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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 <i>treeiso</i> based on the cut-pursuit graph algorithm was proposed to isolate individual-tree points from plot-level TLS scans. The <i>treeiso</i> 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 <i>treeiso</i>’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 <i>treeiso</i> optimization, and it was robust against parameter variations. This new <i>treeiso</i> method is operationally simple and addresses the growing need for practical 3D tree segmentation tools.