Advertisement

Izvestiya, Atmospheric and Oceanic Physics

, Volume 54, Issue 9, pp 1291–1302 | Cite as

Models of Pattern Recognition and Forest State Estimation Based on Hyperspectral Remote Sensing Data

  • V. V. KozoderovEmail author
  • E. V. Dmitriev
METHODS AND MEANS OF SATELLITE DATA PROCESSING AND INTERPRETATION

Abstract

Model applications of airborne hyperspectral remote sensing data for the recognition of forest stand objects and parameterization of the environmental role of forests in climatic models are discussed. The article is focused primarily on a comparison of the data obtained by ground-based forest inspections and the results of processing of hyper-spectral images of a test area. The examples of such a comparison intended to determine the net primary productivity of forests and other parameters characterizing the biodiversity of forest vegetation are considered.

Keywords:

hyperspectral airborne imaging pattern recognition of forest stand objects parameterization of forest environments 

Notes

ACKNOWLEDGMENTS

The study was financially supported by the Russian Science Foundation (project no. 16-11-00007) and the Russian Foundation for Basic Research (project nos. 16-01-00107 and 16-51-55019).

REFERENCES

  1. 1.
    Abend, K., Harley, T.J., and Kanal, L.N., Classification of binary random patterns, IEEE Trans. Inf. Theory, 1965, vol. 11, pp. 538–544.CrossRefGoogle Scholar
  2. 2.
    Alekseyev, V.A., Svetovoy rezhim lesa (The Light Regime of Forests], Leningrad: Nauka, 1975. Besag, J., Towards Bayesian image analysis, J. Appl. Stat., 1989, vol. 16, pp. 395–406.CrossRefGoogle Scholar
  3. 3.
    Cost, S. and Salzberg, S., A weighted nearest neighbor algorithm for learning with symbolic features, Mach. Learn., 1993, vol. 10, pp. 57–78.Google Scholar
  4. 4.
    Dietterich, T.G. and Bakiri, G., Solving multiclass learning problems via error-correcting output codes, J. Artif. Intell. Res., 1995, vol. 2, pp. 263–286.CrossRefGoogle Scholar
  5. 5.
    Gower, S.T., Krankina, O., Olson, M., Apps, M., Linder, S., and Wang, C., Net primary production and carbon allocation patterns of boreal forest ecosystems, Ecol. Appl., 2001, vol. 11, pp. 1395–1411.CrossRefGoogle Scholar
  6. 6.
    Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P.J., Asner, G.P., Francois, C., and Ustin, S.L., PROSPECT + SAIL models: A review of use for vegetation characterization, Remote Sens. Environ., 2009, vol. 113, pp. S56–S66.CrossRefGoogle Scholar
  7. 7.
    Kozoderov, V.V. and Dmitriev, E.V., Remote sensing of soils and vegetation: Regional aspects, Int. J. Remote Sens., 2008, vol. 29, no. 9, pp. 2733–2748.CrossRefGoogle Scholar
  8. 8.
    Kozoderov, V.V. and Dmitriev, E.V., Remote sensing of soils and vegetation: Quantitative parameters retrieval using pattern-recognition techniques and forest stand structure assessment, Int. J. Remote Sens., 2011, vol. 32, pp. 5699–5717.CrossRefGoogle Scholar
  9. 9.
    Kozoderov, V.V. and Dmitriev, E.V., Testing different classification methods in airborne hyperspectral imagery processing, Opt. Express, 2016, vol. 24, no. 10, pp. A956–A965.CrossRefGoogle Scholar
  10. 10.
    Kozoderov, V.V., Kondranin, T.V., Kosolapov, V.S., Golovko, V.A., and Dmitriev, E.V., Restoration of biomass and other parameters of the state of the soil–vegetation cover from processed multispectral satellite images, Issled. Zemli Kosmosa, 2007, no. 1, pp. 57–65.Google Scholar
  11. 11.
    Kozoderov, V.V., Kondranin, T.V., Dmitriev, E.V., and Sokolov, A.A., Retrieval of forest attributes using optical airborne remote sensing data, Opt. Express, 2014a, vol. 22, no. 13, pp. 15410–15423.CrossRefGoogle Scholar
  12. 12.
