Distribution and seasonal biomass of drift macroalgae in ...Sonar signals were obtained using a 50 kHz signal from a Suzuki TGN60-50H-12L transducer and a 200 kHz signal from a Suzuki

www.elsevier.com/locate/jembe

Journal of Experimental Marine Biolog

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

aNational Coral Reef Institute, Nova Southeastern University Oceanographic Center, 8000 North Ocean Drive, Dania Beach, FL 33004, USAbSt. John’s River Water Management District, 4049 Reid Street, Palatka, FL 32177, USA

cDepartment 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

0022-0981/$ - s

doi:10.1016/j.jem

* Correspondi

E-mail addre

y and Ecology 326 (2005) 89–104

ee front matter D 2005 Elsevier B.V. All rights reserved.

be.2005.05.009

ng author. Tel.: +1 954 262 3671; fax: +1 954 262 4027.

ss: [email protected] (B.M. Riegl).

B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–10490

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, Syrin-

godium filiforme, and Thalassia testudinum (Morris et

al., 2000). These species of seagrass often occur in

dense beds together with or adjacent to drift macro-

algae where biomass has been estimated at 3 times, in

extreme cases 100 times, that of seagrass (Morris and

Fig. 1. Study areas in the Indian Rive

Hall, 2001). Due to their great abundance, drift macro-

algae are considered to have a habitat value compa-

rable 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 distribu-

tion 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 hyper-

spectral, Green et al., 2000; Dierssen et al., 2003;

r Lagoon, central Florida, USA.

B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104 91

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 sea-

floor discrimination provides an alternative that is not

dependent on water clarity. The method is well estab-

lished (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, Bio-

sonics, Echoplus, QTCView; for reviews see Hamil-

ton 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 shal-

low waters of mostly less than 2 m depth. It was our

goal to estimate seasonal biomass and map distribu-

tion 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, Flor-

ida, 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 immedi-

ately 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 to-

wards 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

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 posi-

tioning system (dGPS) that used coast-guard beacon

differential corrections to obtain real-time horizontal

positioning accuracies of mostly less than 1.2 m hor-

izontal 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 mon-

itoring of vessel position with respect to the imagery

and planned survey lines. Sonar signals were obtained

using a 50 kHz signal from a Suzuki TGN60-50H-

12L 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 fre-

quency 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 ba-

thymetry 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,

B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–10492

2005) and will not be reiterated here. We used the

QTCView Series 5 and Echoplus turn-key survey

systems which perform acoustic ground discrimina-

tion 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 sea-

floor (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 time-

stamped, 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 signal-

strengths. 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 ba-

thymetry plot, where recorded depths are checked

against the blanking (minimum recordable) and max-

imum 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 ampli-

tudes and ratios of cumulative amplitudes, amplitude

quantiles, amplitude histograms, power spectra, wave-

let 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 reduc-

tion. The first three principle components of each

echo are retained (called Q values), according to the

assumption that these explain the majority of variabil-

ity in the data set (Quester Tangent Corporation,

2002). Datapoints are then projected into pseudo-

three-dimensional space along these three compo-

nents, and subjected to cluster analysis using a Bayes-

ian 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 (ClusterPerformance Index), bChi2Q and bTotal ScoreQ. TotalScore decreases to an inflection point which is dastrong 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 split-

ting, 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-col-

umn 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 self-

contained 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 influ-

ence 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 exclu-

sively along the survey tracks) to a regular grid of 10-

B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104 93

m pixel size. Interpolation used a nearest-neighbor

algorithm for categorical data, i.e. the classes pro-

duced 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 com-

pared 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 some-

thing 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, respec-

tively. 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 cal-

culated 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-

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 Univer-

sity 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 en-

compass 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) wereadded 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 posi-

tion 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 sea-

grass 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 un-

known, 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 color-

coded pixels assigned to each substratum class in

the extrapolated maps for each survey area. Values

were calculated as total biomass in kg per sample

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 (428opening angle) at 1.5 m depth.

B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–10494

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 bio-

mass of the clumps used for verification was com-

patible 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

Fig. 3. Differentiation of algae from seagrass from bare substratum using

analysis. Right figure shows depth and sequence of signals. Algae and

transducer. Trials with pure bare substratum provided a clear and unique

were comparable. March and August 2003 encom-

passed 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 sea-

grass was introduced under the transducer, a clear 4-

QTCView. Left figure shows PCA and class assignment by cluster

substratum signals occur together when algae are put under the

acoustic signature.

