Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment
Bibliographic record
Abstract
An airborne scanning light detection and ranging (lidar) survey using a discrete pulse return airborne laser terrain mapper (ALTM) was conducted over the Utikuma boreal wetland area of northern Alberta in August 2002. These data were analysed to quantify vegetation class dependent errors in lidar ground surface elevation and vegetation canopy surface height. The sensitivity of lidar-derived land-cover frictional parameters to these height errors was also investigated. Aquatic vegetation was associated with the largest error in lidar ground surface definition (+0.15 m, SD = 0.22, probability of no difference in height P < 0.01), likely a result of saturated ground conditions. The largest absolute errors in lidar canopy surface height were associated with tall vegetation classes; however, the largest relative errors were associated with low shrub (63%, –0.52 m, P < 0.01) and aquatic vegetation (54%, –0.24 m, P < 0.01) classes. The openness and orientation of vegetation foliage (i.e., minimal projection of horizontal area) were thought to enhance laser pulse canopy surface penetration in these two classes. Raster canopy height models (CHMs) underestimated field heights by between 3% (aspens and black spruce) and 64% (aquatic vegetation). Lidar canopy surface height errors led to hydraulic Darcy–Weisbach friction factor underestimates of 10%–49% for short (<2 m) vegetation classes and overestimates of 12%–41% for taller vegetation classes.
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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".