Using Geographically Weighted Regression for Analysing Elevation Error of High-resolution DEMS

Michal Gallay1, Chris Lloyd1, Jennifer McKinley1 and Michal Gallay2
School of Geography, Archaeology and Palaeoecology, Queen's University Belfast, Belfast, UK Institute of Geography, University of Pavol Jozef Safarik, Košice, Slovakia
{mgallayO 1, c.lloyd, j.mckinley}@qub.ac.uk; 2. michal.gallay@upjs.sk

Abstract: This case study compares five different high- resolution digital elevation models (DEMs) originated from light detection and ranging (LiDAR), interferometric SAR, photogrammetric acquisition and contour maps digitizing. 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. The findings also suggest that the assumption of stationarity and Gaussian distribution of the DEM error field is questionable.

Keywords: GWR, DEM error, LiDAR, roughness, local analysis

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