Abstract
Terrestrial laser scanning (TLS) can obtain tree point clouds with high precision and high density. The efficient classification of wood points and leaf points is essential for the study of tree structural parameters and ecological characteristics. Using both intensity and geometric information, we present an automated wood–leaf classification with a three-step classification and wood point verification. The tree point cloud was classified into wood points and leaf points using intensity threshold, neighborhood density and voxelization successively, and was then verified. Twenty-four willow trees were scanned using the RIEGL VZ-400 scanner. Our results were compared with the manual classification results. To evaluate the classification accuracy, three indicators were introduced into the experiment: overall accuracy (OA), Kappa coefficient (Kappa), and Matthews correlation coefficient (MCC). The ranges of OA, Kappa, and MCC of our results were from 0.9167 to 0.9872, 0.7276 to 0.9191, and 0.7544 to 0.9211, respectively. The average values of OA, Kappa, and MCC were 0.9550, 0.8547, and 0.8627, respectively. The time costs of our method and another were also recorded to evaluate the efficiency. The average processing time was 1.4 s per million points for our method. The results show that our method represents a potential wood–leaf classification technique with the characteristics of automation, high speed, and good accuracy.
Highlights
Trees are very ecologically important to the environment [1]
Due to the different physiological functions of leaves and woody parts, separating leaves and woody parts is the basis for many studies, such as leaf area index (LAI) estimation, tree crown volume estimation, and diameter at breast height (DBH) estimation
It is clear that the points were gradually classified into were of wood points and leaf points by using the three-step classification method, and they improved by using the wood point verification technique
Summary
Trees are very ecologically important to the environment [1]. Living trees and plants in terrestrial ecosystems store approximately one trillion tons of carbon dioxide [2]. Forests play an important role in mitigating global climate change due to their ability to sequester carbon [3,4]. Above-ground biomass (AGB) is the main form of tree carbon stocks, comprising trunks, branches, and leaves [5]. Leaves are associated with photosynthesis, respiration, transpiration, and carbon sequestration, whereas trunks, composed of xylem and conduits, are mainly used to transport water and nutrients. Due to the different physiological functions of leaves and woody parts, separating leaves and woody parts is the basis for many studies, such as leaf area index (LAI) estimation, tree crown volume estimation, and diameter at breast height (DBH) estimation
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