Adding Value to Broad-Scale Ocean Exploration Mapping Data Through Standardized Geomorphic Classification and Backscatter Data Analysis

TitleAdding Value to Broad-Scale Ocean Exploration Mapping Data Through Standardized Geomorphic Classification and Backscatter Data Analysis
Publication TypeConference Abstract
AuthorsSowers, D, Masetti, G, Mayer, LA, Johnson, P, Gardner, JV, Armstrong, AA
Conference Name2019 Fall Meeting, American Geophysical Union (AGU)
Conference LocationSan Francisco, CA
Conference DatesDecember 9-13

Accurate maps of ocean bathymetry and seafloor habitats serve as a fundamental basis for understanding marine ecosystems and guiding marine spatial planning efforts. From 2004-2015, a vast region of the Atlantic Ocean continental margin offshore of the United States has been systematically mapped using multibeam sonars in support of the U.S. Extended Continental Shelf (ECS) Project and for baseline characterization of the Atlantic canyons. Now that these high cost and high quality datasets have been collected, there is a rich opportunity to further analyze and interpret these data to generate value-added spatial datasets on seafloor geomorphology, substrate, and potential habitat suitability for deep sea biota. This study presents a methodology to generate geomorphology and predicted substrate spatial datasets using semi-automated classification methods that are transparent and repeatable, and utilizing a standardized classification scheme (the Coastal and Marine Ecological Classification Standard - CMECS).  The approach developed through this work provides a model of how to consistently classify seafloor attributes using CMECS as an organizing framework across large potential regions nationally or globally.

This study utilized an automatic segmentation approach to identify landform features from the bathymetry of the region, then translated these results into complete coverage geomorphology (CMECS geoform component) maps of the region. Results provide a characterization of the marine landscape that serves as an inventory of the cumulative area and abundance of geoforms and the spatial relationships among them. Geoform summary statistics were calculated over the study region to quantify the area of each geoform type. These analyses represent a first step in identifying regions of consistent morphology within which the consistency of the backscatter can then be determined. Detailed analysis of the backscatter response for insights into predicted substrate types within the region represents the next phase of the study, and preliminary results will be presented. Key benefits of the study’s semi-automated approach include computational efficiency for large datasets, and the ability to apply the same methods to large regions with consistent results.


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