25nd Scandinavian Symposium on Physics Acoustics
Ustaoset 27-30 January 2002
Fish detection in rivers with split-beam sonars
Helge Balk and Torfinn Lindem
University of Oslo, Department of Physics, Pb 1048 Blindern, 0316 Oslo, Norway. Fax:
22856422. Email: helge.balk@fys.uio.no, torfinn.lindem@fys.uio.no.
Introduction
For the management, knowledge of
the fish stocks is of great interest.
Migrating fish in a river can be monitored
with mechanical, conductivity, or optical
sensors. These sensors have short range
and the fish has to be led close by. When
this is inconvenient or not possible,
acoustic methods provide an alternative.
Especially the split beam transducer with
its capability of directly detecting the
targets acoustic size and 3D-position has
become popular [8]. Placed horizontally
normal to the river at a suited place, the
conical sound beam can cover major parts
of the river cross-section and register
passing fish.
This method has been studied
through a four years Dr. Scient. project:"Development of hydroacoustic methods
for fish detection in shallow water" [2]. A
summery of experiences and results is
given in this proceeding.
Method
The study was carried out by in
turns performing fieldwork and analysing
recorded material. Data collected by other
scientists was studied as well. The data
was basically recorded with a Simrad
EY500 scientific split-beam echo sounder
equipped with a 120 kHz, 4x10 deg.
transducer ES120-4 [1]. Sound pulses were
transmitted with duration of 0.1 and 0.3
mS, with a power of 63 watt, and a
repetition rate of 5-10 pulses per. sec.
Long term monitoring projects was
carried out in River Tornionjokki (Fi),
River Tana and River Lysaker (No).
Controlled experiments was carried out on
the ice of lake Semsvann (No), and in a
pond in Rimov (Cz).
To perform the analysis a software
program (Sonar5) was developed by the
application
of
Borland's
Delphi
programming environment. Methods for
visualising the data, for noise reduction
and for automatic fish detection were
implemented. Tests were performed with
simulated data and with data from rivers
and lakes.
Why it is difficult to count
fish in a river
a) We can not cover the total cross
section area of the river with the sonar
beam. This can be solved by the
application of area expansion [6].
However, area expansion is complicated
because we will have to adjust for changes
in the water level, transducer alignment
and position. Even the river bottom might
change during a monitoring period. The
beam has a range dependent height and
width, and it is necessary to make
assumption about the fish density
distribution.
b) In the recorded echograms, the
noise level will depend on time, range and
position. At higher range the beam will
approach the bottom and /or the surface,
which will increase the amount of reflected
energy. Reflections from the surface will
change with the weather (waves and rain)
while the reflections from the bottom will
depend on the transducer position and pan.
Changes in the tilt will affect the reflection
from both boundaries.
c) In a river there are more than
fish to be detected. Debris, air bubbles,
boat wakes and birds diving for food can
produce fish like tracks as well. Multiple
fish species and fish schools are also
difficulties one has to consider [9].
It may be fruitful to discuss the
points in (a), (b) and (c) in terms of a
detection probability function (DPF). The
DPF is a multidimensional function with
variables such as time, range, off-axis
position, fish-size, fish distribution etc.
The monitoring systems efficiency will
vary with the DPF. Hence, lack of control
and knowledge of its variations may easily
result in meaningless results.
Figure 1. Horizontal transducer placement in a river.
Results
Interpretation
In order to solve stated problems,
two approaches are possible. i) Gain
control of the DPF by monitoring or
estimating its variables and ii) reduce
variations in the DPF by reducing the
influence from the variables.
The temporal variations in the
covered cross section area can be
calculated by monitoring the water level
and by mapping the bottom profile. The
fish density distribution can be estimated
from alternative studies of the fish
migration pattern or one can apply a "thin"
beam and let it scan the water cross section
in steps.
Reducing the possibility for fish to
slip under the sound beam or behind the
transducer can be done by modifying the
bottom or by guiding the fish with ropes
and nets. In River Tana we mounted a
coloured rope along the lower edge of the
sonar beam at a place where the river
bottom had an unreachable trench. From
video recordings we got indications that
fish preferred to raise from the bottom
when they passed the rope. Due to the
nature of the sound beam the systems DPF
will be low at short and long range. Hence,
limiting the fish to pass through the most
effective parts of the beam will reduce the
influence from the DPF.
