Investigating the Consistency of Uncalibrated Multispectral Lidar Vegetation Indices at Different Altitudes
Bibliographic record
Abstract
Multi-spectral (ms) airborne light detection and ranging (lidar) data are increasingly used for mapping purposes. Geometric data are enriched by intensity digital numbers (DNs) and, by utilizing this additional information either directly, or in the form of active spectral vegetation indices (SVIs), enhancements in land cover classification and change monitoring are possible. In the case of SVIs, the indices should be calculated from reflectance values derived from intensity DNs after rigorous calibration. In practice, such calibration is often not possible, and SVIs calculated from intensity DNs are used. However, the consistency of such active ms lidar products is poorly understood. In this study, the authors reported on an ms lidar mission at three different altitudes above ground to investigate SVI consistency. The stability of two families of indices—spectral ratios and normalized differences—was compared. The need for atmospheric correction in case of considerable range difference was established. It was demonstrated that by selecting single returns (provided sufficient point density), it was possible to derive stable SVI products. Finally, a criterion was proposed for comparing different lidar acquisitions over vegetated areas.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".