UTILISATION CONJOINTE DE TRAINS D'ONDES LIDAR VERT ET INFRAROUGE POUR LA BATHYMÉTRIE DES EAUX DE TRÈS FAIBLE PROFONDEURS.

Bathymetry and topography are crucial factors for the sustainable management of rivers and coastal areas. In this context, bathymetrie LiDAR appears as a useful technology for coastal and river mapping, offering an high spatial data density, an high acquisition rate and providing a continuous map between emerged and immersed area. Although some studies have investigated this technique's precision in moderately deep coastal areas, few have focused on very shallow waters (< 3 m). In this paper we introduce a new signal processing method for very shallow waters bathymetry. The methods is based on a simultaneous process of green and near-infrared (NIR) LiDAR waveforms. We then evaluate the density and accuracy of the resulting estimations of very shallow waters bathymetry. In this paper, we present our work on a dataset from the Golfe du Morbihan, France, acquired by SHOM (French Navy Hydrographic and Oceanographic Service) in 2005 with a SHOALS system providing Raman, NIR, and green LiDAR waveforms. This work focuses on the data quality resulting from two different processing methods using near-infrared and green signals for surface and water bottom topography: the delivered data processed method and the proposed algorithm. In order to perform a better comparison of altimetrical data between GPS ground control points (pinpoints) and LiDAR footprints(diameter around 2 m), we also used a specific aggregation method (MAUP). In selected very shallow areas, we show that our algorithm extracts 41% of additional measurements from initially delivered data with a similar bias (around 5 cm) and a lower standard deviation of errors (26.1 cm vs. 41.1 cm). 55% of theses additional points are located between 1.5 m and 2 m and our algorithm can detect depths 80 cm shallower than the delivered data (1 m vs. 1.8 m). [ABSTRACT FROM AUTHOR]