@article{RungeGrosse2020,
  author    = {Runge, Alexandra and Grosse, Guido},
  title     = {Mosaicking Landsat and Sentinel-2 Data to Enhance LandTrendr Time Series Analysis in Northern High Latitude Permafrost Regions},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {15},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2072-4292},
  doi       = {10.3390/rs12152471},
  pages     = {23},
  year      = {2020},
  abstract  = {Permafrost is warming in the northern high latitudes, inducing highly dynamic thaw-related permafrost disturbances across the terrestrial Arctic. Monitoring and tracking of permafrost disturbances is important as they impact surrounding landscapes, ecosystems and infrastructure. Remote sensing provides the means to detect, map, and quantify these changes homogeneously across large regions and time scales. Existing Landsat-based algorithms assess different types of disturbances with similar spatiotemporal requirements. However, Landsat-based analyses are restricted in northern high latitudes due to the long repeat interval and frequent clouds, in particular at Arctic coastal sites. We therefore propose to combine Landsat and Sentinel-2 data for enhanced data coverage and present a combined annual mosaic workflow, expanding currently available algorithms, such as LandTrendr, to achieve more reliable time series analysis. We exemplary test the workflow for twelve sites across the northern high latitudes in Siberia. We assessed the number of images and cloud-free pixels, the spatial mosaic coverage and the mosaic quality with spectral comparisons. The number of available images increased steadily from 1999 to 2019 but especially from 2016 onward with the addition of Sentinel-2 images. Consequently, we have an increased number of cloud-free pixels even under challenging environmental conditions, which then serve as the input to the mosaicking process. In a comparison of annual mosaics, the Landsat+Sentinel-2 mosaics always fully covered the study areas (99.9-100 \%), while Landsat-only mosaics contained data-gaps in the same years, only reaching coverage percentages of 27.2 \%, 58.1 \%, and 69.7 \% for Sobo Sise, East Taymyr, and Kurungnakh in 2017, respectively. The spectral comparison of Landsat image, Sentinel-2 image, and Landsat+Sentinel-2 mosaic showed high correlation between the input images and mosaic bands (e.g., for Kurungnakh 0.91-0.97 between Landsat and Landsat+Sentinel-2 mosaic and 0.92-0.98 between Sentinel-2 and Landsat+Sentinel-2 mosaic) across all twelve study sites, testifying good quality mosaic results. Our results show that especially the results for northern, coastal areas was substantially improved with the Landsat+Sentinel-2 mosaics. By combining Landsat and Sentinel-2 data we accomplished to create reliably high spatial resolution input mosaics for time series analyses. Our approach allows to apply a high temporal continuous time series analysis to northern high latitude permafrost regions for the first time, overcoming substantial data gaps, and assess permafrost disturbance dynamics on an annual scale across large regions with algorithms such as LandTrendr by deriving the location, timing and progression of permafrost thaw disturbances},
  language  = {en}
}
@misc{RungeGrosse2020,
  author    = {Runge, Alexandra and Grosse, Guido},
  title     = {Mosaicking Landsat and Sentinel-2 Data to Enhance LandTrendr Time Series Analysis in Northern High Latitude Permafrost Regions},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1009},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-48031},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-480317},
  pages     = {25},
  year      = {2020},
  abstract  = {Permafrost is warming in the northern high latitudes, inducing highly dynamic thaw-related permafrost disturbances across the terrestrial Arctic. Monitoring and tracking of permafrost disturbances is important as they impact surrounding landscapes, ecosystems and infrastructure. Remote sensing provides the means to detect, map, and quantify these changes homogeneously across large regions and time scales. Existing Landsat-based algorithms assess different types of disturbances with similar spatiotemporal requirements. However, Landsat-based analyses are restricted in northern high latitudes due to the long repeat interval and frequent clouds, in particular at Arctic coastal sites. We therefore propose to combine Landsat and Sentinel-2 data for enhanced data coverage and present a combined annual mosaic workflow, expanding currently available algorithms, such as LandTrendr, to achieve more reliable time series analysis. We exemplary test the workflow for twelve sites across the northern high latitudes in Siberia. We assessed the number of images and cloud-free pixels, the spatial mosaic coverage and the mosaic quality with spectral comparisons. The number of available images increased steadily from 1999 to 2019 but especially from 2016 onward with the addition of Sentinel-2 images. Consequently, we have an increased number of cloud-free pixels even under challenging environmental conditions, which then serve as the input to the mosaicking process. In a comparison of annual mosaics, the Landsat+Sentinel-2 mosaics always fully covered the study areas (99.9-100 \%), while Landsat-only mosaics contained data-gaps in the same years, only reaching coverage percentages of 27.2 \%, 58.1 \%, and 69.7 \% for Sobo Sise, East Taymyr, and Kurungnakh in 2017, respectively. The spectral comparison of Landsat image, Sentinel-2 image, and Landsat+Sentinel-2 mosaic showed high correlation between the input images and mosaic bands (e.g., for Kurungnakh 0.91-0.97 between Landsat and Landsat+Sentinel-2 mosaic and 0.92-0.98 between Sentinel-2 and Landsat+Sentinel-2 mosaic) across all twelve study sites, testifying good quality mosaic results. Our results show that especially the results for northern, coastal areas was substantially improved with the Landsat+Sentinel-2 mosaics. By combining Landsat and Sentinel-2 data we accomplished to create reliably high spatial resolution input mosaics for time series analyses. Our approach allows to apply a high temporal continuous time series analysis to northern high latitude permafrost regions for the first time, overcoming substantial data gaps, and assess permafrost disturbance dynamics on an annual scale across large regions with algorithms such as LandTrendr by deriving the location, timing and progression of permafrost thaw disturbances},
  language  = {en}
}
@misc{TostEhmelHeidmannetal.2018,
  author    = {Tost, Jordi and Ehmel, Fabian and Heidmann, Frank and Olen, Stephanie M. and Bookhagen, Bodo},
  title     = {Hazards and accessibility},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {710},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-42785},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-427853},
  pages     = {8},
  year      = {2018},
  abstract  = {The assessment of natural hazards and risk has traditionally been built upon the estimation of threat maps, which are used to depict potential danger posed by a particular hazard throughout a given area. But when a hazard event strikes, infrastructure is a significant factor that can determine if the situation becomes a disaster. The vulnerability of the population in a region does not only depend on the area's local threat, but also on the geographical accessibility of the area. This makes threat maps by themselves insufficient for supporting real-time decision-making, especially for those tasks that involve the use of the road network, such as management of relief operations, aid distribution, or planning of evacuation routes, among others. To overcome this problem, this paper proposes a multidisciplinary approach divided in two parts. First, data fusion of satellite-based threat data and open infrastructure data from OpenStreetMap, introducing a threat-based routing service. Second, the visualization of this data through cartographic generalization and schematization. This emphasizes critical areas along roads in a simple way and allows users to visually evaluate the impact natural hazards may have on infrastructure. We develop and illustrate this methodology with a case study of landslide threat for an area in Colombia.},
  language  = {en}
}