ASSESSMENT OF SUB-CANOPY STRUCTURE IN A COMPLEX CONIFEROUS FOREST
Bibliographic record
Abstract
LiDAR technology emits narrow beams of laser light that are able to exploit gaps in the forest canopy and detect sub-canopy surfaces. In this study, we explore the potential o f airborne LiDAR to quantify understorey vegetation cover in a dense and structurally diverse conifer forest on Vancouver Is land, British Columbia, Canada. The cover of unders torey vegetation, defined below an arbitrary height threshold of 4 m, was rec orded in the field both horizontally and vertically at 12 plots for comparison with LiDAR data. Results showed significant relationship s between field and LiDAR-based estimates of understorey vegetation cover at both the plot (30 x 30 m area, r 2 = 0.87) and sub-plot scale (15 x 15 m areas, n = 4 per plot, r 2 = 0.68) (p < 0.05). In addition, the variability (coefficient of variation) of understor ey vegetation cover estimated in the field and with LiDAR data was found to be significantly correlated (r 2 =0.88, p < 0.001). Overall, this work suggests that small-footprint LiDAR is sensitive to large changes in understorey vegetation cover which can benefit key forestry applications at the landscape scale such a s examining stand regeneration success.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".