Abstract
Using data sets collected north of San Francisco Bay (CA) an ArcGIS classification toolset was developed using supervised image classification tools to characterize potential shallow marine benthic habitats. First-derivative images and a topographic algorithm, called Bathymetric Position Index were created from the bathymetry data set using ArcGIS Spatial Analyst tools. Backscatter intensity was also analyzed by creating training samples based on the collected sediment samples and then applying multivariate statistical tools to delinate textural classes. The data collected revealed a rugged and complex seafloor and imaged in detail basement and bedrock outcrops, sand and gravel bedforms, and flat sediment covered seabed.
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