Skip to main content

Monitoring the changes in impervious surface ratio and urban heat island intensity between 1987 and 2011 in Szeged, Hungary

Environmental Monitoring and Assessment volume 189, Article number: 86 (2017) Cite this article

Abstract

Landsat time series data make it possible to continuously map and examine urban land cover changes and effects on urban environments. The objectives of this study are (1) to map and analyse an impervious surface and its changes within a census district and (2) to monitor the effects of increasing impervious surface ratios on population and environment. We used satellite images from 1987, 2003 and 2011 to map the impervious surface ratio in the census district of Szeged, Hungary through normalized spectral mixture analysis. Significant increases were detected from 1987 to 2011 in industrial areas (5.7–9.1%) and inner residential areas (2.5–4.8%), whereas decreases were observed in the city centre and housing estates due to vegetation growth. Urban heat island (UHI) values were derived from the impervious surface fraction map to analyse the impact of urban land cover changes. In 2011, the average value in the industrial area was 1.76 °C, whereas that in the inner residential area was 1.35–1.69 °C. In the city centre zones and housing estates, values ranging from 1.4 to 1.5 °C and from 1.29 to 1.5 °C, respectively, were observed. Our study reveals that long-term land cover changes can be derived at the district level from Landsat images and that their effects can be identified and analysed, providing important information for city planners and policy makers.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

References

  • Adams, J. B., Smith, M. O., & Johnson, P. E. (1986). Spectral mixture modeling: a new analysis of rock and soil types at the Viking Lander 1 site. Journal of Geophysical Research, 91(8), 8098–8112.

    Article  Google Scholar 

  • Bauer, M. E., Loffelholz, B. C., & Wilson, B. (2007). Estimating and mapping impervious surface area by regression analysis of Landsat imagery. In Q. Weng (Ed.), Remote sensing of impervious surfaces (pp. 3–19). Boca Raton, Florida: CRC Press.

    Google Scholar 

  • Blazovich, L. (2005). (1943-) Szeged rövid története (Brief history of Szeged), Szeged Csongrád megyei levéltár.

  • Borel, C. C., & Gerstl, S. A. W. (1994). Nonlinear spectral mixing models for vegetative and soil surfaces. Remote Sensing of Environment, 47(3), 403–416.

    Article  Google Scholar 

  • Chander, G., & Markham, B. L. (2003). Revised Landsat-5 TM radiometric calibration procedures, and postcalibration dynamic ranges. IEEE Transactions on Geoscience and Remote Sensing, 41(11), 2674–2677.

    Article  Google Scholar 

  • Chavez Jr., P. S. (1996). Image-based atmospheric corrections—revisited and improved. Photogrammetric Engineering and Remote Sensing, 62(9), 1025–1036.

    Google Scholar 

  • Chen, X. L., Zhao, H. M., Li, P. X., & Yin, Z. Y. (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104(2), 133–146.

    Article  Google Scholar 

  • Chormanski, J., Voorde, T. V. D., Roeck, T. D., Batelaan, O., & Canters, F. (2008). Improving distributed runoff prediction in urbanized catchments with remote sensing based estimates of impervious surface cover. Sensors, 8(2), 910–932.

    Article  Google Scholar 

  • Cracknell, A. P. (1998). Synergy in remote sensing—What’s in a pixel? International Journal of Remote Sensing, 19(11), 2025–2047.

    Article  Google Scholar 

  • Deng, C., & Wu, C. (2013). Examining the impacts of urban biophysical compositions on surface urban heat island: a spectral unmixing and thermal mixing approach. Remote Sensing of Environment, 131, 262–274.

    Article  Google Scholar 

  • Dewan, A. M., & Yamaguchi, Y. (2009). Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka metropolitan of Bangladesh during 1960–2005. Environmental Monitoring and Assessment, 185(2), 1699–1709.

    Google Scholar 

  • Elvidge, C. D., Baugh, K. E., Kihn, E. A., Kroehl, H. W., Davis, E. R., & Davis, C. W. (1997). Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. International Journal of Remote Sensing, 18(6), 1373–1379.

    Article  Google Scholar 

  • Franke, J., Roberts, D. A., Halligan, K., & Menz, G. (2009). Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments. Remote Sensing of Environment, 113(8), 1712–1723.

    Article  Google Scholar 

  • Geymen, A., & Baz, I. (2007). Monitoring urban growth and detecting land-cover changes on the Istanbul metropolitan area. Environmental Monitoring and Assessment, 136(1), 449–459.

