@article{KoehlerBauerDietzetal.2022,
  author    = {Koehler, Jonas and Bauer, Andr{\´e} and Dietz, Andreas J. and Kuenzer, Claudia},
  title     = {Towards forecasting future snow cover dynamics in the European Alps — the potential of long optical remote-sensing time series},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {18},
  issn      = {2072-4292},
  doi       = {10.3390/rs14184461},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-288338},
  year      = {2022},
  abstract  = {Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we, therefore, explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985-2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5-8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash-Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolute Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a combined forecast based on the best-performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022-2029. In the majority of the catchments, the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast, a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach.},
  language  = {en}
}
@article{RoesslerWittIkonenetal.2021,
  author    = {R{\"o}ßler, Sebastian and Witt, Marius S. and Ikonen, Jaakko and Brown, Ian A. and Dietz, Andreas J.},
  title     = {Remote sensing of snow cover variability and its influence on the runoff of S{\´a}pmi's rivers},
  series = {Geosciences},
  volume    = {11},
  journal   = {Geosciences},
  number    = {3},
  issn      = {2076-3263},
  doi       = {10.3390/geosciences11030130},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-234261},
  year      = {2021},
  abstract  = {The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR's Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM).},
  language  = {en}
}
@article{DietzConradKuenzeretal.2014,
  author    = {Dietz, Andreas J. and Conrad, Christopher and Kuenzer, Claudia and Gesell, Gerhard and Dech, Stefan},
  title     = {Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data},
  series = {Remote Sensing},
  volume    = {6},
  journal   = {Remote Sensing},
  number    = {12},
  issn      = {2072-4292},
  doi       = {10.3390/rs61212752},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-114470},
  pages     = {12752-12775},
  year      = {2014},
  abstract  = {Central Asia consists of the five former Soviet States Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, therefore comprising an area of similar to 4 Mio km(2). The continental climate is characterized by hot and dry summer months and cold winter seasons with most precipitation occurring as snowfall. Accordingly, freshwater supply is strongly depending on the amount of accumulated snow as well as the moment of its release after snowmelt. The aim of the presented study is to identify possible changes in snow cover characteristics, consisting of snow cover duration, onset and offset of snow cover season within the last 28 years. Relying on remotely sensed data originating from medium resolution imagers, these snow cover characteristics are extracted on a daily basis. The resolution of 500-1000 m allows for a subsequent analysis of changes on the scale of hydrological sub-catchments. Long-term changes are identified from this unique dataset, revealing an ongoing shift towards earlier snowmelt within the Central Asian Mountains. This shift can be observed in most upstream hydro catchments within Pamir and Tian Shan Mountains and it leads to a potential change of freshwater availability in the downstream regions, exerting additional pressure on the already tensed situation.},
  language  = {en}
}
@article{DirscherlDietzKneiseletal.2020,
  author    = {Dirscherl, Mariel and Dietz, Andreas J. and Kneisel, Christof and Kuenzer, Claudia},
  title     = {Automated mapping of Antarctic supraglacial lakes using a Machine Learning approach},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {7},
  issn      = {2072-4292},
  doi       = {10.3390/rs12071203},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-203735},
  year      = {2020},
  abstract  = {Supraglacial lakes can have considerable impact on ice sheet mass balance and global sea-level-rise through ice shelf fracturing and subsequent glacier speedup. In Antarctica, the distribution and temporal development of supraglacial lakes as well as their potential contribution to increased ice mass loss remains largely unknown, requiring a detailed mapping of the Antarctic surface hydrological network. In this study, we employ a Machine Learning algorithm trained on Sentinel-2 and auxiliary TanDEM-X topographic data for automated mapping of Antarctic supraglacial lakes. To ensure the spatio-temporal transferability of our method, a Random Forest was trained on 14 training regions and applied over eight spatially independent test regions distributed across the whole Antarctic continent. In addition, we employed our workflow for large-scale application over Amery Ice Shelf where we calculated interannual supraglacial lake dynamics between 2017 and 2020 at full ice shelf coverage. To validate our supraglacial lake detection algorithm, we randomly created point samples over our classification results and compared them to Sentinel-2 imagery. The point comparisons were evaluated using a confusion matrix for calculation of selected accuracy metrics. Our analysis revealed wide-spread supraglacial lake occurrence in all three Antarctic regions. For the first time, we identified supraglacial meltwater features on Abbott, Hull and Cosgrove Ice Shelves in West Antarctica as well as for the entire Amery Ice Shelf for years 2017-2020. Over Amery Ice Shelf, maximum lake extent varied strongly between the years with the 2019 melt season characterized by the largest areal coverage of supraglacial lakes (~763 km\(^2\)). The accuracy assessment over the test regions revealed an average Kappa coefficient of 0.86 where the largest value of Kappa reached 0.98 over George VI Ice Shelf. Future developments will involve the generation of circum-Antarctic supraglacial lake mapping products as well as their use for further methodological developments using Sentinel-1 SAR data in order to characterize intraannual supraglacial meltwater dynamics also during polar night and independent of meteorological conditions. In summary, the implementation of the Random Forest classifier enabled the development of the first automated mapping method applied to Sentinel-2 data distributed across all three Antarctic regions.},
