A semiautomated approach to estimating fish size, abundance, and behavior from dual-frequency identification sonar (DIDSON) data

We present a semiautomated analytical approach incorporating both image and acoustic processing techniques to apply to dual-frequency identification sonar (DIDSON) data. Our objectives were (1) to develop a standardized analysis pathway in order to reduce the effort associated with counting, measuring, and tracking fish targets; and (2) to empirically obtain estimates of basic target information (e.g., size, abundance, speed, and direction of travel). Analyses were conducted on DIDSON data collected at three different locations (the Kenai River, Alaska; Mobile River, Alabama; and Port Fourchon, Louisiana) with different equipment and deployment configurations. We developed an efficient postprocessing approach that can be applied to a variety of data sets, independent of user and deployment method. For two of the three data sets analyzed, the estimates of fish abundance derived from DIDSON analyses were not significantly different from the manual counts of DIDSON files. The analyses produced estimates of mean fish length, direction and speed of travel, and target surface area for all targets within each data set. A consistent analysis platform increases the acceptance and reliability of the DIDSON as a tool for fisheries surveys and further demonstrates the usefulness of DIDSON technology in fisheries applications.