Journal of Great Lakes Research Supplement 39 (2013) 173–182

Contents lists available at ScienceDirect

Journal of Great Lakes Research

j ourna l homepage: www.e lsev ie r .com/ locate / jg l r

A review of low cost underwater acoustic remote sensing for largefreshwater systems

Guy A. Meadows ⁎Great Lakes Research Center, Michigan Technological University, 1400 Townsend Dr., Houghton, MI 49931, USA

⁎ Tel.: +1 906 487 1106.E-mail address: gmeadows@mtu.edu.

0380-1330/$ – see front matter © 2013 Published by Elhttp://dx.doi.org/10.1016/j.jglr.2013.02.003

a b s t r a c t

a r t i c l e i n f o

Article history:Received 28 December 2011Accepted 1 November 2012Available online 2 April 2013

Communicated by Robert Shuchman

Keywords:SummarySONARSubstrate mappingBottom classification

In recent years, low cost and highly accurate underwater remote sensing instruments and technologies haveadvanced at an astonishing rate. Intense competition among manufacturers, coupled with advances in digitalsignal processing has brought about these breakthroughs, all to the benefit of the scientific community. Com-mercial Off the Shelf Technology (COST) is now available and incorporated into current acoustic sensors, withquality and resolution that was previously reserved for only large-scale ocean exploration. The corollary tothis observation is that the entire field of acoustic remote sensing of the aquatic environment is rapidly evolv-ing and is likely to continue to do so over the next decade. Acoustic bottom mapping, Doppler sensing andremote and autonomous vehicle imaging systems are now becoming commonplace for large lake scienceand are approaching maturity. Given this evolution, this summary reviews two major categories of underwa-ter, acoustic remote sensing technologies; bottom mapping systems and Doppler sensing systems. Bottommapping systems are primarily used to determine the presence of the bottom, map its features and classifyits composition. Doppler systems make use of target motion to deduce the velocity of the target, in up tothree spatial dimensions. For each category of acoustic remote sensing a brief description of the theory ofoperation is provided, followed by examples of the types of data produced by the technology. When possible,estimates of range and accuracy of typical units in each class is provided. Finally, examples of new utilizationsof combined remote sensing technologies are discussed.

© 2013 Published by Elsevier B.V. on behalf of International Association for Great Lakes Research.

Introduction

Acoustic propagation through water has long been recognizedas an effective mechanism to make the underwater environment“transparent.” In the early 1800's scientists recognized that the speedof sound through water was nearly constant (for a given temperature,depth and salinity) and hence, may represent a useful tool for unlockingthe secrets of the underwater world (Fish and Carr, 1990). The firstscientific measurements of the speed of sound through water wereconducted in 1826 by Daniel Colladon over a 16 km range on LakeGeneva. Colladon's experiment produced a speed of sound of 1435 m/sat a temperature of 8 °C (Fish and Carr, 1990). Using a currently ac-cepted seawater equation of state, one deduces a theoretical surface,freshwater, speed of sound velocity of 1439.1 m/s, remarkably close tothe original 1826 estimate of Colladon.

Not only is the speed of acoustic propagation through water ap-proximately four times faster than through air, but the travel distanceis also far greater. The dissipation of sound waves in water is relative-ly low due to the characteristics of the medium. Because attenuationis inversely related to acoustic frequency, higher frequency soundtransmissions are attenuated more rapidly than are lower frequency

sevier B.V. on behalf of Internationa

transmissions. Fish and Carr (1990) provide a useful table of theworking relationship between acoustic frequency, wavelength andtwo-way working ranges traveled (distance) in water (see Table 1).

Based on the information provided in Table 1, a typical soundsource, operating at a frequency of 100 KHz creates a transmittedwave of approximately 1.5 cm in length with an effective range onthe order of 300 m (600 m round trip). Moving to a higher acoustic fre-quency of 500 KHzwill greatly enhance the resolution (by transmittinga waveform of only 3 mm in wavelength). However, this increasedresolution comes at the price of greatly reduced bottom coverage of ap-proximately 75 m in maximum range.

It is clear from the above example that acoustic remote sensing can bean effective, long range, tool for interrogating the aquatic environmentwhen properly designed and applied. In addition, major, recent ad-vances in signal processing and in transmitter/receiver technologyhave combined tomake thewater environment, virtually transparentto acoustic interrogation. It is also clear from Table 1 that attenuation ofacoustic energy through water is strongly affected by the frequency ofthe transmitting source. Acoustic attenuation is proportional to fre-quency squared, providing very rapid loss of range with increasingfrequency and increasing resolution.

All aquatic, acoustic remote sensors, discussed in this context, are“active remote sensors.” An active sensor transmits to the target andthat portion of the returned energy that is backscattered to the receiver

l Association for Great Lakes Research.

Table 1Relationship between acoustic frequency, wavelength and two-way working rangestraveled (distance).Modified from Fish and Carr (1990).

