The transferability of a dNBR-derived model to predict burn severity across 10 wildland fires in western Canada
Bibliographic record
Abstract
Wildfire is a complex and critical ecological process that is an integral component of western Canadian terrestrial ecosystems. Therefore, Canadian land management agencies such as Parks Canada require detailed burn severity data for the monitoring and managing of both wildland and prescribed fires. In order to gather this type of information for our study, the acquisition of Landsat imagery was acquired and the differenced Normalised Burn Ratio was computed, a technique that has been shown to be sensitive to field measured burn severity. It is less known if this technique can be transferred and extrapolated over space and time so as to make routine and operational application of the approach feasible. To answer this question, we combined burn severity data from 10 fires across western Canada to test the transferability of an overall model as well as one stratified by land cover and ecozone. Finally, we tested the statistical benefit of incorporating pre- and post-fire data directly into the statistical model using transformations of the remote sensing imagery. Our results indicated that an overall differenced Normalised Burn Ratio derived model successfully estimated burn severity for the majority of fires in the study, which supports its transferability across multiple western Canadian landscapes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".