The accuracy of estimating individual tree variables with airborne laser scanning in a boreal nature reserve
Bibliographic record
Abstract
This study examines the ability of high-density laser scanning to produce single-tree estimates in mixed stands of heterogeneous structure. Individual trees were detected from a constructed digital canopy height model by locating local maxima of the height values. The reference material comprised accurately measured field data for 10 mapped sample plots containing Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and different birches. To verify the accuracy of height measurements of single trees in more detail, the height of 29 Scots pine trees and their annual shoots of the last few years was carefully measured with a tacheometer and a glass fibre rod. The considered variables were the proportion of detected trees and tree height. As more than 80% of the dominant trees were detected, the results indicated that laser scanning can accurately describe the trees of the dominant tree layer. Because of the dense understorey tree layer in most of the sample plots, about 40% of all trees were detected. On the plot level, the stand structure affected the accuracy of the results considerably. The scanning-based tree height was most accurate for Norway spruce and least accurate for birches. The height of the separately measured 29 Scots pine trees was obtained with an accuracy of ±50 cm or better.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".