Deriving ground surface digital elevation models from LiDAR data with geostatistics

Lloyd, C.D. and Atkinson, P.M. (2006) Deriving ground surface digital elevation models from LiDAR data with geostatistics International Journal of Geographical Information Science, 20, (5), pp. 535-563. (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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1080/13658810600607337
ISSNs: 1365-8816 (print)
Keywords: LiDAR, Kriging, DEM

ePrint ID: 54957
Date :
Date Event
3 February 2004Submitted
5 May 2006Published
Date Deposited: 01 Aug 2008
Last Modified: 16 Apr 2017 17:45
Further Information:Google Scholar

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