Optimizing photogrammetric DEMs for glacier volume change assessment using laser-scanning derived ground-control points
Bibliographic record
Abstract
Abstract Photogrammetric processing of archival stereo imagery offers the opportunity to reconstruct glacier volume changes for regions where no such data exist, and to better constrain the contribution to sea-level rise from small glaciers and ice caps. The ability to derive digital elevation model (DEM) measurements of glacier volume from photogrammetry relies on good-quality, well-distributed ground reference data, which may be difficult to acquire. This study shows that ground-control points (GCPs) can be identified and extracted from point-cloud airborne lidar data and used to control photogrammetric glacier models. The technique is applied to midtre Lovénbreen, a small valley glacier in northwest Svalbard. We show that the amount of ground control measured and the elevation accuracy of GCP coordinates (based on known and theoretical error considerations) has a significant effect on photogrammetric model statistics, DEM accuracy and the subsequent geodetic measurement of glacier volume change. Models controlled with fewer than 20 lidar control points or GCPs from sub-optimal areas within the swath footprint overestimated volume change by 14–53% over a 2 year period. DEMs derived from models utilizing 20–25 or more GCPs, however, gave volume change estimates within ∼4% of those from repeat lidar data (−0.51 m a−1 between 2003 and 2005). Our results have important implications for the measurement of glacier volume change from archival stereo-imagery sources.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".