Acoustic frequency Wavelength Two-way distance

100 Hz 15 m 1000 km or more1 kHz 1.5 m 100 km or more10 kHz 15 cm 10 km25 kHz 6 cm 3 km50 kHz 3 cm 1 km100 kHz 1.5 cm 600 m500 kHz 3 mm 150 m1 mHz 1.5 mm 50 m

174 G.A. Meadows / Journal of Great Lakes Research Supplement 39 (2013) 173–182

is processed to extract the desired information. Hence, the key to active,acoustic remote sensing is the understanding of the propagation ofsound through the water. Because these sensors all sense range to tar-get, an accurate description of the speed of sound through the wateris required. To calculate speed of sound, knowledge of the state vari-ables of the fluid is necessary. For seawater this includes temperature,salinity (conductivity and temperature) and fluid hydrostatic pressure.Over the natural environmental ranges of these variables in seawa-ter, temperature changes are the most important in determining thespeed of sound, followed by salinity changes and finally hydrostaticpressure changes. Hence, in the freshwater environment, knowledgeof the temperature distribution along the propagation path is most crit-ical. From the temperature profile, a sound velocity profile can be deter-mined and an accurate range to the target calculated.

One of several, accepted seawater formulations for the speed ofsound is provided by Wilson (1960). Wilson's formula is accepted bythe National Oceanographic Data Center (NODC) USA for computerprocessing of hydrological information. Also, this formula formedthe basis for hydroacoustical calculations in the former USSR (see:Leroy, 1969).

Wilson's formula follows:

c S;T;Pð Þ ¼ c0 þ DcT þ DcS þ DcP þ DcSTP;

where:

c0 1449.14,DcT 4.5721 T−4.4532·10−2 T2−2.6045·10−4

T3+7.9851·10−6 T4,DcS 1.39799(S-35)−1.69202·10−3(S-35)2,DcP 1.63432P−1.06768·10−3P2+3.73403·10−6P3−

3.6332·10−8P4,DcSTP (S-35)(−1.1244·10−2 T+7.7711·10−7

T2+7.85344·10−4P−1.3458·10−5P2+3.2203·10−7

PT+1.6101·10−8T2P)+P(−1.8974·10−3

T+7.6287·10−5 T2+4.6176·10−7 T3)+P2(−2.6301·10−5 T+1.9302·10−7 T2)+P3(−2.0831·10−7 T),

and where c(S,T,P) is the speed of sound in m/s; T is the temperature in

°C; S is the salinity in parts per mille; P is the hydrostatic pressure inMPa, and D is the departure of the real ocean from a standard oceanof S=35 parts per mille, T=0 °C and surface pressure.

Wilson's formula is valid for the following ranges of temperature,salinity, and hydrostatic pressure:

• temperature from −4° to 30 °C;• salinity from 0 to 37 parts per mille;• hydrostatic pressure from 0.1 to 100 MPa.

The mean-square error of calculation of the speed of sound viathis formula with regard to Wilson's experimental data is 0.22 m/s.Utilizing this formulation, the speed of sound for freshwater at 10 °Cat the water surface is 1447.3 m/s. The speed of sound increases with

increasing temperature, increasing salinity and increasing pressure.No abnormal behavior is experienced in the calculated speed ofsound in the vicinity of the temperature of maximum density of fresh-water, ~4 °C. Speed of sound monotonically increases with increasingtemperature from 0 °C and higher. This behavior is due to the depen-dence of the speed of sound on both fluid density and on its bulkmodulus of elasticity.

In recent years (beginning in the mid 1970's with the introductionof the first water-proof, consumer oriented, depth sounder), techno-logical advances and intense competition between manufacturers,including those in both the commercial and sport fishing industries,have dominated acoustic sensing and imaging technology developmentin all areas of utilization. As a result, the entire field of acoustic remotesensing of the aquatic environment is evolving rapidly and is likelyto continue to do so for the foreseeable future, benefiting the scientificconsumer.

Given this evolution, this review considers twomajor categories ofunderwater, acoustic remote sensing technologies, bottom mappingsystems and Doppler sensing systems. In general, bottom mappingsystems are primarily used to determine the presence of the bottom,map its features, and classify its composition. Doppler systems makeuse of target motion to deduce the velocity of the target in up tothree spatial dimensions. For each category of acoustic remote sens-ing a brief description of the theory of operation is provided, followedby examples of the types of data produced by the technology.When possible, estimates of range and accuracy of typical units in eachclass are provided. Finally, examples of new utilizations of combined re-mote sensing technologies are discussed.

Underwater acoustic remote sensing and theory of operation

This review considers two major categories of active, underwater,acoustic remote sensing technologies, bottom mapping systems andDoppler sensing systems. Bottom mapping systems can further besubdivided into four primary classes of sensors: single-beam, side scan,multi-beam and imaging systems.

The second major category of underwater, acoustic remote sens-ing is that of Doppler sensing systems. The principle of operation ofthis class of sensors is to deduce the motion of the fluid in one, twoor three spatial dimensions (and with time) from the Doppler phaseshift of the reflected acoustic pulse. Doppler sensing systems can besub-divided into two primary classes; point measurement and profil-ing measurement systems.

