Izvestiya, Atmospheric and Oceanic Physics

, Volume 54, Issue 9, pp 997–1007 | Cite as

Mapping Age Stages of Forest Vegetation Based on an Analysis of Landsat Multiseasonal Satellite Images

  • I. V. DanilovaEmail author
  • M. A. Korets
  • V. A. Ryzhkova


The possibility of increasing the efficiency of forest vegetation mapping is studied using Landsat multitemporal satellite images of medium spatial resolution. To simplify the process of supervised satellite-image classification, the method of automated generation of reference samples is applied based on forest inventory materials. Using the test forest areas of the southern part of Yenisei Siberia, it is demonstrated that native and secondary stands of different ages, from which regeneration series of dark coniferous and light coniferous forests are developed in different forest growing conditions, are mapped as a result of the classification of multiseasonal images with a sufficient level of significance (Kappa coefficient more than 0.7).


Landsat satellite images forest inventory materials reforestation processes Central Siberia 



This work was supported by the Government of Russian Federation (grant no. 14.B25.31.0031) and the Russian Foundation for Basic Research (grant no. 15-04-04013).


  1. 1.
    Aksenov, D.E. and Yaroshenko, A.U., Space images for forestry and forest management, Earth from Space, 2009, no. 1, pp. 10–15.Google Scholar
  2. 2.
    Bartalev, S., Erchov, D., Isaev, A., and Belward, A., A new SPOT4-Vegetation derived land cover map of Northern Eurasia, Int. J. Remote Sens., 1999, vol. 24, no. 9, pp. 1977–1982.CrossRefGoogle Scholar
  3. 3.
    Bartalev, S.A., Egorov, V.A., Ershov, D.V., Isaev, A.S., Loupian, E.A., Plotnikov, D.E., and Uvarov, I.A., Satellite mapping of vegetation cover in Russia using MODIS spectroradiometer data, Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2011, vol. 8, no. 4, pp. 285–302.Google Scholar
  4. 4.
    Berlyant, A.M., Gedymin, A.V., Kelner, Yu.G., et al., Spravochnik po kartografii (Cartography: A Reference Book), Moscow: Nedra, 1988.Google Scholar
  5. 5.
    Bontemps, S., Defourney, P., van Bogaert, E., and Arino, O., GLOBCOVER2009 Products Description and Validation Report. Scholar
  6. 6.
    Britsyna, M.P., The relief and soil-forming species in the central part of the Krasnoyarsk Region), in Prirodnoe raionirovanie tsentral’noi chasti Krasnoiarskogo kraia i nekotorye voprosy prirodnogo khozyaistva (Natural Zoning of the Central Part of the Krasnoyarsk Region and Some Issue of the Natural Management), Moscow: AN USSR, 1962.Google Scholar
  7. 7.
    Buzykin, A.I., Cherednikova, Yu.S., and Perevoznikova, V.D., Forest growth zoning and forest types, in Regional’nye problemy ekosistemnogo lesovodstva (Regional Problems of Ecosystem Forestry), Onuchin, A.A., Ed., Krasnoyarsk: Inst. lesa im. V.N. Sukacheva SO RAN, 2007, pp. 15–45.Google Scholar
  8. 8.
    Classifier of thematic problems of the assessment of natural resources and environment that can be solved using tools of remote sensing of the Earth, in Zemlya iz kosmosa. Naibolee effektivnye resheniya (The Earth from Space: Most Efficient Solutions), 2009, no. 3, pp. 46–53.Google Scholar
  9. 9.
    Danilova, I.V., Ryzhkova, V.A., and Onuchin, A.A., Using of satellite imagery, digital elevation model (DEM) and field data for mapping of forest regeneration dynamics), Geod. Kartogr., 2013, no. 9, pp. 25–32.Google Scholar
  10. 10.
