Day and night-time active fire detection over North America using NOAA-16 AVHRR data
Bibliographic record
Abstract
Presents an investigation of the applicability of NOAA-16/AVHRR (N-16) satellite data for detecting and mapping active wildfires across North American forest ecosystems. Two fire detection algorithms were developed for application to N-16 day and night-time daily imagery. The algorithms exploit both the multi-spectral and thermal information from the AVHRR daily images. For daytime data collection N-16 provides 3 reflective bands, namely, red, near infrared and short-wave infrared (SWIR), in addition to two thermal brightness temperature bands centred at 10.8 /spl mu/m and 12 /spl mu/m. During nighttime data collection the SWIR band is switched to the thermal brightness temperature band centred at 3.7 /spl mu/m. This band in particular has been extensively used for detecting active fires. The fire algorithms have two major steps: detection of potential fires followed by false fire elimination. The threshold tests developed for fire identification and false fire removal were optimised through a trial-and-error approach using a database of active fire pixels over the whole of North America. The database was generated from a large number of single-daytime and night-time AVHRR scenes, where the fire pixels were identified visually on the images aided by the associated daytime smoke plumes. The SWIR band was found to be sensitive only to large burning fires leading to a noticeable spike in the measured reflectance. However, a serious limitation with the SWIR band is that small burning fires are not easily detectable and quite a number of active fires go undetected. Night detection does not pose such a limitation and has allowed for significantly higher detection rates. Overall night detection provides reasonably good alternative for the reduced sensitivity of the SWIR band. The set of day and night algorithms was used to generate daily active fire maps across North America. Such a combined approach for fire detection leads to an improved detection rate. Selected validation sites in western Canada and the United States, showed reasonable correspondence with the location of fires mapped by the Canadian Forest Service and the USDA Forest Service using conventional means.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".