Wildfire Detection and Mapping by Satellite With an Enhanced Configuration of the Normalized Hotspot Indices: Results From Sentinel-2 and Landsat 8/9 Data Integration
Bibliographic record
Abstract
The Operational Land Imager (OLI) and the Multispectral Instrument (MSI), respectively, aboard Landsat-8/9 (L8/9) and Sentinel-2 (S2) satellites, by providing data in near infrared (NIR) and short-wave infrared (SWIR) bands, with a mid-high spatial resolution (20/30 m), enable the identification, mapping, and characterization of high-temperature features. Here, we exploit this potential by presenting and testing the normalized hotspot indices algorithm tailored to fire mapping (NHI-F). Results were achieved by investigating the devastating fire events occurring in California and Hawaii islands (USA), Yellowknife (Canada), Tenerife islands (Spain), North Attica (Greece), and Northern Territory (Australia), during the intense fire seasons of 2023, show the high performance of the NHI-F in detecting and mapping wildfires, despite multispectral misregistration and striping effects affecting S2-MSI imagery. These effects may be directly minimized from the used indices, as demonstrated in this work. By investigating the wildfires of Yellowknife and California by means of L8/9 OLI/OLI2 data, we found that the NHI-F flagged up to 99% of fire pixels detected by the operational Landsat Fire and Thermal Anomaly (LFTA) product. Moreover, the additional fire pixels from NHI-F (up to 70% in night-time conditions) better detailed the fire fronts and provided unique information also about some small-fire outbreaks. The analysis of fire dynamics, performed integrating L8/9 (nighttime/daytime) and S2 (daytime) observations, demonstrates that the NHI-F configuration may highly support the fire monitoring activities at different spatial scales, complementing information from systems using satellite data at high-temporal/low spatial resolution.
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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 | 0.000 |
| 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".