Evaluation of mobile laser scanning acquisition scenarios for automated wood volume estimation in a temperate hardwood forest using quantitative structural models
Bibliographic record
Abstract
This study explores how data from a handheld mobile laser scanning (MLS) system and quantitative structural models (QSM) can be used to estimate tree structural attributes. Four MLS acquisition scenarios were investigated in a 1 ha temperate hardwood stand, including 15 and 35 m parallel lines, nine circular plots, and a 20 m × 20 m grid. Results were compared against terrestrial laser scanning and destructive field measurements. All acquisition scenarios yielded comparable results, except for the 35 m scenario, which showed greater variability. The 20 m × 20 m grid scenario showed the highest accuracy, with an RMSE of 0.41 m (2.07%) for tree height, 3.98 cm (14.93%) for diameter at breast height, 0.21 m³ (19.28%) for merchantable wood volume, and 0.07 m³ (10.11%) for merchantable stem volume. A bias < 5% was observed for these key attributes, except for an 11.68% bias in merchantable wood volume. Overestimation of branch volume was identified as the primary source of bias related to merchantable wood volume. This study highlights MLS’s potential for accurate, non-destructive estimation of tree structural attributes, while pointing out the need to refine noise removal and to assess the most suitable acquisition scenarios for various forest types.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".