Assessment and Evaluating the Effects of Generalisation Approaches and DEM Resolution on the Extraction of Terrain Indices in Kwazulu Natal, South Africa

Digital elevation model (DEM) data are elemental in deriving primary topographic attributes which
serve as input variables to a variety of hydrologic and geomorphologic studies. There is, however, still
varied consensus on the effect of DEM source and resolution on the application of these topographic
attributes to landscape characterization. At the same time, elevation data for South Africa are
available from several major sources and resolutions: Shuttle Radar Topographic Mission (SRTM),
EarthEnv and Stellenbosch University DEM (SUDEM). Limited research has been conducted in a
local context comparing the extraction of terrain attributes to high-resolution Digital Terrain Data
(DTM) such as LiDAR (Light Detection and Ranging) that are becoming increasingly available.
However, the utility of LiDAR to topographic analyses presents its challenges in terms of operationalrelevant
resolution, processing demands and limited spatial coverage. There is a need to quantify the
impact that generalization approaches have on simplifying detailed DEMs and to compare the
accuracy and reliability of results between high resolution and coarse resolution data on the extraction
of localized topographic variables. In this regional study, we analyze the efficiency on selected local
terrain attributes: elevation, slope, topographic wetness index and surface roughness derived from
DEMs from varying sources, at different spatial resolutions and using three generalization algorithms,
namely: mean cell aggregation, nearest neighbour and hydrological corrected topo-to- raster. We
show that topographic variable extraction is highly dependent on DEM source and generalization
approach. While higher resolution DEMs may represent the “true” surface more accurately, they do
not necessarily offer the best results for all extracted variables. Our results highlight the caveats of
selecting DEMs not “fit-for-purpose” for topographic analysis and offer a simple yet effective solution
for reconciling the selection of DEMs based on neighbourhood size resolution before terrain analyses
and topographic feature characterization. Finding the right combination of when to upscale surface
data, what DEMS to use and what spatial scale operate at to ensure that surface integrity is most
optimal is mostly still context-specific. However, this study demonstrates a robust framework to
interpret optimal sensor choice and spatial scale for understanding the geomorphological processes in
the landscape for the southern-coastal area of KZN.