First retrogressive thaw slump (RTS) inventory for the Kanin Peninsula (NW Russia)

Retrogressive thaw slumps (RTS) are thermokarst landforms resulting from the thawing of ice-rich permafrost in the pan-Arctic and high mountain regions, recognized as climate-related phenomena. Using 2022 ESRI Wayback satellite imagery, we manually digitized 900 RTS polygons on the Kanin Peninsula (NW Russia), categorizing them into 633 inactive and 267 active features based on morphology and vegetation patterns. The primary goal was not to create a complete RTS inventory but to develop a highly representative dataset for training machine learning models to automatically detect and classify RTS into active and inactive categories. This approach will facilitate regional-scale monitoring of permafrost degradation and enhance understanding of RTS impacts, including their effects on indigenous Nenets reindeer herders' travel routes. By classifying RTS polygons into morphologically active and inactive features, this dataset aims to enable ongoing analysis of change patterns over time, offering critical insights in the current climate context.

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Publication Details

Journal
Scientific Data
Published
2025-07-14
DOI
https://doi.org/10.1038/s41597-025-05592-7
Citations
2
Primary Topic
Climate change and permafrost
Type
article
Field-Weighted Citation Impact
2.60
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article

First retrogressive thaw slump (RTS) inventory for the Kanin Peninsula (NW Russia)

Hakan Tanyaş, Luigi Lombardo, Ionuţ Cristi Nicu, Lena Rubensdotter
2 citations
Scientific Data
Climate change and permafrost
2.60
article

First retrogressive thaw slump (RTS) inventory for the Kanin Peninsula (NW Russia)

Hakan Tanyaş, Luigi Lombardo, Ionuţ Cristi Nicu, Lena Rubensdotter
article en
2 citations

Abstract

Retrogressive thaw slumps (RTS) are thermokarst landforms resulting from the thawing of ice-rich permafrost in the pan-Arctic and high mountain regions, recognized as climate-related phenomena. Using 2022 ESRI Wayback satellite imagery, we manually digitized 900 RTS polygons on the Kanin Peninsula (NW Russia), categorizing them into 633 inactive and 267 active features based on morphology and vegetation patterns. The primary goal was not to create a complete RTS inventory but to develop a highly representative dataset for training machine learning models to automatically detect and classify RTS into active and inactive categories. This approach will facilitate regional-scale monitoring of permafrost degradation and enhance understanding of RTS impacts, including their effects on indigenous Nenets reindeer herders' travel routes. By classifying RTS polygons into morphologically active and inactive features, this dataset aims to enable ongoing analysis of change patterns over time, offering critical insights in the current climate context.

Scientific DataVol. 12(1)
Geological Survey of Norway (NO), Norwegian Institute for Cultural Heritage Research (NO), University of Twente (NL)
Climate action
Openalex Percentile: Top 11%
Climate change and permafrost
2.60
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