The surface is responsible for a
major part of the temporal fluctuations in
the DPF. From data recorded in River Tana
it was clearly seen that the noise level
increased during periods of waves or rain.
Keeping the beam lower in the water
reduced the influence from the surface, but
this led to increased disturbance from the
bottom. Selecting a site sheltered from
wind and waves will help, and it is also
possible to smooth the surface with
floaters.
In periods with high noise level,
smaller fish will tend to disappear in the
recorded material. In a long monitoring
project lasting for years, noise levels
should be measured to find an overall size
threshold. In periods when the noise level
exceeds this threshold, the number of
smaller fish should be estimated by time
expansion.
Another way of reducing the
influence of the DPF is by improving the
data analysis methods. Data from rivers are
often analysed by interactive methods. An
operator looks at the echograms and count
fish tracks. In echograms with low SNR
this method easily becomes biased. If the
operator knows that many fish have been
caught in a particular period, noise based
tracks will easily be accepted as fish.
Hence, automation is important. This
system must be able to separate noise from
solid targets and to recognise fish from the
other targets.
Data analysis
Single echo detection and tracking
If the sound pulse hits one fish
without disturbance from any other
elements, the returned echo will have
nearly the same duration as the transmitted
pulse, one single peak, and a stable phase.
This has led to the development of the
single echo detectors (SED) used by most
echo sounders. They work well in open
water situations. However, in shallow
rivers, echoes from fish frequently avoid
the single echo description, while echoes
from unwanted targets and peaks in the
background reverberation level often are
accepted. When the ratio between wanted
and unwanted echoes (WUR) becomes
low, tracking and fish detection becomes
difficult.
Our first attempt to improve the
ratio was to remove most of the traditional
criteria except the echo duration. Setting
the echo duration criteria much wider than
normal increased the detection of passing
fish. Naturally this also increased the
amount of unwanted detections, but by
applying 2 dimensional low-pas filters in
the time and range domain the WUR could
be substantially improved relative to the
WUR found with traditional methods.
Image analysis
A second solution to the detection
problem was found by means of image
analysis. We discovered that different echo
sources had different frequency spectra in
the time and range domain. A low-pass
filter with a low cut of frequency in the
range domain could be used to detect the
instant background reverberation level. A
filter with high cut of frequency in the
range domain and a medium cut-of
frequency in the time domain could
improve tracks from fish and suppress
noise pulses. Variations in echo intensity
and echo phase from passing fish are well
known in rivers and leads to gaps in the
tracks. Gaps make tracking difficult [3].
LP-filters enhance the tracks by "moving"
energy into weaker parts of the tracks,
which reduce the gap problem.
With filters it was possible both to
highlight and to suppress tracks from
passing targets. Based on this observation a
new target detection method "Adaptive
thresholding by cross filtering and
subtraction" was developed. (Patented:
PTC/NO00/00288).
Let H be a matrix containing lowpas filter coefficients and F the original
sampled echogram. Then folding F with H
will produce the filtered echograms Q1 and
Q2. With an echogram recorded in a
shallow river with the described
equipment, Q3 will according to our
experience mostly contain tracks from
passing targets.
Q1(m1, m2 ) =
Eq. 1
Q2 (m1, m2 ) =
3
1
∑ ∑ F (r,t ) H (m1 − r + 1, m2 − t )
t =−3 r =−1
1
21
∑ ∑ F (r, t )H (m1 − r + 1, m2 − t )
t =−1 r = −21
F (m1, m2),
Q3 (m1, m2 ) = 
 0,
Q1(m1, m2 ) − Q2 (m1, m2 ) > threshold
Q1(m1, m2 ) − Q2 (m1, m2 ) ≤ threshold
Figure 1 Left: Up-stream migrating fish track detected in River Tana summer 1999. Right:
Same part of the echogram processed b the adaptive cross-filter threshold method.
Classification
The tracks detected by the
described methods might origin from
different kinds of targets like reminiscent
noise, debris, fish, lures, swimming or
diving birds or reflections from stones on
the bottom. Hence, classification is
necessary. To classify one needs track
features.
