LE3 .A278 2021

2021

Webster, Tim
Spooner, Ian

Acadia University

Master of Science

Masters

Applied Geomatics

Earth & Environmental Sciences

Coastal zones play a vital role in aquatic ecosystems, marine industry, environmental monitoring and protection, and meteorological forecasting. Traditionally sonar-based remote sensing technologies have been the dominant technologies used to map coastal regions, but in recent decades topo-bathymetric LiDAR has been more widely employed as it enables the user to cover a larger study area quicker in shallow areas difficult to survey by boat. This thesis seeks to use topo-bathyemtric LiDAR derived bathymetry and identify a method of accurately classifying the seafloor morphology using geometric and geomorphometric variables. In previous scientific studies on the topic the features used in the classification have been relatively simple and normally occupied a role of secondary importance to spectral reflectance data, LiDAR intensity, or acoustic backscatter. While these variables are of no doubt valuable to seafloor classification, it is believed that the introduction of more complex and descriptive variables will result in a more accurate classification of the seafloor. The methodology developed in this research uses only the digital elevation model (DEM) and information that can be extracted from the DEM in order to provide an analysis that can be used regardless of data-collection technique, or where the data is being collected. The classification was done using the K-nearest neighbour algorithm and achieves accuracies of 72.8% and 81.9%. These accuracies are comparable to the accuracies achieved in other studies that look to classify seafloor morphology or habitat.

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https://scholar.acadiau.ca/islandora/object/theses:3631