Abstract
Various sectors of the economy such as transport and renewable energy have
shown great interest in sea bed models. The required measurements are usually carried
out by ship-based echo sounding, but this method is quite expensive. A relatively new
alternative is data obtained by airborne lidar bathymetry. This study investigates the
accuracy of these data, which was obtained in the context of the project ‘Investigation
on the use of airborne laser bathymetry in hydrographic surveying’. A comparison to
multi-beam echo sounding data shows only small differences in the depths values of
the data sets. The IHO requirements of the total horizontal and vertical uncertainty for
laser data are met. The second goal of this paper is to compare three spatial interpolation
methods, namely Inverse Distance Weighting (IDW), Delaunay Triangulation (TIN), and
supervised Artificial Neural Networks (ANN), for the generation of sea bed models. The
focus of our investigation is on the amount of required sampling points. This is analyzed
by manually reducing the data sets. We found that the three techniques have a similar
performance almost independently of the amount of sampling data in our test area.
However, ANN are more stable when using a very small subset of points.
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