Improving user assessment of error implications in digital elevation models

A digital representation of a terrain surface is an approximation of reality and is inherently prone to some degree of error and uncertainty. Research in uncertainty analysis has produced a vast range of methods for investigating error and its propagation. However, the complex and varied methods proposed by researchers and academics create ambiguity for the dataset user. In this study, existing methods are combined and simplified to present a prototype tool to enable any digital elevation model (DEM) user to access and apply uncertainty analysis. The effect of correlated gridded DEM error is investigated, using stochastic conditional simulation to generate multiple equally likely representations of an actual terrain surface. Propagation of data uncertainty to the slope derivative, and the impact on a landslide susceptibility model are assessed. Two frameworks are developed to examine the probable and possible uncertainties in classifying the landslide hazard: probabilistic and fuzzy. The entire procedure is automated using publicly available software and user requirements are minimised. A case study example shows the resultant code can be used to quantify, visualise and demonstrate the propagation of error in a DEM. As a tool for uncertainty analysis the method can improve user assessment of error and its implications.