Adding Value to Broad-scale Ocean Exploration Mapping Data Through Standardized Geomorphic Classification and Backscatter Data Analysis
Abstract
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 to 2015, a vast region of the Atlantic Ocean continental margin offshore of the U.S. was systematically mapped using multibeam sonars in support of the U.S. Extended Continental Shelf (ECS) Project and for baseline characterization of the Atlantic canyons. The extensive bathymetry and backscatter datasets from this region are now being further analyzed and interpreted 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 the standardized classification scheme CMECS (Coastal and Marine Ecological Classification Standard). The approach developed through this work provides a model of how to consistently classify seafloor attributes using CMECS as an organizing framework across large 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 components) maps of the region. Geoform summary statistics were calculated to provide an inventory of the cumulative area and abundance of geoforms within the margin. The next phase of the study is a detailed analysis of the backscatter response for insights into predicted substrate facies within the region and preliminary results will be presented. Rugosity, standard deviation of landform type, slope, median backscatter intensity and angular range analysis backscatter response will be evaluated for their utility in generating predicted maps of three broad CMECS substrate subclasses: Bedrock, Coarse Unconsolidated Substrate, and Fine Unconsolidated Substrate. 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.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMOS11A..03S
- Keywords:
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- 3045 Seafloor morphology;
- geology;
- and geophysics;
- MARINE GEOLOGY AND GEOPHYSICS;
- 4894 Instruments;
- sensors;
- and techniques;
- OCEANOGRAPHY: BIOLOGICAL;
- 4260 Ocean data assimilation and reanalysis;
- OCEANOGRAPHY: GENERAL;
- 4262 Ocean observing systems;
- OCEANOGRAPHY: GENERAL