Orthophotokarte Vernagtferner 1979 in jpg-Format (54 MB)

For the production of the orthophoto map Vernagtferner 1979, scale I: 10000, photographs of the flight "Hintereisferner 1979" were used, which have been found to be very suitable for differential rectification. Control points were determined before the flight took place. The processing of nine stereopairs was carried out on an analytical plotter. Simultaneously with the on-line plotting of the contour lines the reference data for the computation of the profiles for the differential rectification were recorded. The orthophoto map was covered by four aerial photographs. A smooth data transfer was ensured because the same computer was used for the data acquisition and the differential rectification. Two printing originals were prepared, one for the outline drawings with contour lines and another for the orthophoto. The print was done in black for the two copies. The data acquisition, the computation of the scanning profiles for the othoprojector and the procedure of the differential rectification are described. The reason for the use of on-line drawn contour lines is explained. Further applications, also for digital contour lines, are introduced. Possibilities for the achievement of high photo quality during the reproduction are discussed.

Antarctic dataset in NetCDF format

The dataset described in this document has been put together for the purposes of numerical ice sheet modelling of the Antarctic Ice Sheet (AIS), containing data on the ice sheet configuration (e.g. ice surface and ice thickness) and boundary conditions, such as the surface air temperature and accumulation. It is now possible to download a community ice sheet model (e.g. Glimmer-CISM, Rutt et al., 2009 doi:10.1029/2008JF001015), but without adequate data it is difficult to utilise such models. More specifically, ice sheet models that are initialised and run forward from the present day ice sheet configuration, need input data to represent the present-day ice sheet configuration as closely as possible (unlike those spun-up from ice free conditions, which only require the bed/bathymetry). Whilst the BEDMAP dataset (Lythe et al., 2001) was a step forward when it was made, there are a number of inconsistencies within the dataset (see Section 3), and since its release, more data has become available. The dataset described here incorporates some major new datasets (e.g. AGASEA/BBAS ice thickness, Nitsche et al. (2006) bathymetry doi:10.1029/2007GC001694), but by no means incorporates all the new data available. This considerable task is left for a 'BEDMAP2', (an updated version of BEDMAP), however, the processing carried out in this document illustrates the requirements of a dataset for the purpose of high resolution ice sheet modelling, and bridges the gap until a BEDMAP2 is published. It is envisaged, however, that updated versions of the data set will be made available periodically when new regional data sets become available and can be readily incorporated.

Environmental variables and multivariate seabed classification maps via k means clustering of Potter Cove, Antarctica, link to input and result files in GeoTIFF format

This study subdivides the Potter Cove, King George Island, Antarctica, into seafloor regions using multivariate statistical methods. These regions are categories used for comparing, contrasting and quantifying biogeochemical processes and biodiversity between ocean regions geographically but also regions under development within the scope of global change. The division obtained is characterized by the dominating components and interpreted in terms of ruling environmental conditions. The analysis includes in total 42 different environmental variables, interpolated based on samples taken during Australian summer seasons 2010/2011 and 2011/2012. The statistical errors of several interpolation methods (e.g. IDW, Indicator, Ordinary and Co-Kriging) with changing settings have been compared and the most reasonable method has been applied. The multivariate mathematical procedures used are regionalized classification via k means cluster analysis, canonical-correlation analysis and multidimensional scaling. Canonical-correlation analysis identifies the influencing factors in the different parts of the cove. Several methods for the identification of the optimum number of clusters have been tested and 4, 7, 10 as well as 12 were identified as reasonable numbers for clustering the Potter Cove. Especially the results of 10 and 12 clusters identify marine-influenced regions which can be clearly separated from those determined by the geological catchment area and the ones dominated by river discharge.