Skip to main content

Advertisement

Log in

Multi-scale remote sensing estimates of urban fractions and road widths for regional models

  • Published:
Climatic Change Aims and scope Submit manuscript

Abstract

Landuse in East Asia has changed substantially during the last three decades, featured with expansion of urban built-up at unprecedented scale and speed. The fast expansion of urban areas could contribute to local and even regional climate change. However, current spatial datasets of urban fractions do not well represent the extent and expansion of urban areas in the regions, and that best available satellite data and remote sensing techniques have not been well applied to serve regional modeling of urbanization impacts on near surface temperature and other climate variables. Better estimates of localized urban fractions are badly needed. Here we use high and mid resolution satellite data to estimate urban fractions and road width at local and regional scales. With our fractional cover, data fusion, and differentiated threshold approaches, more spatial details of urban cover are demonstrated than previously reported in many global datasets. Many city clusters were merging into each other, with gradual blurring of boundaries and disappearance of gaps among member cities. Cities and towns were more connected with roads and commercial corridors, while wildland and urban green areas have become more isolated as patches among built-up areas. Average road width in commercial areas was 37.2 m in Beijing (north, temperate) and 24.2 m in Guangzhou (south, tropical), which are greater than these listed in model default values. Those new estimates could effectively improve climate simulation at local and regional scales in East Asia.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Brazel AJ, Selover N, Vose R, Heisler G (2000) The tale of two climates: Baltimore and Phoenix LTER sites. Clim Res 15:123–135

    Article  Google Scholar 

  • Brown MJ (1999) Urban parameterizations for mesoscale meteorological models. Mesoscale Atmospheric Dispersion. Edited by Z. Boybeyi, WIT Press, pp. 193–255, LA-UR-99-5329

  • Crutzen P (2004) New directions: the growing urban heat and pollution ‘island’ effect—impact on chemistry and climate. Atmos Environ 38:3539–3540

    Article  Google Scholar 

  • Dupont S, Otte TL, Ching JKS (2004) Simulation of meteorological fields within and above urban and rural canopies with a mesoscale model (MM5). Bound-Layer Meteorol 113:111–158

    Article  Google Scholar 

  • Elvidge CD, Baugh K, Dietz JB, Bland T, Sutton PC, Kroehl H (1998) Radiance calibration of DMSP-OLS low-light imaging data of human settlements. Remote Sens Environ 68(1):77–88

    Article  Google Scholar 

  • Fang C, Qi W, Song J (2008) Researches on comprehensive measurement of compactness of urban agglomerations in China. Acta Geograph Sin 63(10):1011–1021

    Google Scholar 

  • Fang C, Yao S, Liu S (2011) Development of major city clusters in China. Science Press, Beijing, pp 89–112

    Google Scholar 

  • Feng J, Wang Y, Ma Z, Liu Y (2012) Simulating the regional impacts of urbanization and anthropogenic heat release on climate across China. J Clim 25(20):7187–7203

    Article  Google Scholar 

  • Foley JA et al (2005) Global consequences of land use. Science 309:570–574

    Article  Google Scholar 

  • Fu CB (2003) Potential impacts of human-induced land cover change on East Asia monsoon. Glob Planet Chang 37:219–229

    Google Scholar 

  • Gao H, Jia G (2013) Assessing disagreement and tolerance of misclassification of satellite—derived land cover products used in WRF model applications. Adv Atmos Sci 30(1):125–141

    Article  Google Scholar 

  • Grimm NB, Grove JM, Redman CL, Pickett SA (2000) Integrated approaches to long-term studies of urban ecological systems. Bioscience 70:571–584

    Article  Google Scholar 

  • Grossman-Clarke S, Zehnder JA, Stefanov WL, Yubao L, Zoldak MA (2005) Urban modifications in a mesoscale meteorological model and the effects on near-surface variables in an arid metropolitan region. J Appl Meteorol 44:1281–1297

    Article  Google Scholar 

  • Guindon B, Zhang Y, Dillabaugh C (2004) Landsat urban mapping based on a combined spectral–spatial methodology. Remote Sens Environ 92:218–232

    Article  Google Scholar 

  • Han JY, Baik JJ (2008) A theoretical and numerical study of urban heat island-induced circulation and convection. J Atmos Sci 65:1859–1877

    Article  Google Scholar 

  • He Y, Jia G (2012) A dynamic way to quantify natural warming in urban area. Atmos Ocean Sci Lett 5(5):408–413

