Pan-Arctic thermokarst lagoon distribution, area and classification
Notice bibliographique
Résumé
Thermokarst lagoons develop in permafrost lowlands along the ice-rich Arctic coast when thermokarst lakes or basins with bottom elevations at or below sea level are breached by the sea due to erosion, sea-level rise, or connection via drainage channels. Thermokarst lagoons, as dynamic landforms at the interface of terrestrial permafrost and marine systems, play a crucial role in the transformation of permafrost carbon under rising marine influence. Here we present a comprehensive dataset consisting of the first manual thermokarst lagoon area mapping, a more precise number of thermokarst lagoons and a detailed lagoon classification for thermokarst lagoons along the pan-Arctic coast from Taymyr Peninsula in Russia to the Tuktoyaktuk Peninsula in Canada. This is an updated dataset based on the previous work of Jenrich et al. 2021 and Jenrich et al. 2023. The main improvements include (1) counting thermokarst lagoons individually within a lagoon system, as long as the distinct round form of former lake basins is visible; (2) manually calculating the area for all mapped thermokarst lagoons based on the updated Global Surface Water Dataset from 1984-2021 by Pekel et al., 2016; and (3) classifying lagoons based on connectivity to the sea into 5 connectivity classes. We identified 520 thermokarst lagoons covering an area of 3457 km2. Methods: Pan-Arctic thermokarst lagoon distribution and area were mapped using QGIS version 3.34 and Google Earth Engine. The updated Global Surface Water Dataset by Pekel et al., 2016, based on Landsat-5, -7, and -8 satellite images from 1984-2021, was used to create masks with a threshold of >75% based on water occurrence, which enabled the manual splitting of polygons from the resulting mask vector data and extraction of thermokarst lagoon areas. Mapping and area extraction also relied on Sentinel-2 imagery from 2023/07/01-2023/08/30, basemaps Google Satellite and ESRI Satellite, and the digital elevation model ArcticDEM and its hillshade HSarcticDEM (Porter et al., 2018). The thermokarst lagoon classification employed a geomorphological approach based on Sentinel-2 imagery and basemaps Google Satellite and ESRI Satellite. Connectivity classes were visually defined and attributed to thermokarst lagoons based on: 1) the size of the lagoon opening relative to its overall size, 2) whether it was directly connected or subsequent within a lagoon system, and 3) interactivity within the lagoon system. The five classes range from 5 - very high connectivity to 1 - very low connectivity: 5 - Lagoon, always open (Lao) - Very high connectivity - Lagoon in direct exchange with the sea.4 - Lagoon, mostly open (Lmo) - High connectivity - Barrier islands or sand spits only slightly block exchange with the sea or subsequent lagoon which is very well connected to the primary lagoon.3 - Lagoon, semi-open (Lso) - Medium connectivity - Exchange limited either temporally or spatially due to barrier islands and sand spits or subsequent lagoon which is well connected to the primary lagoon. 2 - Lagoon, limited open (Llo) - Low connectivity - Exchange very limited due to very small opening or narrow channel or subsequent lagoon which is less connected to primary lagoon due to small channel or high distance.1 - Lagoon, nearly-closed (Lnc) - Very low connectivity - Exchange strongly limited due to long and narrow channel, or subsequent lagoon with very limited exchange. Temporary lake characteristics are possible.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 tête enseignante, 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 ».