Towards precise drone-based measurement of elevation change in permafrost terrain experiencing thaw and thermokarst
Bibliographic record
Abstract
Measuring ground elevation changes plays a crucial role in several environmental applications. For instance, permafrost soils undergo seasonal active layer freezing and thawing that causes cyclic elevation changes. Permafrost thaw can result in unidirectional ground subsidence, which may be gradual and uniform, or rapid and irregular in the case of thermokarst landforms such as slumps and degrading ice-wedges. Photogrammetric drone surveys have effectively characterized large (> 0.1 m) ground elevation changes resulting from thermokarst, yet many permafrost processes of interest lead to more subtle elevation changes. In this study, we assessed various drone-based surveying strategies for their precision to measure smaller (< 0.1 m) ground elevation changes to better characterize permafrost-driven surface dynamics. The strategies were compared by examining the short-term reproducibility of modeled elevation for 76 bare ground targets, derived from six repeat drone surveys captured under variable illumination. We found that the Phantom 4 RTK drone using direct georeferencing, combined with one fixed ground control point, could reproduce elevations with a mean absolute deviation of 0.6 cm, suggesting a minimum level of change detection of 1.4 cm at 95% confidence. Drone-based methods for measuring permafrost elevation changes should be complementary to in situ and satellite-based (e.g. differential interferometric Synthetic Aperture Radar) approaches.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".