Detecting Short-Term Surface Melt on an Arctic Glacier Using UAV Surveys
Bibliographic record
Abstract
Current understanding of glacier mass balance changes under changing climate is limited by scarcity of in situ measurements in both time and space, as well as resolution of remote sensing products. Recent innovations in unmanned aerial vehicles (UAVs), as well as structure-from-motion photogrammetry (SfM), have led to increased use of digital imagery to derive topographic data in great detail in many fields, including glaciology. This study tested the capability of UAV surveys to detect surface changes over glacier ice during a three-day period in July 2016. Three UAV imaging missions were conducted during this time over 0.185 km 2 of the ablation area of Fountain Glacier, NU. These were processed with the SfM algorithms in Agisoft Photoscan Professional and overall accuracies of the resulting point clouds ranged from 0.030 to 0.043 m. The high accuracy of point clouds achieved here is primarily a result of a small ground sampling distance (0.018 m), and is also influenced by GPS precision. Glacier surface change was measured through differencing of point clouds and change was compared to ablation stake measurements. Surface change measured with the UAV-SfM method agreed with the coincident ablation stake measurements in most instances, with RMSE values of 0.033, 0.028, and 0.042 m for one-, two-, and three-day periods, respectively. Total specific melt over the study area measured with the UAV was 0.170 m water equivalent (w.e.), while interpolation of ablation measurements resulted in 0.144 m w.e. Using UAVs to measure small changes in glacier surfaces will allow for new investigations of distribution of mass balance measurements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 teacher head, 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".