Using Landsat data to assess fire and burn severity in the North American boreal forest region: an overview and summary of results
Bibliographic record
Abstract
There has been considerable interest in the recent literature regarding the assessment of post-fire effects on forested areas within the North American boreal forest. Assessing the physical and ecological effects of fire in boreal forests has far-reaching implications for a variety of ecosystem processes – such as post-fire forest succession – and land management decisions. The present paper reviews past assessments and the studies presented in this special issue that have largely been based on the Composite Burn Index and differenced Normalized Burn Ratio (dNBR). Results from relating and mapping fire/burn severity within the boreal region have been variable, and are likely attributed, in part, to the wide variability in vegetation and terrain conditions that are characteristic of the region. Satellite remote sensing of post-fire effects alone without proper field calibration should be avoided. A sampling approach combining field and image values of burn condition is necessary for successful mapping of fire/burn severity. Satellite-based assessments of fire/burn severity, and in particular dNBR and related indices, need to be used judiciously and assessed for appropriateness based on the users’ need. Issues unique to high latitudes also need to be considered when using satellite-derived information in the boreal forest region.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".