    Kozoderov, V.V., Kondranin, T.V., Dmitriev, E.V., and Kamentsev, V.P., A system for processing hyperspectral imagery: Application to detecting forest species, Int. J. Remote Sens., 2014b, vol. 35, no. 15, pp. 5926–5945.Google Scholar
  13. 13.
    Kozoderov, V.V., Dmitriev, E.V., and Sokolov, A.A., Improved technique for retrieval of forest parameters from hyperspectral remote sensing data, Opt. Express, 2015a, vol. 23, no. 24, pp. A1342–A1353.CrossRefGoogle Scholar
  14. 14.
    Kozoderov, V.V., Kondranin, T.V., Dmitriev, E.V., and Kamentsev, V.P., Bayesian classifier applications of airborne hyperspectral imagery processing for forested areas, Adv. Space Res., 2015b, vol. 55, no. 11, pp. 2657–2667.CrossRefGoogle Scholar
  15. 15.
    Kozoderov, V.V., Kondranin, T.V., Dmitriyev, E.V., and Kamentsev, V.P., Validation of information products for processing of aircraft hyperspectral images, Issled. Zemli Kosmosa, 2015c, no. 1, pp. 32–43.Google Scholar
  16. 16.
    Kozoderov, V.V., Dmitriev, E.V., and Kamentsev, V.P., Kognitivnye tekhnologii distantsionnogo zondirovaniya v prirodopol’zovanii (Cognitive Technologies of Remote Sensing in the Use of Natural Resources), Tver’: Tverskoi Gos. Univ., 2016.Google Scholar
  17. 17.
    Kozoderov, V.V., Kondranin, T.V., and Dmitriev, E.V., Comparison analysis of recognition algorithms of forest-cover objects on hyperspectral air-borne and space-borne images, Izv., Atmos. Ocean. Phys., 2017, vol. 53, no. 6, pp. 1132–1141.Google Scholar
  18. 18.
    Li, Z., Kurz, W.A., Apps, M.J., and Beukema, S.J., Belowground biomass dynamics in the carbon budget model of the Canadian forest sector: Recent improvements and implications for the estimation of NPP and NEP, Can. J. Forest Res., 2003, vol. 33, pp. 126–136.CrossRefGoogle Scholar
  19. 19.
    Reyer, C., Lasch-Born, P., Suckow, F., Gutsch, M., Murawski, A., and Pilz, T., Projections of regional changes in forest net primary productivity for different tree species in Europe driven by climate change and carbon dioxide, Ann. Forest Sci., 2014, vol. 71, pp. 211–225.CrossRefGoogle Scholar
  20. 20.
    Ross, Yu.K., Radiatsionnyi rezhim i arkhitektonika rastitel’nogo pokrova (Radiation Regime and Vegetation Architectonics), Leningrad: Gidrometeoizdat, 1975.Google Scholar
  21. 21.
    Shvidenko, A.Z., Nilsson, S., Stolbovoi, V.S., et al., Aggregated assessment of main indicators of the bioproduction and carbon budget of terrestrial ecosystems of Russia. 2. Net-primary production of ecosystems, Ekologiya, 2001, no. 2, pp. 83–90.Google Scholar
  22. 22.
    Shvidenko, A.Z., Shchepashchenko, D.G., Vaganov, E.A., and Nilsson, S., Net primary production of forest ecosystems in Russia: A new estimate, Dokl. Earth Sci., 2008a, vol. 421, no. 6, pp. 1009–1011.CrossRefGoogle Scholar
  23. 23.
    Shvidenko, A.Z., Shchepashchenko, D.G., Nilsson, S., and Bului, Yu.I., Tablitsy i modeli khoda rosta i produktivnosti nasazhdenii osnovnykh lesoobrazuyushchikh porod Severnoy Yevrazii (normativno-spravochnye materialy) (Tables and Models of Growth and Productivity of Plantations of the main Forest-Producing Species of Northern Eurasia (Reference Data)), Moscow: Federal Forestry Agency, 2008b.Google Scholar
  24. 24.
    Vapnik, V. and Chapelle, O., Bounds on error expectation for support vector machines, Neural Comput., 2000, vol. 12, pp. 2013–2036.CrossRefGoogle Scholar
  25. 25.
    Zamolodchikov, D.G. and Utkin, A.I., System of conversion relations for calculating the net primary production of forest ecosystems from tree stocks, Lesovedeniye, 2000, no. 6, pp. 54–63.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  1. 1.Moscow State UniversityMoscowRussia
  2. 2.Marchuk Institute of Numerical Mathematics, Russian Academy of SciencesMoscowRussia

Personalised recommendations