The evaluation and application of an urban land cover map with image data fusion and laboratory measurements

  • László Mucsi Department of Physical Geography and Geoinformatics, University of Szeged, Hungary
  • Csilla Mariann Liska Department of Physical Geography and Geoinformatics, University of Szeged, Hungary
  • László Henits Department of Physical Geography and Geoinformatics, University of Szeged, Hungary
  • Zalán Tobak Department of Physical Geography and Geoinformatics, University of Szeged, Hungary
  • Bálint Csendes Department of Physical Geography and Geoinformatics, University of Szeged, Hungary
  • László Nagy Department of Medical Physics and Informatics, University of Szeged, Hungary


High spatial and spectral resolution aerial images make it possible to develop detailed and large-scale (about 1:5,000) urban land cover maps. The main objectives of this study are (1) to evaluate the correlation between laboratory and hyperspectral image spectra to select proper bands and training samples for classification; (2) to develop a classification process to combine the spectral and spatial information of multispectral and hyperspectral images and make an urban land cover map for the study area in Szeged, Hungary; and (3) to examine the effect of different roof types on the modification of surface temperature. Reference materials were collected from the training area and their spectral characteristics were measured by a laboratory spectrometer. The hyperspectral image and laboratory spectral data between 500-800 nm showed a very strong correlation, the correlation coefficient was 0.99. The urban land cover map was produced by the combination of segmentation procedure and Spectral Angle Mapper (SAM) method using the spatial information derived from multispectral image and the spectral information of the hyperspectral image. Eight land cover classes were identified as impervious surfaces (asphalt, 4 types of tiled roof), water, and green vegetation. The overall accuracy of urban land cover map was 87.9 per cent. According to the results, an accurate large-scale urban land cover map can be generated from the fusion of multispectral and hyperspectral images. We presented that certain roof types have significant effect on surface temperature, which is strongly connected to the urban heat island phenomenon, and influences population health.


Al Khudairy, D.H., Caravaggi, I. and Glada, S. 2005. Structural damage assessments from Ikonos data using change detection, object-oriented segmentation, and classification techniques. Photogrammetric Engineering & Remote Sensing 71. (7): 825-837.

Alonzo, M., Bookhagen, B. and Roberts, D. A. 2014. Urban tree species mapping using hyperspectral and lidar data fusion. Remote Sensing of Environment 148. 70-83.

Bassani, C., Cavalli, R.M., Cavalcante, F., Cuomo, V., Palombo, A., Pescucci, S. and Pignatti, S. 2007. Deterioration status of asbestos-cement roofing sheets assessed by analyzing hyperspectral data. Remote Sensing of Environment 109. (3): 361-378.

Bitelli, G., Conte, P., Csoknyai, T., Franci, F., Girelli, V.A. and Mandanici, E. 2015. Aerial Thermography for Energetic Modelling of Cities. Remote Sensing 7. (2): 2152-2170.

Blaschke, T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65. 2-16.

Chen, C.M., Hepner, G.F. and Forster, R.R. 2003. Fusion of Hyperspectral and radar data using the HIS transformation to enhance urban surface features ISPRS Journal of Photogrammetry and Remote Sensing 58. (1-2): 19-30.

Chen, Y., Shi, P., Fung, T., Wang, J. and Li, Y. 2007. Object-oriented classification for urban land cover mapping with ASTER imagery. International Journal of Remote Sensing 28. (20): 4645-4651.

Chubey, M.S., Franklin, S.E. and Wulder, M.A. 2006. Object-based analysis of IKONOS-2 imagery for extraction of forest inventory parameters. Photogrammetric Engineering & Remote Sensing 72. (4): 383-394.

Dell'Acqua, F., Gamba, P., Ferrari, A. Palmason, J.A., Benediktsson, J.A. and Arnason, K. 2004. Exploiting spectral and spatial information in hyperspectral urban data with high resolution. IEEE Geoscience and Remote Sensing Letters 1. (4): 322-326.

