Mapping bedfast and floating thermokarst lake ice and determining lake depth using Sentinel 1 Synthetic Aperture Radar Remote Sensing on the west shore of Hudson Bay, Canada and Prudhoe Bay, Alaska
Bibliographic record
Abstract
Thermokarst lakes are an abundant feature in Arctic permafrost regions and cover up to 40 percent of the land area. During winter shallow lakes freeze to the bed (bedfast ice) while lakes which are deeper than the maximum ice thickness up to 2 m preserve perennial liquid water below the ice (floating ice). The different lake ice regimes have an impact on the energy distribution to the surrounding permafrost, available aquatic habitat and geomorphological processes. Completely frozen lakes contribute less energy and gas fluxes to the landscape and atmosphere while floating ice conditions support the development of a talik, a continuously unfrozen layer, as the remaining liquid water provides energy to the surrounding permafrost. This has an impact on permafrost thawing and geomorphological development as taliks can favour subsurface lake drainage, permafrost degradation and lateral lake erosion. Bedfast or floating ice conditions are dependant on the maximum ice thickness. Ice growth is determined by winter temperatures and snow conditions as a thicker snow cover provides insulation and reduce ice growth. In this study Sentinel 1 synthetic aperture radar (SAR) data for four winters from 2015 to 2018 was used to investigate thermokarst lakes and compare lake ice regimes in two study areas with permafrost conditions. One is in the area of Prudhoe Bay, North Slope Borough, Alaska and the other on the west shore of Hudson Bay near Churchill, Manitoba, Canada. Synthetic aperture radar remote sensing allows to distinguish between bedfast and floating ice due to different backscatter intensities. While bedfast ice absorbs the radar signal and appears dark on the radar image, floating ice shows a strong reflectance and appears bright. This is due to differences in the dielectric contrast between ice and sediment (lake bed) and ice and liquid water, respectively. \nAdditionally the maximum ice thickness was approximated by calculating ice growth based on freezing degree days from MODIS land surface temperature data. With the resulting ice growth curve the maximum water depth of lakes which freeze completely to the ground was determined through the date when they became bedfast. Bedfast lake ice percentages decreased over the study period in Prudhoe Bay while they varied widely in Churchill. The average proportions were similar for both study areas with 68 % in Prudhoe Bay and 62 % in Churchill. The lakes in Prudhoe Bay showed a trend towards floating ice regimes which was not detectable in Churchill. Relationships between winter temperatures and the amount of bedfast ice were not linear and indicate the presence of tipping points. Maximum ice thickness was estimated to be 160 cm in Prudhoe Bay which seems valid, while the similar ice thickness in Churchill is most likely overestimated by the used method. Future work in permafrost regions and the establishment of long term observations should help to understand trends more reliable and detect relationships between climate and resulting landscape responses
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".