Earth Observation-based Time Series \nAnalysis of Retrogressive Thaw Slump \nDynamics in the Russian High Arctic
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".