Distribution and seasonal biomass of drift macroalgae

Journal of Experimental Marine Biology and Ecology 326 (2005) 89 – 104
www.elsevier.com/locate/jembe
Distribution and seasonal biomass of drift macroalgae in the Indian
River Lagoon (Florida, USA) estimated with acoustic seafloor
classification (QTCView, Echoplus)
Bernhard M. Riegl a,*, Ryan P. Moyer c, Lori J. Morris b,
Robert W. Virnstein b, Samuel J. Purkis a
a
National Coral Reef Institute, Nova Southeastern University Oceanographic Center, 8000 North Ocean Drive, Dania Beach, FL 33004, USA
b
St. John’s River Water Management District, 4049 Reid Street, Palatka, FL 32177, USA
c
Department of Earth and Environmental Sciences, University of Pennsylvania, Philadelphia PA 19104
(formerly NCRI, Nova Southeastern University), USA
Received 13 September 2004; received in revised form 20 May 2005; accepted 20 May 2005
Abstract
Three areas of the Indian River Lagoon, Florida (USA) were surveyed to show seasonal changes in the distribution and
biomass of macroalgae and seagrass. Acoustic seafloor discrimination based on first and second echo returns of a 50 kHz
and 200 kHz signal, and two different survey systems (QTCView and ECHOplus) were used. System verification in both the
field and a controlled environment showed it was possible to distinguish acoustically between seagrass, sparse algae, and
dense algae. Accuracy of distinction of three classes (algae, seagrass, bare substratum) was around 60%. Maps were
produced by regridding the survey area to a regular grid and using a nearest-neighbor interpolation to provide filled
polygons. Biomass was calculated by counting pixels assigned to substratum classes with known wet-weight biomass values
(sparse algae 250 g m 2, dense algae 2000 g m 2, seagrass 100 g m 2) that were measured in the field. In three study areas
(Melbourne, Sebastian Inlet, and Cocoa Beach), a dependence of algal biomass on depth and season was observed. Seagrass
most frequently occurred in water less than 1 m deep, and in November, seagrass beds tended to be covered by dense algae
that also extended up- and downstream of shoals in the Lagoon. In March, the pattern was similar, with the exception that
some areas of previously dense algae had started thinning into sparse algae. Macrophyte biomass was lowest in May in the
Melbourne and Cocoa Beach study areas, with the opposite situation in the Sebastian Inlet study area. In May, seagrass areas
were largely devoid of dense algae and most algae accumulations were sparse. In August, dense algae covered large areas of
the deep Lagoon floor while shoals were largely free of algae or had only sparse cover. We suggest this summer pattern to
reflect moribund algae being washed from the shallows to deeper channels and from there being removed from the lagoonal
ecosystem either through tidal passages into the open ocean or by degradation and breakdown in situ. The differences
* Corresponding author. Tel.: +1 954 262 3671; fax: +1 954 262 4027.
E-mail address: [email protected] (B.M. Riegl).
0022-0981/$ - see front matter D 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.jembe.2005.05.009
90
B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104
between the study areas indicate high spatial and temporal variability in biomass and distribution of macrophyte biomass in
the Indian River Lagoon.
D 2005 Elsevier B.V. All rights reserved.
Keywords: Acoustic seafloor discrimination; Algae; ECHOplus; Florida; Indian River Lagoon; Macroalgal biomass; QTCView; Seagrass
1. Introduction
Seagrass and macroalgae beds are key ecosystems
in the Indian River Lagoon (Florida, USA), which is a
shallow, largely enclosed, coastal waterbody separated
from the Atlantic Ocean by a series of barrier islands
(Fig. 1). The Lagoon’s salinity is variable with tidal
movements, but generally lower than normal marine
(35 ppt) and water is typically turbid. There are seven
species of seagrass present. The three most abundant,
canopy forming species are Halodule wrightii, Syringodium filiforme, and Thalassia testudinum (Morris et
al., 2000). These species of seagrass often occur in
dense beds together with or adjacent to drift macroalgae where biomass has been estimated at 3 times, in
extreme cases 100 times, that of seagrass (Morris and
Hall, 2001). Due to their great abundance, drift macroalgae are considered to have a habitat value comparable to that of seagrass and since the densities of
animals on the two vegetation-types are similar and
they share about 75% of the same species (Virnstein
and Howard, 1987), the drift macroalgae are generally
considered an extension of the seagrass habitat. The
estimation of drift algae biomass is important for the
determination of overall nutrient release and/or uptake
in the Indian River Lagoon system (Virnstein and
Carbonara, 1985). Hence, maps of seasonal distribution are needed.
An easy and accurate way of mapping shallow
benthic habitats is by passive optical remote-sensing
using airphotos or satellite imagery (multi- or hyperspectral, Green et al., 2000; Dierssen et al., 2003;
Fig. 1. Study areas in the Indian River Lagoon, central Florida, USA.
