@phdthesis{Runge2021,
  author    = {Runge, Alexandra},
  title     = {Multispectral time series analyses with Landsat and Sentinel-2 to assess permafrost disturbances in North Siberia},
  doi       = {10.25932/publishup-52206},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-522062},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xxiv, 134},
  year      = {2021},
  abstract  = {Permafrost is warming globally, which leads to widespread permafrost thaw and impacts the surrounding landscapes, ecosystems and infrastructure. Especially ice-rich permafrost is vulnerable to rapid and abrupt thaw, resulting from the melting of excess ground ice. Local remote sensing studies have detected increasing rates of abrupt permafrost disturbances, such as thermokarst lake change and drainage, coastal erosion and RTS in the last two decades. All of which indicate an acceleration of permafrost degradation. In particular retrogressive thaw slumps (RTS) are abrupt disturbances that expand by up to several meters each year and impact local and regional topographic gradients, hydrological pathways, sediment and nutrient mobilisation into aquatic systems, and increased permafrost carbon mobilisation. The feedback between abrupt permafrost thaw and the carbon cycle is a crucial component of the Earth system and a relevant driver in global climate models. However, an assessment of RTS at high temporal resolution to determine the dynamic thaw processes and identify the main thaw drivers as well as a continental-scale assessment across diverse permafrost regions are still lacking. In northern high latitudes optical remote sensing is restricted by environmental factors and frequent cloud coverage. This decreases image availability and thus constrains the application of automated algorithms for time series disturbance detection for large-scale abrupt permafrost disturbances at high temporal resolution. Since models and observations suggest that abrupt permafrost disturbances will intensify, we require disturbance products at continental-scale, which allow for meaningful integration into Earth system models. The main aim of this dissertation therefore, is to enhance our knowledge on the spatial extent and temporal dynamics of abrupt permafrost disturbances in a large-scale assessment. To address this, three research objectives were posed: 1. Assess the comparability and compatibility of Landsat-8 and Sentinel-2 data for a combined use in multi-spectral analysis in northern high latitudes. 2. Adapt an image mosaicking method for Landsat and Sentinel-2 data to create combined mosaics of high quality as input for high temporal disturbance assessments in northern high latitudes. 3. Automatically map retrogressive thaw slumps on the landscape-scale and assess their high temporal thaw dynamics. We assessed the comparability of Landsat-8 and Sentinel-2 imagery by spectral comparison of corresponding bands. Based on overlapping same-day acquisitions of Landsat-8 and Sentinel-2 we derived spectral bandpass adjustment coefficients for North Siberia to adjust Sentinel-2 reflectance values to resemble Landsat-8 and harmonise the two data sets. Furthermore, we adapted a workflow to combine Landsat and Sentinel-2 images to create homogeneous and gap-free annual mosaics. We determined the number of images and cloud-free pixels, the spatial coverage and the quality of the mosaic with spectral comparisons to demonstrate the relevance of the Landsat+Sentinel-2 mosaics. Lastly, we adapted the automatic disturbance detection algorithm LandTrendr for large-scale RTS identification and mapping at high temporal resolution. For this, we modified the temporal segmentation algorithm for annual gradual and abrupt disturbance detection to incorporate the annual Landsat+Sentinel-2 mosaics. We further parametrised the temporal segmentation and spectral filtering for optimised RTS detection, conducted further spatial masking and filtering, and implemented a binary object classification algorithm with machine-learning to derive RTS from the LandTrendr disturbance output. We applied the algorithm to North Siberia, covering an area of 8.1 x 106 km2. The spectral band comparison between same-day Landsat-8 and Sentinel-2 acquisitions already showed an overall good fit between both satellite products. However, applying the acquired spectral bandpass coefficients for adjustment of Sentinel-2 reflectance values, resulted in a near-perfect alignment between the same-day images. It can therefore be concluded that the spectral band adjustment succeeds in adjusting Sentinel-2 spectral values to those of Landsat-8 in North Siberia. The number of available cloud-free images increased steadily between 1999 and 2019, especially intensified after 2016 with the addition of Sentinel-2 images. This signifies a highly improved input database for the mosaicking workflow. In a comparison of annual mosaics, the Landsat+Sentinel-2 mosaics always fully covered the study areas, while Landsat-only mosaics contained data-gaps for the same years. The spectral comparison of input images and Landsat+Sentinel-2 mosaic showed a high correlation between the input images and the mosaic bands, testifying mosaicking results of high quality. Our results show that especially the mosaic coverage for northern, coastal areas was substantially improved with the Landsat+Sentinel-2 mosaics. By combining data from both Landsat and Sentinel-2 sensors we reliably created input mosaics at high spatial resolution for comprehensive time series analyses. This research presents the first automatically derived assessment of RTS distribution and temporal dynamics at continental-scale. In total, we identified 50,895 RTS, primarily located in ice-rich permafrost regions, as well as a steady increase in RTS-affected areas between 2001 and 2019 across North Siberia. From 2016 onward the RTS area increased more abruptly, indicating heightened thaw slump dynamics in this period. Overall, the RTS-affected area increased by 331 \% within the observation period. Contrary to this, five focus sites show spatiotemporal variability in their annual RTS dynamics, alternating between periods of increased and decreased RTS development. This suggests a close relationship to varying thaw drivers. The majority of identified RTS was active from 2000 onward and only a small proportion initiated during the assessment period. This highlights that the increase in RTS-affected area was mainly caused by enlarging existing RTS and not by newly initiated RTS. Overall, this research showed the advantages of combining Landsat and Sentinel-2 data in northern high latitudes and the improvements in spatial and temporal coverage of combined annual mosaics. The mosaics build the database for automated disturbance detection to reliably map RTS and other abrupt permafrost disturbances at continental-scale. The assessment at high temporal resolution further testifies the increasing impact of abrupt permafrost disturbances and likewise emphasises the spatio-temporal variability of thaw dynamics across landscapes. Obtaining such consistent disturbance products is necessary to parametrise regional and global climate change models, for enabling an improved representation of the permafrost thaw feedback.},
  language  = {en}
}