NDLI: Classification of tree species composition using a combination of multispectral imagery and airborne laser scanning data

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Classification of tree species composition using a combination of multispectral imagery and airborne laser scanning data

Content Provider	Scilit
Author	Sedliak, Maroš Sačkov, Ivan Kulla, Ladislav
Copyright Year	2017
Abstract	Remote Sensing provides a variety of data and resources useful in mapping of forest. Currently, one of the common applications in forestry is the identification of individual trees and tree species composition, using the object-based image analysis, resulting from the classification of aerial or satellite imagery. In the paper, there is presented an approach to the identification of group of tree species (deciduous - coniferous trees) in diverse structures of close-to-nature mixed forests of beech, fir and spruce managed by selective cutting. There is applied the object-oriented classification based on multispectral images with and without the combination with airborne laser scanning data in the eCognition Developer 9 software. In accordance to the comparison of classification results, the using of the airborne laser scanning data allowed identifying ground of terrain and the overall accuracy of classification increased from 84.14% to 87.42%. Classification accuracy of class “coniferous” increased from 82.93% to 85.73% and accuracy of class “deciduous” increased from 84.79% to 90.16%.
Related Links	https://content.sciendo.com/downloadpdf/journals/forj/63/1/article-p1.pdf http://www.degruyter.com/downloadpdf/j/forj.2017.63.issue-1/forj-2017-0002/forj-2017-0002.xml
Ending Page	9
Page Count	9
Starting Page	1
e-ISSN	24540358
DOI	10.1515/forj-2017-0002
Journal	Central European Forestry Journal
Issue Number	1
Volume Number	63
Language	English
Publisher	Walter de Gruyter GmbH
Publisher Date	2017-03-28
Access Restriction	Open
Subject Keyword	Central European Forestry Journal Remote Sensing Object-based Classification Tree Species Aerial Images Airborne Laser Scanning Journal: Central European Forestry Journal, Vol- 63
Content Type	Text
Resource Type	Article

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