Individual Tree Segmentation from ALS Point Clouds Based on Layers Stacking Algorithm
[Objective]This paper proposed an individual tree segmentation algorithm utilizing hierarchical layer stacking approach to optimize the use of high-density LiDAR point cloud data,thereby improving the accuracy of individual tree segmentation in the understory of forest stands.[Method]Diverging from traditional algorithms which utilize canopy vertices as cluster seeds,this hierarchical overlay-based segmentation algorithm selects local maxima of each layer after horizontal slicing of point clouds for tiered clustering.It diminishes the over-segmentation due to branches through layered overlay and iterative refinement,securing segmentation precision of canopy trees and boosting extraction of understory trees.[Result]The tree segmentation algorithm based on layer stacking exhibits high precision in larch stands of various stem densities,with a maximum matching success rate of 94%between extracted and observed trees,and up to 92%in medium to high density stands.Compared to other algorithms,the matching rate for mid and lower-layer trees can be improved by 20%to 40%.In terms of individual tree height extraction precision,the correlation coefficient between extracted and observed tree heights is 0.8,with a relative root mean square error of 8.45%.The highest correlation coefficient between extracted and observed crown widths is 0.83,with a relative root mean square error of 16.5%.[Conclusion]By stacking hierarchical clustering and optimizing seed point selection,the comprehensive use of point cloud data across forest layers enhances individual tree segmentation accuracy,providing valuable data support for forest management and operations.
airborne LiDARlarch plantationunderstory treeslayers stackingseed point optimization
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