Webster, T.; McGuigan, K.; Crowell, N.; Collins, K., and MacDonald, C., 2016. Optimization of data collection and refinement of post-processing techniques for Maritime Canada's first shallow water topographic-bathymetric lidar survey. In: Brock, J.C.; Gesch, D.B.; Parrish, C.E.; Rogers, J.N., and Wright, C.W. (eds.), Advances in Topobathymetric Mapping, Models, and Applications. Journal of Coastal Research, Special Issue, No. 76, pp. 31–43. Coconut Creek (Florida), ISSN 0749-0208.An airborne topographic-bathymetric lidar survey was conducted for five coastal study sites in Maritime Canada in fall 2014 using the shallow water Leica AHAB Chiroptera II sensor. The sensor utilizes near-infrared (NIR) and green lasers to map topography, water surface, and bathymetry, and is equipped with a 60 MPIX camera, which results in 5-cm resolution color and NIR orthophotos. Depth penetration of the lidar sensor is limited by water clarity, and because the coastal zone is vulnerable to reduced water clarity/increased turbidity due to fine-grained sediment suspended by wind-induced waves, several techniques were employed to obtain maximum depth penetration of the sensor. These included monitoring wind speed, direction, and water clarity at study locations, surveying a narrow pass of the study area to assess depth penetration, and quickly adapting to changing weather conditions by altering course to an area where water clarity was less affected by wind-induced turbidity. These techniques enabled 90% depth penetration at all five of the shallow embayments surveyed and up to 6 m depth penetration in the exposed coastal region. Synchronous ground truth surveys were conducted to measure water depth and clarity and seabed cover during the surveys. GPS checkpoints on land indicated that the topographic lidar had an accuracy of better than 10 cm RMSE in the vertical. The amplitude of the green laser bathymetric returns provides information on bottom type and can be useful for generating maps of vegetation distribution. However, these data are not automatically compensated for water depth attenuation and signal loss in post-processing, which results in difficulties in interpreting the amplitude imagery derived from the green laser. An empirical approach to generating a depth-normalized amplitude image which is merged with elevation derivatives to produce a 2-m resolution map product that is easily interpreted by end users is presented. An eelgrass distribution model was derived from the bathymetric elevation parameters with 80% producer's accuracy.