Standardized Geomorphic Classification of Seafloor Within the United States Atlantic Canyons and Continental Margin

TitleStandardized Geomorphic Classification of Seafloor Within the United States Atlantic Canyons and Continental Margin
Publication TypeJournal Article
Year2020
AuthorsSowers, D, Masetti, G, Mayer, LA, Johnson, P, Gardner, JV, Armstrong, AA
JournalFrontiers in Marine Science
Volume7(9)
Pages1-9
Date PublishedJanuary 28
KeywordsAtlantic, bathymorphon, classification, coastal and marine ecological classification standard, geoform, geomorphology, geomorphometry, seafloor

Accurate seafloor maps serve as a critical component for understanding marine ecosystems and guiding informed ocean management decisions. From 2004 to 2015, the Atlantic Ocean continental margin offshore of the United States has been systematically mapped using multibeam sonars. This work was done in support of the U.S. Extended Continental Shelf (ECS) Project and for baseline characterization of the Atlantic canyons, but the question remains as to the relevance of these marginwide data sets for conservation and management decisions pertaining to these areas. This study utilized an automatic segmentation approach to initially identify landform features from the bathymetry of the region, then translated these results into complete coverage geomorphology maps of the region utilizing the coastal and marine ecological classification standard (CMECS) to define geoforms. Abyssal flats make up more than half of the area (53%), with the continental slope flat class making up another 30% of the total area. Flats of any geoform class (including continental shelf flats and guyot flats) make up 83.06% of the study area. Slopes of any geoform class make up a cumulative total of 13.26% of the study region (8.27% abyssal slopes, 3.73% continental slopes, and 1.25% seamount slopes). While ridge features comprise only 1.82% of the total study area (1.03% abyssal ridges, 0.63 continental slope ridge, and 0.16% seamount ridges). 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 Linkhttps://doi.org/10.3389/fmars.2020.00009
DOI10.3389/fmars.2020.00009
Refereed DesignationRefereed
Region
Check When Not CCOM Publication