SAR interferometry for permafrost monitoring
Bibliographic record
Abstract
Synthetic Aperture Radar interferometry (InSAR) is a technique that can be used to measure ground movement from two or more SAR acquisitions. The SAR data sets must be of the same area, acquired with exactly the same radar properties and the same viewing geometry, and separated by a period of time. When the SAR data are processed carefully and controlled for errors, the resulting patterns of phase shift can be converted to patterns of ground movement. While the theory of InSAR for permafrost environments is well established, SAR satellite limitations have made it difficult, if not impossible, to carry out regular monitoring. In the past four years three new SAR satellites have been launched with dramatically improved capabilities for InSAR. These sensors are ALOS-PALSAR (Lband SAR), RADARSAT-2 (C-band SAR) and TerraSAR-X (X-band SAR).The project at CCRS is a comprehensive exploration of these new sensors and their capabilities for use in permafrost environments. Preliminary results show that the quality of the InSAR data pairs from the new sensors is very high and that ground movement patterns can be clearly identified. Figure 1 shows the ground displacement detected over Herschel Island between August 19 and October 4, 2007, using ALOS-PALSAR data. Significant subsidence is observed on the north coast and over the higher elevation areas. Figure 2. shows the ground displacement detected over a one year period for the same area, also using ALOS-PALSAR data. Again significant subsidence is noted along the north coast. Other subsidence patterns seem more related to surface hydrology and breaks in surface slope.The SAR sensors recently launched appear to hold significant promise for detecting ground displacement patterns over larger areas than are possible with ground surveys. Both seasonal and longer term trends can be detected. Future work includes plans for field validation and investigation of the high resolution modes of RADARSAT-2 and TerraSAR-X.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".