Preliminary hard and soft bottom seafloor substrate map (40m grid) derived from an unsupervised classification of gridded backscatter and bathymetry derivatives at Rose Atoll, Territory of American Samoa, USA.

Processing steps used to generate the backscatter and bathymetry grids used in the unsupervised classification process are described in their own metadata that can be found at www.soest.hawaii.edu/pibhmc. Rugosity grid was derived from the 40m gridded multibeam bathymetry (clipped as described above) using the Benthic Terrain Modeler (BTM). The BTM may be downloaded from https://coast.noaa.gov/digitalcoast//tools/btm/index.html. The BTM, written for ArcGIS ArcGIS 8.x or 9.x, includes a component for deriving rugosity using methods by Jeff Jenness that he wrote for ArcView 3.x. Bathymetric variance was calculated from the 40 m multibeam bathymetry in a two-step process using ArcMap Version 9.2. The first step involved calculating the standard deviation of the bathymetry layer using the Neighborhood Statistic tool from the Spatial Analyst pull-down menu in ArcMap Version 9.2. Rectangular neighborhood settings were set to a height and width of 3 grid cells to create a small-scale variance layer. The standard deviation output was then squared using the ArcMap Raster Calculater tool. A large-scale variance layer was also generated using a rectangular neighborhood height and width of 5 grid cells. The fine-scale variance is useful for defining small features that may indicate the presence of sediment ripples or possible coral-rich areas whereas the large-scale variance is useful for defining seafloor ridges, pinnacles, and significant changes in slope. Bathymetric variance proved to be a useful tool in previous efforts to map seafloor habitats by Dartnell and Gardner (2004). Their publication can be downloaded from www.soest.hawaii.edu/pibhmc/pibhmc_documentation.htm. The layers were then opened as ESRI GRIDS in the image processing software package ENVI (Environment for Visualizing Images) Version 4.3. A multi-band image was then created with the backscatter, small- and large-scale variance, and rugosity. A mask was also created in ENVI for the dataset. Image classification was performed on the multi-band image using the K-Means unsupervised classification method, which calculates initial class means evenly distributed in the data space and then iteratively clusters the pixels into the nearest class using a minimum distance technique. More information about K-Means algorithm can be found in the ENVI documentation available at www.ittvis.com/envi/. The number of classes was set to two and all other options were set to default. Class color mapping was performed on the resulting classified images. The substrate types of hard and soft were assigned based on a visual interpretation of backscatter intensity and corresponding classes.