Exploring the use of satellite Earth observation active wildland fire hotspot data via open access web platforms
Bibliographic record
Abstract
Globally, managing wildland fire is increasing in complexity. Satellite Earth Observation (EO) data, specifically active fire ‘hotspot’ data, is often used to inform wildland fire management. This study explores hotspot data usage via web traffic data (‘user counts’) for the FIRMS, GWIS and EFFIS web portals between September 2019 and April 2023. Global active fire data use is characterized by multi-month periods of relatively low, stable user counts, interspersed with periodic spikes (4.1x median monthly activity) of activity broadly aligned with the North American / European fire season (late summer-fall). Users from the Americas (45%) and Europe (36%) dominate web traffic. We also examined correlative relationships between web page user counts and environmental and social variables at multiple spatial scales. Globally, the strongest relationships were found between user counts and the total number of fires, total burned area, number of fire disaster events; country population levels and internet proliferation. Notably, at a country level, the strongest (and weakest) relationships between weekly user counts and fire activity were found in a diverse range of countries suggesting that a variety of hotspot data use situations exist and further investigation is needed to better understand EO active fire data usage.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".