Mapping Snowpack Depth beneath Forest Canopies Using Airborne Lidar
Bibliographic record
Abstract
An evaluation of airborne lidar (Light Detection And Ranging) technology for snow depth mapping beneath different forest canopy covers (deciduous, coniferous, and mixed) is presented. Airborne lidar data were collected for a forested study site both prior to and during peak snowpack accumulation. Manual field measurements of snow depth were collected coincident with the peak snowpack lidar survey, and a comparison between field and lidar depth estimates was made. It was found that (1) snow depth distribution patterns can be mapped by subtracting a “bare-earth” DEM from a “peak snowpack” DEM, (2) snow depth estimates derived from lidar data are strongly related to manual field measures of snow depth, and (3) snow depth estimates are most accurate in areas of minimal understory. It has been demonstrated that airborne lidar data provide accurate snow depth data for the purpose of mapping spatial snowpack distribution for volume estimations, even under forest canopy conditions.
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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 | 0.000 |
| 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".