For each of the previously described applications of acoustic remotesensing (single-beam echo-sounder, dual-beam side scan sonar,multi-beam sonar, imaging sonar andDoppler sonar) a brief descriptionof the theory of operation is provided. In each case, this is followedby examples of representative commercially available units as well asexamples of the types of data produced by the technology.When possi-ble, estimates of range and accuracy of typical units in each class areprovided.

Single-beam echo-sounders

Early systems, commonly referred to as echo-sounders or depthfinders, use a single, narrow acoustic beam, typically transmitted froma near-surface platform to determine the presence and range to thebottom. These systems employ a known speed of sound through thewater column to calculate the total water depth along the track of thesurvey vessel.Muchof current bathymetric chart data has been acquiredin this manner.

Single-beam echo-sounders and sub-bottom profilers (typicallysingle-beam systems designed to penetrate the bottom substrate ratherthan reflect from it) operate at relatively low acoustic frequencies.For single-beam echo-sounders, 50 to 200 kHz is the standard. Oper-ating in this range provides excellent spatial resolution (order of a

175G.A. Meadows / Journal of Great Lakes Research Supplement 39 (2013) 173–182

few centimeters) as well as a reasonably-sized acoustic transducer.At the lower end of this frequency range (50 kHz) the beam spread is,15°–25°,while at the higher frequencies (200 kHz) a 1°–6° beam spreadis typical. The narrower beam width provides a more accurate depthreading directly below the survey vessel. The wider the beam, thelarger the bottom “footprint” covered by the beam, and the moreattributes of the water column, bottom and living resources can beidentified. In order to provide sediment penetration, sub-bottom pro-filers operate at much lower frequencies, typically 2–25 kHz. Manleyet al. (2011) provides an example of the use of sub-bottom acousticprofiling to characterize the composition and structure of two lacustrinesediment drifts located in Lake Champlain's Juniper Deep.

All transmitted patterns (acoustic or electromagnetic) have un-avoidable side lobes associated with the directional distribution of theenergy. Extensive efforts are made to minimize these patterns withthe goal being to produce as much transmitted energy as possible, inthe narrowest of desired directions. In Fig. 1, an example of a manufac-turer diagram showing the tradeoffs associated with acoustic beamangle and associated side lobes is provided. This acoustic transmittedpattern is typical of, single-beam, echo-sounder systems and exists tosome degree with all active acoustic systems. The images show thatwith substantial acoustic energy located in side lobes of the transmit-ted pattern, the ability of the system to accurately characterize abruptchanges in bathymetry may be compromised. In most cases, the goalfor the successful acquisition of accurate depth information is to extractfrom the returned echo, the first encounter with the bottom. In rapidlyvarying bottom topography, the first echo may be from a side lobe en-counter with an acoustic reflector and not from the bottom directly be-neath the survey vessel. Thus, applications of this technology at siteswith rapidly varying bottom topography should be performedwith cau-tion. Typical ranges and accuracies of single-beam systems are extreme-ly well suited for application in most freshwater environments. Costeffective commercial systems typically produce reliable results to depthsof 600 to 800 m with depth accuracies of ±0.5% of the total depth.Hence in 100 m of water the depth measurement should be ±50 cm.

Some applications of single-beam surveying are given in Fig. 2. Anexample of a nearshore, single-beam, acoustic survey acquired from asmall survey vessel with an echo-sounder operating at 200 kHz is shownin Fig. 2A. This example (University of Michigan, Ocean EngineeringLaboratory, 2006), is a regional survey of the active sediment transport re-gion of Tawas Point, a recurve spit in Lake Huron. Along-track depth pro-files are also possible and prove useful for documenting bottom changesover time. An example of Lake Michigan nearshore changes over the

Fig. 1. Example of a single-beam acoustic transmit pattern (A) superimposed on a rapidly chManual).

period 1988–2000 are presented in Fig. 2B. These repeated acoustic, sin-gle beam, surveys from Big Sable Point (Lake Michigan) extend fromshore to beyond the “depth of closure” (the nearshore region of active,storm wave induced, bottom sediment movement), approximately8.2 m (27 ft) of water depth in this region. This dynamic nearshore re-gion is characterized by highly mobile, very large amplitude sand barswith trough to crest heights of approximately 7 m in elevation thatshift dramatically over the 12-year study period. Systems of this typecan be used in combination to extract information on biological charac-teristics of interest. For example, Depew et al. (2011) used two single-beam transducers in an integrated system to assess spatial patterns ofCladophora cover and stand height in Lakes Erie, Huron and Ontario. Al-ternatively, Holbrook et al. (2006) used a high-resolution split-beamsystem to develop an algorithm to relate backscatter intensity to zoo-plankton biomass in Lake Superior.

