An optimization technique for addressing DEM misregistration in hilly terrain

Digital elevation models (DEMs) represent the Earth’s topography and support a variety of applications, ranging from extracting watershed drainage structure to measuring glacier volume. Applications that require conflation of multiple DEMs are problematic, however, because of misregistration. We explore a spatial optimization technique to quantify the misregistration on the pixel level between NASA’s Shuttle Radar Topography Mission (SRTM) elevation data set and the USGS’s National Elevation Dataset (NED). The misregistration, estimated within blocks by horizontal offset and direction, is modelled spatially in terms of typical topographical parameters: slope, aspect and elevation. A Nelder–Mead algorithm was implemented to compute the local shift at two study sites in the San Gabriel Mountains (SGM) and Santa Monica Mountains (SMM) in Los Angeles County, California. The magnitude of misregistration is generally less than the distance between DEM postings, and varies by terrain type, being larger in steeper terrains; nevertheless, misregistration is systematic within these continuous terrains that have similar topographical features.