Speaker
Description
Accurate estimation of a tree’s expected leaning direction is essential for safe and controlled felling, particularly in complex crown architectures or winch-assisted motor-manual felling operations. In practice, such decisions rely primarily on visual assessment by experienced forestry workers. The extent to which this estimation reflects the tree’s actual mechanical mass distribution and the direction of its centre of gravity (CoG) remains insufficiently quantified. This study compares personal visual tree assessments with terrestrial laser scanning (TLS)-derived CoG directions and extends the analysis to operational implications in winch-assisted motor-manual felling.
54 trees were independently assessed by three experienced workers (162 assessments in total). Each worker estimated the direction in which the tree was most likely to fall. For each tree, a three-dimensional CoG was calculated using TLS-based quantitative structure models (QSM). Monte Carlo simulations were applied to consider structural and density-related uncertainties. Across all assessments, the mean absolute deviation between personal visual assessments and TLS-received CoG directions was 48° (median 35°), with extreme deviations reaching 141° (95th percentile). Differences between workers were small relative to the overall variability, indicating that uncertainty was not just limited to individual performance. Only 13% of assessments fell within ±10°, and less than half were within ±30°. This demonstrates limited precision at operationally relevant tolerances. Error patterns revealed systematic directional tendencies rather than random scatter. The forest workers frequently showed similar assessments of individual trees. Nevertheless, these assessments did not consistently align with the TLS-derived CoG directions, suggesting that visual assessment does not automatically imply mechanical accuracy. The observed deviations suggest that visually prominent features, such as stem curvature or inclination, strongly influence judgments, while less visible crown mass asymmetries that shift the CoG may be underestimated. Building on the three-dimensional CoG modelling, the study is being extended to quantify the mechanically required winch cable attachment height for each tree. This allows the geometric consequences of mass displacement to be assessed beyond directional estimation. These attachment heights will be compared with practitioner-based height selection in winch-assisted motor-manual felling, providing an operational perspective on CoG displacement.
Overall, the research indicates that personal visual tree assessment alone is insufficient to reliably capture the mechanical mass distribution of complex tree structures. The integration of laser-based CoG modelling with practitioner-based assessment establishes a framework for quantifying the boundaries of visual judgment, thereby promoting safety-oriented decision-making in forestry operations.
| Keywords | QSM; Direction; Assessment; Felling |
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