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Earth Observation-based Time Series
\nAnalysis of Retrogressive Thaw Slump
\nDynamics in the Russian High Arctic

2022· dissertation· en· W6990626756 sur OpenAlexaboutno aff

Notice bibliographique

RevueHelmholtz-Zentrum für Polar-und Meeresforschung (Alfred-Wegener-Institut) · 2022
Typedissertation
Langueen
DomaineEarth and Planetary Sciences
ThématiqueClimate change and permafrost
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPermafrostThermokarstSlumpingTable (database)Arctic
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

While temperatures are rising globally, they are rising more than twice as fast in the \nArctic. Landscapes underlain by permafrost are especially vulnerable to this changing \nclimate and experience increased thaw and degradation. The proceeding warming of \norganic-rich frozen ground is a highly relevant driver of carbon release into the atmosphere. \nRetrogressive Thaw Slumps (RTSs) are dynamic thermokarst features which develop when \nice-rich permafrost thaws and thus are important indices when it comes to the assessment \nof potential carbon sources in permafrost landscapes. \nThousands of RTSs have been inventoried in northwestern Canada. These inventories \nshowed that thaw slumping modifies terrain morphology and alters the discharge into \naquatic systems resulting amongst others in infrastructure instabilities and ecosystem \nchanges. Furthermore, recent studies project that abrupt thermokarst processes contribute \nsignificant amounts of greenhouse gas emissions. \nAs observed in most arctic regions, RTS activity has increased in the Russian High Arctic, \nhowever, little research has been done on RTSs in this region. The objective of this study \nis to better understand growth pattern and development rates of RTSs in northern Russia \nduring the last decade. The study area consists of five different sites in the Russian High \nArctic covering an area of more than 600 km². The sites are located on the Novaya Zemlya \nArchipelago, Kolguev Island, Bol’shoy Lyakhovsky Island and Taymyr Peninsula in ice-rich \npermafrost characterized by either buried glacial ice deposits or syngenetically formed \nYedoma permafrost. To assess changes in number and extent, a GIS based inventory of \nmanually mapped RTSs was created. The inventory is based on multispectral imagery of \nhigh-resolution satellite sensors, including PlanetScope, RapidEye, Pléiades and SPOT. \nCloud free images were acquired between 2011 and 2020 and exist for each or every \nfew years depending on their availability. Additional data sets such as ArcticDEM, Esri \nSatellite base map and Tasseled Cap Landsat Trends were used to support the mapping \nprocess. From the extracted individual RTS objects, changes in number and surface \narea were calculated. Furthermore, for coastal slumps thermal denudation and thermal \nabrasion rates were computed. \nThe results show that RTS activity was high at the study sites during the investigation \nperiod and that the diverse sites revealed different RTS characteristics, with non-coastal RTSs showing a much larger increase in area. At the non-coastal sites, RTS-affected area \nincreased by a factor of 2 (100 %) in West Taymyr, a factor of 4 (400 %) in Novaya Zemlya, \nand a factor of 33 (3300 %) in East Taymyr, with particularly large increases in more \nrecent years. At the coastal sites, total RTS area increased by a factor of 1.2 (20%) in \nNorth Kolguev, remained the same in South Kolguev, and decreased slightly by a factor of \n0.95 (5%) in Bol’shoy Lyakhovsky. Headwall and base of the coastal slumps retreated at \ndifferent rates. However, at all coastal sites, erosion of the headwall and base progressed, \ndemonstrating that RTS activity cannot be determined by area changes alone because \ncoastal RTSs are strongly influenced by thermal abrasion and thermal denudation which \ndiminishes areal changes. Moreover, the number of RTS did not necessarily increase with \nincreasing RTS activity. At all study sites except East Taymyr, increased RTS activity \nresulted from RTS growth rather than new RTS initiation. In addition, climate analysis \nrevealed that the mean temperature increased significantly, within the last decade at all \nsites, potentially favouring RTS initiation and growth. \nThe findings of this study contribute substantially to our understanding of regional \npermafrost thaw in the Russian High Arctic. Nevertheless, further research is needed \nto quantify volumetric permafrost loss and associated carbon release comprehensively \nthroughout the Russian High Arctic to better understand RTS dynamics and their impact \non greenhouse gas release.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,200
Score d'incertitude au seuil0,398

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,015
Tête enseignante GPT0,251
Écart entre enseignants0,236 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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