Computational and Methodological Aspects of Terrestrial Surface Analysis based on Point Clouds

Computers & GeosciencesComputers & Geosciences, Volume 42, May 2012, Pages 64-70

Igor Rychkov, James Brasington, and Damià Vericat


  • We present a software toolkit for processing terrestrial point clouds.
  • The toolkit can be applied to TLS surveys of gravel river beds.
  • Improved DEM differencing is one outcome.
  • Estimating surface roughness and grain size distribution is possible now with point-based, statistical metrics.
  • Other applications and extensions are enabled by the library being freely available and open source.

“Processing of high-resolution terrestrial laser scanning (TLS) point clouds presents methodological and computational challenges before a geomorphological analysis can be carried out. We present a software library that effectively deals with billions of points and implements a simple methodology to study the surface profile and roughness. Adequate performance and scalability were achieved through the use of 64-bit memory mapped files, regular 2D grid sorting, and parallel processing. The plethora of the spatial scales found in a TLS dataset were grouped into the “ground” model at the grid scale and per cell, sub-grid surface roughness. We used centroid-thinning to build a piecewise linear ground model, and studied “detrended” standard deviation of relative elevations as a measure of surface roughness. Two applications to the point clouds from gravel river bed surveys are described. Linking empirically the standard deviation to the grain size allowed us to retrieve morphological and sedimentological models of channel topology evolution and movement of the gravel with richer quantitative results and deeper insights than the previous survey techniques.”