Operational Processing of Big Satellite Data for Monitoring Glacier Dynamics: Case Study of Muldrow Glacier
Bibliographic record
Abstract
Frequent acquisition of Synthetic Aperture Radar (SAR) data by the European Sentinel-1 satellites provides an opportunity for monitoring the dynamics of worldwide glaciers. We present a fully-automated processing system for producing multi-dimensional time series of glacier flow. We then use this fully-automated processing system to investigate the dynamics of Muldrow Glacier, located in the Denali National Park and Preserve (Alaska, AK, USA) during the October 2014—November 2021 period. We compute north, east, and vertical Surface-Parallel-Flow (SPF) and non-Surface-Parallel-Flow (nSPF) components of flow velocity and displacement with an average temporal resolution of 9 days and grid spacing of 100 m. During this period, we observe a glacier surge, a manifold increase in glacier flow velocity, that started as early as 2017 and continues until the present; however, the near completion of this surge is apparent. This glacier previously surged in 1906–1912 (the exact date is unknown) and in 1956–1957. We present our results in different ways to emphasize various aspects of the observed surge and demonstrate the full capability of our processing system. As the availability of SAR data improves, we expect that the fully-automated processing systems, similar to the one presented here, will play an increasingly dominant role and soon entirely replace manual processing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".