Application of Sentinel-1 data to quantify Arctic Coastal Retreat
Bibliographic record
Abstract
In the arctic region many coastal areas exhibit rapid erosion, with coastal retreat or erosion rates of 10 meters per year (m/yr.) or higher in places. This poses a threat primarily to all manner of infrastructure built directly on and near the coastline. With climate change the coastal erosion is expected to increase. This effect is expected to be especially severe in the continuous permafrost region, as the coastal erosion is linked with the increase in thermoerosion of the permafrost. A remote sensing method with high coverage and sufficient temporal observation frequency at lower cost than from aerial photography would be practical to mitigate the problem. This would enable assessing and predicting (potential) damage to existing infrastructure and planning of its future locations. A thresholding method based on TerraSAR-X x-band synthetic aperture radar observations is applied to lower resolution Sentinel-1 c-band synthetic aperture radar observations monitor the coastal erosion rates. This study aims to determine the feasibility of using this method with Sentinel- 1 data. For this purpose, the method is applied to Senteinel-1 Backscatter scenes and the Coherence between scenes within each year from 2016 to 2020 at three sample sites on Herschel Island (Beaufort Sea, northern Canada). The method was successfully applied to the Sentinel-1 Backscatter data, yielding reliable and accurate results for one of the sample sites, with the highest estimated erosion rate of the three sites. The same technique was applied to the Coherence data. The obtained results were less reliable compared to the results from the Backscatter data, showing too high variance. The results indicate that the application is generally limited to the summer season and to coastlines oriented towards or parallel to the looking direction of the SAR sensor. The results are compared to previous studies of coastal erosion rates on Hershel Island or the nearby northern Yukon coastline region.
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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 | 0.000 | 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".