Evaluation of SRTM3 and GTOPO30 Terrain Data in Germany

  • H. Denker
Part of the International Association of Geodesy Symposia book series (IAG SYMPOSIA, volume 129)


High-resolution terrain data are crucial for gravity field modelling in mountainous regions. In areas without national digital elevation models (DEMs) available, fill-ins from global models have to be used. For this purpose, the global models GTOPO30 (30″ resolution) and SRTM3 (3″ resolution) are considered. The SRTM3 model has been released recently from the analysis of the Shuttle Radar Topography Mission and covers the latitudes between 60°N and 54°S, while the GTOPO30 model is a global public domain data set completed already in 1996.

In this contribution, 1″ × 1″ national DEMs for Germany are used to evaluate the global models. The differences between the best national models and the SRTM3 data show a standard deviation of 7.9 m with maximum differences up to about 300 m. The largest differences are located in opencast mining areas and result from the different epochs of the DEMs. Histograms of the differences reveal a clear deviation from the normal distribution with a long tail towards too high SRTM3 elevations. The evaluation of GTOPO30 shows that the longitudes should be increased by 30″ (one block) in Germany. For the shifted GTOPO30 DEM, the standard deviation of the differences with respect to the best national model is 6.8 m, roughly 75 % smaller than for the original model.


Digital elevation model DEM terrain data DEM evaluation SRTM3 GTOPO30 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. AMilGeo (1992). Elevation Model DHM/M745. Amt für Militärisches Geowesen, Euskirchen, pers. comm..Google Scholar
  2. Bamler, R. (1999). The SRTM Mission: A World-Wide 30 m Resolution DEM from SAR Interferometry in 11 Days. In: D. Fritsch and R. Spiller (eds.): Photogrammetric Week 99, Wichmann Verlag Heidelberg: 145–154.Google Scholar
  3. Denker, H. (1988). Hochauflösende regionale Schwerefeldmodellierung mit gravimetrischen und topographischen Daten. Wiss. Arb. Fachr. Verm.wesen, Univ. Hannover, Nr. 156.Google Scholar
  4. Denker, H., W. Torge (1998). The European gravimetric quasigeoid EGG97 — An IAG supported continental enterprise. IAG Symposia, Vol. 119:249–254, Springer Verlag.Google Scholar
  5. Forsberg, R., M.G. Sideris (1989). On topographic effects in gravity field approximation. In: E Keylso, K. Poder, C.C. Tscherning (eds.): Festschrift to Torben Krarup, Geodaetisk Institut, Meddelelse No. 58: 129–148, Copenhagen.Google Scholar
  6. Forsberg, R., C.C. Tscherning (1981). The use of height data in gravity field approximation by collocation. Journal of Geophys. Research 86: 7843–7854.Google Scholar
  7. JPL (2004). SRTM — The Mission to Map the World. Jet Propulsion Laboratory, California Inst. of Techn., Scholar
  8. LP DAAC (2004). Global 30 Arc-Second Elevation Data Set GTOPO30. Land Process Distributed Active Archive Center, Scholar
  9. NGA (1996). Performance specification Digital Terrain Elevation Data (DTED). National Geospatial-Intelligence Agency, Document MIL-PRF-89020A.Google Scholar
  10. Sideris, M.G., R. Forsberg (1991). Review of geoid prediction methods in mountainous regions. IAG Symposia, Vol. 106: 51–62, Springer Verlag.Google Scholar
  11. Showstack, R. (2003). Digital Elevation Maps Produce Sharper Image of Earth’s topography. EOS, Transactions, American Geophysical Union, Vol. 84, No. 37: 363.Google Scholar
  12. USGS (2004). US Geological Survey, Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • H. Denker
    • 1
  1. 1.Institut für ErdmessungUniversität HannoverHannoverGermany

Personalised recommendations