Toward extending terrestrial laser scanning applications in forestry: a case study of broad- and needle-leaf tree classification

Yi Lin; Miao Jiang

doi:10.1117/1.JRS.11.016037

14 March 2017 Toward extending terrestrial laser scanning applications in forestry: a case study of broad- and needle-leaf tree classification

Yi Lin, Miao Jiang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 1, 016037 (March 2017). https://doi.org/10.1117/1.JRS.11.016037

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.

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Yi Lin and Miao Jiang "Toward extending terrestrial laser scanning applications in forestry: a case study of broad- and needle-leaf tree classification," Journal of Applied Remote Sensing 11(1), 016037 (14 March 2017). https://doi.org/10.1117/1.JRS.11.016037

Received: 24 October 2016; Accepted: 28 February 2017; Published: 14 March 2017

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