Mapping the Activity and Evolution of Retrogressive Thaw Slumps by Tasselled Cap Trend Analysis of a Landsat Satellite Image Stack
Bibliographic record
Abstract
ABSTRACT Retrogressive thaw slumps are a dominant agent of geomorphic change in ice‐rich permafrost landscapes and may remain active for decades. Previous studies of slump activity have used aerial photographs and/or high‐resolution satellite images acquired at (multi)‐decadal time intervals. This study investigates if the calculation of the three Tasselled Cap transformations (brightness, greenness and wetness) from a dense stack of Landsat Thematic Mapper and Enhanced Thematic Mapper+ images can be used to identify slump activity and map slump evolution at near‐annual resolution. Results obtained from analysis of slumps in the Richardson Mountains‐Peel Plateau region of the Northwest Territories, Canada, suggest that Tasselled Cap linear trend images effectively identify both active and stable thaw slumps. In addition, the analysis of single‐date Tasselled Cap values at the pixel level can be used to map the initiation, growth and stabilisation of slumps at near‐annual timescales. The Tasselled Cap trend analysis method therefore offers the possibility to: (1) map the distribution of thaw slumps by activity level (active, stable or relict); (2) derive headwall retreat rates at near‐annual resolution; and (3) determine patterns of stabilisation and re‐vegetation over the period of available Landsat images. The rich temporal information provided by Landsat analysis complements conventional, higher spatial resolution (but lower temporal resolution) methods that map slumps from pairs of aerial photographs and high‐resolution satellite imagery. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".