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Relationships between land cover and the surface urban heat island: seasonal variability and effects of spatial and thematic resolution of land cover data on predicting land surface temperatures

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Abstract

We investigated the seasonal variability of the relationships between land surface temperature (LST) and land use/land cover (LULC) variables, and how the spatial and thematic resolutions of LULC variables affect these relationships. We derived LST data from Landsat-7 Enhanced Thematic Mapper (ETM+) images acquired from four different seasons. We used three LULC datasets: (1) 0.6 m resolution land cover data; (2) 30 m resolution land cover data (NLCD 2001); and (3) 30 m resolution Normalized Difference Vegetation Index data derived from the same ETM+ images (though from different bands) used for LST calculation. We developed ten models to evaluate effects of spatial and thematic resolution of LULC data on the observed relationships between LST and LULC variables for each season. We found that the directions of the effects of LULC variables on predicting LST were consistent across seasons, but the magnitude of effects, varied by season, providing the strongest predictive capacity during summer and the weakest during winter. Percent of imperviousness was the best predictor on LST with relatively consistent explanatory power across seasons, which alone explained approximately 50 % of the total variation in LST in winter, and up to 77.9 % for summer. Vegetation related variables, particularly tree canopy, were good predictor of LST during summer and fall. Vegetation, particularly tree canopy, can significantly reduce LST. The spatial resolution of LULC data appeared not to substantially affect relationships between LST and LULC variables. In contrast, increasing thematic resolution generally enhanced the explanatory power of LULC on LST, but not to a substantial degree.

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References

  • Akaike H (1978) On the likelihood of a time series model. Stat 27:217–235

    Google Scholar 

  • 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 J 70:295–310

    Article  Google Scholar 

  • Alhamad MN, Alrababah MA, Feagin RA, Gharaibeh A (2011) Mediterranean drylands: the effect of grain size and domain of scale on landscape metrics. Ecol Indic 11:611–621

    Article  Google 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 23:1–26

    Article  Google Scholar 

  • Balling RC, Brazel SW (1988) High resolution surface temperature pattern in a complex urban terrain. Photogramm Eng Remote Sens 54:1289–1293

    Google Scholar 

  • Barsi JA, Schott JR, Palluconi FD, Hook SJ (2005). Validation of a web-based atmospheric correction tool for single thermal band instruments. Earth observing systems X. In: Proceedings of SPIE Vol. 5882, San Diego, CA

  • Ben-Dor E, Saaroni H (1997) Airborne video thermal radiometry as a tool for monitoring microscale structures of the urban heat island. Int J Remote Sens 18(4):3039–3053

    Article  Google Scholar 

  • Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New York

    Google Scholar 

  • Buyantuyev A, Wu J (2010) Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns. Landscape Ecol 25(1):17–33

    Article  Google Scholar 

  • Connors JP, Galletti CS, Chow WTL (2012) Landscape configuration and urban heat island effects: assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona. Landscape Ecol. doi:10.1007/s10980-012-9833-1

    Google Scholar 

  • Duneier M (2006) Ethnography, the ecological fallacy, and the 1995 Chicago heat wave. Am Sociol Rev 71:679–688

    Article  Google Scholar 

  • Greenfield EJ, Nowak DJ, Walton J (2009) Assessment of 2001 NLCD percent tree and impervious cover estimates. Photogram Eng Remote Sens 75(11):1279–1286

    Article  Google Scholar 

  • Guhathakurta S, Gober P (2007) The impact of the Phoenix urban heat island on residential water use. J Am Plan Assoc 73:317–329

    Article  Google Scholar 

  • Homer C, Huang C, Yang L, Wylie B, Coan M (2004) Development of a 2001 National Land Cover Database for the United States. Photogram Eng Remote Sens 70(7):829–840

    Article  Google Scholar 

  • Huang GW, Zhou, Cadenasso ML (2010) Understanding the relationship between urban land surface temperature, landscape heterogeneity and social structure. In: Proceedings of the 2010 IEEE international geoscience and remote sensing symposium, 3933–3936

