The estimation of
underwater features of channel bed surfaces without the use of bathymetric
sensors results in very high levels of uncertainty.
A revised approach enabling an automatic extraction of the wet areas to
create more accurate and detailed Digital Terrain Models (DTMs) is here
presented. LiDAR-derived elevations of dry surfaces, water depths of wetted
areas derived from aerial photos and a predictive depth-colour relationship
were adopted. This methodology was applied at two different reaches of a
northeastern Italian gravel-bed river (Tagliamento) before and after two
flood events occurred in November and December 2010. In-channel dGPS survey points were performed taking different
depth levels and different colour scales of the river bed. More than 10,473 control
points were acquired, 1107 in 2010 and 9366 in 2011 respectively. A
regression model that calculates channel depths using the correct intensity
of three colour bands (RGB) was implemented. LiDAR and water depth points were
merged and interpolated into DTMs which features an average error, for the
wet areas, of ±14 cm. The different number of calibration points obtained for
2010 and 2011 showed that the bathymetric error is also sensitive to the number
of acquired calibration points. The morphological evolution calculated
through a difference of DTMs shows a prevalence of deposition and erosion areas
into the wet areas.
Cite this paper
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