HR: 11:55h
AN: OS32C-03    [Abstracts]
TI: Automated Rugosity Values From High Frequency Multibeam Sonar Data for Benthic Habitat Classification
AU: * Diurba, E S
EM: diurba@hawaii.edu
AF: University of Hawaii at Manoa, 1680 East West Road Post 842 C, Honolulu, HI 96822
AU: Appelgate, B
EM: bruce@soest.hawaii.edu
AF: Hawaiian Mapping Research Group, University of Hawaii at Manoa 1680 East West Road, Honolulu, HI 96822
AB: Rugosity literally means wrinkled, or marked with folds and ridges. When applied to seafloor texture, rugosity is one piece of the puzzle that is necessary in order to understand benthic habitats. On a simple level, rugosity is a proxy for habitat complexity and serves as a basis for predicting population density as well as species inhabitants (Friedlander, 1998). Traditionally, rugosity was calculated by draping a chain of known length over the seafloor and comparing the total chain length with the distance the chain covered. The equation Rugosity=100*(length of chain/ actual length of chain) was then applied to get a percent rugosity. However, the process is time consuming and provides little information about the overall bottom roughness. Our objective was to develop a new acoustic method for measuring rugosity and employ this method in conjunction with other types of data, to rapidly characterize the seafloor of a coral reef environment. We developed an automated algorithm to determine the acoustic rugosity of seafloor surfaces from a 240 kHz multibeam sonar data on a ping-by-ping basis. The multibeam data was collected in 2003 during a survey of an anchorage along the NW side of Saipan. This data, collected by the National Oceanic and Atmospheric Administration's (NOAA) Coral Reef Ecosystem Division (CRED), utilized a RESON SeaBat 8101 multibeam sonar that has a vertical resolution of 1.25cm and a range capability of 300m. Each 150 degree ping consists of 101 beams that are each individually logged with x (distance across the ping), and z (depth) coordinates. The rugosity algorithm bins the beams along each ping and uses the x and z coordinates to calculate rugosity. By binning the data solely in the cross-track direction, all rugosity calculations preserve the spatial resolution without distortions from along-track averaging and gridding. This method allows for extremely detailed data sets and quick information about surveyed areas that would otherwise have taken years for divers to manually collect to the same level of resolution. The final product is an objectively derived, quantitative map that is easily comparable to the bathymetry and sidescan data. We present results of this method, using various bin sizes, to show how they compare with other spatial indications of rugosity, as well as with manual and optical rugosity measurements.
DE: 0520 Data analysis: algorithms and implementation
DE: 0933 Remote sensing
DE: 0994 Instruments and techniques
DE: 3045 Seafloor morphology, geology, and geophysics
DE: 4220 Coral reef systems (4916)
SC: Ocean Sciences [OS]
MN: Fall Meeting 2005