    Article  Google Scholar 

  • Henits, L., Jürgens, C., & Mucsi, L. (2016). Seasonal multitemporal land-cover classification and change detection analysis of Bochum, Germany, using multitemporal Landsat TM data. International Journal of Remote Sensing. doi:10.1080/01431161.2015.1125558.

    Google Scholar 

  • Hungarian Central Statistical Office. (2012). Magyarország közigazgatási helynévkönyve, Központi Statisztikai Hivatal. Budapest, 2012, 228.

  • Imhoff, M. L., Zhang, P., Wolfe, R. E., & Bounoua, L. (2010). Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sensing of Environment, 114, 504–513.

    Article  Google Scholar 

  • Jianyaa, G., Haiganga, S., Guoruia, M., & Qimingb, Z. (2008). A review of multi-temporal remote sensing data change detection algorithms (pp. 757–762). Beijing: ISPRS Congress.

    Google Scholar 

  • Li, G., & Weng, Q. (2007). Measuring the quality of life in city of Indianapolis by integration of remote sensing and census data. International Journal of Remote Sensing, 28(2), 249–267.

    CAS  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 Sensing of Environment, 115, 3249–3263.

    Article  Google Scholar 

  • Lo, C. P., & Faber, B. J. (1997). Integration of Landsat Thematic Mapper and census data for quality of life assessment. Remote Sensing of Environment, 62(2), 143–157.

    Article  Google Scholar 

  • Lu, D., Mausel, P., Brondízio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365–2401.

    Article  Google Scholar 

  • Lu, D., & Weng, Q. (2006a). Use of impervious surface in urban land-use classification. Remote Sensing of Environment, 102(1–2), 146–160.

    Article  Google Scholar 

  • Lu, D., & Weng, Q. (2006b). Spectral mixture analysis of ASTER images for examining the relationship between urban thermal features and biophysical descriptors in Indianapolis, Indiana, USA. Remote Sensing of Environment, 104(2), 157–167.

    Article  Google Scholar 

  • Lu, D., Weng, Q., & Li, G. (2006). Residential population estimation using a remote sensing derived impervious surface approach. International Journal of Remote Sensing, 27(16), 3553–3570.

    Article  Google Scholar 

  • Madhavan, B. B., Kubo, S., Kurisaki, N., & Sivakumar, T. V. L. N. (2001). Appraising the anatomy and spatial growth of the Bangkok metropolitan area using a vegetation-impervious-soil model through remote sensing. International Journal of Remote Sensing, 22(5), 789–806.

    Article  Google Scholar 

  • Mesev, V. (1998). The use of census data in urban image classification. Photogrammetric Engineering and Remote Sensing, 64(5), 431–438.

    Google Scholar 

  • Michishita, R., Jiang, Z., & Xu, B. (2012). Monitoring two decades of urbanization in the Poyang Lake area, China through spectral unmixing. Remote Sensing of Environment, 117, 3–18.

    Article  Google Scholar 

  • Rashed, T., Weeks, J. R., & Gallada, M. S. (2001). Revealing the anatomy of cities through spectral mixture analysis of multispectral satellite imagery: a case study of the greater Cairo region, Egypt. Geocarto International, 16(4), 5–15.

    Article  Google Scholar 

  • Rashed, T., Weeks, J. R., Stow, D., & Fugate, D. (2005). Measuring temporal compositions of urban morphology through spectral mixture analysis: toward a soft approach to change analysis in crowded cities. International Journal of Remote Sensing, 26(4), 699–718.

    Article  Google Scholar 

  • Ridd, M. K. (1995). Exploring a VIS (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. International Journal of Remote Sensing, 16(12), 2165–2185.

    Article  Google Scholar 

  • Roberts, D. A., Gardner, M., Church, R., Ustin, S., Scheer, G., & Green, R. O. (1998). Mapping chaparral in the Santa Monica mountains using multiple endmember spectral mixture models. Remote Sensing of Environment, 65(3), 267–279.

    Article  Google Scholar 

  • Schneider, A. (2012). Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach. Remote Sensing of Environment, 124, 689–704.

    Article  Google Scholar 

  • Schwarz, N., Lautenbach, S., & Seppelt, R. (2011). Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures. Remote Sensing of Environment, 115(12), 3175–3186.

    Article  Google Scholar 

  • Setiawan, H., Mathieu, R., & Thompson-Fawcett, M. (2006). Assessing the applicability of the V–I–S model to map urban land use in the developing world: case study of Yogyakarta, Indonesia. Computers, Environment and Urban Systems, 30(4), 503–522.

    Article  Google Scholar 

  • Sharma, R., Ghosh, A., & Joshi, P. K. (2013). Spatio-temporal footprints of urbanisation in Surat, the Diamond City of India (1990–2009). Environmental Monitoring and Assessment, 185(4), 3313–3325.

