Cybernetics and Systems Analysis

, Volume 52, Issue 1, pp 127–138 | Cite as

Large-Scale Classification of Land Cover Using Retrospective Satellite Data

  • M. S. Lavreniuk
  • S. V. Skakun
  • A. Ju. Shelestov
  • B. Ya. Yalimov
  • S. L. Yanchevskii
  • D. Ju. Yaschuk
  • A. Ì. Kosteckiy


Large-scale mapping of land cover is considered in the paper as a problem of automated processing of big geospatial data, which may contain various uncertainties. To solve it, we propose to use three different paradigms, namely, decomposition method, the method of active learning from the scope of intelligent computations, and method of satellite images reconstruction. Such an approach allows us to minimize the participation of experts in solving the problem. Within solving the problem of land cover classification we also investigated three different approaches of data fusion. The most efficient data fusion method is one that could be reduced to the problem of classification on the base of time-series images. Developed automated methodology was applied to land cover mapping and classification for the whole territory of Ukraine for 1990, 2000, and 2010 with a 30-meter spatial resolution.


land cover classification geospatial data data fusion satellite data neural network training and test samples 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    M. A. Friedl, D. K. McIver, J. C. F. Hodges, X. Y. Zhang, D. Muchoney, A. H. Strahler, C. E. Woodcock, S. Gopal, A. Schneider, A. Cooper, A. Baccini, F. Gao, and C. Schaaf, “Global land cover mapping from MODIS: Algorithms and early results,” Remote Sensing of Environment, 83, No. 2, 287–302 (2002).CrossRefGoogle Scholar
  2. 2.
    J. Gallego, N. Kussul., S. Skakun, O. Kravchenko, A. Shelestov, and O. Kussul, “Efficiency assessment of using satellite data for crop area estimation in Ukraine,” Intern. J. Applied Earth Observation and Geoinformation, 29, 22–30 (2014).CrossRefGoogle Scholar
  3. 3.
    N. Kussul, S. Skakun, A. Shelestov, O. Kravchenko, J. F. Gallego, and O. Kussul, “Crop area estimation in Ukraine using satellite data within the MARS project 2012,” IEEE Intern. Geoscience and Remote Sensing Symposium (IGARSS) (2012), pp. 3756–3759.Google Scholar
  4. 4.
    T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, The MIT Press, Cambridge, MA (2009).MATHGoogle Scholar
  5. 5.
    D. Tuia, F. Ratle, F. Pacifici, M. F. Kanevski, and W. J. Emery, “Active learning methods for remote sensing image classification,” IEEE Trans. on Geoscience and Remote Sensing, 47, No. 7, 2218–2232 (2009).CrossRefGoogle Scholar
  6. 6.
    A. Yu. Shelestov, A. N. Kravchenko, S. V. Skakun, S. V. Voloshin, and N. N. Kussul, “Geospatial information system for agricultural monitoring,” Cybern. Syst. Analysis, 49, No. 1, 124–132 (2013).CrossRefGoogle Scholar
  7. 7.
    N. Kussul, S. Skakun, A. Shelestov, O. Kussul, and B. Yailymov, “Resilience aspects in the sensor Web infrastructure for natural disaster monitoring and risk assessment based on Earth observation data,” IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, 7, No. 9, 3826–3832 (2014).CrossRefGoogle Scholar
  8. 8.
    N. Kussul, D. Mandl, K. Moe, J. P. Mund, J. Post, A. Shelestov, S. Skakun, J. Szarzynski, G. Van Langenhove, and M. Handy, “Interoperable infrastructure for flood monitoring: Sensor Web, Grid, and cloud,” IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, 5, No. 6, 1740–1745 (2012).CrossRefGoogle Scholar
  9. 9.
    N. Kussul, A. Shelestov, and S. Skakun, “Grid and sensor Web technologies for environmental monitoring,” Earth Science Informatics, 2, No. 1–2, 37–51 (2009).CrossRefGoogle Scholar
  10. 10.
    N. Kussul, A. Shelestov, and S. Skakun, “Flood monitoring from SAR data,” NATO Science for Peace and Security Series C: Environmental Security, 19–29,  10.1007/978-90-481-9618-0_3 (2011).
  11. 11.
