Remotely sensed vegetation phenology drives large fire spread in northwestern Europe
Bibliographic record
Abstract
Background Increasing frequency of large fires in northwest Europe, a region under-represented in fire studies, with different ecosystem processes from those most studied, indicates the need to understand the drivers of hazardous fire behavior. Aims This study characterizes rate of spread variation in the region and delves into vegetation and weather drivers through remote sensing. Methods For 58 large fires, we analyzed phenology (using the temporal variation of satellite-measured vegetation indices) and weather (using as the Canadian Fire Weather Index System). Their relations and capability of predicting fire spread rates were assessed. Key results Low vegetation greenness correlated non-linearly with high rate of spread, and fires in the growing season showed a drastic reduction in spread. Low levels of weather-related danger were correlated with high rate of spread. Conclusions In NW Europe, the integration of phenology into fire behavior analyses helps predict spread rate. Analyzing vegetation indices variation can help estimate times when ignition could generate fast-spreading fires. Contrary to expectations, high danger related to fire weather was associated with low spread. Implications This study highlights the need for including timing of vegetation greenness in wildfire risk modeling and for a fire weather index systems tailored to regional conditions that relate to high-hazard fire behavior.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".