  • Huang G, Zhou W, Cadenasso ML (2011) Is everyone hot is the city? Spatial pattern of land surface temperatures, land cover and neighborhood socioeconomic characteristics in Baltimore city, MD. J Environ Manag 92(7):1753–1759

    Article  Google Scholar 

  • Jenerette GD, Harlan SL, Brazel A, Jones N, Larsen L, Stefanov WL (2007) Regional relationships between surface temperature, vegetation, and human settlement in a rapidly urbanizing ecosystem. Landscape Ecol 22:353–365

    Article  Google Scholar 

  • Jenerette GD, Harlan SL, Stefanov WL, Martin CA (2011) Ecosystem services and urban heat riskscape moderation: water, green spaces, and social inequality in Phoenix, USA. Ecol Appl 21:2637–2651

    Article  PubMed  Google Scholar 

  • Klinenberg E (2002) Heat Wave: a social autopsy of disaster in Chicago. University of Chicago Press, Chicago

    Book  Google Scholar 

  • Landsat Project Science Office. Landsat 7 Science Data Users Handbook (2009). Available via http://landsathandbook.gsfc.nasa.gov/handbook.html. Accessed on 22 Oct 2010

  • Lechner AM, Langford WT, Bekessy SA, Jones SD (2012) Are landscape ecologists addressing uncertainty in their remote sensing data? Landscape Ecol 27(9):1249–1261

    Article  Google Scholar 

  • Li J, Song C, Cao L, Zhu F, Meng X, Wu J (2011) Impacts of landscape structure on surface urban heat islands: a case study of Shanghai, China. Remote Sens Environ 115:3249–3263

    Article  Google Scholar 

  • Li X, Zhou W, Ouyang Z, Xu W, Zheng H (2012) Spatial pattern of greenspace affects land surface temperature: evidence from the heavily urbanized Beijing metropolitan area, China. Landscape Ecol 27:887–898

    Article  Google Scholar 

  • Liu H, Weng Q (2009) Scaling-up effect on the relationship between landscape pattern and land surface temperature. Photogram Eng Remote Sens 75(3):291–304

    Article  Google Scholar 

  • Lo CP, Quattrochi DA (2003) Land-use and land-cover change, urban heat island phenomenon, and health implications: a remote sensing approach. Photogram Eng Remote Sens 69(9):1053–1063

    Article  Google Scholar 

  • Lu D, Hetricka S, Morana E (2011) Impervious surface mapping with quickbird imagery. Int J Remote Sens 32(9):2519–2533

    Article  PubMed Central  PubMed  Google Scholar 

  • Myint S, Wentz E, Brazel A, Quatrochi D (2013) The impact of distinct anthropogenic and vegetation features on urban warming. Landscape Ecol 28:959–978

    Article  Google Scholar 

  • Nichol JE (1994) A GIS-based approach to microclimate monitoring in Singapore’s high-rise housing estates. Photogram Eng Remote Sens 60:1225–1232

    Google Scholar 

  • Nichol JE (1996) High-resolution surface temperature patterns related to urban morphology in a tropical city: a satellite-based study. J Appl Meteorol 35:135–146

    Article  Google Scholar 

  • Nichol JE (1998) Visualisation of urban surface temperatures derived from satellite images. Int J Remote Sens 19(9):1639–1649

    Article  Google Scholar 

  • Nichol JE, Fung WY, Lam K, Wong MS (2009) Urban heat island diagnosis using ASTER satellite images and ‘in situ’ air temperature. Atmos Res 94(2):276–284

    Article  Google Scholar 

  • Nowak DJ, Greenfield EJ (2010) Evaluating the national land cover database tree canopy and impervious cover estimates across the conterminous United States: a comparison with photo-interpreted estimates. Environ Manag 46:378–390

    Article  Google Scholar 

  • Oke TR (1995) The heat island of the urban boundary layer: characteristics, causes and effects. In: Cermak JE (ed) Wind climate in cities. Kluwer Academic Publishers, Netherlands, pp 81–107