    Google Scholar 

  • Hu Y, Jia G (2010) Influence of land use change on urban heat island derived from multi-sensor data. Int J Climatol 30:1382–1395

    Google Scholar 

  • Huang J, Lu X, Sellers JM (2007) A global comparative analysis of urban form: applying spatial metrics and remote sensing. Landsc Urban Plan 82:184–197

    Article  Google Scholar 

  • Jia G, Fu C, Zhou Y, Li X (2011) Towards a sustainable Asia: environment and climate change. Springer-Verlag, 106 p. ISBN: 978-3-642-16671-6

  • Kusaka H, Kimura F (2004) Thermal effects of urban canyon structure on the nocturnal heat island: Numerical experiment using a mesoscale model coupled with an urban canopy model. J Appl Meteorol 43:1899–1910

    Article  Google Scholar 

  • Li B, Avissar R (1994) The impact of spatial variability of land-surface characteristics on land-surface heat fluxes. J Climatol 7:527–537

    Article  Google Scholar 

  • Loridan T, Grimmond CSB (2011) Characterization of energy flux partitioning in urban environments: links with surface seasonal properties. J Appl Meteorol Climatol 51:219–241

    Article  Google Scholar 

  • Martilli A (2009) On the derivation of input parameters for urban canopy models from urban morphological datasets. Bound-Layer Meteorol 130:301–306

    Article  Google Scholar 

  • Masson V (2006) Urban surface modelling and the meso-scale impact of cities. Theor Appl Climatol 84:35–45

    Article  Google Scholar 

  • Offerle B, Grimmond CSB, Oke TR (2003) Parameterization of net all-wave radiation for urban areas. J Appl Meteorol 42:1157–1173

    Article  Google Scholar 

  • Oke TR (1988) Street design and urban canopy layer climate. Energy Build 11:103–113

    Article  Google Scholar 

  • Oke TR (2006) Towards better communication in urban climate. Theor Appl Climatol 84:179–189

    Article  Google Scholar 

  • Oleson KW, Bonan GB, Feddema J, Vertenstein M, Grimmond CSB (2008) An urban parameterization for a global climate model. Part I: formulation and evaluation for two cities. J Appl Meteorol Climatol 47:1038–1060

    Article  Google Scholar 

  • Otte TL, Lacser A, Dupont S, Ching JKS (2004) Implementation of an urban canopy parameterization in a mesoscale meteorological model. J Appl Meteorol 43:1648–1665

    Article  Google Scholar 

  • Rosenfeld D (2000) Suppression of rain and snow by urban and industrial air pollution. Science 287:1793–1796

    Article  Google Scholar 

  • Sarrat C, Lemonsu A, Masson V, Guedalia G (2006) Impact of urban heat island on regional atmospheric pollution. Atmos Environ 40:1743–1758

    Article  Google Scholar 

  • Seto KC, Fragkias M, Güneralp B, Reilly MK (2011) A meta-analysis of global urban land expansion. PLoS ONE 6:e23777

    Article  Google Scholar 

  • Simon D (2008) Urban environments: issues on the peri-urban fringe. Annu Rev Environ Resour 33:167–185

    Article  Google Scholar 

  • Tewari M, Chen F, Kusaka H (2007) Coupled WRF/Unified Noah/Urban-canopy modeling system, NCAR WRF Documentation, pp 1–20

  • Turner BL II, Lambin EF, Reenberg A (2007) The emergence of land change science for global environmental change and sustainability. Proc Natl Acad Sci 104:20666–20671

    Article  Google Scholar 

  • Wang J, Feng J, Yan Z, Hu Y, Jia G (2012) Nested high-resolution modeling of the impact of urbanization on regional climate in three vast urban agglomerations in China. J Geophys Res 117, D21103. doi:10.1029/2012JD018226

    Google Scholar 

Download references

Acknowledgments

This study was supported by CAS Strategic Research Program (XDA05090200) and China Basic Research Program (2009CB723904). We thank Dr. Weidong Liu for help facilitating our field investigation along urban–rural transects.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gensuo Jia.

Additional information

This article is part of a Special Issue on “Regional Earth System Modeling” edited by Zong-Liang Yang and Congbin Fu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jia, G., Xu, R., Hu, Y. et al. Multi-scale remote sensing estimates of urban fractions and road widths for regional models. Climatic Change 129, 543–554 (2015). https://doi.org/10.1007/s10584-014-1114-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10584-014-1114-3

Keywords

Navigation