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Record W4404021357 · doi:10.5200/gm.2023.4

Analysis of continuous and discontinuous permafrost surface deformations using sentinel-1 insar, a case of iškoras and longyearbyen

2023· article· en· W4404021357 on OpenAlexaboutno aff
Elzė Buslavičiūtė

Bibliographic record

VenueGeografijos metraštis / The Geographical Yearbook · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsInterferometric synthetic aperture radarPermafrostGeologyGeodesySurface (topology)Environmental scienceRemote sensingMathematicsGeometrySynthetic aperture radarOceanography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.027
GPT teacher head0.256
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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