Abstract
The detection of individual trees in a larch plantation could improve the management efficiency and production prediction. This study introduced a two-stage individual tree crown (ITC) segmentation method for airborne light detection and ranging (LiDAR) point clouds, focusing on larch plantation forests with different stem densities. The two-stage segmentation method consists of the region growing and morphology segmentation, which combines advantages of the region growing characteristics and the detailed morphology structures of tree crowns. The framework comprises five steps: (1) determination of the initial dominant segments using a region growing algorithm, (2) identification of segments to be redefined based on the 2D hull convex area of each segment, (3) establishment and selection of profiles based on the tree structures, (4) determination of the number of trees using the correlation coefficient of residuals between Gaussian fitting and the tree canopy shape described in each profile, and (5) k-means segmentation to obtain the point cloud of a single tree. The accuracy was evaluated in terms of correct matching, recall, precision, and F-score in eight plots with different stem densities. Results showed that the proposed method significantly increased ITC detections compared with that of using only the region growing algorithm, where the correct matching rate increased from 73.5% to 86.1%, and the recall value increased from 0.78 to 0.89.
Highlights
Natural forests and plantations are significant ecosystems because they strongly modulate stores and fluxes of water and carbon near the Earth’s surface
The morphology segmentation based on the original point cloud increased the accuracy and clearity of individual tree structure, as it was not affected by the interpolation and the elevation normalization
It was commonthat threshold T-spacing in region growing segmentation was difficult to set appropriately in a high-density plantation plot
Summary
Natural forests and plantations are significant ecosystems because they strongly modulate stores and fluxes of water and carbon near the Earth’s surface. The station density is one of the most important factors controlling the productivity of managed larch forests [2]. Accurate individual tree detection could improve the management efficiency and forestry production. The algorithms of three-dimensional (3D) individual tree crown (ITC) extraction using airborne laser scanning (ALS) data have been commonly exploited to minimize the traditional time-consuming and manpower-demanding forest inventory practices [6,7]. Once a single tree is accurately segmented, tree structural attributes, such as height [8], species [9], crown size [8,10], stem density [11], wood volume [12], and biomass [13,14], can be derived
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