Arrangement of terrestrial laser scanner positions for area-wide stem mapping of natural forests
Bibliographic record
Abstract
With the development of terrestrial laser scanning (TLS) and its applications in forestry, the question arises as to how the scanners should be ideally placed for the best possible data acquisition. We searched for an optimal scanning distance for recognition of stems in natural beech-dominated forests, focusing particularly on the shading effect of tree stems and terrain. Recognised tree stems in TLS point clouds were compared with reference stem maps. A GIS-based visibility simulation was carried out to enhance the quantitative assessment and generalizability of results. The analyses also include the additive effect of multiple scanning positions. Single scans only have a tree recognition rate above 80% up to a distance of 15 m from the scanner; using at least three scanning positions a comparable recognition rate was attained up to 20–25 m. A simulated coverage of a beech-dominated natural forest by laser beams using a 40 m square grid of scanning positions captured at least half of the stem perimeter for more than 90% of trees with a DBH ≥ 10 cm. In sites with rough terrain, the relief configuration has a more significant effect of occlusion than present tree stems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".