B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104
Zimmermann, 2003). However, the typically turbid
conditions in the Indian River Lagoon, largely driven
by nutrient input and sediment re-suspension, usually
render optical methods inappropriate. Acoustic seafloor discrimination provides an alternative that is not
dependent on water clarity. The method is well established (Chivers et al., 1990; Preston et al., 2000;
Lawrence and Bates, 2001; Ellingsen et al., 2002;
Riegl and Purkis, 2005) and several commercially
available systems exist (among others RoxAnn, Biosonics, Echoplus, QTCView; for reviews see Hamilton et al., 1999; Lawrence and Bates, 2001; Bates and
Whitehead, 2001; Sabol et al., 2002; Kenny et al.,
2003) that are capable of detecting differences in
sediment types (Hamilton et al., 1999; Preston et al.,
2000; Freitas et al., 2003a,b) and can differentiate
artifacts from sediments (Lawrence and Bates,
2001). It has also been used to detect biotopes by
jointly using biological and acoustic sampling (Freitas
et al., 2003a,b). In this present study we examined the
suitability of the QTCView Series V (Quester Tangent
Co., Sidney, B.C.) and Echoplus (SEA Ltd, Bath, UK)
systems to detect boundaries of areas covered by
different densities of seagrass and drift algae in shallow waters of mostly less than 2 m depth. It was our
goal to estimate seasonal biomass and map distribution in three test areas of the Indian River Lagoon
(Melbourne, Sebastian Inlet, Cocoa Beach).
2. Materials and methods
Three areas within the Indian River Lagoon, Florida, USA (Fig. 1) were surveyed in November 2002,
March 2003, May 2003 and September 2003. The
winter (November 2002) and summer (May 2002)
surveys covered the biggest area in order to account
for maximum seasonal variability in seagrass and
algal standing stock.
The Melbourne survey area was situated immediately to the south of the US 192 causeway (Fig. 1) and
was characterized by a navigation channel (4 m) in the
middle, shallowing to depths of less than 1 m on the
Lagoon’s sides. Also, two headlands existed on the
eastern shoreline, from which shoals extended towards the Lagoon’s center. The Sebastian Inlet survey
area (Fig. 1) was situated immediately to the south of
Sebastian Inlet. Maximum depth in the area was 3.6 m
91
while the shallowest areas were at 0.9 m. No deep
navigation channel was found within the Sebastian
Inlet study area. The Cocoa Beach survey area was
situated between the Port Canaveral and Cocoa Beach
causeways and was characterized by shallow sides
and a deeper navigation channel in the middle,
where water depths reached over 5 m. It had the
deepest and widest central trough of all three study
areas. Also, two headlands existed on the eastern
shoreline, from which shoals extended towards the
Lagoon’s center. Of these, the northern shoal was
bigger. Both shoals were, at least over the survey
period, permanent features.
The surveys were conducted from a 7-m survey
vessel equipped with a Trimble Ag132 global positioning system (dGPS) that used coast-guard beacon
differential corrections to obtain real-time horizontal
positioning accuracies of mostly less than 1.2 m horizontal dilution of precision. Data were logged as
NMEA-GGA string, which encodes the horizontal
accuracy of each position, to allow quality control
of positioning during data processing. Geo-rectified
aerial photographs of the survey areas were loaded
into the softwares Fugawi and Hypack, that were
interfaced with the dGPS unit to allow real-time monitoring of vessel position with respect to the imagery
and planned survey lines. Sonar signals were obtained
using a 50 kHz signal from a Suzuki TGN60-50H12L transducer and a 200 kHz signal from a Suzuki
TGW50-200-10L omnidirectional transducer, both
with 0.4-ms pulse width and a 5-Hz sampling frequency with a beam angle of 428 (50 kHz) and 128
(200 kHz), respectively, provided by a Suzuki 5200/
6dB depth sounder. Signal saturation was avoided by
an autogain control feature in both survey systems.
Depth was determined by using the QTCView bottom
picking algorithm, which was accurate to at least 10
cm (tested with bar checks; Brinker and Minnick,
1994) and by direct cross-checks of displayed bathymetry and measured depth with a weighted line
at several sites. Survey lines were spaced variably, but
mostly not more than 100 m apart.
The principles of acoustic ground-discrimination
based on single-beam echosounders employed by
the systems are well-reviewed elsewhere (Chivers et
al., 1990; Hamilton et al., 1999; Preston et al., 2000;
Lawrence and Bates, 2001; Bates and Whitehead,
2001; Freitas et al., 2003a,b; Riegl and Purkis,
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B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104
2005) and will not be reiterated here. We used the
QTCView Series 5 and Echoplus turn-key survey
systems which perform acoustic ground discrimination based on the shape of sonar returns (Hamilton et
al., 1999; Quester Tangent Corporation, 2002). The
two systems are based on similar assumptions and
record the characteristics of reflected waveforms to
generate habitat classifications based on the acoustic
diversity of collected echoes which encode scattering
and penetration properties of different types of seafloor (Chivers et al., 1990; Preston et al., 1999;
Hamilton et al., 1999). The typical process involves
a hydrographic survey during which acoustic data are
collected. In QTCView, these are recorded as timestamped, dGPS-geolocated digitized envelopes of the
first echo. Data are then processed in the software
QTC Impact and checked by the operator for correct
time-stamps, correct depths, and correct signalstrengths. All signals that do not pass an appropriate
level of quality control are discarded and not used for
further processing. Data are also displayed on a bathymetry plot, where recorded depths are checked
against the blanking (minimum recordable) and maximum depths set for the survey and any faulty depth
picks are removed manually before further processing.
