Predicting post-fire canopy mortality in the boreal forest from dNBR derived from time series of Landsat data
Bibliographic record
Abstract
Regulatory and certification agencies need historical fire pattern information across the Canadian boreal forest to support natural disturbance-based management. Landsat-derived spectral indices have been used extensively to map burn severity in North America. However, satellite-derived burn severity is difficult to define and quantify, and relies heavily on ground truth data for validation, which hinders fire pattern analysis over broad scales. It is therefore critical to translate burn severity estimates into more quantifiable measurements of post-fire conditions and to provide more cost-effective methods to derive validation data. We assessed the degree to which Landsat-derived indices and ancillary data can be used to classify canopy mortality for 10 fires in the boreal forest of Alberta and Saskatchewan, Canada. Models based on two and three mortality classes had overall accuracies of 91 and 72% respectively. The three-level classification has more utility for resource management, with improved accuracy at predicting unburned and complete canopy mortality classes (93 and 66%), but is relatively inaccurate for the partial mortality class (56%). The results presented here can be used to assess the suitability of different canopy mortality models for forest fire management goals, to help provide objective, consistent and cost-effective results to analyse historical fire patterns across the Canadian boreal forest.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 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".