<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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%.</p></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%.

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

Central European Forestry Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Remote Sensing provides a variety of data and resources useful in mapping of forest. Currently, one of the common applications in forestry is the...

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

Published Online: Jun 13, 2017

Page range: 1 - 9

DOI: https://doi.org/10.1515/forj-2017-0002

Keywords
object-based classification, tree species, aerial images, airborne laser scanning

© by Maroš Sedliak

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Published Online: Jun 13, 2017

Page range: 1 - 9

DOI: https://doi.org/10.1515/forj-2017-0002

Keywordsobject-based classification, tree species, aerial images, airborne laser scanning

© by Maroš Sedliak

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
object-based classification, tree species, aerial images, airborne laser scanning