Dual-beam and side scan sonar systems

To observe a broader swath of the sea floor, two beams orientedwith depression angles of only a few degrees below horizontal, areused to extend the capabilities of a single acoustic beam. The transmit-ter and receiver are typically located aboard a towed body (towfish).This type of arrangement is referred to as side scan sonar (SSS). Thisinnovation, first developed following World War II with the first com-mercial release in the early 1960's, allows relatively large areas of thebottom to be mapped by subsequent acoustic pulses as the survey ves-sel proceeds with the SSS towfish submerged at a known depth.Hence, the system provides a two-dimensional (along-track distancevs. range) image of the bottom. Early systems where termed “shad-owgraphs” owing to the analog recording medium and to the acous-tic shadowsproduced on the bottom from the acoustic viewing geometry.Hence, the resultant image ismore complex than the downward orientedsystems, requiring an understanding of the system geometry for properinterpretation of the imagery.

Dual-beam and side scan sonar systems employ many of the samefundamental principles of single-beam systems; however in general, theviewing geometry is altered from the vertical to the horizontal. SSS sys-tems typically project acoustic beams with a slight, downward depres-sion angle from horizontal, from both sides of a streamlined towfish.The towfish is usually operated below and to the stern of the survey ves-sel. All modern systems are now fully digital, including transmitbeam-forming processing of the return echo and data storage and dis-play, providing great advantages over older analog systems.

anging bathymetry (B) (adapted from SYQUEST, Incorporated — HydroBox, Operations

Fig. 2. (A) Example of a nearshore, single-beam, acoustic survey acquired from a small survey vessel with an echo-sounder operating at 200 kHz. Colored scale display providesdepth soundings in feet (0–34 ft) as a visual reference superimposed on a Google image of the region. (B) Repetitive acoustic, single-beam, surveys taken over a period of12 years from Big Sable Point (Lake Michigan) from shore to beyond “depth of closure”, approximately 8.2 m (27 ft) for this region. Note the changing position of the dune struc-tures over time.

176 G.A. Meadows / Journal of Great Lakes Research Supplement 39 (2013) 173–182

Fish and Carr (1990) provide a description of the typical viewinggeometry for the SSS towfish (Fig. 3). In this view, the towfish is mov-ing through the water column at depth (A) below the surface, emit-ting acoustic pulses from both sides, slightly below the horizontal. As istypical with SSS systems, there exists a “blind spot” directly below thepath of the towfish, located at point (D) in the diagram. The transmittedacoustic beam is designed to be broad (wide angle) in the cross-trackdimension and narrow in the along-track dimension. This geometry pro-vides for excellent range coverage of the bottom with a correspondinglyhigh resolution of targets in the azimuthal direction. Fig. 3 shows thatthe first return echoes are most likely to occur from the water surfaceand not the bottom. This is true inmost cases andmust be consideredin the interpretation of the return pattern. In addition, by knowingthe distance of the towfish from the bottom (A–D) and the lengthof the acoustic shadow from an object with elevation above the bottom(G), the height of the object can be directly calculated. Similarly, byknowing the speed of the towfish, the along-track length of an objectcan be estimated. Finally, by knowing the speed of sound, the range di-mension of a target can be determined.

A modern example of digital SSS technology is presented in Fig. 4A.This SSS mosaic image was obtained using an Imagenex Yellowfin dig-ital SSS, operating at 260 kHz, mounted on an Iver2, fully autonomous

Fig. 3. (A) Typical side scan sonar towfish acoustic viewing geometAdapted from Fish and Carr (1990).

underwater vehicle (AUV). In this application the AUV providesuntethered forward motion of the SSS system and offers the advan-tage of being able to place the SSS below the thermocline, thus reducingpotential challenges associated with sound propagation through astrong density gradient. Removing the variation of temperature (andhence the variation of speed of sound) generally provides for sharperand less distorted acoustic images. It is interesting to note the objectidentified in the upper right hand corner of the SSS search pattern.This object is 6.9 m in length and appears cylindrical in shape. The clarityof the resolution of this object provides a vivid example of the widearea search capabilities of modern SSS systems. The accuracies andranges of operation of SSS systems are similar to those of single-beamecho-sounders. However, because the acoustic transducer is typicallynot at thewater surface in SSS, the acoustic beamgeometrymust be con-sidered in relation to the distance above the bottom. The “blotchy” pat-tern on the bottom evident throughout the SSS acoustic return inFig. 4A may, at first glance, appear as an artifact. One significant advan-tage of AUV-borne SSS systems is the ability to simultaneously acquirebottom visual imagery using an onboard camera. Fig. 4B shows a videoscreen image from the AUV camera, also acquired at a depth of 30 mbelow the surface (8 m above the bottom) under ambient light. Fig. 4Cprovides a video screen image from a remote operated vehicle

ry. (B) Corresponding SSS image matching viewing geometry.

Fig. 4. (A) Digital SSS mosaic (collected at 260 kHz) of Lake Michigan bottom (depth=38 m) acquired on September 2011 by an AUV operating approximately 30 m below thesurface. The entire bottom image (180×100 m) shows a “blotchy” appearance associated with patches of dense bottommussel growth separated by sand substrate. (B) AUV digitalimage of bottom conditions, acquired concurrently with SSS image in Fig. 4A. (C) ROV screen captured image of details of quagga mussel bed from the same area at a camera depthof 35 m.