In QTC Impact software, the echoes are digitized,
subjected to a variety of analyses (cumulative amplitudes and ratios of cumulative amplitudes, amplitude
quantiles, amplitude histograms, power spectra, wavelet packet transforms) by the acquisition software
(Preston et al., 2001, 2004). After being normalized
to a range between 0 and unity, they are subjected to
Principal Components Analysis (PCA) for data reduction. The first three principle components of each
echo are retained (called Q values), according to the
assumption that these explain the majority of variability in the data set (Quester Tangent Corporation,
2002). Datapoints are then projected into pseudothree-dimensional space along these three components, and subjected to cluster analysis using a Bayesian approach (Quester Tangent Corporation, 2002).
The user decides on the number of desirable clusters
and also chooses which cluster is split how often.
Clustering decisions are guided by three statistics
that are offered by the program called bCPIQ (Cluster
Performance Index), bChi2Q and bTotal ScoreQ. Total
Score decreases to an inflection point which is da
strong indication of best split levelT (Quester Tangent
Corporation, 2002). CPI increases with more cluster
splitting (Kirlin and Dizaji, 2000; Freitas et al.,
2003b), while Chi2 decreases with more cluster splitting, reaching maximum/minimum values at optimal
split level (Quester Tangent Corporation, 2002). In
our case, the number of desired clusters was easily
identified, since we knew from the surveys which
seafloor classes were encountered. These were always
4 or fewer classes (dense algae, sparse algae, seagrass,
and bare seafloor that was not further differentiated).
Reviews of the functioning of the QTC system and
critiques can be found in Hamilton et al. (1999),
Hamilton (2001), Legendre et al. (2002), Preston
and Kirlin (2002), von Szalay and McConnaughey
(2002), Ellingsen et al. (2002), Freitas et al.
(2003a,b), Riegl and Purkis (2005), Moyer et al. (in
press). The QTC dataset was reduced to a three-column matrix consisting of a single x,y geo-referenced
class category z that was obtained from the cluster
analysis.
Echoplus uses first and second echo. It timegates
the first echo signal to ignore the first strong peak(s)
and processes only the latter part plus the entire
second echo. The Echoplus is similar to RoxAnn
(Hamilton, 2001), which was extensively tested by
Hamilton et al. (1999). Echoplus is entirely selfcontained and internally compensates for frequency,
depth, power level and pulse length. Pulse amplitude
and length are measured on every transmission, the
outputs scaled accordingly, and absorption corrections
factored in. The first echo is digitized and time-gated
in a way that only its tail (backscatter component) is
used for analysis along with the entire second echo.
The measurements from first and second echo are
collapsed into two indices, E1 and E2, for the first
and second echo respectively. The user has no influence on the formation of these indices and collects a
geo-referenced string of variables (latitude, longitude,
E1, E2). All data above the 95th and below the 5th
percentile were rejected as outliers and all data were
normalized to the 95th percentile, resulting in a range
between 0 and unity. First return (E1) was plotted
against second return (E2) data in a scatterplot,
which showed whether data clustered or not.
In order to produce spatially continuous habitat
maps and bfill in the blanksQ between survey lines,
we resampled the irregular survey data (grid exclusively along the survey tracks) to a regular grid of 10-
B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104
m pixel size. Interpolation used a nearest-neighbor
algorithm for categorical data, i.e. the classes produced by QTC cluster analyses (Davis, 2002; Riegl
and Purkis, 2005) or geostatistics, in this case ordinary
point kriging, based on the spatial autocorrelation
inherent in landscape patterns, for the continuous
variable output of the Echoplus (Matheron, 1971;
Greenstreet et al., 1997; Middleton, 2000; Hamilton,
2001; Papritz and Stein, 2002; Walter et al., 2002). On
categorical data, kriging is not the method of choice,
since fractional classes, such as will be produced by
kriging, appear non-sensical. To evaluate the accuracy
of the acoustic ground discrimination, groundtruthing
transects consisting of geo-referenced images
obtained by video camera drops were collected. The
habitat types observed in the videos were then compared to the extrapolated maps. Accuracy of these
maps was then assessed using a confusion matrix
approach (Ma and Redmond, 1995), which assesses
how often classes are confused (i.e. mapped as something that groundtruthing proves them not to be). An
Atlantis AUW-5600 color underwater camera was
used to capture video images along groundtruthing
survey lines. The video signal was time stamped and
merged with positioning information. Incoming video
with GPS and time-stamp information was recorded in
Digital Hi-8 format using a Video Walkman. Total
linear distances of 1.4 km and 5.5 km were surveyed
in the Sebastian Inlet and Melbourne areas, respectively. In Cocoa Beach, groundtruthing was performed
by a stratified-random arrangement of video-camera
drops. Data were more equally spaced over the survey
area, rather than along lines, such as in the other two
study areas. Groundtruthing statistics were only calculated for the Melbourne and Sebastian River survey
areas.
