Multi-Spectral Lidar: Radiometric Calibration, Canopy Spectral Reflectance, and Vegetation Vertical SVI Profiles
Bibliographic record
Abstract
Multi-spectral (ms) airborne lidar data are enriched relative to traditional lidar due to the multiple channels of intensity digital numbers (DNs), which offer the potential for active Spectral Vegetation Indices (SVIs), enhanced classification, and change monitoring. However, in case of SVIs, indices should be calculated from spectral reflectance values derived from intensity DNs after calibration. In this paper, radiometric calibration of multi-spectral airborne lidar data is presented. A novel low-cost diffuse reflectance coating was adopted for creating radiometric targets. Comparability of spectral reflectance values derived from ms lidar data for coniferous stand (2.5% for 532 nm, 17.6% for 1064 nm, and 8.4% for 1550 nm) to available spectral libraries is shown. Active vertical profiles of SVIs were constructed and compared to modeled results available in the literature. The potential for a new landscape-level active 3D SVI voxel approach is demonstrated. Results of a field experiment with complex radiometric targets for estimating losses in detected lidar signals are described. Finally, an approach for estimating spectral reflectance values from lidar split returns is analyzed and the results show similarity of estimated values of spectral reflectance derived from split returns to spectral reflectance values obtained from single returns (p > 0.05 for paired test).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".