Dear Colleagues,

The topographic features of our Earth have always been the key to our orientation in geographic space. A digital elevation model (DEM) is a powerful surface model of the Earth or of any other planets. It can provide explicate and more inherently hidden information of the topographic complexity, in a simplified way. DEM is promising for various applications in the broader wider field of geoscience, engineering, and virtual reality, as well as in natural, environmental and social sciences, and even in entertainment. A relevant global DEM plays a role in the connectivity of our society.

At present, a number of terrestrial and bathymetric global DEMs can be obtained. They can be commercial, with a typical horizontal resolution of around 10 m, or under free licenses or public domain, with resolution of around 30 m (e.g., SRTM, GMTED, ASTER GDEM, AW3D, and ETOPO). In addition to these, there are a number of freely available DEMs in regional (e.g., EU-DEM and ArcticDEM) and national scales. Most DEMs are actually digital surface models (DSMs), which include the tops of buildings, vegetation cover, temporal snow cover, and so on. However, digital terrain models (DTMs) with pure bare Earth are mostly required by users.

The key research questions are as follows: “Which elements can improve the usability of freely available global DEMs?” and, closely linked, “How can we improve such DEMs’ quality when using geomorphometric methods?”. This Issue is fundamental in order to ensure the best performance of any spatial analysis involving DEM and for reducing uncertainties.

The authors are invited to explore any of the following related critical topics, but are not only limited to these. Firstly, is it possible to involve a geomorphometry in automatic DEM quality improvement, bearing in mind the relevant experiences in order to determine the remarkable geomorphological features, which are, let us say five-times more detailed than the grid size? The issues are the vertical positional accuracy, temporal and conceptual consistency, and details without any artefacts. Because a more detailed DEM usually contains other kinds of errors and uncertainties, the question is, if it is more important to have a high spatial resolution DEM with noises, or to have an overall consistent DEM in an optimal scale. In fact, an optimal scale is usually a multi-scale DEM, where the choice of a particular scale can significantly depend on the processing time of the selected spatial analysis of these massive datasets. There is a need to associate geometrical, geomorphological, and semantical aspects in DEM processing. As there are already a number of global and other DEM datasets available, the question is whether the geomorphometric methods can be used to harmonize them with integration, fusion, or conflation, aiming for a considerably better DEM quality. The geomorphometric methods can also help in the transition from DSM to DTM. The spatial data cleaning principles can be involved in comprehensive generalization tools based on geomorphometry, bearing in mind vertical positional accuracy and different scales. There is also a need to improve a concept using a DEM definition in different scales or resolutions. Nevertheless, does global DEM distribution need a more practical solution in a projected horizontal coordinate system with Cartesian coordinates that optimally preserve their metric properties (areas, distances, and angles), instead of the current geographic coordinates?

This Special Issue is well-timed, because of the recent advances in geomorphometry and spatial data quality approaches. Answers to the research questions can lead to a step forward in the (global) DEM processing, which will further escalate the interoperability and usability.

Dr. Tomaž Podobnikar
Prof. Dr. Juha Oksanen
Guest Editors

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• global digital elevation model
• DEM, DSM, and DTM
• geomorphometry
• geomorphology
• spatial analysis
• data quality
• uncertainty
• spatial data cleaning
• usability
• spatial data generalization
• spatial data integration, conflation, and fusion
• big data processing