Cassification of Sugi and Hinoki using high density airborne LiDAR data and two canopy shape parameters.

Abstract

Objectives of this study were creating canopy shape-feature parameters using the sampled canopy point data and classifying the two species using the point data and parameters. A canopy sharpness parameter, which was standard deviation of canopy height of the points within distance of 1.5 m from the top, and a canopy top roundness parameter, which was an angle between the top point and two points at distances of 0.3 m and 1.3 m,were contrived. Larger absolute canopy sharpness values showed sharper and narrower canopies and smaller roundness parameter showed rounder canopy top. Sample trees of sugi and hinoki were selected for modeling and validation separately and a discriminant function was created (r=0.675) with accuracy of 79.2% for species classification. Classification accuracy of the validation samples was 84% and it suggested usefulness of the discriminant function for species classification of sugi and hinoki trees with similar canopy shape to the modeling samples.