177G.A. Meadows / Journal of Great Lakes Research Supplement 39 (2013) 173–182

(ROV) deployed simultaneously during this mission for selective bot-tom sampling. The blotchy texture in these images is beds of quaggamussels separated by patches of very fine sand and not an artifact ofthe SSS system. The resolution of the SSS system is such that it can pro-vide evidence of the presence of these mussels over the site.

Multi-beam sonar

The development of dual-beam SSS and the advent of advanced dig-ital systems and processing, have led to the expansion of multi-beamsystems. These advances include the development of transmitter rapidpulsing (multiple times per second) over awide range of cross-track an-gles and returned signals received over a digitally synthesized, phasedarray. Multi-beam systems are primarily vertically downward-lookingsystems that incorporate several individual beams spread over a widerange of transmitted angles, pulsing nearly instantaneously, with thereturn signal also captured over this same range of angles. These sys-tems have advanced to incorporate between 256 to over 500 beams.When corrected for vessel motions (pitch, roll, yaw, heave, surge andsway) and slant-to-ground range corrections, accurate depth maps canbe produced overwide areas of the lake bed in a single pass. This greatlyreduces survey vessel time and generally increases survey quality.

Themajor advantage ofmulti-beam sonar systems, over single-beam,is the ability to produce nearly 100% coverage of the bottom within thefield of view of the sensor. Vessel track lines can be adjusted to providea survey pattern that will capture a complete mosaic of the bottom inthe area of interest. Hence, omission of important bottom topographicfeatures can be virtually eliminated. SeaBeam (2000) provides an excel-lent reference on multi-beam sonar theory and signal processing. As arule of thumb, a multi-beam sonar operating with an array of beamsencompassing a cross track angle of 120° provides a slightly greaterthan 3:1 ratio between swath width covered on the seafloor for every

meter of water depth. For a cross track array beam angle of 150° thisratio increases to approximately 7.5:1.

Typical accuracies and ranges of operation of multi-beam systemsare dependent on the specific unit and conditions under which it isoperated. As a byproduct of the development of totally digital systems,the user is given unprecedented control over the instrument setupand function. For example, frequency of the transmitted pulse, pulselength, number of transmitted beams, and other operational param-eters are typically under user control. Hence, dependent upon the spe-cific application, and user knowledge of the setup and operation ofthese systems,wide ranges of accuracies are obtainable. In general, how-ever, absolute accuracies on the order of 0.2% of water depth are notuncommon. Several examples ofmulti-beam sonar surveys are availablefrom NOAA's, National Ocean Service, Hydrographic Office. See: http://celebrating200years.noaa.gov/breakthroughs/hydro_survey/welcome.html#methods.

Imaging sonar systems

Applying the multi-beam concept in the horizontal plane producesan imaging sonar system. These systems typically employ relativelyhigh acoustic frequencies (500–800 kHz) for underwater vehicle, shortrange navigation. Scanning the acoustic energy in a user-defined sectorrelative to the vehicle provides a very detailed map of the near fieldlandscape. This orientation produces sonar systems that form an imageof the underwater scene over a user-defined sector, extending from afew degrees (for rapid refresh rates of the scene) to a full, 360° fieldaround the vehicle. Most present systems utilize “point and shoot” tech-niques employing a pulsed acoustic transducer coupled with an angulartransducer stepping motor. Hence, a series of single-beam echoes arequickly assembled to produce a pseudo-multi-beam image over an an-gular sector. The image produced in this manner provides a very de-tailed map of the near field landscape. These systems have found great

178 G.A. Meadows / Journal of Great Lakes Research Supplement 39 (2013) 173–182

application in the offshore oil and gas industry as well as in scientificinvestigations and in search and recovery operations.

An early example of the application of scanning-imaging sonar tothe sunken vessel E.B. Allen in the Thunder Bay National Marine Sanc-tuary (NMS), in Lake Huron is given in Fig. 5. A clear delineation of theentire sunken vessel is apparent in this image, from which its overalldimensions are obtainable and even details along the deck are visible.The wreck is reported by theNational Oceanic and Atmospheric Admin-istration (NOAA)/NMS as being 40.9 m (134 ft.) in length with a 7.9 m(26 ft.) beam and was constructed of wood in 1864. It is apparent thatthese high-resolution imaging sonar systems are excellent underwatermeasurement tools when operating at high frequency over relativelyshort ranges and provides for excellent accuracy and measurement ca-pability. Hence, scanning-imaging sonars have attained common use inprecision underwater archeological investigations both from mobileROV's (O'Shea and Meadows, 2009) as well as from stationary platformsinstalled by divers over sites of interest. Range accuracies are on the orderof a few centimeters over ranges of generally less than 100 m.