    Chapter  Google Scholar 

  • Pu R, Gong P, Michishita R, Sasagawa T (2006) Assessment of multi-resolution and multi-sensor data for urban surface temperature retrieval. Remote Sens Environ 104:211–225

    Article  Google Scholar 

  • Quattrochi DA, Luvall JC (1999) Thermal infrared remote sensing for analysis of landscape ecological processes: methods and applications. Landscape Ecol 14:577–598

    Article  Google Scholar 

  • Shao G, Wu J (2008) On the accuracy of landscape pattern analysis using remote sensing data. Landscape Ecol 23:505–511

    Article  Google Scholar 

  • Smith MA, Zhou W, Cadenasso M, Grove M, Band L (2010) Evaluation of the national land cover database for hydrologic applications in urban and suburban Baltimore Maryland. J Am Water Resour Assoc 46(2):429–442

    Article  Google Scholar 

  • Snyder W, Wan Z, Zhang Y, Feng Y, Feng Z (1998) Classification-based emissivity for land surface temperature measurement from space. Int J Remote Sens 19:2753–2774

    Article  Google Scholar 

  • Sun RH, Chen AL, Chen LD, Lü YH (2012) Cooling effects of wetlands in an urban region: the case of Beijing. Ecol Indic 20:57–64

    Article  Google Scholar 

  • University of Maryland (UMD) (2001) Normal precipitation and temperature values for Baltimore city from 1961 to 1990. Baltimore, Maryland

    Google Scholar 

  • Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86:370–384

    Article  Google Scholar 

  • Weng Q, Lu D, Schubring J (2004) Estimation of land surface temperature—vegetation abundance relationship for urban heat island studies. Remote Sens Environ 89:467–483

    Article  Google Scholar 

  • White MA, Nemani RR, Thornton PE, Running SW (2002) Satellite evidence of phenological differences between urbanized and rural areas of the eastern United States deciduous broadleaf forest. Ecosystems 5:260–277

    Article  Google Scholar 

  • Wu J (2004) Effects of changing scale on landscape pattern analysis: scaling relationships. Landscape Ecol 19:125–138

    Article  Google Scholar 

  • Yuan F, Bauer ME (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 106:375–386

    Article  Google Scholar 

  • Zhou W, Troy A (2008) An object-oriented approach for analyzing and characterizing Urban landscape at the parcel level. Int J Remote Sens 29:3119–3135

    Article  Google Scholar 

  • Zhou W, Troy A (2009) Development of an object-based framework for classifying and inventorying human-dominated forest ecosystems. Int J Remote Sens 30(23):6343–6360

    Article  Google Scholar 

  • Zhou W, Huang G, Cadenasso ML (2011) Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landscape Urban Plan 102(1):54–63

    Article  Google Scholar 

Download references

Acknowledgments

The support of the State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, is gratefully acknowledged. This research also was supported by the National Science Foundation LTER program (DEB 042376). The authors would like to thank the editor and the anonymous reviewers for their helpful comments and suggestions. Comments from Dr. Brian Voigt improved the early draft of this manuscript.

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Authors and Affiliations

  1. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Shuangqinglu 18, Haidian District, Beijing, 100085, China

    Weiqi Zhou, Yuguo Qian, Xiaoma Li, Weifeng Li & Lijian Han

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  1. Weiqi Zhou
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  2. Yuguo Qian
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  3. Xiaoma Li
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  4. Weifeng Li
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  5. Lijian Han
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Correspondence to Weiqi Zhou.

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Zhou, W., Qian, Y., Li, X. et al. Relationships between land cover and the surface urban heat island: seasonal variability and effects of spatial and thematic resolution of land cover data on predicting land surface temperatures. Landscape Ecol 29, 153–167 (2014). https://doi.org/10.1007/s10980-013-9950-5

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  • DOI: https://doi.org/10.1007/s10980-013-9950-5

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