Return to search

Fish stock assessment by a statistical analysis of echo sounder signals

A means of assessing the quantity of exploitable fish in the sea is a requirement for effective management of the resource. Sonar is widely used in this regard, as it provides a rapid means of assessment. Two acoustic assessment techniques currently used are the echo counting and echo integration· methods. The echo counting method requires that only single fish echoes are present in the backscatter from the shoal, while the echo integration technique requires an a-priori knowledge of the average target strength of the fish in the shoal. A novel method of assessment has been proposed. It relies on the relationship between the statistics of the backscatter from a volume distribution of scatterers and the number of scatterers contributing to the backscatter at any one time. The attraction of the method when applied to the estimation of number density of fish, is that estimates can be produced in the presence of overlapping echoes, and that knowledge of the target strength of the fish is unnecessary. The application of this method to acoustic fish stock assessment is investigated in this work. Current methods of assessment are reviewed and the theory of the statistical method is given. A computer simulation of the scattering problem gives a useful insight into the effects of sample size and density on the accuracy of the method. The method has been applied to the assessment of fish at sea, where it was run in tandem with an echo integrator. The results obtained with the two techniques are compared. Reasons for discrepancies are proposed and problems in the application of the method are identified.

Identiferoai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/19509
Date January 1986
CreatorsWeintroub, Jonathan
ContributorsDenbigh, P N
PublisherUniversity of Cape Town, Faculty of Engineering and the Built Environment, Department of Electrical Engineering
Source SetsSouth African National ETD Portal
LanguageEnglish
Detected LanguageEnglish
TypeMaster Thesis, Masters, MSc (Eng)
Formatapplication/pdf

Page generated in 0.0023 seconds