2.1. Verification
In order to verify that the classes obtained by
QTCView and Echoplus really reflected seagrass,
sparse algae, dense algae and bare seafloor, two
approaches were taken: (1) for verification in the
study area where the survey vessel was positioned
over a discrete habitat patch, the habitat was verified
by means of video drop-camera or spot-dive, and then
a small dataset, containing between 1000 and 1500
echo traces, was obtained over dense algae (approxi-
93
mately 2000 g wet weight m 2, which is equivalent to
approximately 230 g dry weight, was spread in the
transducer footprint to cover the entire insonified
seafloor), sparse algae (approximately 250 g wet
weight m 2 were concentrated as a single clump in
the center of the transducer footprint), seagrass, and
bare seafloor. (2) verification in the Nova SE University marina (comparable salinity and temperature to
the field trial area), where collected drift algae were
placed in various densities underneath a suspended
transducer. Data files of 1500 echo traces were
obtained with an empty basket large enough to encompass the entire area of the footprint. Then, 250 g
of algae were added into the basket (bsparse algaeQ)
and another file of 1500 echo traces was obtained,
then, an additional 1750 g algae (bdense algaeQ) were
added and another file of about 1500 traces was
obtained. This procedure was repeated in 1.8 m and
1.3 m depth. Additionally, separate files with different
settings of blanking depth and signal length were
obtained to evaluate whether these factors had any
influence. Since the transducer was in a stable position and not moving, signal stacking performed by
QTC View did not affect footprint size, which could
easily be determined by trigonometry. Data were then
subjected to cluster analysis in order to evaluate
whether discrimination was obtained. Because seagrass and drift algae differed between the study
areas in species composition, length, density, and
other factors important for echo formation, and also
differences in composition of sediment (category
bbare sedimentQ) between the study areas was unknown, we considered it unwise to use the verification
files as calibration files, but rather as a reasonable
approximation of expected conditions. If during the
verification trials all seagrass, algae and sediment
categories that could be encountered in the field had
been tested and true calibration datasets produced,
evaluation of survey data could have proceeded
using discriminant functions (Davis, 2002) rather
than cluster analysis.
2.2. Estimation of macroalgal biomass
Biomass was estimated by counting the colorcoded pixels assigned to each substratum class in
the extrapolated maps for each survey area. Values
were calculated as total biomass in kg per sample
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B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104
Fig. 2. Illustration of the density of algae referred to in the text. (A) sparse algae (~ 250 g m 2 uncleaned wet weight) (B) dense algae (~ 2000 g
m 2 uncleaned wet weight). Both within a footprint of 57 cm radius (outlined by a rope), as would be realistic with the 50 kHz transducer (428
opening angle) at 1.5 m depth.
area. Uncleaned wet weight (including animals,
water, dirt) biomass was calculated as number of 1
m2 pixels times 2 kg for dense algae, 0.25 kg for
sparse algae. These values were the same as those
used for the acoustic verification described above.
When adequately cleaned and remeasured, the biomass of the clumps used for verification was compatible to that occurring naturally (Morris et al.,
2000). Seagrass biomass was considered to be on
average 0.1 kg per m2 (range between 0.4 and 0.04
kg m 2 wet weight, with more sparse than dense
seagrass occurring in the study area. We therefore
biased data towards a lower value). The sizes of the
survey areas were calculated from the total number
of pixels in the extrapolated maps times pixel-size.
Biomass was only calculated for the November 2002
and May 2003 surveys since the total surveyed areas
were comparable. March and August 2003 encompassed smaller areas.
All data evaluation in this paper was done with
code written in Matlab 6.1.
3. Results
3.1. Verfication of QTCView’s ability to detect algae
In the Sebastian Inlet survey area, algae were
successively added into the transducer’s footprint at
depths of 1.2 and 1.5 m (Fig. 2). Data split into three
classes and a new unique class only appeared when
algae were added over the sand. This class represented
the acoustic signal of the algae (Fig. 3). When seagrass was introduced under the transducer, a clear 4-
Fig. 3. Differentiation of algae from seagrass from bare substratum using QTCView. Left figure shows PCA and class assignment by cluster
analysis. Right figure shows depth and sequence of signals. Algae and substratum signals occur together when algae are put under the
transducer. Trials with pure bare substratum provided a clear and unique acoustic signature.
B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104
class split was achieved, which again corresponded to
the a priori known number of seafloor classes. Fig. 3
also shows that not all signals were uniform when
algae were introduced under the transducer. After
cluster-splitting, a relatively high percentage was
grouped with the category bare substratum. The physical reasons for this phenomenon remain unclear.
In the verification experiments at the university
marina (Fig. 4), data from trials at 1.8-m depth
allowed a three-class split, where at least one class
could be assigned to dense algae, while results were
not clear for the sparse algae. Clear separation between data taken with dense algae, sparse algae and
empty basket were found. A distinct class (black in
Fig. 5) represented dense algae, and another class
(grey in Fig. 5) represented the sparser algae and a
95
third class (white in Fig. 5), which was closer to the
sparse algae class than the dense algae class, represented the absence of algae. The depth-plot of Trial 1
in Fig. 5 clearly shows that dense and sparse algae
created a signal by reflecting the acoustic pulse at the
depth of the basket (0.7 m). When removed, the
acoustic pulse was reflected at the depth of the substratum (1.6 m) and not at that of the now empty
basket indicating that indeed the algae, and not the
basket, were responsible for the scatter. Also, the
depth plot of Trial 2 (Fig. 5) showed that dense
algae scattered the acoustic signal at their surface,
which was about 5cm above the substratum. Sparse
algae formed clumps resulting in a different depthreading than the bare substratum.
3.2. Echoplus results
Fig. 4. The experimental setup consisting of a wire basket suspended underneath the transducer used to determine whether algae
and seagrass really produced an acoustic signature that could be
differentiated from other substrata. The signal encountered either the
empty basket (recording the seafloor underneath), or dense algae
(biomass ~ 2000 g m 2 uncleaned wet weight) or sparse algae
(~ 250 g m 2 wet weight).
