Full-polarimetric burn scar mapping – the differences of active fire and post-fire situations
Bibliographic record
Abstract
Fire is a threat to human lives, infrastructure, and forestry. Satellite-based Earth observations enable a fast, efficient, and reliable estimation of burnt area. In most cases, optical satellite data are used for burn scar detection. However, smoke and cloud coverage strongly limits the suitability of optical imagery for rapid mapping of burn scars. Here, an automated procedure based on quad-polarized L-band Synthetic Aperture Radar (SAR) data is presented to enable fast burn scar mapping independently of the weather and smoke conditions. Full-polarimetric SAR data enable the decomposition of the SAR backscatter into different scattering mechanisms, describing the scatterer more precisely. Important differences in the polarimetric backscattering behaviour during the active fire and post-fire situations are reported. While the SPAN difference is best suited for burn scar mapping during active fire situations, the Entropy-Anisotropy-Alpha and the generalized Freeman–Durden decompositions showed the best suitability for burnt area mapping several months after the end of the fire. The proposed methodology is an object-based image analysis approach based on change detection. The big fire event which affected Fort McMurray, Alberta, Canada, in May–June 2016 was investigated as a case study.
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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 | 0.000 | 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".