ERDAS Field Guide 2013. ERDAS, Inc. Norcross - GA30092-2500 USA 812.

Gál, T., Skarbit, N. and Unger, J. 2016. Urban heat island patterns and their dynamics based on an urban climate measurement network. Hungarian Geographical Bulletin 65. (2): 105-116.

Gevaert, C.M., Tang, J., Garcia-Haro, F.J., Suomalainen, J.M. and Kooistra, L. 2014. Combining hyperspectral UAV and multispectral Formosat-2 imagery for precision agriculture applications. In Proceedings WHISPERS 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in remote sensing. Lausanne, Switzerland, 24-26. 06. 2014.

Goetz, A.F.H. 2009. Three decades of hyperspectral remote sensing of the Earth: A personal view. Remote Sensing of Environment 113. (Supplement 1), 5-16.

Greiwe, A. and Ehlers, M. 2005. Combined analysis of hyperspectral and high resolution image data in an object oriented classification approach. In Proceedings of 3rd International Symposium on RemoteSensing and Data Fusion over Urban Areas. Citeseer.

Gusella, L., Adams, B.J., Bitelli, G., Huyck, C.K., Eeri, M. and Mognol, A. 2005. Object-oriented image understanding and post-earthquake damage assessment for the 2003 Bam, Iran, Earthquake. Earthquake Spectra 21. (1): 225-238.

Heiden, U., Segl, K., Roessner, S. and Kaufmann, H. 2007. Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data. Remote Sensing of Environment 111. (4): 537-552.

Henits, L., Mucsi, L. and Liska, C. M. 2017. Monitoring the changes in impervious surface ratio and urban heat island intensity between 1987 and 2011 in Szeged, Hungary. Environmental Monitoring and Assessment 189. (2): 1-13.

Herbel, I., Croitoru, A.E., Rus, I., Harpa, G.V. and Ciupertea, A.F. 2016. Detection of atmospheric urban heat island through direct measurements in Cluj-Napoca city, Romania. Hungarian Geographical Bulletin 65. (2): 117-128.

Herold, M. and Roberts, D.A. 2010. The Spectral Dimension in Urban Remote Sensing. In Remote Sensing of Urban and Suburban Areas, Remote Sensing and Digital Image Processing. Eds.: Rashed, T. and Jürgens, C., Dordrecht, Springer, 47-66.

Jay, S. and Guillaume, M. 2014. A novel maximum likelihood based method for mapping depth and water quality from hyperspectral remote-sensing data. Remote Sensing of Environment 147, 121-132.

Jin, H. and Mountrakis, G. 2013. Integration of urban growth modelling products with image-based urban change analysis. International Journal of Remote Sensing 34. (15): 5468-5486.

Jung, A., Götze, C. and Glässer, C. 2012. Overview of experimental setups in spectroscopic laboratory measurements - the SpecTour Project. Photogrammetrie-Fernerkundung-Geoinformation 4. 433-442.

Kolokotsa, D., Santamouris, M., Zerefos, S.C. 2013. Green and cool roofs' urban heat island mitigation potential in European climates for office buildings under free floating conditions Solar Energy 95. 118-130.

Kruse, F.A. 2012. Mapping surface mineralogy using imaging spectrometry. Geomorphology 137. 41-56.

Kruse, F.A., Lefkoff, A.B., Boardman, J.W., Heidebrecht, K.B., Shapiro, A.T., Barloon, P.J. and Goetz, A.F.H. 1993. The Spectral Image Processing System (SIPS) Interactive Visualization and Analysis of Imaging Spectrometer Data. Remote Sensing of Environment 44. 145-163.

Lagacherie, P., Baret, F., Feret, J.B., Netto, J. M. and Robbez-Masson, J.M. 2008. Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements. Remote Sensing of Environment 112. (3): 825-835.

Lechner, L. 1891. Szeged újjáépítése. (Rebuilding Szeged) Reprint edition in 2000 (in Hungarian).

