Extracting recent short-term glacier velocity evolution over Southern Alaska from a large collection of Landsat data
Bibliographic record
Abstract
Abstract. The measurement of glacier velocity fields using repeat satellite imagery has become a standard method of cryospheric research. However, the reliable discovery of important glacier velocity variations on a large scale from noisy time-series of such data is still problematic. In this study we propose a new post-processing procedure for assembling a set of velocity fields in time-series that generates a better visualization of glacier speed changes when the velocity fields are sparse or noisy. We demonstrate this automatic method on a large glacier area in Alaska/Canada. The visualization tool provides an overview of where and when interesting glacier dynamics are occurring. The goal is not to improve accuracy or precision, but the timing and location of ice flow events such as glacier surges. Building upon existing glacier velocity products from the GoLIVE data set (https://nsidc.org/data/golive), we compile a multi-temporal stack of velocity data over the Saint Elias Mountain range and vicinity. Each layer has a time separation of 32 days, making it possible to observe details such as within-season velocity change over an area of roughly 600 000 km2. Our methodology is robust as it is based upon a fuzzy voting scheme to filter multiple outliers. The multi-temporal data stack is then smoothed to facilitate interpretation. This results in a spatio-temporal dataset where one can identify short-term glacier dynamics on a regional scale. Our implementation is fully automatic and the approach is independent of geographical area or satellite system used. Within the Saint Elias and Kluane mountain ranges, several surges and their propagation characteristics are identified and tracked through time, as well as more complicated dynamics in the Wrangell's mountains.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.001 | 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".