Interannual changes of active fire detectability in North America from long‐term records of the advanced very high resolution radiometer
Bibliographic record
Abstract
This paper addresses practical issues related to the processing of 1‐km National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR) data for producing a consistent, long‐term time series of active fire locations over the Continental United States and Canada. The effects of the interannual changes in measured background temperatures, caused by the orbital drift of the afternoon NOAA satellites and by environmental factors, are investigated. Background temperature changes are analyzed using a time series of monthly mean clear‐sky brightness temperatures from the NOAA National Environmental Satellite, Data, and Information Service (NESDIS) Pathfinder Atmosphere (PATMOS) data set at a 1° × 1° resolution. Examples of target areas over four predominant land cover types, as defined in the International Geosphere‐Biosphere Programme (IGBP) global 1 km data set, are presented. The results indicate that over forests (defined as >60% tree canopy cover) the contrast between nonburning background and fire pixels is nearly always sufficient for successful fire detection. Over nonforested areas, however, the low saturation temperature of the mid‐IR channel on the NOAA 7 to NOAA 14 satellites often sets a physical limit to the separation of valid fire pixels and false ones. Moreover, the severity of this effect changes over the years with the changing background temperatures. The results suggest that because of the potential spurious trends in the number of fires, nonforested areas be excluded from the multiyear analysis. However, a detailed assessment of the emissions from nonforest fires is needed to quantify the effect of this on continental‐scale emission estimates.
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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 | 0.000 | 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 | 0.000 | 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".