Simulating the effects of lidar scanning angle for estimation of mean tree height and canopy closure
Bibliographic record
Abstract
A three-dimensional simulation model was used for modeling the scanning angle effect when measuring tree height and canopy closure in boreal forest with a laser scanner. The height distribution of the laser returns and the proportion of laser returns from the canopy were simulated using ray-tracing applied to a computer modeled forest. The proportion of canopy returns is commonly used as a measure of canopy closure, and height percentiles are commonly used to estimate mean tree height. Laser scanner data and field measurements of tree position, tree height, crown diameter, and crown base height were used for validating the simulation model. The correlation coefficient between simulated and real laser height percentiles was 0.96 and the simulation model systematically overestimated the laser height percentiles by 2.25 m. Simulations show that laser height percentiles and proportion of canopy returns changed more with an increased scanning angle for long crown species like spruce, compared with short crown species like pine. The change of height percentiles due to scanning angle was greater in forests with low stem numbers than with high stem numbers. The proportion of canopy returns was more affected by scanning angle than were the laser height percentiles.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".