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Researchers created a dataset of retrogressive thaw slumps (RTS) to train machine learning models for monitoring permafrost thaw and its impacts on Arctic landscapes and indigenous communities.

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Area of Science:

  • Geosciences
  • Environmental Science
  • Remote Sensing

Background:

  • Retrogressive thaw slumps (RTS) are significant thermokarst landforms indicative of permafrost degradation.
  • These features are prevalent in Arctic and high mountain regions, directly linked to climate change.
  • Understanding RTS dynamics is crucial for assessing landscape changes and impacts on ecosystems and human activities.

Purpose of the Study:

  • To develop a representative dataset of retrogressive thaw slumps (RTS) for machine learning model training.
  • To enable automated detection and classification of active versus inactive RTS features.
  • To facilitate regional-scale monitoring of permafrost thaw and its consequences.

Main Methods:

  • Manual digitization of 900 RTS polygons using 2022 ESRI Wayback satellite imagery on the Kanin Peninsula, NW Russia.
  • Classification of digitized RTS into active and inactive categories based on morphological and vegetation characteristics.
  • Creation of a curated dataset for training machine learning algorithms.

Main Results:

  • A dataset of 900 manually digitized RTS polygons was generated, with 633 classified as inactive and 267 as active.
  • The dataset provides a foundation for developing machine learning models for RTS detection and classification.
  • This work enables future regional-scale analysis of permafrost degradation patterns.

Conclusions:

  • The developed dataset is a valuable resource for advancing automated RTS monitoring and permafrost research.
  • Machine learning approaches can significantly enhance the efficiency of mapping and classifying thermokarst features.
  • This research contributes to understanding climate change impacts on sensitive Arctic environments and indigenous livelihoods.