Warner et al. (2009) showed that acoustic sampling could be auseful complement to other sampling tools used in large fish restora-tion research in the Great Lakes. They made use of an ROV-mountedimaging sonar to identify spawning locations by rapidly locatingaggregations of large fish while simultaneously describing distribu-tion, and estimating abundance in southern Lake Michigan. Their workshowed that lake trout were preferentially spawning over rough sub-strates (rubble and cobble). Other examples of application of sonarimaging to fisheries research are provided by Dunlop et al. (2010) andStockwell et al. (2006).

Doppler sensing

Doppler sensing systems represent the other major category ofunderwater, acoustic remote sensing systems. The principle of opera-tion of this class of sensors is to deduce the motion of the ambientfluid in one, two or three special dimensions (and with time) fromtheDoppler phase shift of the reflected acoustic pulse. In this application,the acoustic pulse is not intended to reflect primarily off the bottom,as in the previous classes of instruments, but instead off suspendedparticulates naturally occurring within the fluid itself. Acoustic reflec-tions from both the bottom and from the surface can also contain signif-icant information and are not discarded. For mobile platforms, Doppler

Fig. 5. Scanning imaging sonar record taken in June 1994 of a Great Lakes shipwrecklying on the bottom. The sonar was operated at 675 kHz at 50 m range (with 10 mrange rings displayed) from a medium duty, Benthos ROV approximately 30 mbelow the surface.

bottom returns provide valuable navigational information and fromstationary platforms, surface returns can provide both wave and iceinformation (Morse et al., 2003). Doppler sensing can be sub-dividedinto two primary classes; point measurement and profiling measure-ment systems.

Point measurement systems

Acoustic Doppler velocimeters (ADVs), typically provide very highresolution, 3-dimensional flow velocities as a function of time. This isaccomplished by transmitting a focused acoustic pulse and receivingthe backscattered energy with 3 or 4 receivers from a well-definedfluid volume, thus, providing a time series of fluid velocity compo-nents at the prescribed location. ADVs of this type are available inboth laboratory and field deployable versions. The advantage of thisclass of sensors is the ability to obtain very detailed and precise mea-surements of flow structure at a single point. The point of measurementis separated from the physical sensor itself, allowing for limited in-terference of the measurement device with the flow field in carefullydesigned experiments. These classes of instruments typically operateat very high acoustic frequencies, ~10 MHz, producing correspondinglyhigh precision measurements at high sampling rates. Velocity resolu-tion is typically 0.01 cm/s with an accuracy of 1% of the mean flow ve-locity. Cotel et al. (2006) and Webb et al. (2009) report on the use ofADVs in the measurement of turbulent quantities in nearshore lacus-trine habitats. Measurements of this type were coupledwith visible ob-servations of substrate, flora and fauna to determine the effects of smallscale eddies on the persistence of fish habitation.

Profiling measurement systems

Profiling measurement systems, called, acoustic Doppler currentprofilers (ADCPs) apply the same basic acoustic concepts, but add thecapability of providing several measurements of Doppler velocities at avariety of ranges from the instrument. ADCP systems can be mountedin many configurations, such as bottom-mounted (upward looking),and ship or buoy mounted (downward looking) or horizontal in thewater column, e.g. for harbor entrancemonitoring. In general, the num-ber ofmeasurement bins and bin size are controlled by a combination ofacoustic frequency, internal processing of the acoustic signal and userdesires.

Bottom-mounted and ship-borne ADCPs have been used exten-sively throughout the Great Lakes. Bottom-mounted units have the ad-vantage of being capable of remaining in place through the harsh, icecovered, northern winters, making measurements when conventionalsurface observations are generally not possible. There are several exam-ples of innovative applications of ADCPs. For example,Miller (2003) usedanADCP to track the verticalmigration ofMysis; Czuba et al. (2011) usedADCPs to map the flow structure of the upper St. Clair River, in a similarmanner to the earlier work of Holtschlag and Koschik (2002), and Zhaoet al. (2012) used measurements from three ADCP moorings to aid inthe development of a tracer dispersion model for Lake Winnipeg. Theseselected recent references provide an indication of the wide spread ac-ceptance of this technology and the equally wide range of applicationsit has presented.

A hybrid ADCP-buoy concept has received recent popularity, throughthe combination of the significant advantages of ADCP measurementsthroughout the water column with a surface buoy presence, which pro-vides power and real-time transmission of data (see Fig. 6). This concepthas been employed by NOAA's Great Lakes Environmental ResearchLaboratory and by Grand Valley State University's AnnisWater ResourcesInstitute (among others). A recent variation of this concept has been toplace the ADCP in a buoy on the surface. This variation greatly simplifiesthe mooring requirements, but adds the complication that buoy motionsmust be removed from the ADCP measurements. For a complete discus-sion of the comparison of buoy-mounted and bottom-mounted ADCP

Fig. 7. TIDAS 900 buoy with through hull, motion compensated, Nortek AquadoppZ-Cell Profiler (ADCP).