A plot of first against second echoes from the
August 2003 survey in the Sebastian Inlet survey
area (Fig. 6A) collected by the Echoplus, showed
three distinct clusters: one group of low E1 and E2
values, one group of high E2 but low E1 values, and
one group of low E2 and high E1 values (Fig. 6).
Since E1 largely encodes surface roughness and E2
surface hardness, low values in both were believed to
correspond to flat and soft, muddy substratum (i.e.,
the category bare substratum), low E1 (roughness)
and high E2 (hardness) indicated a stronger substratum than algae signal and thus was believed to encode
shelly, hard sand. High roughness (E1) and low hardness (E2) indicated a stronger surface scatter component and thus was believed to encode dense algae.
Sparse algae formed a cloud of data in between the
dense algae and hard substratum clusters. These
assumptions were verified with groundtruthing video
camera drops.
The bdigital numbersQ (numeric codes for E1 and
E2) were also plotted in sequence (Fig. 6B), allowing
for further groundtruthing of the assumption that E1
encoded the density of algae. Since the time sequence
of the digital numbers corresponded to their geographical position, it was possible to groundtruth the information contained in the signals with camera drops.
High E1 values (between 0.75 and 1) corresponded to
dense algae, medium E1 values (0.6 to 0.75) corresponded to sparse algae, and low E1 values (0.55–0.6)
corresponded to bare substratum.
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B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104
Fig. 5. Differentiation of algae from bare substratum using QTCView. Upper two figures represent PCA and cluster assignment by cluster
analysis, lower figures plot the depth of each signal. Trial 1 was performed in the marina using a basket suspended under the transducer into
which algae were introduced (see methods). Trial 2 was in the field at Sebastian Inlet where different densities of algae were put on bare seafloor
underneath the transducer. PCA shows three distinct groups of echoes (black=dense algae, gray=sparse algae, white=bare substratum). Depthplot of Trial 1 demonstrates that dense and sparse algae create a signal return—they reflect the acoustic signal at the depth of the basket (0.7 m).
When removed, the signal is reflected at the depth of the substratum (1.6 m). Second depth plot (Trial 2) shows that dense algae scatter the
acoustic signal near their surface (about 5 cm above the substratum).
A similar relationship was found for E2 values.
High E2 values (0.8 to 1) were found associated with
bare, shelly substratum and sparse algae, medium E2
values (0.5–0.7) corresponded to dense algae, and low
E2 values (0.3–0.5) corresponded to bare substratum
(Fig. 6C).
3.3. Distribution of algal biomass in Melbourne survey area
The QTCView survey in November 2002 (50 kHz)
discriminated clearly all four classes of substratum,
with bsparse algaeQ covering less area than bdense
algaeQ and having a far lower cumulative biomass in
the survey area (Table 3). Algae were concentrated
along the lateral shoals of the study area, the biggest
patches occurring on the northeastern and northwestern shoals (Fig. 7A). Much of the entire eastern fringe
was covered by dense algae. Where seagrass was
found, it was usually associated with algae. Dense
algae were also found covering large patches of the
central survey area (Fig. 7A). The QTCView survey in
March 2003 (not illustrated in Fig. 7) encompassed
the two northern shoals that had shown the densest
coverage. The dense algae had remained but decreased in areal coverage on both shoals, and the
western shoal showed some seagrass signal but
remained algae-dominated. Previously, in November,
the dense algae cover in the seagrass had drowned
most seagrass signals. Also on the eastern shoal, the
overall amount of dense algae appeared to have decreased in March, replaced by sparse algae.
In May 2003, using a 200 kHz signal and
QTCView, four substratum classes were observed
(Fig. 7B) which coincided with classes observed in
November. Dense algae covered more area than sparse
B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104
B
1
1
Bare substratum
(shelly)
0.9
E1 = first echo, roughness parameter
E2=second echo, hardness parameter
A
97
0.8
0.7
sparse algae
0.6
0.5
bare
substratum
(muddy)
0.4
0.3
0.2
0.5
dense algae
0.95
dense algae
0.9
0.85
sparse algae
0.8
0.75
0.7
0.65
0.6
0.55
(shelly)
(muddy)
bare substratum
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
0
2000
4000
6000
8000
10000
12000
14000
signal sequence
E1 = first echo, roughness parameter
E2=second echo, hardness parameter
C
1
0.9
0.8
0.7
sparse
algae
bare
substratum
(shelly)
0.6
0.5
dense algae
0.4
0.3
0.2
(muddy)
0
2000
4000
6000
8000
10000
12000
14000
signal sequence
Fig. 6. Differentiation of algae from bare substratum using Echoplus (results from the Sebastian Inlet August 2003) (A) Scatterplot of first
versus second echo digital number; (B) The first echo digital numbers in time sequence; and (C) The second echo digital numbers in time
sequence. High E1 (roughness) and low E2 (hardness) values correspond to drift algae, high E2 (hardness) and low E1 (roughness) values
correspond to bare substratum. Sparse algae form a point cloud in between the two clusters.
algae and seagrass and had a higher biomass than both
other categories together (Table 3). The northern
shoals were covered by dense algae and seagrass
occupied similar areas as in November. The northwestern shoal showed some mixture of seagrass and
sparse algae, but algae still dominated. The northwestern shoal had less algae. Only few areas of dense
algae cover remained on both the northern and southern sides of the shoal and in the deep channel. Also
the southeastern shoal showed seagrass cover with a
fringe of sparse algae and patches of dense algae on
the upstream and downstream sides (Fig. 7 B).
