Seasonally-decomposed Sentinel-1 backscatter time-series are useful indicators of peatland wildfire vulnerability
Bibliographic record
Abstract
Peatlands throughout the boreal forest are expected to experience changes in precipitation, evapotranspiration and temperature due to climate change. Correspondingly, changes in hydrologic regimes could lead to increased drought and occurrence of wildfire. Fire management agencies require information about near-real time wildfire vulnerability in boreal peatlands. Remote sensing tools (e.g., NDVI, NDII) to monitor changing wildfire vulnerability focus on monitoring changes in vascular vegetation and are not necessarily applicable to moss-dominated peatlands. We use time series analysis of Sentinel-1 SAR backscatter data to compare the trends in peatlands that have burned to unburned peatlands and show that the Theil-Sen slopes of seasonally decomposed SAR backscatter reflects prolonged drought conditions that can lead to burning. Seasonally decomposed Sentinel-2 NDVI and NDII were also tested but no statistical differences were found between burned and unburned peatlands. Overall, we found that 6 months prior to a wildfire the slope of seasonally decomposed Sentinel-1 VV SAR backscatter was significantly different in burned and unburned peatlands, and can be used to spatially identify fire vulnerability and identify fire-prone areas.
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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 | 0.000 |
| 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) | 0.001 | 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".