  language  = {en}
}
@article{BaumhoerDietzKneiseletal.2019,
  author    = {Baumhoer, Celia A. and Dietz, Andreas J. and Kneisel, C. and Kuenzer, C.},
  title     = {Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning},
  series = {Remote Sensing},
  volume    = {11},
  journal   = {Remote Sensing},
  number    = {21},
  issn      = {2072-4292},
  doi       = {10.3390/rs11212529},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193150},
  pages     = {2529},
  year      = {2019},
  abstract  = {Sea level rise contribution from the Antarctic ice sheet is influenced by changes in glacier and ice shelf front position. Still, little is known about seasonal glacier and ice shelf front fluctuations as the manual delineation of calving fronts from remote sensing imagery is very time-consuming. The major challenge of automatic calving front extraction is the low contrast between floating glacier and ice shelf fronts and the surrounding sea ice. Additionally, in previous decades, remote sensing imagery over the often cloud-covered Antarctic coastline was limited. Nowadays, an abundance of Sentinel-1 imagery over the Antarctic coastline exists and could be used for tracking glacier and ice shelf front movement. To exploit the available Sentinel-1 data, we developed a processing chain allowing automatic extraction of the Antarctic coastline from Seninel-1 imagery and the creation of dense time series to assess calving front change. The core of the proposed workflow is a modified version of the deep learning architecture U-Net. This convolutional neural network (CNN) performs a semantic segmentation on dual-pol Sentinel-1 data and the Antarctic TanDEM-X digital elevation model (DEM). The proposed method is tested for four training and test areas along the Antarctic coastline. The automatically extracted fronts deviate on average 78 m in training and 108 m test areas. Spatial and temporal transferability is demonstrated on an automatically extracted 15-month time series along the Getz Ice Shelf. Between May 2017 and July 2018, the fronts along the Getz Ice Shelf show mostly an advancing tendency with the fastest moving front of DeVicq Glacier with 726 ± 20 m/yr.},
  language  = {en}
}
@article{DirscherlDietzKneiseletal.2021,
  author    = {Dirscherl, Mariel and Dietz, Andreas J. and Kneisel, Christof and Kuenzer, Claudia},
  title     = {A novel method for automated supraglacial lake mapping in Antarctica using Sentinel-1 SAR imagery and deep learning},
  series = {Remote Sensing},
  volume    = {13},
  journal   = {Remote Sensing},
  number    = {2},
  issn      = {2072-4292},
  doi       = {10.3390/rs13020197},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-222998},
  year      = {2021},
  abstract  = {Supraglacial meltwater accumulation on ice sheets can be a main driver for accelerated ice discharge, mass loss, and global sea-level-rise. With further increasing surface air temperatures, meltwater-induced hydrofracturing, basal sliding, or surface thinning will cumulate and most likely trigger unprecedented ice mass loss on the Greenland and Antarctic ice sheets. While the Greenland surface hydrological network as well as its impacts on ice dynamics and mass balance has been studied in much detail, Antarctic supraglacial lakes remain understudied with a circum-Antarctic record of their spatio-temporal development entirely lacking. This study provides the first automated supraglacial lake extent mapping method using Sentinel-1 synthetic aperture radar (SAR) imagery over Antarctica and complements the developed optical Sentinel-2 supraglacial lake detection algorithm presented in our companion paper. In detail, we propose the use of a modified U-Net for semantic segmentation of supraglacial lakes in single-polarized Sentinel-1 imagery. The convolutional neural network (CNN) is implemented with residual connections for optimized performance as well as an Atrous Spatial Pyramid Pooling (ASPP) module for multiscale feature extraction. The algorithm is trained on 21,200 Sentinel-1 image patches and evaluated in ten spatially or temporally independent test acquisitions. In addition, George VI Ice Shelf is analyzed for intra-annual lake dynamics throughout austral summer 2019/2020 and a decision-level fused Sentinel-1 and Sentinel-2 maximum lake extent mapping product is presented for January 2020 revealing a more complete supraglacial lake coverage (~770 km\(^2\)) than the individual single-sensor products. Classification results confirm the reliability of the proposed workflow with an average Kappa coefficient of 0.925 and a F\(_1\)-score of 93.0\% for the supraglacial water class across all test regions. Furthermore, the algorithm is applied in an additional test region covering supraglacial lakes on the Greenland ice sheet which further highlights the potential for spatio-temporal transferability. Future work involves the integration of more training data as well as intra-annual analyses of supraglacial lake occurrence across the whole continent and with focus on supraglacial lake development throughout a summer melt season and into Antarctic winter.},
  language  = {en}
}