Rapid retreat of permafrost coastline observed with aerial drone photogrammetry
Bibliographic record
Abstract
Abstract. Permafrost landscapes are changing around the Arctic in response to climate warming, with coastal erosion being one of the most prominent and hazardous features. Using drone platforms, satellite images and historic aerial photos, we observed the rapid retreat of a permafrost coastline on Qikiqtaruk–Herschel Island, Yukon Territory, in the Canadian Beaufort Sea. Erosion of this coast increasingly threatens the settlement located on the Kuvluraq–Simpson Point gravel spit. This spit accommodates several culturally significant sites and is the logistical base for the Qikiqtaruk–Herschel Island Territorial Park operations. The objectives of this study were to demonstrate the effective use of low-cost lightweight drones for: (i) assessing short-term coastal erosion dynamics over fine temporal resolution, (ii) evaluating short-term change detection in the context of long-term observations of shoreline change, and (iii) demonstrating the potential of these measurement tools for park management and decision makers. Using drones, we resurveyed a 500 m permafrost coastal reach at high temporal frequency (seven surveys over 40 days in 2017). The observed intra-seasonal shoreline changes were related to meteorological and oceanographic variables to understand intra-seasonal erosion dynamics. To put our short-term observations into historical context, we integrated analysis of shoreline positions in 2016 and 2017 with historical observations from 1952, 1970, 2000, and 2011. We found drone surveys analysed with image-based modelling yield fine-grain and accurately geolocated observations that are highly suitable to observe intra-seasonal erosion dynamics. In 2017, we observed coastal retreat of 14.5 m a−1, more than six times faster than the long-term average rate of 2.2 ± 0.2 m a−1 (1952–2017). Over a single 4 day period, coastline retreat exceeded 1 ± 0.1 m d−1. Our findings highlight the episodic nature of shoreline change, which is poorly understood along permafrost coastlines. We conclude that the data available from drones is an effective tool to understand better the mechanistic short-term controls on coastal erosion dynamics and thus long-term coastline change, and has strong potential to support local management decisions regarding coastal settlements in rapidly changing Arctic landscapes.
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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 | 0.001 |
| 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) | 0.001 | 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".