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Journal of Applied Remote Sensing

Toward extending terrestrial laser scanning applications in forestry: a case study of broad- and needle-leaf tree classification
Author(s): Yi Lin; Miao Jiang
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Paper Abstract

Tree species information is essential for forest research and management purposes, which in turn require approaches for accurate and precise classification of tree species. One such remote sensing technology, terrestrial laser scanning (TLS), has proved to be capable of characterizing detailed tree structures, such as tree stem geometry. Can TLS further differentiate between broad- and needle-leaves? If the answer is positive, TLS data can be used for classification of taxonomic tree groups by directly examining their differences in leaf morphology. An analysis was proposed to assess TLS-represented broad- and needle-leaf structures, followed by a Bayes classifier to perform the classification. Tests indicated that the proposed method can basically implement the task, with an overall accuracy of 77.78%. This study indicates a way of implementing the classification of the two major broad- and needle-leaf taxonomies measured by TLS in accordance to their literal definitions, and manifests the potential of extending TLS applications in forestry.

Paper Details

Date Published: 14 March 2017
PDF: 10 pages
J. Appl. Rem. Sens. 11(1) 016037 doi: 10.1117/1.JRS.11.016037
Published in: Journal of Applied Remote Sensing Volume 11, Issue 1
Show Author Affiliations
Yi Lin, Peking Univ. (China)
Miao Jiang, Institute of Mineral Resources Research (China)

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