Analysis of continuous and discontinuous permafrost surface deformations using sentinel-1 insar, a case of iškoras and longyearbyen
Notice bibliographique
Résumé
Covering around a quarter of the exposed land surface in the Northern Hemisphere (Zhang et al., 2008), permafrost has a vital impact on the sustainability of the Arctic and global ecosystems. Frozen soils are carbon sinks that hold around twice as much carbon as the atmosphere (Schuur et al., 2015). It is generally accepted that the Arctic is warming two or three times faster than the global average (Pithan & Mauritsen, 2014) and that permafrost temperatures have increased during the last three decades (Brown & Romanovsky, 2008; Vaughan et al., 2013). Due to these increasing temperatures, carbon dioxide (CO2), as well as methane (CH4), can be released and result in further strengthening of positive climate feedback (Biskaborn et al., 2019; Schuur et al., 2015; Schuur et al., 2009; van Huissteden & Dolman, 2012; Zimov et al., 2006). It is estimated that from 2020 to the end of the century, cumulative net C loss from the frozen soils could reach 4.18–10.00 kgC/m2 (Schuur et al., 2021). Around 50 to 90 percent of near-surface permafrost can be lost by the end of the century with devastating consequences (Chadburn et al., 2017; Nitze et al., 2018). Groundwater flow and the physical surface and near-surface processes in sub-arctic topography and are largely determined by permafrost freezing and thawing dynamics (Walvoord & Kurylyk, 2016). Freeze-thaw cycles can also result in surface deformations, which are hazardous for settlements and infrastructure laid on once firm soils (Hjort et al., 2018, 2022; Nelson et al., 2001; Raynolds et al., 2014). Groundwater flow and the physical surface and near-surface processes in sub-arctic topography and are largely determined by permafrost freezing and thawing dynamics (Walvoord & Kurylyk, 2016). Freeze-thaw cycles can also result in surface deformations, which are hazardous for settlements and infrastructure laid on once firm soils (Hjort et al., 2018, 2022; Nelson et al., 2001; Raynolds et al., 2014). The relevance of the issues mentioned before determines that in recent years there has been considerable effort in the application of different remote sensing techniques to monitor permafrost degradation (Philipp et al., 2021). Optical, as well as thermal and microwave remote sensing has been used to monitor landslides (Hao et al., 2019; Kääb, 2002), pingos (Samsonov et al., 2016), patterned ground (Lousada et al., 2018), active layer thickness (Schaefer et al., 2015), greenhouse gas emissions (Curasi et al., 2016; Song et al., 2012) and other processes and characteristics connected to permafrost. Many studies of permafrost degradation have been performed using InSAR interferometry. Because thawing ice-rich permafrost is one of the main natural causes of land subsidence (Kok & Costa, 2021), techniques, such as GNSS interferometric reflectometry (Zhang & Liu, 2021), Differential GPS (Little et al., 2003; Liu & Larson, 2018; J. Zhang et al., 2020) and Differential Interferometric SAR (D-InSAR) (e.g., Chen et al., 2020; Rykhus & Lu, 2008; Strozzi et al., 2018; Z. Wang & Li, 1999) have been used to monitor vertical surface deformations in permafrost areas. Studies in this field focused mainly on Alaska (Chen et al., 2020; Liu et al., 2010; Rykhus & Lu, 2008; Wang & Li, 1999), Canada (Short et al., 2011; Wang et al., 2020) and Qinghai-Tibet Plateau (Chen et al., 2013; Chen et al., 2022; Daout et al., 2017; Wang et al., 2022; Wang et al., 2019; Zhang et al., 2019). Only a few authors chose Svalbard (Rouyet et al., 2019) or Greenland (Strozzi et al., 2018) for analysis of land subsidence due to permafrost thawing. To our knowledge, no multi-temporal studies have yet been performed in northern Norway lowlands, which is one of the focus areas in this study. The majority of the mentioned studies used C-band SAR, with the exception of a few using L-band (Abe et al., 2020). Sentinel-1, used in this study, carries a C-band SAR instrument. Its high temporal resolution of 6 to 12 days has been acknowledged as a major advantage in monitoring permafrost dynamics (e.g., Rouyet et al., 2019; Strozzi et al., 2018; Zhang et al., 2019). Our study aims to analyze surface deformation patterns in continuous and discontinuous permafrost areas using InSAR remote sensing technique. This study has the following tasks: 1) to create seasonal surface subsidence maps and databases using high temporal frequency interferometric data, 2) to evaluate surface subsidence trends over different sediment areas, 3) to compare coherence values over two study areas.
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 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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».