Assessing morphological changes in gravel bed rivers using LiDAR data and colour bathymetry

Estimating underwater features of channel bed surfaces without the use of bathymetric sensors results in very high levels of uncertainty. A novel approach to create more accurate and detailed Digital Terrain Models (DTMs) integrates LiDAR-derived elevations of dry surfaces, water depth of wetted areas derived from aerial photos and a predictive depth-colour relationship. This method was applied in three different sub-reaches of a northeastern Italian gravel-bed river (Brenta) before and after flood events occurred in November and December 2010 (recurrence interval: 8 and 10 years). From the data collected through channel field survey, a regression model which calculates channel depths using the correct intensity of three colour bands was implemented. LiDAR and depth points were merged and interpolated into a DTM which features an average error of ±18 cm. The morphological evolution and the sediment volume change calculated through a difference of DTMs shows deposition and erosion areas, indicating a deficit which reduces as it goes downstream.