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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».