    Article  Google Scholar 

  • Small, C. (2002). Multitemporal analysis of urban reflectance. Remote Sensing of Environment, 81(2–3), 427–442.

    Article  Google Scholar 

  • Small, C., & Lu, J. (2006). Estimation and vicarious validation abundance by spectral mixture analysis. Remote Sensing of Environment, 100(4), 441–456.

    Article  Google Scholar 

  • Smith, M. O., Johnson, P. E., & Adams, J. B. (1985). Quantitative determination of mineral types and abundances from reflectance spectra using principal components analysis. Journal of Geophysical Research, 90, 792–804.

    Google Scholar 

  • Smith, M. O., Ustin, S. L., Adams, J. B., & Gillespie, A. R. (1990). Vegetation in deserts: I. A regional measure of abundance from multispectral images. Remote Sensing of Environment, 31(1), 1–26.

    Article  Google Scholar 

  • Song, C., & Woodcock, C. E. (2003). Monitoring forest succession with multitemporal Landsat images: factors of uncertainty. IEEE Transactions on Geoscience and Remote Sensing, 41(11), 2557–2567.

    Article  Google Scholar 

  • Unger, J., Bottyán, Z., Sümeghy, Z., & Gulyás, Á. (2000). Urban heat island development affected by urban surface factors. Időjárás, 104, 253–268.

    Google Scholar 

  • Unger, J., Sümeghy, Z., Gulyás, Á., Bottyán, Z., & Mucsi, L. (2001). Land-use and meteorological aspects of the urban heat island. Meteorological Applications, 8, 189–194.

    Article  Google Scholar 

  • United Nations (2014). World urbanization prospects the 2014 revision, World Urbanization Prospects, Department of Economic and Social Affairs, Population Division 2014.

  • Vogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3), 370–384.

    Article  Google Scholar 

  • Weng, Q., & Hu, X. (2008). Medium spatial resolution satellite imagery for estimating and mapping urban impervious surfaces using LSMA and ANN. IEEE Transaction on Geosciences and Remote Sensing, 46(8), 2397–2406.

    Article  Google Scholar 

  • Weng, Q., & Lu, D. (2008). A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States. International Journal of Applied Earth Observation and Geoinformation, 10(1), 68–83.

    Article  Google Scholar 

  • Wu, C., & Murray, A. T. (2003). Estimating impervious surface distribution by spectral mixture analysis. Remote Sensing of Environment, 84(4), 493–505.

    Article  Google Scholar 

  • Wu, C. (2004). Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sensing of Environment, 93(4), 480–492.

    Article  Google Scholar 

  • Wu, C., & Yuan, F. (2007). Seasonal sensitivity analysis of impervious surface estimation with satellite imagery. Photogrammetric Engineering & Remote Sensing, 73(12), 1393–1402.

    Article  Google Scholar 

  • Xian, G., & Crane, M. (2006). An analysis of urban thermal characteristics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data. Remote Sensing of Environment, 104(2), 147–156.

    Article  Google Scholar 

  • Yuan, F., & Bauer, M. B. (2007). Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing of Environment, 106(3), 375–386.

    Article  Google Scholar 

  • Yuan, F., Wu, C., & Bauer, M. E. (2008). Comparison of spectral analysis techniques for impervious surface estimation using Landsat imagery. Photogrammetric Engineering and Remote Sensing, 74(8), 1045–1055.

    Article  Google Scholar 

  • Zhang, J. Q., & Wang, Y. P. (2008). Study of the relationships between the spatial extent of surface urban heat islands and urban characteristic factors based on Landsat ETM plus data. Sensors, 8(11), 7453–7468.

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank the Department of Climatology and Landscape Ecology at the University of Szeged for providing UHI intensity data collected by mobile observations in Szeged.

Author information

Affiliations

  1. Department of Physical Geography and Geoinformatics, University of Szeged, Szeged, Hungary

    László Henits, László Mucsi & Csilla Mariann Liska

Authors
  1. László Henits
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. László Mucsi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Csilla Mariann Liska
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to László Henits.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Henits, L., Mucsi, L. & Liska, C.M. Monitoring the changes in impervious surface ratio and urban heat island intensity between 1987 and 2011 in Szeged, Hungary. Environ Monit Assess 189, 86 (2017). https://doi.org/10.1007/s10661-017-5779-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-017-5779-8

Keywords

  • Impervious surface ratio
  • Landsat time series data
  • Urban heat island
  • Spectral mixture analysis
  • Land cover change
  • Census data