Forest fires under the lens: needleleaf index - a novel tool for satellite image analysis
Bibliographic record
Abstract
Abstract High-resolution coniferous forest area datasets are needed to understand spatiotemporal variations in forest capacity1–3; however, separating coniferous forests from other vegetation covers remains challenging because of their similar spectral signatures4,5. Here, we propose a new spectral index called the needleleaf index to extract coniferous forest areas in North American boreal forests based on Landsat imagery at a 30-m resolution by utilizing over 24,000 Landsat images. Our analysis revealed that 25% of the total area of coniferous forests burned over the past two decades was destroyed in the 2023 wildfires. Remotely sensed observations showed that the coniferous forest area in the 2018–2023 period increased by 5.62% compared with the 1984–1991 period and decreased by 4.85% since its peak in 1992–2001. While the needleleaf index holds potential for application in coniferous forests of the taiga biome across different continents, further validation is essential to assess its reliability.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".