Mapping Retrogressive Thaw Slumps Using Single-Pass TanDEM-X Observations
Bibliographic record
Abstract
Vast areas of the Arctic host ice-rich permafrost, which is becoming increasingly vulnerable to terrain-altering thermokarst in a warming climate. Among the most rapid and dramatic changes are retrogressive thaw slumps. These slumps evolve by a retreat of the slump headwall during the summer months, making them detectable by comparing digital elevation models over time using the volumetric change as an indicator. Here, we present and assess a method to detect and monitor thaw slumps using time series of elevation models applied on two contrasting study areas in Northern Canada. Our two-step method is tailored to single-pass InSAR observations from the TanDEM-X satellite pair, which have been acquired since 2011. For each acquisition, we derive a digital elevation model and uncertainty estimates. In the first step, we difference digital elevation models and detect the significant elevation changes using a blob-detection algorithm. In the second step, we classify the detections into those due to thaw slumps and other causes using a simple thresholding method (accuracy: 78%), a random forest classifier (87%), and a support vector machine (86%). When our method is applied to other areas, the classifiers should be trained with data from part of the study area or with data obtained from similar areas in terms of topography, vegetation, and thaw slump characteristics to achieve the best performance. The obtained locations of thaw slumps can be used as a starting point to extract important slump properties, such as the headwall height and the volumetric change, which are currently not available on regional scales.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".