@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{UllmannBanksSchmittetal.2017, author = {Ullmann, Tobias and Banks, Sarah N. and Schmitt, Andreas and Jagdhuber, Thomas}, title = {Scattering characteristics of X-, C- and L-Band PolSAR data examined for the tundra environment of the Tuktoyaktuk Peninsula, Canada}, series = {Applied Sciences}, volume = {7}, journal = {Applied Sciences}, number = {6}, doi = {10.3390/app7060595}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-158362}, pages = {595}, year = {2017}, abstract = {In this study, polarimetric Synthetic Aperture Radar (PolSAR) data at X-, C- and L-Bands, acquired by the satellites: TerraSAR-X (2011), Radarsat-2 (2011), ALOS (2010) and ALOS-2 (2016), were used to characterize the tundra land cover of a test site located close to the town of Tuktoyaktuk, NWT, Canada. Using available in situ ground data collected in 2010 and 2012, we investigate PolSAR scattering characteristics of common tundra land cover classes at X-, C- and L-Bands. Several decomposition features of quad-, co-, and cross-polarized data were compared, the correlation between them was investigated, and the class separability offered by their different feature spaces was analyzed. Certain PolSAR features at each wavelength were sensitive to the land cover and exhibited distinct scattering characteristics. Use of shorter wavelength imagery (X and C) was beneficial for the characterization of wetland and tundra vegetation, while L-Band data highlighted differences of the bare ground classes better. The Kennaugh Matrix decomposition applied in this study provided a unified framework to store, process, and analyze all data consistently, and the matrix offered a favorable feature space for class separation. Of all elements of the quad-polarized Kennaugh Matrix, the intensity based elements K0, K1, K2, K3 and K4 were found to be most valuable for class discrimination. These elements contributed to better class separation as indicated by an increase of the separability metrics squared Jefferys Matusita Distance and Transformed Divergence. The increase in separability was up to 57\% for Radarsat-2 and up to 18\% for ALOS-2 data.}, language = {en} }