Satellite remote sensing of active fires: History and current status, applications and future requirements
Bibliographic record
Abstract
Landscape fire is a widespread, somewhat unpredictable phenomena that plays an important part in Earth's biogeochemical cycling. In many biomes worldwide fire also provides multiple ecological benefits, but in certain circumstances can also pose a risk to life and infrastructure, lead to net increases in atmospheric greenhouse gas concentrations, and to degradation in air quality and consequently human health. Accurate, timely and frequently updated information on landscape fire activity is essential to improve our understanding of the drivers and impacts of this form of biomass burning, as well as to aid fire management. This information can only be provided using satellite Earth Observation (EO) approaches, and remote sensing of active fire is one of the key techniques used. This form of EO is based on detecting the signature of the (mostly infrared) electromagnetic radiation emitted as biomass burns. Since the early 1980's, active fire (AF) remote sensing conducted using low Earth orbit (LEO) satellites has been deployed in certain regions of the world to map the location and timing of landscape fire occurrence, and from the early 2000's global-scale information updated multiple times per day has been easily available to all. Geostationary (GEO) satellites provide even higher frequency AF information, more than 100 times per day in some cases, and both LEO- and GEO-derived AF products now often include estimates of a fires characteristics, such as its fire radiative power (FRP) output, in addition to the fires detection. AF data provide information relevant to fire activity ongoing when the EO data were collected, and this can be delivered with very low latency times to support applications such as air quality forecasting. Here we summarize the history of achievements in the field of active fire remote sensing, review the physical basis of the approaches used, the nature of the AF detection and characterization techniques deployed, and highlight some of the key current capabilities and applications. Finally, we list some important developments we believe deserve focus in future years.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".