Remote Sensing-Based Assessment of Fire Danger Conditions Over Boreal Forest
Bibliographic record
Abstract
Forest fire is an integral part in many forested ecosystems including boreal forests, that influences forest productivity, biodiversity and socio-economy, among others. In this paper, we evaluated the potential of three selected satellite (i.e., MODIS)-based variables/indices at 8-day temporal resolution, i.e., surface temperature (TS), normalized multiband drought index (NMDI) and temperature vegetation wetness index (TVWI) in predicting/forecasting the fire danger conditions over boreal forest regions of Alberta during the period 2006-2008. The method was based on the assumption that the fire danger conditions duringi+1 period would be high if the instantaneous values of: (i)TSvalues were either higher or equal; or (ii) NMDI or TVWI values were either lower or equal; with compare to their respective study-area-specific average during i period. The analyses were conducted on the basis of either individual variable or combining all of the three together. We found that 60.59% forTS, 72.41% for NMDI, and 54.19% for TVWI of fires fell under the high fire danger conditions. The combination of all of the three individual variables, it revealed that 91.63% of the fires fell in the categories of “very high” (i.e., all three variables indicated high danger), “high” (i.e., at least two of them indicated high danger), and “moderate” (i.e., at least one of the variables indicated high danger) fire danger classes. These results showed that the applicability of the proposed method in predicting fire danger conditions over the boreal forest regions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".