect of urban impervious surfaces (UIS) is a complex problem. It is thus necessary to study the relationship between UIS and land surface temperatures (LST) using complexity science theory and methods. This paper investigates the long-range cross-correlation between UIS and LST with detrended cross-correlation analysis and multifractal detrended cross-correlation analysis, utilizing data from downtown Shanghai, China. UIS estimates were obtained from linear spectral mixture analysis, and LST was retrieved through application of the mono-window algorithm, using Landsat Thematic Mapper and Enhanced Thematic Mapper Plus data for 1997–2010. These results highlight a positive long-range cross-correlation between UIS and LST across People’s Square in Shanghai. LST has a long memory for a certain spatial range of UIS values, such that a large increment in UIS is likely to be followed by a large increment in LST. While the multifractal long-range cross-correlation between UIS and LST was observed over a longer time period in the W–E direction (2002–2010) than in the N–S (2007–2010), these observed correlations show a weakening during the study period as urbanization increased.

Keywords

urban impervious surface / land surface temperature / long-range cross-correlation / Shanghai

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

[2] Chen Y G (2013). Fractal analytical approach of urban form based on spatial correlation function. Chaos Solitons Fractals, 49: 47−60

[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] Grau J, Méndez V, Tarquis A M, Díaz M C, Saa A (2006). Comparison of gliding box and box-counting methods in soil image analysis. Geoderma, 134(3−4): 349−359

[5] Liu Z H, Wang Y L, Peng J (2012). Quantifying spatiotemporal patterns dynamics of impervious surface in Shenzhen. Geogrpahical Research, 31: 1535−1545 (in Chinese)

[6] Liu Z H, Wang Y L, Peng J, Xie M M, Li Y (2011). Using ISA to analyze the spatial pattern of urban land cover change: a case study in Shenzhen. Acta Geogr Sin, 66(7): 961−971

[7] Podobnik B, Grosse I, Horvati’c D, Ilic S, Ivanov P Ch, Stanley H E (2009). Quantifying cross-correlations using local and global detrending approaches. Eur Phys J B, 71(2): 243−250

[8] Podobnik B, Stanley H E (2008). Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series. Phys Rev Lett, 100(8): 084102

[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

[10] Small C (2001). Estimation of urban vegetation abundance by spectral mixture analysis. Int J Remote Sens, 22(7): 1305−1334

[11] Van de Griend A A, Owe M (1993). On the relationship between thermal emissivity an d the normalized diference vegetation index for nature surfaces. Int J Remote Sens, 14(6): 1119−1131

[12] Vassoler R T, Zebende G F (2012). DCCA cross-correlation coefficient apply in time series of air temperature and air relative humidity. Physica A: Statistical Mechanics and its Applications, 391: 2438−2443

[13] Wang Y, Wei Y, Wu C (2010). Cross-correlations between Chinese A-share and B-share markets. Physica A: Statistical Mechanics and its Applications, 389: 5468−5478

[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

[15] 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

[16] 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

[17] Xie M M, Wang Y L, Li G C (2009). Spatial variation of impervious surface area and vegetation cover based on SubPixel Model in Shenzhen. Resources Science, 31: 257−264 (in Chinese)

[18] 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

[19] 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

[20] Yang L, Huang C, Homer C G, Wylie B K, Coan M J (2003). An approach for mapping large-area impervious surfaces: synergistic use of Landsat-7 ETM+ and high spatial resolution imagery. Can J Rem Sens, 29(2): 230−240

[21] Yang X, Liu Z (2005). Use of satellite-derived landscape imperviousness index to characterize urban spatial growth. Comput Environ Urban Syst, 29(5): 524−540

[22] Yuan F, Bauer M E (2007). Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sens Environ, 106(3): 375−386

[23] 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

[24] Yue W, Xu J, Tan W, Xu L (2007). The relationship between land surface temperature and NDVI with remote sensing: application to Shanghai Landsat 7 ETM+ data. Int J Remote Sens, 28(15): 3205−3226

[25] Zhang Y, Odeh I O A, Han C (2009). Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. Int J Appl Earth Obs Geoinf, 11(4): 256−264

[26] Zhou W X (2008). Multifractal detrended cross-correlation analysis for two nonstationary signals. Phys Rev E Stat Nonlin Soft Matter Phys, 77(6): 066211

Acknowledgments

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

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg