Lloyd, C.D. and Atkinson, P.M.
Deriving ground surface digital elevation models from LiDAR data with geostatistics
International Journal of Geographical Information Science, 20, (5), . (doi:10.1080/13658810600607337).
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This paper focuses on two common problems encountered when using Light
Detection And Ranging (LiDAR) data to derive digital elevation models
(DEMs). Firstly, LiDAR measurements are obtained in an irregular configuration
and on a point, rather than a pixel, basis. There is usually a need to
interpolate from these point data to a regular grid so it is necessary to identify the
approaches that make best use of the sample data to derive the most accurate
DEM possible. Secondly, raw LiDAR data contain information on abovesurface
features such as vegetation and buildings. It is often the desire to
(digitally) remove these features and predict the surface elevations beneath them,
thereby obtaining a DEM that does not contain any above-surface features. This
paper explores the use of geostatistical approaches for prediction in this
situation. The approaches used are inverse distance weighting (IDW), ordinary
kriging (OK) and kriging with a trend model (KT). It is concluded that, for the
case studies presented, OK offers greater accuracy of prediction than IDW while
KT demonstrates benefits over OK. The absolute differences are not large, but to
make the most of the high quality LiDAR data KT seems the most appropriate
technique in this case.
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