Advancing Measurement and Modelling of Glacier Change Using Unmanned Aerial Vehicles and Structure-From-Motion
Bibliographic record
Abstract
Glaciers throughout Canada are responding to climate change with rapid changes in mass balance. There are limitations in current methods of measuring and predicting these changes in mass balance, including accessibility, spatial and temporal resolution of remotely sensed data, and cost of data acquisition. Technological developments in unmanned aerial vehicles (UAVs) and structure-from-motion (SfM) have created new opportunities to overcome these limitations. This dissertation investigated uncertainties in UAV-SfM data and used that data to understand spatial patterns and drivers of summer glacier melt. A study of glacier snow surface reconstruction in the Canadian Rockies used lidar data acquired simultaneously with UAV imagery to assess the spatial distribution of errors in the UAV-SfM data. The study revealed patterns in the errors related to snow surface illumination which must be considered when using UAVs over snow covered glaciers. Short term summer melt in the ablation zone of a glacier in the Canadian Arctic was investigated using UAV surveys. The study showed that UAV-SfM melt measurements agreed with ablation stake measurements and was a reliable method of measuring distributed melt patterns. The study found the lower limits on change detection were related to flying height and dGPS precision. A melt model was used to estimate surface melt for the three-day window where UAV-SfM measurements were collected and model results were validated against spatially distributed measurements. This study revealed patterns in model error which show that simplified melt models fail to capture important melt drivers on the glacier surface. The model errors would have cumulative effects in long term projections, which would lead to significant misrepresentation of total surface melt. UAV and SfM technologies were shown to be an effective method for gathering highly detailed information on glacier surface characteristics and change. However, the technology is not the answer to every problem and limitations still exist in its appropriate application. This work shows the utility of the data in advancing modelling efforts where site visits are not feasible. The dissertation ultimately demonstrates that studies can be strengthened using UAV-SfM data as one tool of many to address questions of glacier change.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".