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Applications of Remote Sensing Data of High Spatial and Spectral Resolution to Estimate Species Composition of Forests and Their Parameters of Biological Productivity
 
PIIS020596140003370-8-1
DOI10.31857/S020596140003370-8
Publication type Article
Status Published
Authors
V. Kozoderov 
Affiliation: Moscow State University Lomonosov
Address: Russian Federation
S. Donskoy
Affiliation: Federal State Budgetary Institute "Roslesinforg"
Address: Russian Federation
P. Melnik
Affiliation: Moscow State Technical Name Bauman
Address: Russian Federation
E. Dmitriev
Affiliation: Institute of Computational Mathematics RAS
Address: Russian Federation
Journal nameIssledovanie Zemli iz kosmosa
EditionIssue 6
Pages77-85
Abstract

Applications encompass comparison of hyperspectral remote sensing data of high spatial resolution and common-used ground-based forest observations on trial plots as a possible alternative the relevant laborious works. Pattern recognition methods have become the basic approaches while solving the related applied problems. Computational procedures of pattern recognition for diff erent classes of objects serve to process the hyperspectral images as well as fi nding such parameters of objects like net primary productivity of forests of diff erent species and ages. The classifi ers used embed the updated representation about pattern recognition while the hyperspectral imagery processing. The classifi cation accuracy is compared with the accuracy of ground-based observations. The comparison shows perspectives of the elaborated new approaches.

Keywordshyperspectral data of remote sensing, pattern recognition of forest canopy objects, estimation of parameters of biological productivity of forests
AcknowledgmentThis work was supported by the Russian Science Foundation (project No. 16–11– 00007) and a grant from the Russian Foundation for Basic Research (project No. 16–01–00107).
Received27.12.2018
Publication date27.12.2018
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1. Bondur V. G. Sovremennye podkhody k obrabotke bol'shikh potokov giperspektral'noj i mnogospektral'noj aehrokosmicheskoj informatsii // Issled. Zemli iz kosmosa. 2014. № 1. S. 4–16.
2. Dmitriev E. V., Kozoderov V. V., Dement'ev A. O., Safonova A. N. Kompleksirovanie klassifikatorov v zadache tematicheskoj obrabotki giperspektral'nykh aehrokosmicheskikh izobrazhenij // Avtometriya. 2018. T. 54. № 3. S. 3–13.
3. Zamolodchikov D. G., Utkin A. I. Sistema konversionnykh otnoshenij dlya rascheta chistoj pervichnoj produktsii lesnykh ehkosistem po zapasam nasazhdenij // Lesovedenie. 2000. № 6. S. 54–63.
4. Kozoderov V. V., Dmitriev E. V. Modeli raspoznavaniya i otsenki sostoyaniya lesnoj rastitel'nosti po giperspektral'nym dannym distantsionnogo zondirovaniya // Issled. Zemli iz kosmosa. 2017. № 6. S. 75–88.
5. Shvidenko A. Z., Nil'sson S., Stolbovoj V. S. i dr. Opyt agregirovannoj otsenki osnovnykh pokazatelej bioproduktsionnogo protsessa i uglerodnogo byudzheta nazemnykh ehkosistem Rossii. 2. Netto-pervichnaya produktsiya ehkosistem // Ehkologi . 2001. № 2. S. 83–90.
6. Shvidenko A. Z., Schepaschenko D. G., Nil'sson S., Buluj Yu. I. Tablitsy i modeli khoda rosta i produktivnosti nasazhdenij osnovnykh lesoobrazuyuschikh porod Severnoj Evrazii (normativno-spravochnye materialy). Izd. 2-e, dop. M.: Federal'noe agentstvo lesnogo khozyajstva, 2008. 886 s.
7. Dietterich T. G., Bakiri G. Solving Multiclass Learning Problems via Error-Correcting Output Codes // J. Artif. Intell. Res. 1995. V. 2. P. 263–286.
8. Gobakken T., N?sset E. Estimation of diameter and basal area distributions in coniferous forest by means of airborne laser scanner data // Scandinavian J. Forest Res. 2004. V. 19. P. 529–542.
9. Gower S. T., Krankina O., Olson M., Apps M., Linder S., Wang C. Net primary production and carbon allocation patterns of boreal forest ecosystems // Ecol. Appl. 2001. V. 11. P. 1395–1411.
10. Keenan R. J. Climate change impacts and adaptation in forest management: a review // Ann. Forest Sci. 2015. V. 72. P. 145–167.
11. Kozoderov V. V., Dmitriev E. V. Remote sensing of soils and vegetation: quantitative parameters retrieval using pattern-recognition techniques and forest stand structure assessment // Int. J. Rem. Sens. 2011. V. 32. P. 5699–5717.
12. Kozoderov V. V., Kondranin T. V., Dmitriev E. V., Soko lov A. A. Retrieval of forest attributes using optical airborne remote sensing data // Opt. Expr. 2014a. V. 22. № 13. P. 15410–15423.
13. 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. Rem. Sens. 2014b. V. 35. № 15. P. 5926–5945.
14. Kozoderov V. V., Dmitriev E. V., Sokolov A. A. Improved technique for retrieval of forest parameters from hyperspectral remote sensing data // Opt. Expr. 2015a. V. 23. № 24. P. A1342-A1353.
15. Kozoderov V. V., Kondranin T. V., Dmitriev E. V., Kamentsev V. P. Bayesian classifi er applications of airborne hyperspectral imagery processing for forested areas // Adv. Space. Res. 2015b. V. 55. № 11. P. 2657–2667.
16. Kozoderov V. V., Dmitriev E. V. Testing diff erent classifi cation methods in airborne hyperspectral imagery processing // Opt. Expr. 2016. V. 24. № 10. P. A956-A965.
17. Kozoderov V. V., Kondranin T. V., Dmitriev E. V. Hyperspectral Remote Sensing Imagery Processing Focused on Forest Applications // Int. Rev. Aerosp. Eng. (I.RE.AS.E). 2017. V. 10. № 5. P. 267–276.
18. Matikainen L, Karila K, Hyyppa J, Litkey P, Puttonen E, Ahokas E. Object-based analysis of multispectral airborne laser scanner data for land cover classifi cation and map updating. 2017 // ISPRS J. Photogr. and Rem. Sens. V. 128. P. 298–313.
19. Nevalainen O., Honkavaara E., Tuominen S., Viljanen N., Hakala T., Yu X., Tommaselli A. M. Individual tree detection and classifi cation with UAV-based photogrammetric point clouds and hyperspectral imaging // Rem. Sens. 2017. V. 9. P. 185–195.
20. Rowell E., Seielstad C., Vierling L., Queen L., Queen W. Using Laser Altimetry-based Segmentation to Refi ne Automated Tree Identifi cation in Managed Forests of the Black Hills, South Dakota // Photogr. Eng. & Rem. Sens. 2006. V. 72. P 1379–1388.
21. Schepaschenko D., Moltchanova E., Shvidenko A., Blyshchyk V., Dmitriev E., Martynenko O., See L., Kraxner F. Improved Estimates of Biomass Expansion Factors for Russian Forests // Forests. 2018. V. 9. № 312. P. 1–23.

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