Search

With scant information available on domestic DEM quality and suitable evaluation methods, particularly in city regions, this study set out to evaluate and compare nine contemporary seamless DEM from different sources and spatial resolutions. The assessment focused on DEM accuracy, as well as determining the variation in data quality across several urban land cover types and slope classes. This was systematically determined against independent LiDAR-derived control points, co-registered reference grids, and resampled data from relevant land cover information. It showed that different sensor configurations and interpolation techniques directly relate to the unique ground cover and topographic variations as manifested by the particular digital surface model (DSM) or terrain model (DTM). An experimental photogrammetric DTM and SA National DEM compared very well with 'off-the-shelf' tri-stereo Elevation4™ and WorldDEM™ DTM products. Their good performance eluded to them being the least affected by land surface cover and gradient variations. The accuracy metrics produced by the AW3D30 DEM throughout were on average substantially higher than those calculated for both the National DEM and the SRTM-X DEM. Two local commercial versions, the older SADTM and more recent SUDEM, and lastly the ASTER GDEM2 in particular, yielded noticeably less accurate results that would restrain their usefulness. [ABSTRACT FROM AUTHOR]

Hydrogeomorphic approaches to wetland classification use landform classes to distinguish wetland functionality at a regional scale. Space-borne radar technology enabled faster regional surveying of surface elevations to digital elevation models, which allowed for automated terrain attribute and landform classification using geographical information systems. Country-wide mapping of landforms remain a challenge though, because of the diversity of landscapes and non-exclusive attributes of each landform class. This article presents the approach, method and steps taken to calculate a country-wide data-set of landforms for wetland-type classification in South Africa, using a freely available Landform Tool. The Landform Tool primarily uses standard deviation from the average elevation, calculated for both small and large neighbourhood distances. The results were compared with 2820 random points, classified manually using Google Earth imagery and topographical maps, and showed a 43% accuracy within 50 m of the manually identified landform class. The Landform Tool tended to overestimate benches and valley floors, and underestimate the extent of the slope class, regardless of changes made in the neighbourhood settings or standard deviation settings. Though the data-set was acceptable as a general reference framework at national scale, improvements will be essential for fine-scale wetland delineation and classification. [ABSTRACT FROM PUBLISHER]