Erosion - deposition evaluation through hybrid DTMs derived by LiDAR and colour bathymetry: the case study of the Brenta, Piave and Tagliamento rivers
AbstractRisk management and flood protection are frequently assessed through geo-morphometric evaluations resulting by floods events. If we aim at elevation models with high resolutions and covering large areas, airborne LiDAR surveys can represent a good compromise among costs, time and uncertainty. The major limitation of the nonbathymetric LiDAR surveys consists in the detection of wet areas. Indeed, accounting for more than 20 cm of water depth, LiDAR signal increases exponentially its error. In this paper we present a comparison of the results concerning the application of a colour bathymetry methodology for the production of hybrid DTMs (HDTM). These elevation models were derived by merging LiDAR data for the dry areas and colour bathymetry for the wet areas. The methodological approach consists in a statistical regression between water depth and RGB band intensity values from contemporary aerial images. This methodology includes the use of filters in order to reduce possible errors due to the application of the model, to estimate precise “in-channel” points. The study areas are three different human impacted gravel-bed rivers of the North-East of Italy. This methodology has been applied in three sub-reaches of Brenta River, two of Piave River and two of Tagliamento River before and after relevant flood events with recurrence interval 10 years. Potentials and limitations of the applied bathymetric method, the comparison of its use in different fluvial contexts and its possibility of employment for geo-morphometric evaluations, were then tested. DGPS control points (1841, 2638, 10473 respectively for Brenta, Piave and Tagliamento River) were finally used to evaluate the accuracy of wet areas. Results showed that, in each model, wet areas vertical errors were comparable to those featured by LiDAR data for the dry areas.
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Copyright (c) 2013 J. Moretto, F. Delai, E. Rigon, L. Picco, M.A. Lenzi
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