In the August 2003 QTCView and Echoplus surveys, only three classes were found in sufficient density to be mapped, the class bsparse algaeQ was rare.
Several areas of dense algae were found in the lee of
the shoals and in the deep channel. The northwestern
shoal now showed more seagrass than algae. Dense
algae clumps appeared moribund, being flaccid with
little pigmentation. We assume that they had previously lived in and around the seagrass and on the shoals.
Total biomass of macrophytes decreased by over
50% between autumn 2002 and spring 2003 in the
Melbourne study area (Table 3).
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B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104
Fig. 7. Spatial distribution of sand, seagrass, and algae in November 2002 (A,C,E) and in May 2003 (B, D, F). The Cocoa Beach survey was
smaller in May 03 than in November 02. Dark gray=dense algae, medium gray=sparse algae, black=seagrass, white=bare substratum.
Coordinates are in UTM, Zone 17R North. Striped areas in side the region of interest are outside the extrapolated areas and of unassigned
bottom type.
3.4. Distribution of algal biomass in Sebastian inlet
survey area
The November 2002 (Fig. 7C) 50-kHz survey
discriminated four classes of substratum. Seagrass
occurred on the northern and southern shoals, and
was surrounded by sparse algae forming a dense
apron. Almost a quarter of the survey area was covered by sparse algae (Table 3), with smaller patches of
dense algae concentrated near the southern shoal.
In March 2003, two surveys (50 and 200 kHz)
found more dense algae on the northern shoal than
in November. Dense algae covered most of the seagrass and sparse algae surrounded these areas, which
only showed in the 50 kHz survey. Most of the deeper
survey area was bare substratum. The 50 kHz survey
found a contiguous area of dense algae towards the
southern shoal, while the 200 kHz survey suggested
several patches of sparse algae in roughly the same
area.
In May 2003 (Fig. 7D), a 200-kHz survey discriminated the same four classes. Unlike in the Melbourne
study area, overall macrophyte cover had not decreased and dense algae covered as much area as
sparse algae and seagrass (Table 3). The northern
shoals, near Sebastian Inlet, were characterized by
seagrass signals with only small areas showing dense
algae cover with surrounding sparse algae (Fig. 7D).
B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104
Another large seagrass area was on the southernmost
shoal. Most dense algae were concentrated in the
southern and western sections and surrounded by
sparse algae. Seagrass and dense algae also existed
in the deep and sheltered northeastern part of the basin,
adjacent to mangroves. Most deeper area were bare.
Between November 2002 and May 2003, the overall biomass of macrophytes had remained almost unchanged (Table 3).
The August 2003 Echoplus surveys were conducted on 200 kHz, discriminating bare substratum,
sparse algae, and dense algae. No seagrass areas were
99
surveyed. Different to Melbourne, algal cover in the
Sebastian Inlet survey area increased in August. Most
of the deeper basin was covered by dense algae or
sparse algae (Fig. 7 D). Large clumps of algae were
found along the waterline. The least algal cover was
found in the western and southern sections.
3.5. Distribution of algal biomass in Cocoa beach
survey area
The November 2002, QTCView survey discriminated four classes of substratum (Fig. 7E). Algae
Fig. 8. Survey results in the Cocoa Beach area using Echoplus (A,C) and QTCView (B,D). (A) and (C) show the location of individual pings
assigned to the algae class, (C) and (D) show a nearest-neighbor interpolation of the algae class versus the non-algae class. Drift algae signals are
indicated as black circled or filled black surfaces. Where survey lines appear incomplete, signals were removed during quality control due to
poor quality. Both areas show a higher concentration of algae signal in the northern section. Coordinates are decimal degrees in WGS 84.
100
B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104
formed an almost continuous fringe along the lateral
shoals, but were also found in the deeper central part
of the study area. Seagrass was almost always neighbored by accumulations of algae and concentrated on
the northwestern shallow fringe of the study area, the
northeastern shoal, and the southern central shoal.
Dense algae covered more area than sparse algae
and seagrass (Table 3).
In May 2003 (Fig. 7F), the area covered by the
survey was smaller than that of the November survey
(Table 3, Fig. 7F). Seagrass was found on the southern
central shoal and along the eastern and western shoals,
while algae cover in the shallows had decreased.
Cover of sparse algae in the deeper parts of the
study area had increased, however. Sparse algae covered far more area than seagrass or dense algae (Table
3). Between November 2002 and May 2003, macrophyte cover had increased due to sparse algae in the
deep Lagoon, but biomass had dropped dramatically
(Table 3).
In August 2003, Echoplus and QTCView surveys
were conducted in the northern part of the survey area
following the same survey lines (Fig. 8). In this
relatively deep area, only algae and bare substratum
occurred. Both surveys differentiated algae from bare
substratum, and identified an area of dense algae in
the northernmost part of the area. For map extrapolation of the Echoplus survey, digital E1 numbers were
binned into algae- and non-algae classes according to
Fig. 6B. The two systems differed in detection of
algae in the southern, sparser, part. The Echoplus
showed few algae signals in this area, while the
QTCView showed a significant number of wellspaced algae signals.
