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Key words: Interpolation, Differential GPS, Digital Photogrammetry, Accuracy, Geomorphology
Despite the last decade’s increasing concern for understanding and working with the uncertainty within DEMs, knowledge about DEM error is still at a primitive stage and incorporation of this knowledge into DEM-based modelling applications has only developed to a limited extent. This research addresses the limitations of using a single RMSE value to portray the uncertainty associated with a DEM by developing a technique for creating a spatially distributed DEM error model - an error surface.
The technique is based on the hypothesis that the distribution and scale of errors within a DEM are at least partly related to characteristics of the terrain. The technique involves the collection of high accuracy elevation measurements to compute DEM error, the generation of a set of terrain parameters to characterise the terrain and developing regression models to define the relationship between DEM error and terrain character. The regression models form the basis for creating a RMSE surface to portray DEM error. These error surfaces provide more detailed information about DEM error than a single global estimate of RMSE and an initial assessment of these surfaces indicates they are of sufficient quality for use in stochastic simulations of the impact of DEM error on spatial modelling applications. Error surfaces also have the potential to open the door to a more deterministic approach towards incorporation of uncertainty into spatial modelling by means of probabilistic modelling techniques.