@article{RungeNitzeGrosse2021, author = {Runge, Alexandra and Nitze, Ingmar and Grosse, Guido}, title = {Remote sensing annual dynamics of rapid permafrost thaw disturbances with LandTrendr}, series = {Remote sensing of environment : an interdisciplinary journal}, volume = {268}, journal = {Remote sensing of environment : an interdisciplinary journal}, publisher = {Elsevier}, address = {New York}, issn = {0034-4257}, doi = {10.1016/j.rse.2021.112752}, pages = {18}, year = {2021}, 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 x 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 highresolution RapidEye and PlanetScope imagery. Our study presents the first automated detection and assessment of RTS and their temporal dynamics at largescale 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: 20,158 ha, 2001-2019: 66,699 ha). Contrary to this, 5 focus sites show spatiotemporal 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.}, language = {en} } @article{RamageIrrgangHerzschuhetal.2017, author = {Ramage, Justine Lucille and Irrgang, Anna Maria and Herzschuh, Ulrike and Morgenstern, Anne and Couture, Nicole and Lantuit, Hugues}, title = {Terrain controls on the occurrence of coastal retrogressive thaw slumps along the Yukon Coast, Canada}, series = {Journal of geophysical research : Earth surface}, volume = {122}, journal = {Journal of geophysical research : Earth surface}, publisher = {American Geophysical Union}, address = {Washington}, issn = {2169-9003}, doi = {10.1002/2017JF004231}, pages = {1619 -- 1634}, year = {2017}, abstract = {Retrogressive thaw slumps (RTSs) are among the most active landforms in the Arctic; their number has increased significantly over the past decades. While processes initiating discrete RTSs are well identified, the major terrain controls on the development of coastal RTSs at a regional scale are not yet defined. Our research reveals the main geomorphic factors that determine the development of RTSs along a 238km segment of the Yukon Coast, Canada. We (1) show the current extent of RTSs, (2) ascertain the factors controlling their activity and initiation, and (3) explain the spatial differences in the density and areal coverage of RTSs. We mapped and classified 287 RTSs using high-resolution satellite images acquired in 2011. We highlighted the main terrain controls over their development using univariate regression trees model. Coastal geomorphology influenced both the activity and initiation of RTSs: active RTSs and RTSs initiated after 1972 occurred primarily on terrains with slope angles greater than 3.9 degrees and 5.9 degrees, respectively. The density and areal coverage of RTSs were constrained by the volume and thickness of massive ice bodies. Differences in rates of coastal change along the coast did not affect the model. We infer that rates of coastal change averaged over a 39year period are unable to reflect the complex relationship between RTSs and coastline dynamics. We emphasize the need for large-scale studies of RTSs to evaluate their impact on the ecosystem and to measure their contribution to the global carbon budget. Plain Language Summary Retrogressive thaw slumps, henceforth slumps are a type of landslides that occur when permafrost thaws. Slumps are active landforms: they develop quickly and extend over several hectares. Satellite imagery allows to map such slumps over large areas. Our research shows where slumps develop along a 238 km segment of the Yukon Coast in Canada and explains which environments are most suitable for slump occurrence. We found that active and newly developed slumps were triggered where coastal slopes were greater than 3.9 degrees and 5.9 degrees, respectively. We explain that coastal erosion influences the development of slumps by modifying coastal slopes. We found that the highest density of slumps as well as the largest slumps occurred on terrains with high amounts of ice bodies in the ground. This study provides tools to better identify areas in the Arctic that are prone to slump development.}, language = {en} }