monitoring migrating fish in rivers

1

Monitoring migrating fish in rivers

Helge Balk

Department of Physics. University of Oslo.

2

3

Acoustics in rivers

Discussion

Sound propagation

Special tools for

rivers

Analysis chain

Introduction

Noise

Meaning of an observation

Time and area expansion

Interpretation

Sonar5 and river work

Noise and DPF

Observations in Tana and Rimov

Waves Current,

stones, rainHydrophone experiments

TS

DPF

TS and positioning

Measurements in Fisca (Au)

4

Sonar5 tools for river workSonar5 tools for river work

Current profile Bottom profile Exact target positioning Transducer positioning Diurnal coverage

5

Position diagram can present the Position diagram can present the water currentwater current

Important to see whether a track

moves against the current or not

Popup menu

6

Position diagram Position diagram exact position exact position and bottom and bottom profileprofile

Draw bottom with a pencil Move or tilt transducer Raise and lower water level Measure distances

Popup menu

Demonstrate it?

7

Description of the sonar file Description of the sonar file

Control the current, bottom profile, and

transducer placement

8

Sound propagationSound propagation

Estimate sound propagation features

Estimate “true” target position and beam behaviour

9

Site calculator Site calculator

Assist in describing the

transducer position

Originally developed to find the same position each year in River

Tana

10

Diurnal coverage and file Diurnal coverage and file informationinformation

Advanced file open dialog

looks into the files and estimate diurnal

coverage

Individual description and

photo from each file can

be presented as well

Aspect correction and fish sizingAspect correction and fish sizing

12

River analysis chain in River analysis chain in

Sonar5-proSonar5-pro

Echo sounder

transducer position and

alignment

River co-ordinates

x,y,z

Size correction

Track Aspect angle detection

TS / aspect / length regressions

beam mapping

Cross filter

detector

Cross filter tracker

Feature extraction

Classification

10,3,4,7

Split and rule

Focus on detecting as many echoes as possible from all targets. Fish, debris and stones

Focus on generating tracks from all targets, not only fish

Classify tracks

Interpretation

13

Acoustics in rivers

Discussion

Sound propagation

Special tools for

rivers

Analysis chain

Introduction

Noise

Meaning of an observation

Time and area expansion

Interpretation

Sonar5 and river work

Noise and DPF

Observations in Tana and Rimov

Waves Current,

stones, rainHydrophone experiments

TS

DPF

TS and positioning

Measurements in Fisca (Au)

14

If we detect a fish, What does it If we detect a fish, What does it mean?mean?

Echo sounder

?

Fish detection software

15

Interpretation: DPF, noise and time Interpretation: DPF, noise and time expansionexpansion

Measure noise level as a function of time and range over years

Apply noise level as an overall fish size threshold

Use noise level to correct the number of fish within each size group

Apply time expansion to find the total fish abundance estimate

Noise level

Threshold

Intensity

1 2 3 4 5 6 7 8 9 10 Time

High noiselevel

Halt inrecording

High noiselevel

Echo sounder

Fish detector Meaningful

statistics

Interpreter

16

Interpretation: Interpretation: Area expansionArea expansion

Echo sounder Meaningful

statistics!

Covered areaUncovered area

1

0

2

3

2

1

What did we cover?

Where did the fish pass?

Index or total run?

Fish detector

Interpreter

!

17

Acoustics in rivers

Discussion

Sound propagation

Special tools for

rivers

Analysis chain

Introduction

Noise

Meaning of an observation

Time and area expansion

Interpretation

Sonar5 and river work

Noise and DPF

Observations in Tana and Rimov

Waves Current,

stones, rainHydrophone experiments

TS

DPF

TS and positioning

Measurements in Fisca (Au)

18

What is the main problem with fish What is the main problem with fish counting in rivers… counting in rivers…

Noise ,Noise and Noise

What can be worse than noise?

Variable noise

Why?

Variable detection probability

19

Detection Probability Function Detection Probability Function (DPF )(DPF )

Varies with Time Range

More likely to detect a large fish than a small fish a fish when the noise level is low a fish in the centre of the beam a fish passing normal towards

the transducer a fish if the cross section

coverage is increased

Noise as a function of range

Noise as a function of time

Rimov experiment, water current Rimov experiment, water current

B

A

C

D2.65 m

0 m 5.8m 9m

21

Rimov experiment, water currentRimov experiment, water current

Observed:Observed:

Increased reverberation Increased reverberation Shift in target positionShift in target positionreduced stability in TS estimatesreduced stability in TS estimates

22

Rimov experiment, Noise from Rimov experiment, Noise from stones and wavesstones and waves

23

Rimov experiment, Noise from Rimov experiment, Noise from waveswaves

24

Acoustics in rivers

Discussion

Sound propagation

Special tools for

rivers

Analysis chain

Introduction

Noise

Meaning of an observation

Time and area expansion

Interpretation

Sonar5 and river work

Noise and DPF

Observations

In River Tana

In RimovHydrophone experiments

TS

DPF

25

Strange observations in River TanaStrange observations in River Tana

26

Strange observations in River TanaStrange observations in River Tana

We observed that the target was

visible

Until it was laying on the bottom

On the outside of the boat

from the moment it touched surface

27

Strange observations in River TanaStrange observations in River Tana

a) meter 6.5 12 27. 42 52 m.b) Depth 2.25 2.7 3.0 3.5 3.5 m.c) beam 0.45 0.84 1.89 2.93 3.63 m.

