Sea floor classification from airborne lidar data

Airborne depth sounding lidar has proven to be a valuable sensor for rapid and accurate sounding of shallow areas. The received lidar pulse echo contains information of the sea floor depth, but also other data can be extracted. We currently perform work on bottom classification and water turbidity estimation based on lidar data. In this paper we present the theoretical background and experimental results on bottom classification. The algorithms are developed from simulations and then tested on experimental data from the operational airborne lidar system Hawk Eye II. We compare the results to field data taken from underwater video recordings. Our results indicate that bottom classification from airborne lidar data can be made with high accuracy.