Potential of RADARSAT-2 stereo radargrammetry for the generation of glacier DEMs
Bibliographic record
Abstract
ABSTRACT The study of glaciers and ice caps in remote and cloudy regions remains difficult using current remote sensing tools. Here the potential of stereo radargrammetry (SRG) with RADARSAT-2 Wide Ultra-Fine images is explored for DEM extraction, elevation changes and mass-balance calculations on Barnes Ice Cap (Nunavut, Canada). Over low-relief terrain surrounding Barnes, a vertical precision of ~7 m (1σ confidence level) is measured, as well as an average vertical bias of ~4 m. Moreover, we show that the C-band penetration depth over the ice cap is insignificant at this time of the year (i.e. late ablation season). This is likely due to a wet surface and the presence of superimposed ice that leads to a surface radar response. Comparing the SRG DEMs with other datasets, an historical glacier-wide mass balance of −0.52 ± 0.19 m w.e. a−1is estimated for 1960–2013, whereas it decreases to −1.06 ± 0.84 m w.e. a−1between 2005 and 2013. This clear acceleration of mass loss is in agreement with other recent studies. Given its all-weather functionality and its possible use without ground control points, the RADARSAT-2 SRG technology represents an appropriate alternative for glacier monitoring in cloudy and remote regions.
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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 | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".