Single photon lidar signal attenuation under boreal forest conditions
Bibliographic record
Abstract
Single-photon lidar (SPL100) is a recently commercialized airborne lidar system facilitating efficient wide-area acquisitions of high-density point clouds due to its capacity for higher altitude acquisitions compared to traditional linear-mode lidar (LML) systems. Increased acquisition efficiency and point densities make SPL100 attractive for forest management applications. SPL100 utilizes 532 nm (green wavelength) lasers, wherein there is reduced reflectance from vegetation, increased sensitivity to solar noise, and increased signal attenuation, which may impact the vertical distribution of SPL100 returns in forest canopies. We assessed SPL100 data acquisitions over managed forests in north-eastern Ontario, Canada, using high-density unmanned aerial vehicle-borne laser scanning (ULS) data as reference over a range of forest conditions with variable vertical structure. Signal attenuation depth of individual SPL100 returns was estimated through a surface model normalization approach stratified by a ULS-derived structural index that compared densities of returns in the upper canopy to low vegetation and near ground. Canopy signal attenuation was closely matched in both systems, particularly in the upper canopy and near the ground surface; however, results showed a 31% reduction in the relative characterization of mid-canopy vegetation layers by SPL100 under conditions identified by the structural index as closed canopy, compared to the ULS system.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".