Modelling Submerged Coastal Environments

Description

Built upon remote sensing and GIS littoral zone characterization methodologies of the past decade, a series of loosely coupled models aimed to test, compare and synthesize multi-beam SONAR (MBES), Airborne LiDAR Bathymetry (ALB), and satellite based optical data sets in the Gulf of St. Lawrence, Canada, eco-region. Bathymetry and relative intensity metrics for the MBES and ALB data sets were run through a quantitative and qualitative comparison, which included outputs from the Benthic Terrain Modeller (BTM) tool. Substrate classification based on relative intensities of respective data sets and textural indices generated using grey level co-occurrence matrices (GLCM) were investigated. A spatial modelling framework built in ArcGISTM for the derivation of bathymetric data sets from optical satellite imagery was also tested for proof of concept and validation. Where possible, efficiencies and semi-automation for repeatable testing was achieved using ArcGISTM ModelBuilder. The findings from this study could assist future decision makers in the field of coastal management and hydrographic studies.
Keywords: Seafloor terrain characterization, Benthic Terrain Modeller (BTM), Multi-beam SONAR, Airborne LiDAR Bathymetry, Satellite Derived Bathymetry, ArcGISTM ModelBuilder, Textural analysis, Substrate classification
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