Beschreibung:
<jats:p>Abstract. Current research questions in the field of geomorphology focus on the impact
of climate change on several processes subsequently causing natural hazards.
Geodetic deformation measurements are a suitable tool to document such
geomorphic mechanisms, e.g. by capturing a region of interest with
terrestrial laser scanners which results in a so-called 3-D point cloud. The
main problem in deformation monitoring is the transformation of 3-D point
clouds captured at different points in time (epochs) into a stable reference
coordinate system. In this contribution, a surface-based registration
methodology is applied, termed the iterative closest proximity algorithm
(ICProx), that solely uses point cloud data as input, similar to the
iterative closest point algorithm (ICP). The aim of this study is to
automatically classify deformations that occurred at a rock glacier and an
ice glacier, as well as in a rockfall area. For every case study, two epochs
were processed, while the datasets notably differ in terms of geometric
characteristics, distribution and magnitude of deformation. In summary, the
ICProx algorithm's classification accuracy is 70 % on average in
comparison to reference data.
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