|Title||The geomorphological characterisation of digital elevation models|
|Year of Publication||1996|
|University||University of Leicester|
Techniques and issues are considered surrounding the characterisation of surface form represented by Digital Elevation Models (DEMs). A set of software tools suitable for use in a raster based Geographical Information System (GIS) is developed. Characterisation has three specific objectives, namely to identify spatial pattern, to identify scale dependency in form and to allow visualisation of results. An assessment is made of the characteristics of error in DEMs by identifying suitable quantitative measures and visualisation processes that may be enabled within a GIS. These are evaluated by contour threading a fractal surface and comparing four different spatial interpolations of the contours. The most effective error characterisations are found to be those that identify high frequency spatial pattern. Visualisation of spatiall arrangement of DEMs error is used to develop a deterministic error model based on local surface slope and aspect. DEMs are parameterised using first and second derivatives of quadratic surfaces fitted over a range of scales. This offers advantages over traditional methods based on a 3 by 3 local window, as geomorphometric form can be characterised at any scale. Morphometric parameters are combined to give a feature classification that may also be applied over a range of scales. Multi-scale measurements are combined to give a feature membership function that describes how properties change with scale. These functions are visualised using modal and entropy measures of variability. An additional method of visualising scale dependency is suggested that graphically represents statistical measures of spatial pattern over a variety of spatial lags. This is found to be most appropriate for detecting structural anisotropy in a surface. Characterisation tools are evaluated by applying them to uncorrelated surfaces, fractal surfaces and Ordnance Survey DEM of the Lake District, Peak District and Dartmoor.