B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104 95

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 phys-

ical 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 be-

tween 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

Fig. 4. The experimental setup consisting of a wire basket sus-

pended 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).

third class (white in Fig. 5), which was closer to the

sparse algae class than the dense algae class, repre-

sented 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 sub-

stratum (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 depth-

reading than the bare substratum.

3.2. Echoplus results

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 substra-

tum than algae signal and thus was believed to encode

shelly, hard sand. High roughness (E1) and low hard-

ness (E2) indicated a stronger surface scatter compo-

nent 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 geograph-

ical position, it was possible to groundtruth the infor-

mation 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) corre-

sponded to sparse algae, and low E1 values (0.55–0.6)

corresponded to bare substratum.

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). Depth-

plot 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).

B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–10496

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 sur-

vey area

The QTCView survey in November 2002 (50 kHz)

discriminated clearly all four classes of substratum,

with bsparse algaeQ covering less area than bdensealgaeQ 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 northwest-

ern 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 de-

creased 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 de-

creased 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

signal sequence

E2=

seco

nd e

cho,

har

dnes

s pa

ram

eter

A

E1

= fi

rst e

cho,

rou

ghne

ss p

aram

eter

E1 = first echo, roughness parameter

B

E2=

seco

nd e

cho,

har

dnes

s pa

ram

eter

signal sequence

C

sparse algae

dense algae

(muddy)

bare substratum

sparse algae

dense algae

baresubstratum

dense algae

sparsealgae

baresubstratum

(shelly)

(shelly)

(muddy)

(shelly)(muddy)

Bare substratum1

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.20.5 0.55 0.6 0.65 0.7 0.75 0.8

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.9

1

0.85 0.9 10.95 0 2000 4000 6000 8000 10000 12000 14000

0 2000 4000 6000 8000 10000 12000 14000

1

0.95

0.85

0.9

0.75

0.8

0.65

0.7

0.55

0.6

0.5

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.

B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104 97

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 north-

western shoal showed some mixture of seagrass and

sparse algae, but algae still dominated. The northwest-

ern shoal had less algae. Only few areas of dense

algae cover remained on both the northern and south-

ern 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 sur-

veys, only three classes were found in sufficient den-

sity 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 previous-

ly 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).

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.

B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–10498

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 cov-

ered 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 sea-

grass 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 discrim-

inated the same four classes. Unlike in the Melbourne

study area, overall macrophyte cover had not de-

creased 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 99

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 over-

all biomass of macrophytes had remained almost un-

changed (Table 3).

The August 2003 Echoplus surveys were con-

ducted on 200 kHz, discriminating bare substratum,

sparse algae, and dense algae. No seagrass areas were

Fig. 8. Survey results in the Cocoa Beach area using Echoplus (A,C) and

assigned to the algae class, (C) and (D) show a nearest-neighbor interpolatio

indicated as black circled or filled black surfaces. Where survey lines app

poor quality. Both areas show a higher concentration of algae signal in th

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 discrimi-

nated four classes of substratum (Fig. 7E). Algae

QTCView (B,D). (A) and (C) show the location of individual pings

n of the algae class versus the non-algae class. Drift algae signals are

ear incomplete, signals were removed during quality control due to

e northern section. Coordinates are decimal degrees in WGS 84.

B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104100

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 neigh-

bored 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 cov-

ered far more area than seagrass or dense algae (Table

3). Between November 2002 and May 2003, macro-

phyte 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 extrapola-

tion of the Echoplus survey, digital E1 numbers were

Table 1

(A) Error matrix (Ma and Redmond, 1995) for Melbourne study area with

algae pooled as a single class

A Melbourne Sand Seagrass Sparse a

Sand 38 17 48

Seagrass 11 0 29

Sparse algae 47 14 89

Dense algae 2 43 74

Sum 98 74 240

Producer’s accuracy 38.8% 0% 37.1%

B Melbourne Sand Seagrass

Sand 38 17

Seagrass 11 0

Algae 47 57

Sum 98 74

Producer’s accuracy 38.8% 0%

Columns are classified categories on the maps, rows are accuracy assessm

total accuracy.

