Sediment cores were analysed from four coastal wetland sites within the Minas Basin, Bay of Fundy to compare mercury speciation and sediment characteristics. The coastal wetland sediments were low in total mercury (mean=17.4± 9.9 ng g −1 ); however, MeHg concentration was 92 times higher (mean of 249 pg g −1 ) than intertidal mudflat sediment (mean of 2.7 pg g −1 ). Total mercury concentrations in intertidal mudflat cores were also low (0.5-23.7 ng g −1 ) and correlated (Pearson correlation=0.98; p<0.01) with % organic carbon; with low concentrations of MeHg present only below depths of 6 cm (mean=2.7±1.0 pg g −1 ). Total mercury concentrations were negatively correlated (correlation = 0.56, p < 0.05) with inorganic sulphur (acid volatile sulphides (AVS) and pyrite) while MeHg concentrations were inversely correlated (Pearson correlation=−0.68; p<0.05) with the pyrite content but not with AVS. Methyl mercury concentrations were not significantly correlated with organic carbon content in the wetland sediments, and mercury-in-biomass enrichment factors were lower (total mercury mean 1.5±1.9 and MeHg mean=3.6± 4.8) than published measurements from mercury polluted sites. Modelling estimates found on average 4.4 times more total mercury mass in the intertidal mudflat sediments relative to vegetated wetlands. A negative relationship was observed between MeHg concentrations (below 20 cm depth) and modelled tidal inundation. The mineral fraction within wetland sediments contained 96.2% of the total mercury mass; however, the highest concentrations of mercury species were in root biomass. This research confirms that vegetated coastal wetlands are key areas for formation of bioavailable methyl mercury, and mercury distribution is tied to organic carbon and sulphur speciation.

The ability to map and monitor the macroalgal coastal resource is important to both the industry and the regulator. This study evaluates topo-bathymetric lidar (light detection and ranging) as a tool for estimating the surface area, height and biomass of Ascophyllum nodosum, an anchored and vertically suspended (floating) macroalga, and compares the surface area derived from lidar and WorldView-2 satellite imagery. Pixel-based Maximum Likelihood classification of low tide satellite data produced 2-dimensional maps of intertidal macroalgae with overall accuracy greater than 80%. Low tide and high tide topo-bathymetric lidar surveys were completed in southwestern Nova Scotia, Canada. Comparison of lidar-derived seabed elevations with ground-truth data collected using a survey grade global navigation satellite system (GNSS) indicated the low tide survey data have a positive bias of 15 cm, likely resulting from the seaweed being draped over the surface. The high tide survey data did not exhibit this bias, although the suspended canopy floating on the water surface reduced the seabed lidar point density. Validation of lidar-derived seaweed heights indicated a mean difference of 30 cm with a root mean square error of 62 cm. The modelled surface area of seaweed was 28% greater in the lidar model than the satellite model. The average lidar-derived biomass estimate was within one standard deviation of the mean biomass measured in the field. The lidar method tends to overestimate the biomass compared to field measurements that were spatially biased to the mid-intertidal level. This study demonstrates an innovative and cost-effective approach that uses a single high tide bathymetric lidar survey to map the height and biomass of dense macroalgae.

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The Chiroptera II shallow water topo-bathymetric LiDAR sensor has been used to survey several coastal areas in Maritime Canada since 2014. In addition to the production of seamless elevation models, the green laser reflectance amplitude has been used in combination with the seabed roughness to map seagrass beds. The LiDAR is coupled with a 5 MPIX quality assurance camera and a 60 MPIX RCD30 multispectral camera (RGB+NIR). Utilizing the camera information with the LiDAR derivatives has allowed us to improve our bottom mapping capabilities and expand the number of benthic classes that can be derived. The amplitude of the green laser, 515 nm, decays exponentially with water depth. The strength of the signal is dependent on several factors including: water surface specular reflection, local incidence angle (scan angle + aircraft orientation), water column properties and the seabed material. We have developed a method to normalize the amplitude of the green laser points between flight lines. The energy of the light exponentially decays with depth and the amplitude of the signal is not scaled accordingly. We have developed an empirical method to depth normalize the amplitude image so it can be used in image classification. We will present various classification methods using the LiDAR and photo derived products to map the benthic environment. These methods include: semi-empirical, pattern recognition (maximum likelihood, k-means), machine learning such as random forest and object based segmentation. The results are validated using drop camera point based ground truth or echosounding data from a Biosonics system. We will also present research related to extracting attributes directly from the waveform of the green laser return that offers additional potential for habitat classification.