@article{BanksMillardBehnamianetal.2017,
  author    = {Banks, Sarah and Millard, Koreen and Behnamian, Amir and White, Lori and Ullmann, Tobias and Charbonneau, Francois and Chen, Zhaohua and Wang, Huili and Pasher, Jon and Duffe, Jason},
  title     = {Contributions of actual and simulated satellite SAR data for substrate type differentiation and shoreline mapping in the Canadian Arctic},
  series = {Remote Sensing},
  volume    = {9},
  journal   = {Remote Sensing},
  number    = {12},
  doi       = {10.3390/rs9121206},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-172630},
  year      = {2017},
  abstract  = {Detailed information on the land cover types present and the horizontal position of the land-water interface is needed for sensitive coastal ecosystems throughout the Arctic, both to establish baselines against which the impacts of climate change can be assessed and to inform response operations in the event of environmental emergencies such as oil spills. Previous work has demonstrated potential for accurate classification via fusion of optical and SAR data, though what contribution either makes to model accuracy is not well established, nor is it clear what shorelines can be classified using optical or SAR data alone. In this research, we evaluate the relative value of quad pol RADARSAT-2 and Landsat 5 data for shoreline mapping by individually excluding both datasets from Random Forest models used to classify images acquired over Nunavut, Canada. In anticipation of the RADARSAT Constellation Mission (RCM), we also simulate and evaluate dual and compact polarimetric imagery for shoreline mapping. Results show that SAR data is needed for accurate discrimination of substrates as user's and producer's accuracies were 5-24\% higher for models constructed with quad pol RADARSAT-2 and DEM data than models constructed with Landsat 5 and DEM data. Models based on simulated RCM and DEM data achieved significantly lower overall accuracies (71-77\%) than models based on quad pol RADARSAT-2 and DEM data (80\%), with Wetland and Tundra being most adversely affected. When classified together with Landsat 5 and DEM data, however, model accuracy was less affected by the SAR data type, with multiple polarizations and modes achieving independent overall accuracies within a range acceptable for operational mapping, at 89-91\%. RCM is expected to contribute positively to ongoing efforts to monitor change and improve emergency preparedness throughout the Arctic.},
  language  = {en}
}
@article{UllmannSchmittRothetal.2014,
  author    = {Ullmann, Tobias and Schmitt, Andreas and Roth, Achim and Duffe, Jason and Dech, Stefan and Hubberten, Hans-Wolfgang and Baumhauer, Roland},
  title     = {Land Cover Characterization and Classification of Arctic Tundra Environments by Means of Polarized Synthetic Aperture X- and C-Band Radar (PolSAR) and Landsat 8 Multispectral Imagery — Richards Island, Canada},
  doi       = {10.3390/rs6098565},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-113303},
  year      = {2014},
  abstract  = {In this work the potential of polarimetric Synthetic Aperture Radar (PolSAR) data of dual-polarized TerraSAR-X (HH/VV) and quad-polarized Radarsat-2 was examined in combination with multispectral Landsat 8 data for unsupervised and supervised classification of tundra land cover types of Richards Island, Canada. The classification accuracies as well as the backscatter and reflectance characteristics were analyzed using reference data collected during three field work campaigns and include in situ data and high resolution airborne photography. The optical data offered an acceptable initial accuracy for the land cover classification. The overall accuracy was increased by the combination of PolSAR and optical data and was up to 71\% for unsupervised (Landsat 8 and TerraSAR-X) and up to 87\% for supervised classification (Landsat 8 and Radarsat-2) for five tundra land cover types. The decomposition features of the dual and quad-polarized data showed a high sensitivity for the non-vegetated substrate (dominant surface scattering) and wetland vegetation (dominant double bounce and volume scattering). These classes had high potential to be automatically detected with unsupervised classification techniques.},
  language  = {en}
}