Sensitivity of Digital Landscapes to Artifact Depressions in Remotely-sensed DEMs

Abstract

Depressions are often removed from digital elevation models (DEMs) used in hydro-geomorphic applications. Light detection and ranging (lidar) and interferometric synthetic aperture radar (INSAR) DEMs of flat to mountainous landscapes were used to evaluate the occurrence of artifact depressions caused by the representation of surfaces using grids and random elevation error. The number of depressions in DEMs that result from grid representation was inversely related to grid spacing; however, normalizing for the number of grid cells in a DEM demonstrated that coarser grids were relatively more vulnerable to depressions. Flat landscapes containing extensive lakes experienced more depressions related to grid spacing and placement than high-relief areas. Stochastic modelling showed that error magnitude controlled the extent of vulnerability within a landscape to depressions caused by random error. Nevertheless, certain areas were likely to experience depressions regardless of the magnitude of random error, including flat areas, valley bottoms, and highly convergent topography.