Depth (y)(m)0

1.00

2.00

3.00

28

Summary of observations in Summary of observations in River TanaRiver Tana

1. Target observed far outside the theoretical beam

2. Target strength observed to increase with range

3. No clear beam pattern was found at any tested range

4. Vertical phase reading seemed corrupted

5. Horizontal phase reading seemed fine

6. Returned echo length observed to be 1.5 times the transmitted pulse-length

29

Strange observations in RimovStrange observations in Rimov

0 5.14 7 10 Range(m) 0.36 0.49 0.7 Beam thickness (m)

0 0.25 0.50 0.75 1.00 1.25 1.50

Looked like a second beam

under the existing beam

30

Summary of observations in RimovSummary of observations in Rimov1. Target observed outside the theoretical beam

2. The target behaved normally within the position of the theoretical beam

3. A second beam was observed under the first beam

4. The under beam’s intensity show the same profile as the main beam, but with weaker echo intensity

5. The under beam’s angle estimates show a narrower opening angle than the main beam

6. A transition region was observed between the two beams at shorter range. At longer range the two beams melted together into one wide beam

7. Echo-pulse-length did not differ from the transmitted pulse-length

8. The transducer depth and tilt influenced the phenomenon

9. The phenomenon depended on the transducers opening angle

10. The phenomenon varied with range

32

What causes the observed What causes the observed phenomena?phenomena?

Refraction and reflection Sound channels and waveguides Image interference Dipole effect Side lobe effect

Direct sound measurementsDirect sound measurementsShow separate presentation

Direct sound measurementsDirect sound measurements

35

Acoustics in rivers

Discussion

Sound propagation

Special tools for

rivers

Analysis chain

Introduction

Noise

Meaning of an observation

Time and area expansion

Interpretation

Sonar5 and river work

Noise and DPF

Observations in Tana and Rimov

Waves Current,

stones, rainHydrophone experiments

TS

DPF

TS and positioning

Measurements in Fisca (Au)

36

River Fischa in River Fischa in AustriaAustria

37

Target detection possibleTarget detection possible

Parametric SED Crossfilter SED

38

Lifting target from bottom to Lifting target from bottom to surface. Positioning and sizing?surface. Positioning and sizing?

SED, TSc versus ping AMP, TSu versus ping

39

10 meter long salmon10 meter long salmonobserved observed

40

Acoustics in rivers

Discussion

Sound propagation

Special tools for

rivers

Analysis chain

Introduction

Noise

Meaning of an observation

Time and area expansion

Interpretation

Sonar5 and river work

Noise and DPF

Observations in Tana and Rimov

Waves Current,

stones, rainHydrophone experiments

TS

DPF

TS and positioning

Measurements in Fisca (Au)

41

What can we do with the detection What can we do with the detection probability probability

Site selection

Bottom modification

Surface modification

Guide fish

Opening angle

Sound frequency

Estimate the DPF

Reduce the variation in the DPF

42

Can we estimate TS in a river? Can we estimate TS in a river?

Spherical or cylindrical spreading? Corrupted vertical angular measurements? Additive noise? Can we compensate for swimming motion?

Can we compensate for fish aspect?

43

Can we estimate TS in a river?Can we estimate TS in a river?

Alternative methods to establish the size?

Beam intensity mapping?

Apply reference targets?

Multiple narrow beams?

Multiple frequencies?

Aspect detection and correction?

44

x

45

ENDEND

46

Tracking principlesTracking principles Split and rule

1. Focus on detecting as many echoes as possible from all targets. Fish, debris and stones

2. Focus on generating tracks from all targets, not only fish

3. Classify tracks

4. Interpret counting result

47

Fish counting

Interpretation

Track size estimation

Noise level analysis

Time expansion

Area expansion

Analysis

Crossfilter tracking

Track classification

Pre-analysis Noise level Bottom line detection

Cross filter SED detection

Preparation

Bottom profile

Study test recordings

48

Pre-Analysis Pre-Analysis

Use “vertical” bottom detection to avoid analysis of the outer noisy range.

Low-pass filter to reduce noise Cross-filter detector to detect single targets

49

AnalysisAnalysis

Manual tracking and classification, Too subjective? Cross filter tracking Automatic or manual classification Fish baskets, track storing and track sorting Track statistics

50

OutlineOutline Noise

Experiments with rain, waves, stones, current… Detection probability function

Sound propagation observations Refraction and reflection Tana and rimov phenomenon Direct hydrophone measurements

What about target strength? Observations in River Fischa (Au)

Interpretation of results Our analysis procedure for rivers What we have learned about tracking

Split and rule Manual tracking is subjective and time consuming Amp echogram is important

Tools supporting river applications in Sonar5-Pro Discussion