An obvious feature is the
velocity. Targets moving directly against
the current are more likely to be fish than
most of the other targets. Unfortunately the
uncertainty in the split beam systems
angular estimates are too high in a shallow
river for this criterion to be sufficient
alone. Echoes from reminiscent noise and
even from a stone on the bottom can take
on an upstream movement comparable
with the average speed of a migrating fish.
Other criteria such as the number of times
a track changes direction, total moved
distance, fluctuation in echo intensity, the
track's deviation from a straight line or the
track curvature can be calculated.
Discriminant function analysis (DFA)
provides a way to deal with a multivariate,
multiple class system. A training set
containing typical tracks from the different
classes has to be defined. Then the sum of
squares within (W) and between (B) the
classes are calculated from the track
features. In a multivariate system W and B
will be matrices. We want a co-ordinate
system optimised so that the ratio between
B and W (W-1B) are as large as possible.
This can be found by calculating the ratio's
eigenvalues (λ) and eigenvectors (ε). (W1
B)ε=λε. The elements in the eigenvectors
can now be used to orthogonise the room
spanned by the features or variables.
Hence, calculating the Euclidean distance
between a new item and the class centroids
can do classification. In a multiple class
system it is common to express the
distances as a score function for each class
so that the most likely class results in the
highest score [4],[7]
DFA was tested on detected tracks.
It was found that it was possible to filter
out major parts of the unwanted tracks
without "loosing" any of the fish we
wanted to detect.
SC Fish = −2.68 x14 + 0.46 x24 + 39.72 x28 + 0.66 x39 − 59.86
SC Debri = −3.30 x14 + 0.68 x24 + 14.8 x28 + 1.34 x39 − 84.97
Eq. 2
SC Noise = −2.30 x14 + 0.44 x24 + 1.83x28 − 2.34 x39 − 10.44
Eq. 2 demonstrates the sum-score
functions for three target classes. x
represent features extracted from a track
while the factors have been calculated
from the eigenvectors of the training sets.
In one test, 800 tracks were
detected by a traditional SED-tracking
method in River Numedalslågen (Norway).
This set contained 8 tracks believed to
origin from upstream migrating fish. With
DFA, it was possible to reduce the
unwanted / wanted track ratio from 800:8
to 24:8. It remains to test DFA on tracks
detected by the cross-filtering method. It is
reasonable to believe that even better
results will be achieved, because the crossfiltering technique allows more features to
be calculated and produces tracks with
fewer gaps and less surrounding noise.
Strange behaviour of the sound-beam
During fieldwork in River Tana
summer 98 and 99 we observed that a
target could be detected in the entire water
cross-section and not only within the
expected 4-deg. sound cone.
This was observed from 6 to 53 m
from the transducer and with a water depth
stretching from 2.25 to 3.5 m. We have not
been able to explain the observations by
means of refraction, reflections, wave
guides or dipole effects [2].
Summery
Figure 3 demonstrates the most
important post-processing elements needed
to analyse split-beam sonar data from a
river. The target detection (a) is based
solemnly on the output from the echosounders amplitude detector. This was
done because the phase signal was found
too unreliable in many rivers. When a
target has been located, the angular
positions are extracted from the echosounders phase detector (b). A quality
factor is calculated from the shape of the
echo pulse and from the deviation in the
phase samples within each echo pulse. This
enables us to apply all echoes in the
classification while low quality echoes can
be kept out of sensible calculations such as
size and velocity estimates. In (c) the
targets acoustic size is corrected for offaxis positions and for variation due to the
fish aspect [5]. In (d), track features are
calculated and the tracks classified before
the results are interpreted (e).
Echosounder
a) target
detector
b) phase and
quality estimation
c) size
correction
d)
classification
e)
interpretation
Figure 3. The most important elements in the post-processing chain. In the figure, the echosounder outputs the result from the amplitude detector on the left side and the phase signal on
it's right side.
Conclusions
Split-beam echo sounders can
monitor fish in shallow rivers. However,
great care has to be taken especially with
long term monitoring projects lasting for
months and years. In addition to the echo
data it is important to monitor parameters
such as the water level, bottom profile,
sound beam behaviour, and noise level. It
will always be possible to set up the
system so that the results will have no
meaning.
Automatic fish detection is possible
with the described track detection and
classification methods.
References
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