Using geographically weighted regression for analysing elevation error of high-resolution DEMs

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    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.

    Original languageEnglish
    Pages109-112
    Number of pages4
    Publication statusPublished - 01 Jan 2010
    Event9th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Accuracy 2010 - Leicester, United Kingdom
    Duration: 20 Jul 201023 Jul 2010

    Conference

    Conference9th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Accuracy 2010
    CountryUnited Kingdom
    CityLeicester
    Period20/07/201023/07/2010

      Research areas

    • DEM error, GWR, LiDAR, Local analysis, Roughness

    ID: 161925958