Assessment and monitoring of foliage projected cover and canopy height across native vegetation in Queensland, Australia, using laser profiler data
Bibliographic record
Abstract
The aim of this project was to demonstrate the potential of laser profiling for monitoring forest structure. Data were captured from flights at 30, 60, and 100 m above the canopy over three study sites in south-east Queensland at regular intervals over a 2-year period. Field measurements of foliage projected cover (FPC) and tree height were found to be highly correlated with laser derived estimates (R2 from 0.91 to 0.95). Monitoring of changes in FPC and tree height, as a result of logging or growth, was also successful. Tree heights, in particular, were measured accurately over time (residual standard error (RSE) of 0.45 m). The large RSE of the FPC model (from 5.7% to 7.3% FPC) means that subtle changes, such as seasonal variation, may be difficult to monitor. Flying height was found to be a significant explanatory variable in estimating field FPC. A transect of greater than 1000 km was also flown in a single helicopter pass to assess the technology over a range of forest types. Field measurements of FPC were collected for 21 sites along this transect. Strong relationships were observed between laser and field FPC, but these varied with forest type.
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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 | 0.001 |
| 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 | 0.000 |
| 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".