179G.A. Meadows / Journal of Great Lakes Research Supplement 39 (2013) 173–182

measurements, see Seim and Edwards (2007). Most major ADCP man-ufacturers now provide motion compensated ADCPs for buoy mount-ing. An example is the TIDAS 900 buoy (Fig. 7), developed for theGreat Lakes Observing System (GLOS) with a Nortek ADCP mountedthrough the buoy hull.

Next steps in advanced acoustic sensors

Acoustic remote sensing instrumentation has become a mainstayin freshwater systems research. Significant advances in both technol-ogy anduser-friendly interfaces allow for customization and adaptabilityof sensors to a wide variety of underwater applications. In this context,the recent advent of highly capable remotely operated and fully autono-mous surface and underwater vehicles have allowed for newapproachesand applications of basic acoustic remote sensing techniques. Miniaturi-zation of acoustic systems has allowed for effective use on underwatermoving platforms, producing exceptional and new viewing geometriesfor acoustically sensing underwater environments. This point is wellillustrated by the recent advances in acoustic bottom classification. Itis now possible, with a variety of commercially available software pack-ages, to provide mapping and characterization of benthic environmentsbased upon the backscattered acoustic signal. Because of the wide areacoverage inherent in both side scan sonar and multi-beam mapping,these two technologies have received themost attention in this context.A recent and comprehensive bibliography of acoustic seabed classifica-tion is provided by Hamilton (2005). The basic premise of this technol-ogy is that the return echo contains far more information than just thetravel time to and from the target. The details of the intensity and dura-tion of the backscattered echo are controlled by the characteristics of theseabed from which the echo is reflected. With high-speed signal pro-cessing of modern digital systems, accurate and reliable classificationof the seabed can be accomplished. “Sea-truthing” of the actual bottomcharacteristics is an essential component of an accurate classification.Although bottom grab samples are generally used for this purpose, un-derwater digital video or still photography of the undisturbed substratehave distinct advantages and are increasingly utilized.

Bottom acoustic imaging coupled with bottom photography pro-vides a powerful remote sensing tool for bottom classification. AUVs,gliders and ROV platforms are ideally suited for this application. As anexample, Fig. 8 shows a side-by-side comparison between scanningimaging sonar and video images of a northern Lake Michigan site withinthe Sleeping Bear Dunes National Lakeshore, where the bottom is

Fig. 6. Grand Valley State University schemat

covered bymussels and by benthicfilamentous algal beds. Both distinctlydifferent acoustic return images are at 700 kHz operating over 25 to50 m ranges. Furthermore, combining visible imagery with acoustic im-agery, investigators are making use of integrated acoustic sampling sys-tems. For example, Halfman et al. (2006) describe the use of a coupledSSS and sub-bottom acoustic profiling system to characterize deepwatersediments in Lake Ontario.

Two recent examples of automated acoustic bottom classificationfrom remote platforms are available from Lake Huron and northern

ic of hybrid ADCP-buoy mooring system.

Fig. 8. A side by side comparison between scanning imaging sonar (700 kHz) and video images of a northern Lake Michigan site, within the Sleeping Bear Dunes National Lakeshore,where the bottom is covered by mussels (A) and by benthic filamentous algal beds (B).

180 G.A. Meadows / Journal of Great Lakes Research Supplement 39 (2013) 173–182

Lake Michigan. In the first example, both an autonomous underwatervehicle (Iver2), equipped as a bottom survey ship with the ImagenexYellowfin, digital side scan sonar and dual digital cameras, and thesame SSS used in towfish mode were used in conjunction with the newQuester Tangent bottom classification software to characterize a portionof Saginaw Bay, in Lake Huron. In the second example, the sameImagenex Yellowfin, SSS was used, but in this example, the optical bot-tom verification was provided by satellite over a portion of SleepingBear Dunes National Lakeshore, in Lake Michigan. The remote sensinggoal of these techniques is to utilize a relatively small but complexarea of an underwater region within which detailed SSS surveys areconducted, accompaniedwith “sea truth” knowledge of the compositionor characteristics of each of the acoustically reflective bottom types. Thisknowledge is then utilized to characterize other regions based upon thestatistical similarity of the new regions to that of the known regions.

Point Au Gres, Saginaw Bay, in Lake Huron is a shallow habitat re-gion for yellow perch and walleye, two of the most important fisher-ies in the bay. To better understand the impacts of multiple stressors onSaginaw Bay upper trophic levels, bottom substrate surveys of Au Gresand Saganing Reefs were desired. Sonar and ancillary data collected

Fig. 9. Three step process to acoustic classification of bottom substrates. The first imagecolor-coded acoustic delineated similar substrates and the final image provides classificatiothe intensity of the acoustic return from the bottom. In the final image, the color identifies

from these deployments were processed with the Quester Tangentsoftware to determine bottom substrate classification signatures in thesurvey areas. A three-step process of acoustic classification was appliedto determine the bottom substrates (Fig. 9). The first image provides theraw side scan sonar overlapping tracks, the second image also includescolor-coded acoustic-delineated similar substrates and the final imageprovides classification of acoustically similar regions. Based on corre-sponding bottom photographic evidence, the actual composition of thebottom in each region can be identified and classified and then that clas-sification, extended to undocumented areas.

