High resolution terrain models generated from widely available
Interferometric Synthetic Aperture Radar (IfSAR) and digital
photogrammetry are an exciting resource for geomorphological research.
However, these data contain error, necessitating pre-processing to
improve their quality. We evaluate the ability of digital filters to
improve topographic representation, using: M a Gaussian noise removal
filter; (2) the proprietary filters commonly applied to these datasets;
and (3) a terrain sensitive filter, similar to those applied to laser
altimetry data. Topographic representation is assessed in terms of both
absolute accuracy measured with reference to independent check data and
derived geomorphological variables (slope, upslope contributing area,
topographic index and landslide failure probability) from a steepland
catchment in northern England. Results suggest that proprietary filters
often degrade or fail to improve precision. A combination of terrain
sensitive and Gaussian filters performs best for both IfSAR and digital
photogrammetry datasets, improving the precision of photogrammetry
digital elevation models (DEMs) by more than 50 per cent relative to
the unfiltered data. High-frequency noise and high-magnitude gross
errors corrupt geomorphological variables derived from unfiltered
photogrammetry DEMs. However, a terrain sensitive filter effectively
removes gross errors and noise is minimized using a Gaussian filter.
These improvements propagate through derived variables in a landslide
prediction model, to reduce the area of predicted instability by up to
29 per cent of the study area. Interferometric Synthetic Aperture Radar
is Susceptible to removal of topographic detail by oversmoothing and
its errors are less sensitive to filtering (maximum improvement in
precision of 5) per cent relative to the raw data).