Using geographically weighted regression for analysing elevation error of detailed digital elevation models

This case study concerns vertical errors of five different high-resolution digital elevation models (DEMs) originated from light detection and ranging (LiDAR), interferometric SAR (InSAR) and photogrammetric acquisition. The LiDAR DEM derived from last return points was considered as the reference DEM. The aim was to analyse the statistical and spatial distribution of the residuals and their relationship with the DEM surface roughness of the analysed DEMs. Surface roughness measured as area ratio and inverted vector strength were used to parameterise the DEM surface. The results show that globally no linear relationship exists between the surface roughness and DEM residuals but it was found to be very diverse locally. High elevation errors occurred along DEM artefacts and sharply defined landforms. The applied surface roughness parameters were found to be useful predictors of such features and could be used for identification of such features.