Geographical Analysis · 1997 · 198 citations · 18 references
ComputingEarth SciencesGeoinformaticsDigital Elevation ModelingGeographic Information SystemsUncertainty QuantificationDigital Elevation ModelsDigital Terrain ModelingDigital Terrain ModelsTerrain ModelingTerrain MorphometryTerrain Surface ModelingAspectDem Error AnalysisDem Uncertainty QuantificationDigital Elevation Model Error AnalysisSpatial StructureTerrain Uncertainty ModelingTerrain Uncertainty QuantificationUncertainty Quantification In Elevation Data
Slope and aspect estimates derived from digital elevation models are inherently uncertain due to errors in the elevation data. The study proposes a general model of DEM errors using a spatially auto‑regressive random field and a procedure to propagate these errors through GIS operations. The authors employ simulations and worst‑case analysis to examine how the spatial structure of DEM errors influences slope and aspect estimates. The results show that slope and aspect errors depend on the spatial structure of DEM errors, with simulations revealing their magnitude and severity.
Estimates of slope and aspect are commonly made from digital elevation models (DEMs), and are subject to the uncertainty present in such models. We show that errors in slope and aspect depend on the spatial structure of DEM errors. We propose a general‐purpose model of DEM errors in which a spatially auto‐regressive random field is added as a disturbance term to elevations. In addition, we propose a general procedure for propagating such errors through GIS operations. In the absence of explicit information on the spatial structure of DEM errors, we demonstrate the potential utility of a worst‐case analysis. A series of simulations are used to make general observations about the nature and severity of slope and aspect errors.
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