| Abstract | Because benthic environments show ecotonal successions that biophysically fuse geomorphological units with biologicalcoverages, there is a need for cognitive biogeomorphological classification methods. This study introduces a new
 geospatially integrated seafloor classification scheme (G-ISCS) methodology for cognitively interpreting geomorphological
 components and dominant biological covers from a variety of remotely sensed imagery within a geographic
 information system interface. An ordered, hierarchical classification scheme was thus developed to discriminate various
 categories such as physiographic realm, morphodynamic zone, geoform, landform, dominant surface sediment, dominant
 biological cover, and density of dominant biological cover. When applying the G-ISCS method to multispectral satellite
 imagery (e.g., GeoEye IKONOS-2), individual raw scenes were imported into IDRISI  Taiga to enhance frequency
 distributions of spectral reflectance through contrast stretching. Image enhancement was applied to blue, green, and red
 wavelength bands and combined to create 24-bit color composite images that were suitable for interpretation. Using
 ESRI ArcGISt ArcMap software, raster catalogs were created for enhanced imagery data sets and projected on an
 interactive SmartBoardt overlay system for real-time, on-screen digitizing of seafloor feature boundaries at a nominal
 scale of 1:6000. Differentiation of marine environments was based on spectral reflectance characteristics of color, tone,
 saturation, pattern, and texture of the seafloor topology. Individual thematic subset maps were then created by
 hierarchical category and amalgamated in ArcMap to create interactive benthic environment maps. Attribute tables
 were compiled in conjugation with thematic map products to quantify relationships between mapping units, as well as to
 generate accurate biogeomorphological spatial statistics. Overall, the new G-ISCS method can be applied across multiple
 remote sensing platforms to cognitively delineate and interpret benthic biogeomorphological features along shelf
 environments.
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