Accelerated change in the glaciated environments of western Canada revealed through trend analysis of optical satellite imagery
Bibliographic record
Abstract
The retreat of mountain glaciers affects mountain hazards and hydrology, and new methods are needed to rapidly map glacier retreat at planetary scales. We automatically map 14,329 glaciers (30,063 km2) in British Columbia and Alberta, Canada, from 1984 to 2020 using satellite image archives from the Landsat 4, 5, 7 and 8 missions and reveal an acceleration in area loss that commenced in 2011. Glacier fragmentation, disappearance, and proglacial lake development also accelerated, as did the retreat of glaciers to higher elevations. Our annually-resolved method relies on the existence of previously published and manually validated glacier inventories from the mid-1980s and mid-2000's. Our methods performed well for clean ice glaciers, had occasional errors when proglacial lakes were present, and consistently underestimated the area of debris-covered glaciers. Clean ice glacier area loss accelerated sevenfold between the early [1984–2010] and late [2011−2020] epochs. This acceleration yielded rates of area shrinkage of −49 ± 7 km2 a−1 [early] and − 340 ± 40 km2 a−1 [late] with accelerated losses (32-fold increase) for small glaciers on Vancouver Island over the last decade. Glacier fragmentation accelerated from 26 ± 5.6 fragments a−1 to 88 ± 39 fragments a−1. About 1141 glaciers fell below our 0.05 km2 detection limit and so disappeared from our database, representing a loss of 8%. Proglacial lake area growth accelerated from 9.2 ± 1.1 km2 a−1 to 49 ± 4.5 km2 a−1. We also observed an acceleration in the upwards migration of median glacier elevations for clean ice glaciers from 0.31 ± 0.08 m a−1 to 4.7 ± 0.7 m a−1. Our workflow demonstrates the advantages of annual resolution glacier inventories and contributes towards the implementation of planetary mapping of glaciers and glacier attributes at annual resolution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".