Please wait a minute...
Frontiers of Earth Science

ISSN 2095-0195

ISSN 2095-0209(Online)

CN 11-5982/P

Postal Subscription Code 80-963

2018 Impact Factor: 1.205

Front. Earth Sci.    2016, Vol. 10 Issue (1) : 117-125    https://doi.org/10.1007/s11707-015-0512-9
RESEARCH ARTICLE
Long-range cross-correlation between urban impervious surfaces and land surface temperatures
Qin NIE1,*(),Jianhua XU2,Wang MAN1
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, Ministry of Education of China, East China Normal University, Shanghai 200241, China
 Download: PDF(1536 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
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     
Corresponding Author(s): Qin NIE   
Just Accepted Date: 08 April 2015   Online First Date: 20 May 2015    Issue Date: 25 December 2015
 Cite this article:   
Qin NIE,Jianhua XU,Wang MAN. Long-range cross-correlation between urban impervious surfaces and land surface temperatures[J]. Front. Earth Sci., 2016, 10(1): 117-125.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-015-0512-9
https://academic.hep.com.cn/fesci/EN/Y2016/V10/I1/117
Fig.1  Map of the study area.
Fig.2  The RMS frequency distribution and spatial image.
Fig.3  Log–log plots of the detrended fluctuations F2(s) versus scale s for the N–S profile.
Fig.4  Log–log plots of the detrended fluctuations F2(s) versus scale s for the W–E profile.
Fig.5  Dependence of the mass exponent function τxy(q) with respect to the order q on the W–E profile. The plots for 2007 and 2010 are shifted upward by 1 and 2 units for clarity, respectively.
Fig.6  Dependence of the mass exponent function τxy(q) with respect to the order q on the N–S profile. The plots for 2010 are shifted upward by 1 unit for clarity.
Fig.7  Multifractal spectra of the long-range cross-correlation on the W–E profile.
Fig.8  Multifractal spectra of the long-range cross-correlation on the N–S profile.
Profile Year Δα
W?E profile 2002 0.37
2007 0.23
2010 0.19
N?S profile 2007 0.534
2010 0.527
Tab.1  The parameter of multifractal spectra
1 Artis D A, Carnahan W H (1982). Survey of emissivity variability in thermography of urban areas. Remote Sens Environ, 12(4): 313−329
https://doi.org/10.1016/0034-4257(82)90043-8
2 Chen Y G (2013). Fractal analytical approach of urban form based on spatial correlation function. Chaos Solitons Fractals, 49: 47−60
https://doi.org/10.1016/j.chaos.2013.02.006
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
https://doi.org/10.1016/j.geoderma.2006.03.009
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
https://doi.org/10.1140/epjb/e2009-00310-5
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
https://doi.org/10.1103/PhysRevLett.100.084102
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
https://doi.org/10.1080/01431169508954549
10 Small C (2001). Estimation of urban vegetation abundance by spectral mixture analysis. Int J Remote Sens, 22(7): 1305−1334
https://doi.org/10.1080/01431160151144369
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
https://doi.org/10.1080/01431169308904400
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
https://doi.org/10.1016/j.physa.2011.12.015
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
https://doi.org/10.1016/j.physa.2010.08.029
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
https://doi.org/10.1007/s11252-007-0020-0
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
https://doi.org/10.1016/j.jenvman.2006.11.012
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
https://doi.org/10.1016/S1001-0742(07)60041-2
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
https://doi.org/10.1007/s11769-003-0017-4
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
https://doi.org/10.1007/s11769-001-0053-x
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
https://doi.org/10.5589/m02-098
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
https://doi.org/10.1016/j.compenvurbsys.2005.01.005
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
https://doi.org/10.1016/j.rse.2006.09.003
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
https://doi.org/10.1007/s00477-012-0638-1
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
https://doi.org/10.1080/01431160500306906
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
https://doi.org/10.1016/j.jag.2009.03.001
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
https://doi.org/10.1103/PhysRevE.77.066211
[1] Emre ÇOLAK, Filiz SUNAR. Spatial pattern analysis of post-fire damages in the Menderes District of Turkey[J]. Front. Earth Sci., 2020, 14(2): 446-461.
[2] Shiyong YAN, Ke SHI, Yi LI, Jinglong LIU, Hongfeng ZHAO. Integration of satellite remote sensing data in underground coal fire detection: A case study of the Fukang region, Xinjiang, China[J]. Front. Earth Sci., 2020, 14(1): 1-12.
[3] Xiaoting WANG, Zhan’e YIN, Xuan WANG, Pengfei TIAN, Yonghua HUANG. A study on flooding scenario simulation of future extreme precipitation in Shanghai[J]. Front. Earth Sci., 2018, 12(4): 834-845.
[4] Jicai NING, Zhiqiang GAO, Ran MENG, Fuxiang XU, Meng GAO. Analysis of relationships between land surface temperature and land use changes in the Yellow River Delta[J]. Front. Earth Sci., 2018, 12(2): 444-456.
[5] Guan WANG, Yuan LIU, Jiao CHEN, Feifan REN, Yuying CHEN, Fangzhou YE, Weiguo ZHANG. Magnetic evidence for heavy metal pollution of topsoil in Shanghai, China[J]. Front. Earth Sci., 2018, 12(1): 125-133.
[6] Chen CHENG, Chunjuan BI, Dongqi WANG, Zhongjie YU, Zhenlou CHEN. Atmospheric deposition of polycyclic aromatic hydrocarbons (PAHs) in Shanghai: the spatio-temporal variation and source identification[J]. Front. Earth Sci., 2018, 12(1): 63-71.
[7] Kun TAN,Zhihong LIAO,Peijun DU,Lixin WU. Land surface temperature retrieval from Landsat 8 data and validation with geosensor network[J]. Front. Earth Sci., 2017, 11(1): 20-34.
[8] Xianzhe LI,Ping JIANG,Yan ZHANG,Weichun MA. Development of a stationary carbon emission inventory for Shanghai using pollution source census data[J]. Front. Earth Sci., 2016, 10(4): 691-706.
[9] Xiangyu REN,Kai YANG,Yue CHE,Mingwei WANG,Lili ZHOU,Liqiao CHEN. Spatial and temporal assessment of the initial pattern of phytoplankton population in a newly built coastal reservoir[J]. Front. Earth Sci., 2016, 10(3): 546-559.
[10] Haishun XU,Liang CHEN,Bing ZHAO,Qiuzhuo ZHANG,Yongli CAI. Green stormwater infrastructure eco-planning and development on the regional scale: a case study of Shanghai Lingang New City, East China[J]. Front. Earth Sci., 2016, 10(2): 366-377.
[11] Weichun MA,Liguo ZHOU,Hao ZHANG,Yan ZHANG,Xiaoyan DAI. Air temperature field distribution estimations over a Chinese mega-city using MODIS land surface temperature data: the case of Shanghai[J]. Front. Earth Sci., 2016, 10(1): 38-48.
[12] 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.
[13] Qing ZHAO, Wei GAO, Weining XIANG, Runhe SHI, Chaoshun LIU, Tianyong ZHAI, Hung-lung Allen HUANG, Liam E. GUMLEY, Kathleen STRABALA. Analysis of air quality variability in Shanghai using AOD and API data in the recent decade[J]. Front Earth Sci, 2013, 7(2): 159-168.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed