Digital elevation models (DEM) are models that approximate a topographical surface. These models will contain both random and systematic errors that create uncertainty, or unreliability, in elevation measurements. Uncertainty can be defined as a lack of knowledge about the reliability of a measurement, whereas error is the departure of a measurement from its true value. This error is hardly quantifiable very often and is defined as the difference between reality and a representation of reality. In the other hand, quantifying data uncertainty is an important part of spatial analysis, and researchers are still developing methods to assess, describe and reduce random and systematic DEM uncertainty. The common methods used for the assessment of the accuracy of digital elevation models -DEMs (like root mean square error or mean absolute error) are only global non-spatial measures. Their unquestionable advantages are their easy calculation and interpretation. However, as to environmental applications, the knowledge of spatial error variability is very important. Many authors claim that the extent of errors in a DEM depends on a character of a terrain and is spatially variable. The attempts to express the magnitude of error in a space led to the emergence of different methods for spatial visualization of DEM accuracy. Visualizations bring quick and easy method how to be informed about precision of the model and how to understand the nature of the data. The uncertainty has to be also incorporate for deeper understanding of the model in the light of the previous statement. Several visualization techniques for portrayal uncertainty in DEM have been developed, but there is a gap between transferring knowledge between researchers and final users due to lack of knowledge about the effectiveness of these visualizations. An approach incorporating effectiveness of uncertainty visualization on the decision making processes is still missing. The paper deals with the problem of choosing suitable method of uncertainty visualizations based on perception and cognition studies as a tool for the quality assessment of the digital elevation models.
