Evaluation of ERS SAR data for prediction of fire danger in a Boreal region
Bibliographic record
Abstract
Research was conducted to determine the utility of Synthetic Aperture Radar (SAR) data for measuring the fuel moisture status of boreal forests as reflected in Fire Weather Index Codes. Three years (May to August 1992–1995) of SAR data from the European Remote Sensing Satellite (ERS) were analysed over the 1990 Tok Alaska burned and adjacent unburned black spruce forests. Corresponding Fire Weather Index Codes of the Canadian Forest Fire Danger Rating System were obtained from Tok Area Forestry, Station number 500720. Strong relationships were expected between the SAR data and fire codes because of the dependence of ERS SAR backscatter on the moisture status of forests and exposed surfaces (burn scars). Astepwise multilinear regression procedure was used to analyse the relationships. Three statistically significant multilinear regression models resulted from this analysis procedure. The models developed show there is potential for using ERS SAR backscatter to generate indicators that are related to Fire Weather Index, Duff Moisture Code, and Drought Code. This research could lead to the ability for remote prediction of fire danger over large regions at relatively fine spatial resolution with minimal weather information.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".