Predicting Seafloor Facies from Multibeam Bathymetry and Backscatter Data
Abstract
An empirical technique has been developed that is used to predict seafloor facies from multibeam bathymetry and acoustic backscatter data collected in central Santa Monica Bay, California. A supervised classification used backscatter and sediment data to classify the area into zones of rock, gravelly-muddy sand, muddy sand, and mud. The derivative facies map was used to develop rules on a more sophisticated hierarchical decision-tree classification. The classification used four images, the acoustic-backscatter image, together with three variance images derived from the bathymetry and backscatter data. The classification predicted the distribution of seafloor facies of rock, gravelly-muddy sand, muddy sand, and mud. An accuracy assessment based on sediment samples shows the predicted seafloor facies map is 72 percent accurate.
Department
Center for Coastal and Ocean Mapping
Publication Date
9-2004
Volume
70, Issue 9
Journal Title
Photogrammetric Engineering and Remote Sensing
Pages
1081-1091
Publisher Place
Bethesda, MD, USA
Rights
© 2004 American Society for Photogrammetry and Remote Sensing
Publisher
American Society for Photogrammetry and Remote Sensing
Digital Object Identifier (DOI)
10.14358/PERS.70.9.1081
Document Type
Journal Article
Recommended Citation
P. Dartnell and J. V. Gardner, "Predicting Seafloor Facies from Multibeam Bathymetry and Backscatter data," Photogrammetric Engineering & Remote Sensing, vol. 70, no. 9, pp. 1081–1091, Sep. 2004.