@article{DobińskiKneisel2021,
  author    = {Dobiński, Wojciech and Kneisel, Christof},
  title     = {Permafrost and glaciers: perspectives for the Earth and planetary sciences — another step forward},
  series = {Geosciences},
  volume    = {11},
  journal   = {Geosciences},
  number    = {2},
  issn      = {2076-3263},
  doi       = {10.3390/geosciences11020068},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-228766},
  year      = {2021},
  abstract  = {No abstract available},
  language  = {en}
}
@article{PhilippDietzUllmannetal.2023,
  author    = {Philipp, Marius and Dietz, Andreas and Ullmann, Tobias and Kuenzer, Claudia},
  title     = {A circum-Arctic monitoring framework for quantifying annual erosion rates of permafrost coasts},
  series = {Remote Sensing},
  volume    = {15},
  journal   = {Remote Sensing},
  number    = {3},
  issn      = {2072-4292},
  doi       = {10.3390/rs15030818},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-304447},
  year      = {2023},
  abstract  = {This study demonstrates a circum-Arctic monitoring framework for quantifying annual change of permafrost-affected coasts at a spatial resolution of 10 m. Frequent cloud coverage and challenging lighting conditions, including polar night, limit the usability of optical data in Arctic regions. For this reason, Synthetic Aperture RADAR (SAR) data in the form of annual median and standard deviation (sd) Sentinel-1 (S1) backscatter images covering the months June-September for the years 2017-2021 were computed. Annual composites for the year 2020 were hereby utilized as input for the generation of a high-quality coastline product via a Deep Learning (DL) workflow, covering 161,600 km of the Arctic coastline. The previously computed annual S1 composites for the years 2017 and 2021 were employed as input data for the Change Vector Analysis (CVA)-based coastal change investigation. The generated DL coastline product served hereby as a reference. Maximum erosion rates of up to 67 m per year could be observed based on 400 m coastline segments. Overall highest average annual erosion can be reported for the United States (Alaska) with 0.75 m per year, followed by Russia with 0.62 m per year. Out of all seas covered in this study, the Beaufort Sea featured the overall strongest average annual coastal erosion of 1.12 m. Several quality layers are provided for both the DL coastline product and the CVA-based coastal change analysis to assess the applicability and accuracy of the output products. The predicted coastal change rates show good agreement with findings published in previous literature. The proposed methods and data may act as a valuable tool for future analysis of permafrost loss and carbon emissions in Arctic coastal environments.},
  language  = {en}
}
@article{KunzUllmannKneiseletal.2023,
  author    = {Kunz, Julius and Ullmann, T. and Kneisel, C. and Baumhauer, R.},
  title     = {Three-dimensional subsurface architecture and its influence on the spatiotemporal development of a retrogressive thaw slump in the Richardson Mountains, Northwest Territories, Canada},
  series = {Arctic, Antarctic, and Alpine Research},
  volume    = {55},
  journal   = {Arctic, Antarctic, and Alpine Research},
  number    = {1},
  issn      = {1523-0430},
  doi       = {10.1080/15230430.2023.2167358},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-350147},
  year      = {2023},
  abstract  = {The development of retrogressive thaw slumps (RTS) is known to be strongly influenced by relief-related parameters, permafrost characteristics, and climatic triggers. To deepen the understanding of RTS, this study examines the subsurface characteristics in the vicinity of an active thaw slump, located in the Richardson Mountains (Western Canadian Arctic). The investigations aim to identify relationships between the spatiotemporal slump development and the influence of subsurface structures. Information on these were gained by means of electrical resistivity tomography (ERT) and ground-penetrating radar (GPR). The spatiotemporal development of the slump was revealed by high-resolution satellite imagery and unmanned aerial vehicle-based digital elevation models (DEMs). The analysis indicated an acceleration of slump expansion, especially since 2018. The comparison of the DEMs enabled the detailed balancing of erosion and accumulation within the slump area between August 2018 and August 2019. In addition, manual frost probing and GPR revealed a strong relationship between the active layer thickness, surface morphology, and hydrology. Detected furrows in permafrost table topography seem to affect the active layer hydrology and cause a canalization of runoff toward the slump. The three-dimensional ERT data revealed a partly unfrozen layer underlying a heterogeneous permafrost body. This may influence the local hydrology and affect the development of the RTS. The results highlight the complex relationships between slump development, subsurface structure, and hydrology and indicate a distinct research need for other RTSs.},
  language  = {en}
}