3D graph-based individual-tree isolation (treeiso) from terrestrial laser scanning point clouds

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Date
2022
Authors
Xi, Zhouxin
Hopkinson, Christopher
Journal Title
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Volume Title
Publisher
MDPI
Abstract
Using terrestrial laser scanning (TLS) technology, forests can be digitized at the centimeter-level to enable fine-scale forest management. However, there are technical barriers to converting point clouds into individual-tree features or objects aligned with forest inventory standards due to noise, redundancy, and geometric complexity. A practical model treeiso based on the cut-pursuit graph algorithm was proposed to isolate individual-tree points from plot-level TLS scans. The treeiso followed the local-to-global segmentation scheme, which grouped points into small clusters, large segments, and final trees in a hierarchical manner. Seven tree attributes were investigated to understand the underlying determinants of isolation accuracy. Sensitivity analysis based on the PAWN index was performed using 10,000 parameter combinations to understand the treeiso’s parameter importance and model robustness. With sixteen reference TLS plot scans from various species, an average of 86% of all trees were detected. The mean intersection-over-union (mIoU) between isolated trees and reference trees was 0.82, which increased to 0.92 within the detected trees. Sensitivity analysis showed that only three parameters were needed for treeiso optimization, and it was robust against parameter variations. This new treeiso method is operationally simple and addresses the growing need for practical 3D tree segmentation tools.
Description
Open access article. Creative Commons Attribution 4.0 International license (CC BY 4.0) applies
Keywords
LIDAR , Terrestrial laser scanning , 3D , Tree segmentation , Individual-tree crown analysis , Forests
Citation
Xi, Z., & Hopkinson C. (2022). 3D graph-based individual-tree isolation (treeiso) from terrestrial laser scanning point clouds. Remote Sensing, 14(23), Article 6116. https://doi.org/10.3390/rs14236116
URI
https://hdl.handle.net/10133/7079
Collections
Hopkinson, Christopher