A method for creating a burn severity atlas: an example from Alberta, Canada
Bibliographic record
Abstract
Wildland fires are globally widespread, constituting the primary forest disturbance in many ecosystems. Burn severity (fire-induced change to vegetation and soils) has short-term impacts on erosion and post-fire environments, and persistent effects on forest regeneration, making burn severity data important for managers and scientists. Analysts can create atlases of historical and recent burn severity, represented by changes in surface reflectance following fire, using satellite imagery and fire perimeters. Burn severity atlas production has been limited by diverse constraints outside the US. We demonstrate the development and validation of a burn severity atlas using the Google Earth Engine platform and image catalogue. We automated mapping of three burn severity metrics using mean compositing (averaging reflectance values) of pixels for all large (≥200 ha) fires in Alberta, Canada. We share the resulting atlas and code. We compared burn severity datasets produced using mean compositing with data from paired images (one pre- and post-fire image). There was no meaningful difference in model correspondence to field data between the two approaches, but mean compositing saved time and increased the area mapped. This approach could be applied and tested worldwide, and is ideal for regions with small staffs and budgets, and areas with frequent cloud.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".