Mucsi, L. 1996. Urban land use investigation with GIS and RS methods. Acta Universitatis Szegediensis Acta Geographica 25. 111-119.

Mucsi, L., Tobak, Z., van Leeuwen, B., Szatmári, J. and Kovács, F. 2008. Analyses of spatial and temporal changes of the urban environment using multi- and hyperspectral data. In Remote Sensing - New Challenges of High Resolution: proceedings of the EARSeL Joint Workshop. Ed.: Jürgens, C., Bochum, Geographischen Instituts der Ruhr-Universität Bochum, 275-286.

Plaza, A., Benediktsson, J.A., Boardman, J.W., Brazile, J., Bruzzone, L., Camps-Valls, G., Chanussot, J., Fauvel, M., Gamba, P., Gualtieri, A., Marconcini, M., Tilton, J.C. and Trianni, G. 2009. Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment 113. 110-122.

Räsänen, A., Rusanen, A., Kuitunen, M. and Lensu, A. 2013. What makes segmentation good? A case study in boreal forest habitat mapping. International Journal of Remote Sensing 34. 8603-8627.

Roman, K.K., O'Brien, T., Alvey, J.B., Woo, O.J. 2016. Simulating the effects of cool roof and PCM (phase change materials) based roof to mitigate UHI (urban heat island) in prominent US cities. Energy 96. 103-117.

Segl, K., Roessner, S., Heiden, U. and Kaufmann, H. 2003. Fusion of spectral and shape features for identification of urban surface cover types using reflective and thermal hyperspectral data. ISPRS Journal of Photogrammetry and Remote Sensing 58. (1-2): 99-112.

Small, C. 2003. High spatial resolution spectral mixture analysis of urban reflectance. Remote Sensing of Environment 88. (1-2): 170-186.

Szabó, Sz., Burai, P., Kovács, Z., Szabó, Gy., Kerényi, A., Fazekas, I., Paládi, M., Buday, T. and Szabó, G. 2014. Testing of algorithms for the identification of asbestos roofing based on hyperspectral data. Environmental Engineering and Management Journal 143. (11): 2875-2880.

Takács, Á., Kovács, A., Kiss, M., Gulyás, Á., and Kántor, N. 2016. Study on the transmissivity characteristics of urban trees in Szeged, Hungary. Hungarian Geographical Bulletin 65. (2): 155-167.

Unger, J., Gál, T., Rakonczai, J., Mucsi, L., Szatmári, J., Tobak, Z., van Leeuwen, B. and Fiala, K. 2010. Modeling of the urban heat island pattern based on the relationship between surface and air temperatures. Idojárás / Quarterly Journal of the Hungarian Meteorological Service 14. 287-302.

Van der Meer, F., de Jong, S.M. and Bakker, W. 2001. Imaging Spectrometry: Basic analytical techniques. In. Imaging Spectrometry: Basic Principles and Prospective Applications. Eds.: van der Meer, F., Freek, D. and Jong, S.M., Dordrecht, Kluwer Academic Publishers, 17-61.

Vane, G. and Goetz, A.F.H. 1988. Terrestrial imaging spectroscopy. Remote Sensing of Environment 24. 1-29.

World Urbanization Prospects. The 2014 Revision, Highlights, (ST/ESA/SER.A/352) United Nations, Department of Economic and Social Affairs, Population Division. 2014, 27.

Wu, B., Chen, C., Kechadi, T.M. and Sun, L. 2013. A comparative evaluation of filter-based feature selection methods for hyper-spectral band selection. International Journal of Remote Sensing 34. (22): 7974-7990.

Zhou,W., Qian,Q., Li,X., Li, W. and Han, L. 2014. 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 Ecology 29. (1): 153-167.

How to Cite
MucsiL., LiskaC. M., HenitsL., TobakZ., CsendesB., & NagyL. (2017). The evaluation and application of an urban land cover map with image data fusion and laboratory measurements. Hungarian Geographical Bulletin, 66(2), 145-156.