Abstract

Permafrost is warming globally which leads to widespread permafrost thaw. Particularly ice-rich permafrost is vulnerable to rapid thaw and erosion, impacting whole landscapes and ecosystems. Retrogressive thaw slumps (RTS) are abrupt permafrost disturbances that expand by several meters each year and lead to an increased soil organic carbon release. Local Remote Sensing studies identified increasing RTS activity in the last two decades by increasing number of RTS or heightened RTS growth rates. However, a large-scale assessment across diverse permafrost regions and at high temporal resolution allowing to further determine RTS thaw dynamics and its main drivers is still lacking.In this study we apply the disturbance detection algorithm LandTrendr for automated large-scale RTS mapping and high temporal thaw dynamic assessment to North Siberia (8.1×106km2). We adapted and parametrised the temporal segmentation algorithm for abrupt disturbance detection to incorporate Landsat+Sentinel-2 mosaics, conducted spectral filtering, spatial masking and filtering, and a binary machine-learning object classification of the disturbance output to separate between RTS and false positives (F1 score: 0.609). Ground truth data for calibration and validation of the workflow was collected from 9 known RTS cluster sites using very high-resolution RapidEye and PlanetScope imagery.Our study presents the first automated detection and assessment of RTS and their temporal dynamics at large-scale for 2001–2019. We identified 50,895 RTS and a steady increase in RTS-affected area from 2001 to 2019 across North Siberia, with a more abrupt increase from 2016 onward. Overall the RTS-affected area increased by 331% compared to 2000 (2000: 20,158ha, 2001–2019: 66,699ha). Contrary to this, 5 focus sites show spatio-temporal variability in their annual RTS dynamics, with alternating periods of increased and decreased RTS development, indicating a close relationship to thaw drivers. The majority of identified RTS was active from 2000 onward and only a small proportion initiated during the assessment period, indicating that the increase in RTS-affected area was mainly caused by enlarging existing RTS and not by new RTS. The detected increase in RTS dynamics suggests advancing permafrost thaw and underlines the importance of assessing abrupt permafrost disturbances with high spatial and temporal resolution at large-scales. Obtaining such consistent disturbance products will help to parametrise regional and global climate change models.

Highlights

  • Permafrost is warming globally and experiences intensifying rates of degradation (Biskaborn et al, 2019; Vasiliev et al, 2020; Farquharson et al, 2019)

  • In this study we focus on the development of a remote sensing method to automatically identify and map retrogressive thaw slumps (RTS) across large-scale regions

  • Our study includes the adaptation of the LandTrendr algorithm to capture the rapid permafrost disturbance dynamics of Retrogressive thaw slumps (RTS) at high temporal resolution in a first large-scale assessment across North Sibe­ ria

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Summary

Introduction

Permafrost is warming globally and experiences intensifying rates of degradation (Biskaborn et al, 2019; Vasiliev et al, 2020; Farquharson et al, 2019). Change and disturbance detection in northern high latitudes are still challenging as time series studies with optical remote sensing are restricted due to frequent cloud cover, short summer periods, and low illumination an­ gles. This confines data availability drastically and limits algorithm applications that require high temporal input data. The combination of imagery from similar sensors, such as Landsat and Sentinel-2, in­ creases data availability in the northern high latitudes strongly and permits change and disturbance detection at high temporal resolution (Runge and Grosse, 2019, 2020)

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