PermaSAR – Improving TanDEM-X D-InSAR techniques for the detection of small-scale vertical movements \nin arctic permafrost regions
Bibliographic record
Abstract
Surface dynamics, such as subsidence and heave, \nas a result of permafrost thawing and freezing is a \nwell-known phenomenon. Ground measurements are \nindicating that such movements exist and first attempts \nto use satellite data to detect these changes \non a larger scale have been undertaken. In particular \ndata from radar satellites have been used to generate \ndifferential interferograms (D-InSAR) in order \nto detect areas of movements. However there are \nstill many uncertainties and limitations related to \nthis method, such as the influence of vegetation and \nmicrotopography on the radar signal. \nWithin the PermaSAR project a multi-source approach, \nusing TanDEM-X data, ground truth measurements \n(subsidence stations and RTK GNSS), but \nalso highly precise terrestrial 3D LiDAR data shall \nhelp to identify influences of the captured surface \ncharacteristics on high-resolution D-InSAR. In a subsequent \nworking step the identified influences will be \nquantified and a method developed in order to mask, \nreduce, or even eliminate, these effects. \nFor the study, a region in Northwest Canada, 50 km \nNorth of Inuvik has been chosen. The site, so-called \nTrail Valley Creek, lies in the continuous permafrost \nzone and the thickness of the permafrost is up to \n370 m. The dominant vegetation in the basin are \nopen tundra areas consist mostly of grasses, lichens \nand mosses. Research activities of the past reveal a \npotential of subsidence due to permafrost thawing in \nthis region. \nIn 2015 two field campaigns in the region could \nbe realized: One in early June, after the freezing \nperiod and one in late August at the end of the thawing \nperiod. During the first campaign 8 automated \nground temperature loggers and manual 24 subsidence \nstations were installed. Active layer thaw depth, \nas well as subsidence was recorded at 12 sites manually. \nDuring both campaigns the high-performance \nterrestrial LiDAR system Riegl VZ-400 was used to \nsurvey at two different sites (40 × 50 m) the microtopography \nand vegetation in 3D. The used LiDAR has \nfull-waveform recording, with each single 3D measurement \nhaving a range precision and accuracy of about \n3-5 mm at 100 m. Both test sites have been scanned \nwith a point spacing of 3 mm at a distance of 10 m \nfrom 7 different scan positions. Additionally the Leica \nGNSS RTK GS10/GS15 system was used to get exact \ninformation about ground height and coordinates of \ncertain features. \nFirst results indicate \ni) a very good co-registration of the LiDAR data \nand RTK GNSS data of the two campaigns and \nii) a high correlation between the subsidence records \nof the LiDAR data, the RTK GNSS records and \nthe subsidence stations. \nThe corresponding mean subsidence rates derived \nfrom the three independent sources (LiDAR, GNSS \nRTK and subsidence stations) range from -2.31 cm \n(LiDAR) to -2.72 cm (subsidence station) (std. deviations \nfrom 0.89 (LiDAR) to 1.01 (subsidence \nstation)). First analysis of subsidence using the \nTanDEM-X data are shown and compared to our \nmulti-source ground truth measurements.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".