Detecting Altimetric Changes in Arctic Landscapes Using Historical Aerial Imagery-Derived Digital Elevation Models (hDEMs): Case Study of the Black Mountain Alluvial Fan Complex, Canada
Bibliographic record
Abstract
In the rapidly changing Arctic, reconstructing landscapes over the last 50 years is essential to understanding effects due to climate-induced geomorphic change. While region-wide warming became measurable in the 1980s, spatially extensive high-latitude elevation data sets extend temporally back to the 2000s. Historical aerial imagery archives provide data sets of high-resolution imagery from the mid-to late 1900s with stereo-capability that can be harnessed to create historical digital elevation models (hDEMs). Reconstructing a surface from the past is challenging due to a lack of ground control from that era to constrain it in space, especially at high latitudes. The main purpose of this study was to determine whether an hDEM could be used to detect altimetric change in an area of poor ground control. We developed an hDEM from historical aerial imagery over the Black Mountain alluvial fan complex in the Northwest Territories, Canada, and used satellite imagery-derived ground control points to constrain the model in space. The resulting hDEM, when compared with the ArcticDEM, yields a vertical root mean square error of 5.19 m. We were able to isolate approximately 30 to 40 m of altimetric change from a landslide (circa 2013 to 2016) in the Black Mountain Fan catchment, supporting the supervised use of hDEMs for change detection studies. Data produced from this study are available on Dryad (doi:10.5061/dryad.mw6m90691).
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".