HIGH TEMPORAL RESOLUTION PERMAFROST MONITORING USING A MULTIPLE STACK INSAR TECHNIQUE
Bibliographic record
Abstract
Abstract. The combined effect of climate change and accelerated economic development in Northern regions increases the threat of permafrost related surface deformation to buildings and transportation infrastructure. Satellite based InSAR provides a means for monitoring infrastructure that may be both remote and spatially extensive. However, permafrost poses challenges for InSAR monitoring due to the complex temporal deformation patterns caused by both seasonal active layer fluctuations and long-term changes in permafrost thickness. These dynamics suggest a need for increasing the temporal resolution of multi-temporal InSAR methods. To address this issue we have developed a method that combines and jointly processes two or more same side geometry InSAR stacks to provide a high-temporal resolution estimate of surface deformation. The method allows for combining stacks from more than a single SAR sensor and for a combination of frequency bands. Data for this work have been collected and analysed for an area near the community of Umiujaq, Quebec in Northern Canada and include scenes from RADARSAT-2, TerraSAR-X and COSMO-SkyMed. Multiple stack based surface deformation estimates are compared for several cases including results from the three sensors individually and for all sensors combined. The test cases show substantially similar surface deformation results which correlate well with surficial geology. The best spatial coverage of coherent targets was achieved when data from all sensors were combined. The proposed multiple stack method is demonstrated to improve the estimation of surface deformation in permafrost affected areas and shows potential for deriving InSAR based permafrost classification maps to aid in the monitoring of Northern infrastructure.
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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 | 0.001 | 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) | 0.001 | 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".