In deriving primary topographic attributes that serve as input variables for a variety of hydrological and geomorphological studies, digital elevation model (DEM) data is basic. However, consensus on the influence of the source and resolution of DEM on the application of these topographic attributes to landscape characterization is still varied. At the same time, elevation data from many major sources and resolutions are available for South Africa: the Shuttle Radar Topographic Mission (SRTM), the EarthEnv and Stellenbosch University DEM (SUDEM). In a local context, limited research was conducted comparing the extraction of terrain attributes increasingly accessible to high-resolution Digital Terrain Data (DTM) such as LiDAR (Light Detection and Ranging). However, LiDAR ‘s usefulness in topographic analysis poses its difficulties in terms of operational resolution, processing requirements and limited spatial coverage. Elevation, slope, topographic wetness index and surface roughness attributes of terrain derived from DEMs from different sources, at different spatial resolutions and using three generalisation algorithms, namely: mean cell aggregation, nearest neighbour and topo-to-raster corrected hydrological. We demonstrate that topographic variable extraction is heavily dependent on the approach of DEM source and generalisation. Although higher resolution DEMs can reflect the “real” surface more accurately, for all extracted variables, they do not necessarily give the best results. Our findings illustrate the caveats of selecting non-fit-for – purpose DEMs for topographic analysis and provide an easy but efficient solution for reconciling the selection of DEMs before terrain analysis and topographic feature characterization based on neighbourhood size resolution. Finding the best mix of when surface data can be upgraded, what DEMS to use, and what spatial scale works to ensure that The most optimal surface integrity is also still context-specific. This research , however , shows a robust structure for the interpretation of optimal sensor choice and spatial scale for the southern-coastal region of KZN to understand the geomorphological processes in the landscape.
Author (s) Details
Jonathan T. Atkinson
Stellenbosch Department of Soil Science, Matieland, Stellenbosch, South Africa.
Dr. Willem P. De Clercq
Stellenbosch Water Research Institute, Matieland, Stellenbosch, South Africa.
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