    ERDAS Field Guide, Fifth Edition, Atlanta, Georgia, USA: ERDAS Inc., 1999.Google Scholar
  11. 11.
    Gavrilyuk, E.A. and Ershov, D.V., Thematic mapping of forest species from Landsat-TM\ETM+ imagery), in Aerokosmicheskie metody i geoinformatsionnye tekhnologii v lesovedenii i lesnom khozyaistve (Aerospace Methods and Geoinformation Technologies in Forestry), Moscow: TsEPL RAN, 2013, pp. 112–115.Google Scholar
  12. 12.
    Gong, P., Wang, J., Yu, L., Zhao, Y.C., Zhao, Y., Liang, L., Niu, Z.G., Huang, X.M., Fu, H.H., Liu, S., et al., Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data, Int. J. Remote Sens., 2013, vol. 34, pp. 2607–2654.CrossRefGoogle Scholar
  13. 13.
    Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., et al., High-resolution global maps of 21st-century forest cover change, Science, 2013, vol. 342, no. 6160, pp. 850–853. doi 10.1126/science.1244693CrossRefGoogle Scholar
  14. 14.
    Isaev, A.S., Knyazeva, S.V., Puzachenko, M.J., and Chernenkova, T.V., The use of satellite data for monitoring of forest biodiversity), Issled. Zemli Kosmosa, 2009, no. 2, pp. 55–66.Google Scholar
  15. 15.
    Kashkin, V.B. and Sukhinin, A.I., Distantsionnoe zondirovanie zemli iz kosmosa. Tsifrovaya obrabotka izobrazhenii: uch. posob. dlja vuzov (Remote Sensing of the Earth from Space. Digital Image Processing: A Manual for Universities), Moscow: Logos, 2001.Google Scholar
  16. 16.
    Korets, M.A., Danilova, I.V., and Cherkashin, V.P., in Remote indication of the forestry strucure, in Regional’nye problemy ekosistemnogo lesovodstva (Regional Problems of Ecosystem Forestry), Onuchin, A.A., Ed., Krasnoyarsk: Inst. lesa im. V.N. Sukacheva SO RAN, 2007, pp. 52–68.Google Scholar
  17. 17.
    Kozoderov, V.V. and Dmitriev, E.V., Aerospace sensing of the soil–vegetation cover: Models, algorithmic and software tools, ground-based validation), Issled. Zemli Kosmosa, 2010, no. 1, pp. 69–86.Google Scholar
  18. 18.
    Marchukov, V.S. and Stytsenko, E.A., Interpretation of vegetation cover using spectral and temporal characteristics, Issled. Zemli Kosmosa, 2012, no. 1, pp. 77–88.Google Scholar
  19. 19.
    Monserud, R.A. and Leemans, R., Comparing global vegetation maps with the Kappa statistic, Ecol. Modeling, 1992, vol. 62, pp. 275–293.CrossRefGoogle Scholar
  20. 20.
    Puzachenko, Yu.G., Krenke, A.N., Puzachenko, M.Yu., and Sandelskii, R.B., General principles of the use of multispectral remote data for forestry studies, in Aerokosmicheskie metody i geoinformatsionnye tekhnologii v lesovedenii i lesnom khozyaistve (Aerospace Methods and Geoinformation Technologies in Forestry), Moscow: TsEPL RAN, 2013, pp. 59–62.Google Scholar
  21. 21.
    Richards, J.A. and Xiuping, J., Remote Sensing Digital Image Analysis: An Introduction, Birkhäuser, 2005.Google Scholar
  22. 22.
    Ryzhkova, V. and Danilova, I., GIS-based classification and mapping of forest site condition and vegetation, Bosque, 2012, vol. 33, no. 3, pp. 293–297.CrossRefGoogle Scholar
  23. 23.
    Tateishi, R., Uriyangqai, B., Al-Bilbisi, H., Ghar, M.A., Tsend-Ayush, J., Kobayashi, T., Kasimu, A., et al., Production of global land cover data—GLCNMO, Int. J. Digital Earth, 2011, vol. 1, pp. 22–49.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  • I. V. Danilova
    • 1
    Email author
  • M. A. Korets
    • 1
  • V. A. Ryzhkova
    • 1
  1. 1.Sukachev Institute of Forest, Siberian Branch, Russian Academy of SciencesKrasnoyarskRussia

Personalised recommendations