3.6. Groundtruthing and accuracy assessment
Video surveys confirmed the presence of the four
bottom types observed by the acoustic surveys. In the
Melbourne 50 kHz survey area, all four seafloor
classes were also seen in the groundtruthing data.
For this reason, dense and sparse algae were considered separate classes for accuracy assessment. Overall, accuracy was only 36% (Table 1A) and high
confusion was evident among all classes, with highest
confusion between dense and sparse algae. The confusion matrix was then recalculated with dense and
sparse algae classes pooled (Table 1B) resulting in a
three-class accuracy of 59%. The Sebastian Inlet
groundtruthing did not encounter dense algae, resulting in only one dalgaeT class (Table 2).
Two confusion matrices were produced which
compared the extrapolated maps derived from the
acoustic surveys. Overall, accuracy was about 60%
(Table 2). The surveys and resulting extrapolated
maps were very good at predicting areas of algae
Table 1
(A) Error matrix (Ma and Redmond, 1995) for Melbourne study area with dense and sparse algae as separate classes; (B) with dense and sparse
algae pooled as a single class
A Melbourne
Sand
Seagrass
Sparse algae
Dense algae
Sum
User’s accuracy
Sand
Seagrass
Sparse algae
Dense algae
Sum
Producer’s accuracy
38
11
47
2
98
38.8%
17
0
14
43
74
0%
48
29
89
74
240
37.1%
17
1
57
75
150
50%
120
41
207
194
562
31.7%
0%
42.9%
38.6%
35.94%
B Melbourne
Sand
Seagrass
Algae
Sum
User’s accuracy
Sand
Seagrass
Algae
Sum
Producer’s accuracy
38
11
47
98
38.8%
17
0
57
74
0%
65
30
295
390
75.6%
120
41
401
562
31.7%
0%
73.6%
59.3%
Columns are classified categories on the maps, rows are accuracy assessment data obtained from video surveys. Bold number in italics is the
total accuracy.
B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104
Table 2
Class-by-class error matrix for Sebastian Inlet
A Sebastian
50 kHz
Sand
Seagrass Algae
Sum User’s
accuracy
Sand
Seagrass
Algae
Sum
Producer’s accuracy
14
66
12
28
4
16
30
110
46.7% 25.4%
1
81
2
42
115
135
118
258
97.5%
17.2%
66.6%
85.1%
B Sebastian
200 kHz
Sand
Seagrass
Algae
Sum
User’s
accuracy
Sand
Seagrass
Algae
Sum
Producer’s accuracy
0
5
4
9
0%
17
41
0
58
71%
13
64
114
191
59.7%
30
110
118
258
0%
37.7%
96.6%
60.9%
60.1%
Accuracy for individual classes as well as overall performance of
the model. (A) 50 kHz survey (B) 200 kHz survey with sparse and
dense algae pooled as a single class.
(96 and 97%), however, some confusion did exist
between areas of sand and seagrass.
4. Discussion
The study shows that two different acoustic ground
discrimination systems, QTCView and Echoplus,
were capable, within limits, not only of differentiating
sediment types, which is well published (Hamilton et
al., 1999; Morrison et al., 2001; Freitas et al., 2003a,b)
but were also able to detect algae and seagrass. A
three-class confusion matrix based on QTCView surveys, suggested a total accuracy of nearly 60% for
seagrass-algae-bare substratum. The two algae classes
(sparse versus dense) showed high confusion, and
clearly more work is needed to improve discrimination, either through improved signal processing or
data analysis at the post-processing stage. But since
ground-truthing information was collected after surveys for logistical reasons and drift algae are mobile,
it is possible that the observed high confusion values
are partly a sampling artifact caused by the algae
having moved (aggregated, dispersed, etc.) in the
time between the surveys and the groundtruthing.
From both verification experiments and field survey
data, it was apparent that seagrass and drift algae
indeed produced unique echo classes.
101
Under controlled conditions as well as in the field,
it was difficult to obtain files containing only echoes
that could be clearly ascribed to either algae or seagrass with the QTCView instrument. Usually all files
containing echoes from algae or seagrass also included a large proportion of echoes (up to half) from bare
substratum. This high intermixed proportion of bottom signals was more pronounced with the algae than
with the seagrass, but occurred with both vegetation
types. This suggests that the bottom signal occurred as
frequently as the vegetation signals themselves, even
when the area under the transducer appeared to be
entirely covered by vegetation. We cannot provide a
satisfactory explanation why this is so, and several
possibilities exist. The easiest is to assume that unattached drift algae rolled in and out of the transducer’s
footprint, as was indeed sometimes observed. However, even when the algae were anchored, and when
the attached seagrass was used as a target, a proportion of signals still had the acoustic qualities of signals
derived from bare substratum (Figs. 3 and 5). The
Echoplus appeared to give a somewhat clearer distinction (Figs. 6 and 8), which could, however, largely
be due to the way classes were assigned as binned
intervals of the digital number provided by the Echoplus, while in the QTCView system classes are
assigned based on the position of signals in pseudo3-dimensional space after PCA. Depending on the
width of the chosen bins, accuracy of discrimination
could be influenced. The QTCView system uses at
that last step at least three variables (the first three
principal components characterizing each echo) for
the binning into groups, in contrast, we used only
the single digital number (E1) of Echoplus for obtaining Fig. 8.
