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Proceedings Paper

Supervised classification of RESURS MSY-E data for recognizing predominant cone-bearing trees
Author(s): Victor I. Khamarin; Konstantin T. Protasov; Aleksandr P. Serykh
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Paper Abstract

Digital classifications of RESURS MSY-E data of forest Tomsk region were used. One scene acquired on 22 October 1992 was selected for classification. A supervised approach was used to generate training signatures for input to ERDAS classification. The classes (forest predominate composition) for classification were: (1) 80% cedar (pinus sibirica) + 20% spruce (picea); (2) 80 - 100% cedar without spruce; (3) moorland; (4) recent wood-cutting area; (5) old wood-cutting area; (6) 90 - 100% pine. Parametric and non-parametric decision rule were used. The GIS-compatible classifications provided a comprehensive view and evaluation of the results. Comparison of the results of classification with the forest management inventory data shows a satisfactory agreement.

Paper Details

Date Published: 19 November 1999
PDF: 6 pages
Proc. SPIE 3983, Sixth International Symposium on Atmospheric and Ocean Optics, (19 November 1999); doi: 10.1117/12.370491
Show Author Affiliations
Victor I. Khamarin, Sukachev Institute of Forestry (Russia)
Konstantin T. Protasov, Institute of Atmospheric Optics (Russia)
Aleksandr P. Serykh, Tomsk State Univ. (Russia)

Published in SPIE Proceedings Vol. 3983:
Sixth International Symposium on Atmospheric and Ocean Optics
Gennadii G. Matvienko; Vladimir P. Lukin, Editor(s)

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