Discriminating Forest Leaf and Wood Components in TLS Point Clouds at Single-Scan Level Using Derived Geometric Quantities

Abstract

Discriminating leaf and wood components in terrestrial laser scanning (TLS) point clouds is a prerequisite for accurately estimating 3-D structural and biophysical attributes of both individual trees and entire forests. However, most existing separation methods are conducted at local (i.e., individual or plot) level. The local level separation methods need a presegmentation of the acquired point clouds, and the separation accuracy and reliability are greatly influenced by forest occlusion effect and point cloud qualities. A new generalized method merely based on differences in geometric features, including curvature, density, and salient features, is proposed in this study for separating leaf and wood components at the TLS single-scan level. A preliminary separation is conducted using the quantity of normal change rate (i.e., surface variation) given that leaf points often demonstrate sharp local curvature changes. Then, separation is continually conducted on the basis of calibrated density data (i.e., number of points in a given radius) because of the scattered orientations and small sizes of leaves. Finally, a new self-adjusting connectivity segmentation algorithm is proposed to group remaining points into different clusters. Leaf and wood clusters are separated in accordance with salient features and sizes simultaneously. Results indicate that derived geometric quantities from curvature, density, and salient features of individual points and segmented clusters can be jointly used to discriminate leaf and wood components effectively and robustly in single-scan TLS point clouds with a mean overall accuracy of approximately 93%. In addition, results show good performance in terms of the insensitivity to distance, instrument type, occlusion effect, and forest composition of the proposed method.