Fire risk analysis over Portugal in the last decades and contributions of satellite Earth observation to evaluate wildfires
Bibliographic record
Abstract
More intense fire seasons have been favored by climate changes around the world, like Russia, Brazil, the USA, Canada and Portugal. In the last years, Portugal experienced numerous severe fire seasons with catastrophic wildfires that caused enormous impacts. This study aimed to investigate the fire risk evolution in Portugal over the last 40 years and the potential of Sentinel missions to monitor wildfires. First, the Fire Weather Index (FWI) from 1980 to 2020, at 0.25° spatial resolution, provided by the ECMWF ERA5 reanalysis based on meteorological variables, was used. FWI monthly mean values and trends were analyzed for four regions of Southern Portugal (Beja, Evora, Faro and Portalegre). Based on these results, the last five years of daily FWI values for the Faro district were evaluated. The results demonstrate that Faro district presented extreme fire risk values, with a peak on August 2, 2018, the day before the Monchique wildfire, which occurred between August 3 and 9 and was the most calamitous wildfire in Portugal during 2018, with almost 27000 ha burned. Lastly, Sentinel-2 and Sentinel-3 imageries were useful to evaluate the fire evolution and fire severity for this episode.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".