Quantifying canopy height underestimation by laser pulse penetration in small-footprint airborne laser scanning data
Bibliographic record
Abstract
There is a well-reported tendency for canopy height to be underestimated in small-footprint airborne laser scanning (ALS) data of coniferous woodland. This is commonly explained by a failure to record treetops because of insufficient ALS sampling density. This study examines the accuracy of canopy height estimates retrieved from small-footprint ALS data of broadleaf woodland. A novel field sampling method was adopted to collect reference canopy upper surface measurements of known horizontal (x, y) and vertical (z) position that had sub-metre accuracy. By investigating the z differences between ALS and reference canopy measurements with matching x and y locations, the effects of ALS sampling density were removed from the analysis. For raw point-sample ALS data, a negative bias of 0.91 m for sample shrub canopies and 1.27 m for sample tree canopies was observed. These results suggest that for broadleaf woodland, a small-footprint laser pulse hitting the upper surface of a canopy often advances into the canopy before reflecting a signal strong enough to be detected by the scanner as a first return. The depth of laser pulse penetration will vary with canopy structural characteristics and ALS device configuration. Interpolation of the point-sample ALS canopy measurements into a grid-based digital canopy height model (DCHM) propagated the observed errors, resulting in a negative bias of 1.02 m for shrub canopies and 2.12 m for tree canopies. Here the sampling density in relation to canopy surface roughness was important.
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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 | 0.004 |
| 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 | 0.000 | 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".