Abstract

The generalization of digital terrain models (DTMs) is essential for the generation of legible, visually effective, grey-scale maps used in landform mapping. In this research, DTMs of slope gradient and downslope curvature are generalized by reducing the short-wave land-form variation, using a low-pass modal filtering technique. The effect of the filtering on the generalization and on the accuracy of the map is dependent on the size of the neighbourhood. As a consequence, the determination of neighbourhood sizes forms one of the most important processing steps in the filtering process. Neighbourhood sizes are selected on the basis of texture analysis, in conjunction with visual interpretation of the resulting maps. Relative variance is used as a texture measure to select neighbourhood sizes for the filtering process. The analysis shows that variances differ according to terrain roughness and area size, and that the variance increases for terrain parameters of higher-order derivatives. As a result, different neighbourhood sizes have to be selected for filtering different terrain parameters, and areas of different terrain. Visual comparisons of the original with the filtered DTMs show that the readability of the filtered DTMs is improved by using a modal filtering technique. Unwanted detail of short-wave terrain variation is reduced in the filtering process. At the same time, sufficient detail is maintained to distinguish between landform categories of different landform patterns and various dominant-slope gradient classes. Maps of the eliminated short-wave variation are of geomorphologic benefit for extracting specific landform elements. The filtering of the DTMs, on the other hand, is essential to reduce data complexity if the DTMs are to be used in further multilevel modelling procedures. Key words: Terrain generalization, low-pass modal filtering, texture analysis, digital terrain models of slope gradient and curvature, landform mapping in Alberta

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