Estimating canopy fuel load with hemispherical photographs: A rapid method for opportunistic fuel documentation with smartphones
Bibliographic record
Abstract
Abstract Canopy fuel load (CFL) affects wildfire intensity and is a critical input for fire behaviour models; however, measuring CFL in the field is time‐consuming and costly. Until recently, hemispherical photographs have been unable to adequately describe canopy metrics in non‐diffuse light conditions. Recent developments allow calculation of canopy openness (CO) and leaf area index (LAI) using hemispherical photographs taken in sunny conditions. We describe an inexpensive and effective method for estimating CFL in the field opportunistically using CO and LAI values derived from hemispherical photographs taken in variable lighting conditions with a smartphone and fisheye lens attachment. Implications of this work include: new data for modelling effects of forest structure on fire behaviour; inexpensive assessment and monitoring of forest structure changes over time and in relation to management actions; and decision support for fuel treatment planning and prescribed burning operations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".