Sampling design of ground-based lidar measurements of forest canopy structure and its effect on shadowing
Bibliographic record
Abstract
This research was undertaken to study the influence of the sampling design and laser beam density of ground-based light detection and ranging (lidar) measurements of forests on the quality of the collected laser datasets in terms of shadowing. Virtual forest stands generated by stochastic L-systems as tree descriptors are used as a basis depending on the study frame and requirements. The dynamic plant modeler and plant nursery natFX (Bionatics, CIRAD, Montpellier, France) was used to simulate deciduous forest stands of three tree species (Fagus sylvatica L., Platanus acerifolia (Ait.) Willd., and Populus nigra L.) with varying structural characteristics. Hemispherical laser measurements with different laser beam densities were simulated according to three different sampling patterns (single, diamond, corners) inside these virtual forest stands using ray-tracing technology. An adjusted sampling design has proven its effectiveness, since an average shadowing decrease of 29.10% was obtained in comparison with that for a single measurement. This finding contrasts with an average decrease of 13.27% by increasing laser beam density by a factor of 25. In the next step, contact frequency values were calculated from the virtual laser datasets. These values were used to model the shadowed parts of the canopy, demonstrating the potential of ground-based laser scans to capture the three-dimensional leaf distribution inside a forest stand in terms of leaf area density (LAD). On average, the LAD estimates underestimated the true LAD by 19.55%, 12.67%, and 10.54% for the single, diamond, and corners setups, respectively. In each of the cases, the LAD values from the single design resulted in a lower accuracy compared with those for the diamond and corners setups.
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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) | 0.000 | 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) | 0.000 | 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".