    S. V. Skakun and R. M. Basarab, “Reconstruction of missing data in time-series of optical satellite images using self-organizing Kohonen maps,” J. Autom. Inform. Sci., 46, No. 12, 19–26 (2014).CrossRefGoogle Scholar
  12. 12.
    M. C. Hansen, A. Egorov, D. P. Roy, P. Potapov, J. Ju, S. Turubanova, I. Kommareddy, and T. R. Loveland, “Continuous fields of land cover for the conterminous United States using Landsat data: First results from the Web-Enabled Landsat Data (WELD) project,” Remote Sensing Letters, 2, No. 4, 279–288 (2011).CrossRefGoogle Scholar
  13. 13.
    G. Buttner, J. Feranec, G. Jaffrain, L. Mari, G. Maucha, and T. Soukup, “The CORINE land cover 2000 project,” EARSeL eProceedings, 3, No. 3, 331–346 (2004).Google Scholar
  14. 14.
    N. Kussul, S. Skakun, A. Shelestov, and O. Kussul, “The use of satellite SAR imagery to crop classification in Ukraine within JECAM project,” in: IEEE Intern. Geoscience and Remote Sensing Symposium (IGARSS) (2014), pp. 1497–1500.Google Scholar
  15. 15.
    N. Kussul, S. Skakun, A. Shelestov, O. Kravchenko, and O. Kussul, “Crop classification in Ukraine using satellite optical and SAR images,” Models&Analyses, 2, No. 2, 118–128 (2013).Google Scholar
  16. 16.
    J. Gallego, A. N. Kravchenko, N. N. Kussul, S. V. Skakun, A. Yu. Shelestov, Yu. A. Grypych, “Efficiency assessment of different approaches to crop classification based on satellite and ground observations,” J. Autom. Inform. Sci., 44, No. 5, 67–80 (2012).CrossRefGoogle Scholar
  17. 17.
    Chen Jun, Chen Jin, A. Liao, X. Cao, L. Chen, X. Chen, He C., G. Han, S. Peng, M. Lu, W. Zhang, X. Tong, and J. Mills, “Global land cover mapping at 30m resolution: A POK-based operational approach,” ISPRS J. of Photogrammetry and Remote Sensing, 103, 7–27 (2015).CrossRefGoogle Scholar
  18. 18.
    S. Haykin, Neural Networks and Learning Machines, Prentice Hall, Upper Saddle River (NJ) (2008).Google Scholar
  19. 19.
    J. Sim and C. C. Wright, “The Kappa statistic in reliability studies: Use, interpretation, and sample size requirements,” Physical Therapy, 85, No. 3, 257–268 (2005).Google Scholar
  20. 20.
  21. 21.
    P. Olofsson, G. M. Foody, M. Herold, S. V. Stehman, C. E. Woodcock, and M. A. Wulder, “Good practices for estimating area and assessing accuracy of land change,” Remote Sensing of Environment, 148, 42–57 (2014).CrossRefGoogle Scholar
  22. 22.
    G. M. Foody, “Status of land cover classification accuracy assessment,” Remote Sensing of Environment, 80, No. 1, 185–201 (2002).CrossRefGoogle Scholar
  23. 23.
    N. Kussul, S. Skakun, A. Shelestov, and O. Kussul, “The use of satellite SAR imagery to crop classification in Ukraine within JECAM project,” Geoscience and Remote Sensing Symposium (IGARSS), IEEE Intern., Quebec City, QC, Canada (2014), pp. 1497–1500, DOI:  10.1109/IGARSS.2014.6946721.
  24. 24.
    V. Lavreniuk, N. Kussul, S. Skakun, A. Shelestov, and B. Yailymov, “Regional retrospective high resolution land cover for Ukraine: Methodology and results,” Intern. Geoscience and Remote Sensing Symposium (IGARSS) (2015).Google Scholar
  25. 25.
    N. N. Kussul, B. V. Sokolov, Ya. I. Zyelyk, S. V. Skakun, and A. Yu. Shelestov, “Disaster risk assessment based on heterogeneous geospatial information,” J. Autom. Inform. Sci., 42, No. 12, 32–45 (2010).CrossRefGoogle Scholar
  26. 26.
    S. Skakun, N. Kussul, A. Shelestov, and O. Kussul, “Flood hazard and flood risk assessment using a time series of satellite images: A case study in Namibia,” Risk Analysis, 34, No. 8, 1521–1537 (2014).CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Institute of Space ResearchNational Academy of Sciences of Ukraine and State Space Agency of UkraineKyivUkraine
  2. 2.“Integration Plus” Ltd.KyivUkraine
  3. 3.National University of Life and Environmental Sciences of UkraineKyivUkraine
  4. 4.National Center of Control and Testing of Spacecraft, Space Agency of UkraineKyivUkraine

Personalised recommendations