Abstract
Global Digital Elevation Models (GDEMs) are datasets of vital importance for regional-scale analysis in areas such as geomorphology, [paleo]climatology, oceanography and biodiversity. In this work I present a comparative assessment of the datasets ETOPO1 (1’ resolution), GTOPO30, GLOBE, SRTM30 PLUS, GMTED2010 and ACE2 (30”) against the altitude of the world’s ultra prominent peaks. GDEMs’ elevations show an expected tendency of underestimating the peak’s altitude, but differences reach 3,500 m. None of the GDEMs captures the full range of elevation on Earth and they do not represent well the altitude of the most prominent peaks. Some of these problems could be addressed with the release of NASADEM, but the smoothing effect caused by moving-window resampling can only be tackled by using new techniques, such as scale-adaptative kernels and curvature-based terrain generalisation.
Highlights
Global Digital Elevation Models (GDEMs) are datasets of vital importance for regional-scale analysis in areas such as geomorphology, climatology, oceanography and biodiversity
Global elevation datasets are available at fine resolutions such as the 30m global SRTM (NGA, 2014; Jet Propulsion Laboratory (JPL), 2014), ASTER GDEM (Tachikawa et al, 2011), ALOS World3D (Takaku et al, 2014) or WorldDEM (Krieger et al, 2009), coarser resolution data still is widely used, specially in global-scale climatic simulations where grid cell size constrains the computational cost of numeric models and results are usually presented at resolutions of 2◦ or coarser (Thompson and Pollard, 1995; Schmidt et al, 2006)
This author cites the importance of using an independent dataset for analysis and uses satellite radar altimetry data from ERS-1 and ERS-2 (European Remote-Sensing Satellite) for comparisons with GLOBE and JGP95E (Berry, 1999) and with SRTM (Berry et al, 2007)
Summary
Global Digital Elevation Models (GDEMs) are datasets of vital importance for regional-scale analysis in areas such as geomorphology, climatology, oceanography and biodiversity. Berry (1999) points the risks of a comparison of GDEMs based only on statistical correlations, since issues which are common to two datasets will not be highlighted and that errors in mosaicking data from different sources are not usually observed in hypsometric or contour line maps given the large variability of elevation in the planet’s topography This author cites the importance of using an independent dataset for analysis and uses satellite radar altimetry data from ERS-1 and ERS-2 (European Remote-Sensing Satellite) for comparisons with GLOBE and JGP95E (Berry, 1999) and with SRTM (Berry et al, 2007). The GDEMs’ elevation was compared with a database of the altitude of mountain peaks with ultra topographic prominence, in order to evaluate the sensibility of regional-scale data to features distinctively marked in the landscape, of little areal expression
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