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Proceedings Paper

A novel approach to internal crown characterization for coniferous tree species classification
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Paper Abstract

The knowledge about individual trees in forest is highly beneficial in forest management. High density small foot- print multi-return airborne Light Detection and Ranging (LiDAR) data can provide a very accurate information about the structural properties of individual trees in forests. Every tree species has a unique set of crown structural characteristics that can be used for tree species classification. In this paper, we use both the internal and external crown structural information of a conifer tree crown, derived from a high density small foot-print multi-return LiDAR data acquisition for species classification. Considering the fact that branches are the major building blocks of a conifer tree crown, we obtain the internal crown structural information using a branch level analysis. The structure of each conifer branch is represented using clusters in the LiDAR point cloud. We propose the joint use of the k-means clustering and geometric shape fitting, on the LiDAR data projected onto a novel 3-dimensional space, to identify branch clusters. After mapping the identified clusters back to the original space, six internal geometric features are estimated using a branch-level analysis. The external crown characteristics are modeled by using six least correlated features based on cone fitting and convex hull. Species classification is performed using a sparse Support Vector Machines (sparse SVM) classifier.

Paper Details

Date Published: 18 October 2016
PDF: 14 pages
Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100040H (18 October 2016); doi: 10.1117/12.2241452
Show Author Affiliations
A. Harikumar, Fondazione Bruno Kessler (Italy)
Univ. degli Studi di Trento (Italy)
F. Bovolo, Univ. degli Studi di Trento (Italy)
L. Bruzzone, Fondazione Bruno Kessler (Italy)

Published in SPIE Proceedings Vol. 10004:
Image and Signal Processing for Remote Sensing XXII
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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