Yaron A. Felus 
  
MULTI-SOURCE DEM EVALUATION AND INTEGRATION AT THE ANTARCTICA TRANSANTARCTIC 
MOUNTAINS PROJECT 
Yaron A. Felus and Beata Csatho 
Byrd Polar Research Center 
Scott Hall Room 108, 1090 Carmack Road 
The Ohio State University 
Columbus, Ohio 43210-1002, USA 
felus.2@osu.edu ; csatho@ohglas.mps.ohio-state.edu 
Working Group IV/2 
KEY WORDS: Data fusion, DTM/DEM/DSM, Geophysics, Mathematical models. 
ABSTRACT 
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. 
1 INTRODUCTION 
11 The Tamara Project 
The Tamara project is an international research aimed at integrating new aeromagnetic data, acquired by a cooperative 
U.S.-German field campaign, with satellite imagery, geological and structural mapping, and existing ground-based, 
airborne and marine geophysical data. With this comprehensive database we hope to answer outstanding questions 
about the evolution of the Transantarctic Mountains (TAM) - West Antarctic rift- in southern Victoria Land. The 
foundation of this database is a Digital elevation model (DEM) which is an essential tools in many glaciological studies 
and especially for magnetic and gravity modeling. It is important to use a data set which will have the best accuracy and 
with the highest resolution. However, due to the hostile climate and inaccessibility environment of the Antarctic 
continent, there are insufficient elevation databases and their quality is poor. Consequently, we need to apply methods 
to combine and integrate the different DEM's which were acquired from different sources with different spatial 
properties. 
l2 Review of data fusion methods 
Many methods have been proposed for integrating multiple data sources. For a comprehensive review on data fusion we 
refer the interested reader to Abidy and Gonzales (1992). Here we mention only a few methods that are important for 
understanding the procedures described in this paper. Rapp (1984) examines various techniques; that can be used to 
combine satellite gravity field information with terrestrial gravimetry. He is using spherical harmonic expansions 
(Fourier analysis) to interpolate the data and weighted least squares to solve the augmented observation equations and to 
compute the combined interpolation function coefficients. Hahn and Samadzadegan (1999) transform the data using 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part Bl. Amsterdam 2000. 117