Characterizing Forest Structure and its Impact on Airtanker Drop Penetration Using Airborne LiDAR
Bibliographic record
Abstract
Aerial suppression is crucial fire suppression resource for fire managers in the boreal forest. This study investigates how forest structure influences airtanker drop penetration during airtanker suppression in a common boreal forest fuel type. Experimental drops of water from a CL-415 (n = 18) and DHC-6 Twin Otter (n = 10) were analyzed in a closed canopy jack pine stand using the standardized cup-and-grid method and airborne (drone-based) LiDAR based on measurements from field experiments in Dryden, Ontario ranging from 2017-2019. Water recovered from airtanker drops (throughfall) was measured in open-field and canopy conditions, using LiDAR-derived plant area density (PAD) to quantify three-dimensional canopy structure. Canopy interception significantly reduced the spatial variability of water received on the ground, with lower coefficient of variation values beneath the canopy, though median throughfall remained similar across different plant area densities. Water delivered through the canopy from the CL-415 drops maintained an area of water volume consistent with that required to reduce the fire behaviour of Intensity Class 4 thresholds, while the average areas of heaviest volume from the Twin Otter fell below theoretical single drop suppression effectiveness thresholds above Intensity Class 2. These findings show that canopy structure has a significant impact on an airtanker’s ability to reduce fire behaviour, and further research should be done to further investigate the role of LiDAR in aerial suppression research.
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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) | 0.001 | 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".