Spatiotemporal post-fire change analysis using optical and SAR imagery
Bibliographic record
Abstract
Wildfires on permafrost covered with the boreal forest can influence vegetation composition, surface soil moisture, and the active layer. Since wildfires on permafrost occur extensively in unpredictable areas, remote sensing is a useful tool for monitoring burn severity and ecosystem changes. Optical spectral indices such as the differenced normalized burn ratio (dNBR) and normalized difference vegetation index (NDVI) were traditionally used to detect burn severity and vegetation regrowth. However, since optical imagery is significantly affected by cloud cover and weather conditions, there is a limitation in acquiring temporally dense images. Synthetic Aperture Radar (SAR) can obtain images regardless of day/night or weather conditions, so it is possible to densely observe the area of interest spatiotemporally. In addition, SAR images, unlike optical images, can acquire information on the active layer of the permafrost in the winter season. This study aimed to analyze winter season time-series SAR backscattering coefficient change with burn severity in south Northwest Territories, Canada using optical and SAR data. The study area, south Northwest Territories, belongs to the discontinuous permafrost zone and consisted of the taiga. Burn severity and vegetation regrowth were estimated by dNBR and NDVI using optical imagery. To increase the temporal resolution, Landsat-8 OLI and Sentinel-2 MSI were acquired through the cloud-based Google Earth Engine (GEE) in the summer season. C-band dual-polarization Sentinel-1 and X-band single-polarization TerraSAR-X were obtained to understand the multi-frequency backscattering coefficient to fire-induced changes. The changes pattern of the SAR backscattering coefficient varies according to the burn severity, especially in the winter season, not affected by vegetation and soil moisture. It can be seen that the wildfires affected the changes in the scattering mechanism in permafrost on the boreal forests. These results represent that C-band and X-band SAR images have the potential to monitor the changes of the active layer with burn severity.
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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 | 0.002 | 0.002 |
| 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 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".