CRED Preliminary hard and soft bottom seafloor substrate map derived from an unsupervised classification of gridded backscatter and bathymetry derivatives at the U.S. Territory of Guam.

Processing steps used to generate the backscatter, bathymetry, and bathymetric rugosity grids used in the unsupervised classification process are described in their own metadata that can be found at www.soest.hawaii.edu/pibhmc. Backscatter data were resampled to 5 m grid cell size using the Raster Resample Tool in the ArcMap Version 9.2 Toolbox. Resampling of the backscatter was performed after charted data in the GeoTIFF format were mosaicked using the ArcMap Version 9.2 Toolbox Mosaic to New Raster function. Mosaics were created in the ESRI GRID format. Bathymetric variance was calculated from the 5 m 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 2 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. Bathymetric rugosity was calculated using the ArcGIS Benthic Terrain Modeler (D.J. Wright et al., 2005) that is accessible for download at http://www.csc.noaa.gov/products/btm. The layers were then opened as ESRI GRIDS in the image processing software package ENVI (Environment for Visualizing Images) Version 4.4. A multi-band image was then created with the backscatter, small- and large-scale variance, and rugosity. A mask was also created in ENVI. Image classification was performed 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 results from supervised classification of the backscatter data (also performed in ENVI) using hard and soft bottom classes from classified seafloor photographs in other study sites including French Frigate Shoals, Northwestern Hawaiian Islands and Tutuila, American Samoa. Those datasets can be found at the PIBHMC website. In addition to the processing steps mentioned above, the high-intensity, track parallel, nadir signal was removed from the Reson 8101 backscatter after classification because it causes false classification results. The nadir signal was removed by creating a 15 m buffer in ArcGIS around the R/V AHI trackline navigation data sampled every second. The buffer was used to mask the Reson 8101 backscatter data and results in the trackline parallel unclassified data gaps. Methods to most accurately interpolate across the gaps are currently under investigation.