Recent methodological progress in fisheries acoustics

Transcription

1 Recent methodological progress in fisheries acoustics Verena M. Trenkel 1, Laurent Berger 2, Sébastien Bourguignon 3, Ronan Fablet 4, Jaques Massé 1, Valérie Mazauric 5, Cyrille Poncelet 2, Gael Quemener 2, Carla Scalabrin 3, Hector Villalobos 1 Ship route 1 Ifremer, Département EMH; 2 Ifremer, Département NSE; 3 Ifremer, Laboratoire LTH; 4 Telecom Bretagne, CNRS UMR 3192 LabSTICC; 5 Ifremer, Département Direction des Moyens et Opérations Navals;

2 Overview Context of the use of acoustics Extending observation frontiers Data processing challenges Potential applications for an ecosystem approach to pelagic communities of the Bay of Biscay

3 Context of the use of acoustics the technology Non intrusive direct method Scientific calibrated echosounder Vertical echosounders Use of dorsal fish response Very high dynamic of response : «Plankton fish bottom» 5 à 15 Detection range from 100 to 1000m

4 Context of the use of acoustics the method to quantify Routine fish stock assessment surveys : Biomass estimates using echointegration technique Example : PELGAS survey in the bay of Biscay School scattering coefficient Scattering coefficient ESDU

5 Context of the use of acoustics the method to identify Experts interpretation of echograms Sardine Anchovy Horse mackerel Horse mackerel Blue whiting Plancton layer Combined with species composition of trawl hauls

6 Extending observation frontiers From the surface to the bottom in a wider sampling volume

7 Extending observation frontiers third dimension Visualisation and characterization of 3D school structure School of capros aper during PELGAS survey May 2008

8 Extending observation frontiers third dimension Impact of stabilisation and increase resolution The equivalent images of 70kHz transducer not stabilised Three centred beams of the fan 4*3=12 at 77k, 80 and 82 khz, equivalent to the area insonified by single beams taking into account roll and pitch 7±3

9 Extending observation frontiers Surface observations Comparison of sampling by AUV at 50 m with an upward looking transducer with the vessel data (500m behind) The AUV samples additional ten first meters and estimates ten times more biomass during a 20 nm miles dive in the 30 first meters. 14m Example of blind zone on ME70 data Paper submitted IJMS (Scalabrin & Marfia)

10 Extending observation frontiers Bottom observations Deadzone defintion Ona & Mitson θ 3 =7 2θ 3 =3 R=30 m 0,08 0,02 R=50 m 0,14 0,03 R=100 m 0,27 0,05 R=150 m 0,41 0,08 R=200 m 0,55 0,10 Theoretical equivalent dead zone height in meters Comparison of sampling by AUV 30 m above the bottom with a downward looking transducer at ms with the vessel data (500m behind) at 1 ms Hake is not observed by the vessel echosounder Example of ME70 data with echo level above bottom detection depending on the sea floor nature

11 Data processing : data acquisition Multibeam echosounder will supplement single beam multifrequency echosounder in their capacity of analysing frequency response of fishes and other scattering layers : necessity for combined data for complementary information Ifremer HERMES software in charge of coherent remote control of ER60 and ME70 sounders, centralized data storage and broadcasting of the data on the network of the vessel All data logged for further analysis in HAC standard format

12 Data processing : vizualisation MOVIES3D a new tool for data vizualization Combined effect of beam width, ping rate and vessel Effect of speed roll on the sampling of and pitch herring on school in shallow EK60 data water Effect of vessel heading on ME70 data

13 Data processing : modular approach MOVIES3D a toolbox for exploratory research Ability to interface with a third party software such as Matlab

14 Data processing : improving bottom detection Step 1 : Bottom detection in each beam by means of maximum amplitude detection or phase difference cancellation for steered beams Step 2 : Removing erroneous detections (lost pings, dense fish close to seafloor) using contiguity between pings and beams with a Bayesian framework for estimating the bottom depth on beam i of ping k as a function of the preceding ones (estimation of slopes in both direction). Simple application: IBTS 08 campaign with R/V Thalassa, dense herring schools close to the seabed Paper submitted IJMS (Bourguignon et al) for a simple model, work still in progress for the full model

15 Data processing : improving frequency response Objective : account for bias in frequency response estimates caused by beamwidth, transducer position and orientation, and navigation effects on small fish schools Method : Determination of a grid of points corresponding to a school in 3D with ME70 and compute of the percentage of overlap of EK60 data with the school Percentage of overlap of 38 khz with simulated school, effect of school position, beamwidth and navigation Paper submitted IJMS (Berger et al) Influence of filtering (red curve) on frequency response median, 5th and 95th percentiles are displayed

16 Data processing : detecting and describing three dimensional structures Single beam school detector based on contiguity at a given energy threshold value has been extended to multibeam and enables to describe compact schools. A first list of parameters can then be computed: Depth: minimum, maximum and mean Altitude: minimum and maximum Height: maximum Width: maximum Length: maximum Geographic position: geometric and weighted Volume Surface Energy: total and weighted While previous 2D images indicated the presence of many small schools, we now realise that these are often connected to form more diffuse structures. Further descriptors for non-compact schools and ribbons are a logical next step.

17 Potential applications for ecosystem approach of pelagic communities Quantitative characterisation of 3D structures will open up new perspectives for : schooling behaviour in response to the vessel schooling behaviour in response to the environment species identification pelagic habitat mapping In terms of biomass estimates, combined data acquisition will explain and reduce some known bias of single beam echosounder for small structures using the high resolution multibeam technology. The sensor is ready for data acquisition, the pre-processing for extracting relevant information is ongoing and the applications are just started.

18 Example of vertical distribution analysis Image based daily vertical distribution analysis of horse mackerel and anchovy sharing the same habitat during CLASS08 survey horse mackerel in a layer close to the bottom early in the morning horse mackerel with anchovy above it in shallower waters at noon horse-mackerel appeared to move to the sea surface after sunset dispersed anchovy schools in the afternoon

19 Thank you for your attention