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 well-

spaced 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 consid-

ered separate classes for accuracy assessment. Over-

all, accuracy was only 36% (Table 1A) and high

confusion was evident among all classes, with highest

confusion between dense and sparse algae. The con-

fusion 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, result-

ing 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

dense and sparse algae as separate classes; (B) with dense and sparse

lgae Dense algae Sum User’s accuracy

17 120 31.7%

1 41 0%

57 207 42.9%

75 194 38.6%

150 562

50% 35.94%

Algae Sum User’s accuracy

65 120 31.7%

30 41 0%

295 401 73.6%

390 562

75.6% 59.3%

ent data obtained from video surveys. Bold number in italics is the

Table 2

Class-by-class error matrix for Sebastian Inlet

A Sebastian

50 kHz

Sand Seagrass Algae Sum User’s

accuracy

Sand 14 66 1 81 17.2%

Seagrass 12 28 2 42 66.6%

Algae 4 16 115 135 85.1%

Sum 30 110 118 258

Producer’s accuracy 46.7% 25.4% 97.5% 60.9%

B Sebastian

200 kHz

Sand Seagrass Algae Sum User’s

accuracy

Sand 0 17 13 30 0%

Seagrass 5 41 64 110 37.7%

Algae 4 0 114 118 96.6%

Sum 9 58 191 258

Producer’s accuracy 0% 71% 59.7% 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.

B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104 101

(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 sur-

veys, 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 discrimina-

tion, either through improved signal processing or

data analysis at the post-processing stage. But since

ground-truthing information was collected after sur-

veys 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.

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 sea-

grass with the QTCView instrument. Usually all files

containing echoes from algae or seagrass also includ-

ed a large proportion of echoes (up to half) from bare

substratum. This high intermixed proportion of bot-

tom 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 unat-

tached drift algae rolled in and out of the transducer’s

footprint, as was indeed sometimes observed. How-

ever, even when the algae were anchored, and when

the attached seagrass was used as a target, a propor-

tion 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 dis-

tinction (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 Echo-

plus, while in the QTCView system classes are

assigned based on the position of signals in pseudo-

3-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 obtain-

ing 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 proper-

ties 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 sed-

iment 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

B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104102

Echoplus. It is possible that much acoustic energy

passes through the vegetation layer to primarily inter-

act 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 macro-

phytes or other purely surficial structures is desired,

since this should minimize the subsurface reverbera-

tion 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 discrimi-

nation by evaluating the survey lines only. An alterna-

tive 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 sys-

tems, 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

Table 3

Space cover (percentage value of total survey area above) and biomass (

detected bottom types in the Indian River Lagoon before and after summ

Seagrass Dense algae Sparse alg

% area (tons) % area (tons) % area (to

Melbourne Nov. 02 7 (60.04) 26 (4704.2) 3 (71.7)

Melbourne May 03 5 (41.91) 10 (1795) 4 (78.9)

Sebastian Nov.02 6 (54.69) 8 (1623.8) 22 (533.6)

Sebastian May 03 12 (102.5) 12 (2140.6) 12 (267.6)

Cocoa Nov. 02 4 (52.14) 19 (5296) 2 (86.6)

Cocoa May 03 4 (39.56) 2 (343.2) 30 (736.4)

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 shal-

lowest areas (b1 m depth) and algae had a clear

tendency to accumulate around and within these sea-

grass 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 Mel-

bourne 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, accumu-

lation of algae. We assume that the observed absence

of a uniform seasonal pattern in biomass and distri-

bution of algae and seagrass among the study areas is

an expression of a generally high spatial and temporal

variability of macrophyte, in particular algae, dynam-

ics 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 malcroal-

gae in the Indian River Lagoon.

in tons wet weight biomass per survey area below) of acoustically

er (peak growth season of macrophytes)

ae Total macrophyte

cover/biomass

Bare substratum Size of survey

ns) % area (tons) % area in km2

36 (4835.9) 64 8.9

19 (1915.8) 81 8.5

36 (2212.1) 64 9.6

36 (2510.7) 64 8.6

25 (5434.7) 75 13.9

36 (1119.2) 64 9.8

B.M. Riegl et al. / J. Exp. Mar. Biol. Ecol. 326 (2005) 89–104 103

! 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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