Reasons for confusion of algae and bare substratum
echoes may either be a relatively weak scattering
ability of the algae and/or the different signal properties and/or the processing steps. QTCView uses the
entire signal envelope of the first echo (it ignores all
multipath echoes, Collins and Lacroix, 1997) which
Preston et al. (2000) and Freitas et al. (2003a,b)
showed to provide good discrimination ability of sediment geotechnical variables. However, Chivers et al.
(1990) reports that the first peak(s) of the echo is
strongly influenced by subsurface reverberation,
while the echo’s tail primarily encodes scatter, which
is the reasoning followed in signal processing of the
102
B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104
Echoplus. It is possible that much acoustic energy
passes through the vegetation layer to primarily interact with the substratum and many signals may be more
strongly influenced by the underlying substratum than
the overlying vegetation. Thus, at least in theory, some
benefit could be seen in emphasizing the trailing edge
and the multipath echo when discrimination of macrophytes or other purely surficial structures is desired,
since this should minimize the subsurface reverberation component. Sabol and Johnston (2001) and Sabol
et al. (2002) used higher frequency echosounders and
different signal processing in their acoustic evaluations
of submerged aquatic vegetation.
Interpretation of the seafloor classification is aided
by regridding the surveys and benhancingQ them
through extrapolation and spatial statistical methods
(Guan et al., 1999; Middleton, 2000; Davis, 2002;
Walter et al., 2002; Papritz and Stein, 2002), which
allows the production of maps with closed surfaces.
While some artifacts may be introduced, it is difficult
to evaluate the validity and meaning of either the
QTCView or the Echoplus acoustic ground discrimination by evaluating the survey lines only. An alternative would be to cover the entire seafloor with acoustic
signals, but this is not practical since the shallow water
depths result in a small footprint even of swath systems, like side-scanning or multibeam sonars.
Bathymetry influenced the distribution of drift
algae in all three study areas. The pattern was clearer
in Melbourne and Cocoa Beach than in Sebastian
Inlet. In Cocoa Beach and Melbourne, the areas of
densest algae accumulation were always towards the
Lagoon’s edges where the shallow areas (b1.5 m
deep) showed, depending on season, a continuous
fringe of either dense or sparse algae. This situation
was also observed by other surveys in the same area
(Nielsen et al., 2000). In the Sebastian Inlet area,
dense algae were found as well on the shoals as in
the deeper areas. Seagrass was restricted to the shallowest areas (b 1 m depth) and algae had a clear
tendency to accumulate around and within these seagrass meadows. This made the unequivocal acoustic
discrimination of seagrass difficult.
Drift algae were encountered in the deeper parts of
the Lagoon and also accumulated around and within
seagrass areas, particularly in winter. In the Melbourne and Cocoa Beach survey areas, macrophyte
biomass was low in spring, while in the Sebastian
Inlet area it was high (Table 3). Algae density, in
particular of freely drifting algae in the deeper parts
of the Lagoon, in the Melbourne and Cocoa Beach
areas increased again towards autumn, in August. It is
assumed that the higher values in algal biomass in the
Sebastian Inlet study area, which is situated near a
major inlet with a very active hydrodynamic regime,
could have been due to local, current-driven, accumulation of algae. We assume that the observed absence
of a uniform seasonal pattern in biomass and distribution of algae and seagrass among the study areas is
an expression of a generally high spatial and temporal
variability of macrophyte, in particular algae, dynamics in the Indian River Lagoon.
5. Conclusion
! Acoustic ground discrimination using QTCView
and Echoplus proved useful tools not only to
map the distribution of seagrass and drift malcroalgae in the Indian River Lagoon.
Table 3
Space cover (percentage value of total survey area above) and biomass (in tons wet weight biomass per survey area below) of acoustically
detected bottom types in the Indian River Lagoon before and after summer (peak growth season of macrophytes)
Melbourne Nov. 02
Melbourne May 03
Sebastian Nov.02
Sebastian May 03
Cocoa Nov. 02
Cocoa May 03
Seagrass
Dense algae
Sparse algae
Total macrophyte
cover/biomass
Bare substratum
Size of survey
% area (tons)
% area (tons)
% area (tons)
% area (tons)
% area
in km2
7
5
6
12
4
4
26
10
8
12
19
2
3
4
22
12
2
30
36
19
36
36
25
36
64
81
64
64
75
64
8.9
8.5
9.6
8.6
13.9
9.8
(60.04)
(41.91)
(54.69)
(102.5)
(52.14)
(39.56)
(4704.2)
(1795)
(1623.8)
(2140.6)
(5296)
(343.2)
(71.7)
(78.9)
(533.6)
(267.6)
(86.6)
(736.4)
(4835.9)
(1915.8)
(2212.1)
(2510.7)
(5434.7)
(1119.2)
B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104
! The maps of macrophyte distribution extrapolated
from the acoustic surveys also allowed a coarse
estimation of biomass.
! Macrophyte (algae and seagrass) biomass varied
with the seasons but the pattern was not uniform
among study areas.
! More work is necessary to increase the accuracy of
acoustic surveys.
Acknowledgements
The authors would like to thank the St. Johns River
Water Management District who funded this project
through grant SF655AA. Additional funding from
NOAA/NOS via grant NA03NOS4260046 to NCRI
is gratefully acknowledged. G.S. McIntosh captained
and made the surveys possible, provided field support
and logistics. B.K. Walker and A. Shinohara assisted
with data collection. This is NCRI publication # 70.
[RH]
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