Vol. 29, issue 07, article # 1

Kozodyorov V.V., Dmitriev E.V. Direct and inverse problems of hyperspectral remote airborne sensing. // Optika Atmosfery i Okeana. 2016. V. 29. No. 07. P. 533-540 [in Russian].
Copy the reference to clipboard

Evolving cognitive technologies of forest cover pattern recognition of different species and ages while hyperspectral airborne imagery processing, characteristic features of the images formation obtained by optical receiving devices are considered together with models of the registered spectra description and forest cover parameters retrieval. Specific conditions are shown of direct problems solution in the form of dependence of the spectral functional on optical properties of the forest canopy and inverse problems of the forest vegetation phytomass volume retrieval as well as its biological productivity parameters in their possible applications in climate models.


remote sensing, optical imagery processing, pattern recognition of forest vegetation, parameters retrieval, direct and inverse problems


  1. Kozoderov V.V., Dmitriev E.V., Sokolov A.A. Improved technique for retrieval of forest parameters from hyperspectral remote sensing data // Opt. Express. 2015. V. 23, N 24. P. A1342–A1353.
  2. Kozoderov V.V., Kosolapov V.S. Obratnye zadachi atmosfernoj optiki: prilozhenija k ocenke parametrov // Optika atmosf. i okeana. 1993. V. 6, N 5. P. 529–538.
  3. Kozoderov V.V., Kondranin T.V., Dmitriev E.V., Ka-mentsev V.P. Bayesian classifier applications of airborne hyperspectral imagery processing for forested areas // Adv. Space Res. 2015. V. 55, N 11. P. 2657–2667.
  4. Kozoderov V.V., Dmitriev E.V., Kamencev V.P. Kognitivnye tehnologii obrabotki opticheskih izobrazhenij vysokogo prostranstvennogo i spektral'nogo razreshenija // Optika atmosf. i okeana. 2014. V. 27. N 7. P. 593–600; Kozoderov V.V., Dmitriev E.V., Kamentsev V.P. Cognitive technologies for processing optical images of high spatial and spectral resolution // Atmos. Ocean. Opt. 2014. V. 27, N 6. P. 558–565.
  5. Kozoderov V.V., Dmitriev E.V., Sokolov A.A. Cognitive technologies in optical remote sensing data processing // Climate & Nature. 2015. N 1(2). P. 5–45.
  6. Kozoderov V.V., Dmitriev E.V. Distancionnoe zondirovanie lesnogo pokrova: innovacionnyj podhod // Vestnik Moskovskogo gosudarstvennogo universiteta lesa – Lesnoj Vestnik. 2012. N 1(84). P. 19–33.
  7. Kozoderov V.V., Dmitriev E.V., Kamencev V.P. Sistema obrabotki dannyh samoletnogo zondirovanija vysokogo spektral'nogo i prostranstvennogo razreshenija // Issled. Zemli iz kosmosa. 2013. N 6. P. 57–64.
  8. Kozoderov V.V., Kondranin T.V., Dmitriev E.V. Metody obrabotki mnogospektral'nyh i giperspektral'nyh ajerokosmicheskih izobrazhenij. Uchebnoe posobie. M.: MFTI, 2013. 224 p.
  9. Kozoderov V.V., Kondranin T.V., Dmitriev E.V., Sokolov A.A. Retrieval of forest attributes using optical airborne remote sensing data // Opt. Express. 2014. V. 22, N 13. P. 15410–15423.
  10. Kozoderov V.V., Kondranin T.V., Dmitriev E.V., Kamentsev V.P. A system for processing hyperspectral imagery: Application to detecting forest species // Int. J. Remote Sens. 2014. V. 35, N 15. P. 5926–5945.
  11. Kozoderov V.V., Dmitriev E.V. Remote sensing of soils and vegetation: Pattern recognition and forest stand structure assessment // Int. J. Remote Sens. 2011. V. 32, N 20. P. 5699–5717.
  12. Kozoderov V.V. Osobennosti realizacii modelej ocenki fitomassy rastitel'nosti po nabljudenijam iz kosmosa // Issled. Zemli iz kosmosa. 2006. N 2. P. 79–88.
  13. Tompsett M.F., Amelio G.F., Bertram W.J. Jr., Buckley R.R., McNamara W.J., Mikkelsen J.C. Jr., Sealer D.A. Charge-coupled imaging devices: Experimental results // IEEE Trans. Electron. Devices. 1971. V. 18, N 11. P. 992–996. 
  14. Goetz A.F.H., Vane G., Solomon J.E., Rock B.N. Imaging spectrometry for Earth remote sensing // Science. 1985. V. 228, iss. 4704. P. 1147–1153.
  15. Kozoderov V.V., Kondranin T.V., Dmitriev E.V., Kazancev O.Ju., Persev I.V., Shherbakov M.V. Obrabotka dannyh giperspektral'nogo ajerokosmicheskogo zondirovanija // Issled. Zemli iz kosmosa. 2012. N 5. P. 3–11.
  16. Vane G., Goetz A.F.H. Terrestrial imaging spectroscopy // Remote Sens. Environ. 1988. V. 24. P. 1–29.
  17. Thenkabail P.S., Enclonab E.A., Ashtonb M.S., Van Der Meerd B. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications // Remote Sens. Environ. 2004. V. 91. P. 354–376.
  18. Bunting P., Lucas R. The delineation of tree crowns in Australian mixed species forests using hyperspectral Compact Airborne Spectrographic Imager (CASI) data // Remote Sens. Environ. 2006. V. 101. P. 230–248.
  19. Suits G.H. The calculation of directional reflectance of a vegetation canopy // Remote Sens. Environ. 1972. V. 2. P. 117–125.
  20. Verhoef W. Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model // Remote Sens. Environ. 1984. V. 16. P. 125–141.
  21. Goel N.S. Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data // Remote Sens. Rev. 1988. V. 4. P. 1–212.
  22. Roberts G. A review of the application of BRDF models to infer land cover parameters at regional and global scales // Progress Phys. Geog. 2001. V. 25, N 4. P. 483–511.
  23. Rosema A., Verhoef W., Noorbergen H., Borgesius J.J. A new forest light interaction model in support of forest monitoring // Remote Sens. Environ. 1992. V. 42. P. 23–41.
  24. Gastellu-Etchegorry J.P., Demarez V., Pinel V., Zagolski F. Modelling radiative transfer in heterogenous 3-D vegetation canopies // Remote Sens. Environ. 1996. V. 58. P. 131–156.
  25. Kozoderov V.V., Kosolapov V.S. Modelling the fields of outgoing solar radiation from a forest vegetation canopy // Earth Observ. Remote Sens. 1997. V. 14, N 6. P. 959–971.
  26. Tarasenkov M.V., Belov V.V. Kompleks programm vosstanovlenija otrazhatel'nyh svojstv zemnoj poverhnosti v vidimom i UF-diapazonah // Optika atmosf. i okeana. 2014. V. 27, N 7. P. 622–627; Tarasenkov M.V., Belov V.V. Software packade for reconstructing reflective properties of the Earth’s surface in the visible and UV ranges // Atmos. Ocean. Opt. 2015. V. 28, N 1. P. 89–94.
  27. Zarco-Tejada P.J., Miller J.R., Harron J., Hu B., Noland T.L., Goel N., Mohammed G.H., Sampson P. Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies // Remote Sens. Environ. 2004. V. 89. P. 189–199.
  28. North P. Estimation of fAPAR, LAI and vegetation fractional cover from ATSR-2 imagery // Remote Sens. Environ. 2002. V. 80. P. 114−121.