Multi-source DEM evaluation and integration at the Antarctica transantarctic mountains project

Yaron A. Felus, Beata Csatho

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


Digital elevation models are essential tools in many glaciological studies and especially for mass balance studies, structural geology modeling and advance remote sensing and geophysical processing. However, due to the hostile climate and inaccessible environment of the Antarctic continent, there are insufficient elevation databases and their quality is poor. In this paper, we analysis the spatial distribution of error in the different DEMs that exists at the Antarctica Transantarctic Mountains. Based on this analysis, we investigate the various methods to combine elevation models with different properties (resolution, horizontal and vertical accuracy). There are five major data sets in the project area: The USGS 1:50000 maps which, covers the north west part of the project area and have 50 meter contour line interval; USGS 1:250000, taken from the Antarctic Digital Database, which, covers all our project area and have 200 meter contour line interval; satellite radar altimetry data derived from ERS-1 with 5 km resolution; airborne Radio-Echo Sounding profile data at the north east part of the project collected by Scott Polar Research Institute and field surveying control points collected by USGS. Our final goal was to compile all those elevation models into one uniform grid elevation model with the highest accuracy and resolution that can be obtained. Many techniques and algorithm’s exists for integrating database, some are based on interpolation methods in the boundary zone, other techniques perform simple data merging and apply various filtering functions to make the transition smoother. We review those procedures and compare their properties and apply some of them in our study. Last, we propose a method to combine the different DEM into one set using universal Kriging concept. In this process, we compute a covariance matrix for every data set individually and a cross covariance of the individual data set in the predication computation.

Original languageEnglish
Pages (from-to)117-123
Number of pages7
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
StatePublished - 2000
Externally publishedYes
Event19th International Congress for Photogrammetry and Remote Sensing, ISPRS 2000 - Amsterdam, Netherlands
Duration: 16 Jul 200023 Jul 2000


  • Data fusion
  • Geophysics
  • Mathematical models


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