In the current context of climate change and rising sea level, it is imperative for hydrographic services worldwide to monitor coastal waterways for purposes of maritime navigation and coastal infrastructure. Further research is required to determine the most effective means of characterizing coastal bathymetry and seafloor substrates through the use of remote sensing data, and to develop standardized and repeatable methods of doing so. This project compared and synthesized multi-beam SONAR (MBES), Airborne LiDAR Bathymetry (ALB), and satellite-based optical data sets for modelling bathymetry and seafloor substrates in the Gulf of Saint Lawrence ecoregion. Bathymetry and relative intensity metrics for the MBES and ALB data sets were compared. In addition, substrate classification based on relative intensities of respective data sets and textural indices generated using grey-level co-occurrence matrices were investigated. Strong correlations were found between ALB and MBES bathymetric surfaces and their submerged terrain modelling derivatives, which allowed for subsequent generation of comprehensive maps from both data types. A spatial modelling framework for the derivation of bathymetric data from optical satellite imagery was also developed in ArcGIS. Derived bathymetric surfaces showed moderate correlation to ALB, and illustrate the promise for future work on other coastlines.
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