In the case of Sleeping Bear Dunes, in northern Lake Michigan, thebottom classification issue of interest, is associated with the rapid anddetrimental proliferation of benthic algae including Cladophora, a benthicnuisance algal species of wide concern (Byappanahalli and Whitkan,2009). Sparsely covered bottom surveys (widely spaced over selectedregions) using side scan sonar were conducted during the maximumCladophora growth period in the summer of 2012. Fig. 10 is derivedfrom aGeoEye-1 high-resolution image (approximately 2 m spatial res-olution in the color bands) of the region between Sleeping Bear Pointand the southern tip of North Manitou Island, including Good Harbor

provides the raw side scan sonar overlapping tracks, the second image also includesn of acoustically similar regions. In the first two images, the color-coding is scaled toarea of similar acoustic return and hence, inferred similar substrate composition.

Fig. 10. GeoEye-1 high resolution image of the Sleeping Bear Dunes National Lakeshore region of northern Lake Michigan between Sleeping Bear Point and the southern tip of NorthManitou Island, including Good Harbor Bay. Location maps (inset) and true color satellite image in panel (A). Superimposed on this image is a portion of the geo-rectified, side scansonar track (frequency of transducer) (B) across North Manitou shoal with bottom sand waves between Cladophora/rock substrates.

181G.A. Meadows / Journal of Great Lakes Research Supplement 39 (2013) 173–182

Bay within the Sleeping Bear Dunes National Lakeshore of northernLake Michigan. Superimposed on this image is a portion of the geo-rectified, side scan sonar track across North Manitou Shoal showingacoustic delineation between bottom sand waves and Cladophora/rocksubstrates. This composite image consisting of both, high-resolutionsatellite photography and selected side scan sonar “sea-truth,” providesamechanism to identify and classify similar regionswith common sonarreturns throughout the satellite image. This is a powerful tool for effi-cient and repetitive bottom substrate classification over relatively largeaquatic regions.

Underwater acoustic remote sensing is a science in its infancy inmany respects compared to other forms of remote sensing. Althoughthe science began with the first scientific measurements of the speedof sound through water by Daniel Colladon on the freshwater of LakeGeneva in 1826, it has not been until the latter half of the 20th centurythat scientific use of underwater sound has “exploded.” This is due inpart to military applications of underwater sound propagation and re-strictions placed on new technologies and advancements. The previousera of relatively slow growth in both technology and in applications hasended. Digital systems, and advanced signal processing, available from awide range ofmanufacturers, at affordable prices, have opened newho-rizons for the scientific consumer of underwater remote sensing tools.

Acknowledgments

The author would like to acknowledge many sponsors who havesupported portions of the work reviewed in this paper. They include,the Michigan Department of Environmental Quality, the Great LakesRestoration Initiative, the Great Lakes Observing System (GLOS), theSaginaw Bay Multi-Stressors Project, the National Oceanic and At-mospheric Administration and the Cooperative Institute for Limnologyand Ecosystems Research (CILER). In addition, without the dedicatedfield and data analysis assistance of Russ Miller, Heidi Purcell, AndrewLayman, Michael Sayers, Robert Shuchman, Lorelle Meadows and themany students of the Ocean Engineering Laboratory, these efforts

would not have been possible. This is Contribution No. 2 of the GreatLakes Research Center at Michigan Tech.

References

Byappanahalli, M.N., Whitkan, R.L., 2009. Clostridium botulinum type E occurs andgrows in the alga Cladophora glomerata. Can. J. Fish. Aquat. Sci. 66 (6).

Cotel, A.J., Webb, P.W., Tritico, H., 2006. Do trout choose habitats with reduced turbulence?Trans. Am. Fish. Soc. 135, 610–619.

Czuba, J.A., Best, James L., Oberg, K.A., Parsons, D.R., Jackson, P.R., Garcia, M.H., Ashmore,P., Sep 2011. Bed morphology, flow structure, and sediment transport at the outletof Lake Huron and in the upper St. Clair River. J. Great Lakes Res. 37 (3), 480–493.

Depew, D.D., Houben, A.J., Guildford, S.J., Hecky, R.E., 2011. Distribution of nuisanceCladophora in the lower Great Lakes: patterns with land use, near shore waterquality and dreissenid abundance. J. Great Lakes Res. 37, 656–671.

Dunlop, E.S., Milne, S.W., Ridgway, M.S., 2010. Temporal trends in the numbers andcharacteristics of Lake Huron fish schools between 2000 and 2004. J. Great LakesRes. 36 (1), 74–85.

Fish, J.P., Carr, H.A., 1990. Sound Underwater Images, A Guide to the Generation andInterpretation of Side Scan Sonar Data. Lower Cape Publishing, Orleans, MA.

Halfman, J.D., Dittman, D.E., Owens, R.W., Etherington, M.D., 2006. Storm-induced re-distribution of deepwater sediments in Lake Ontario. J. Great Lakes Res. 32 (2),348–360.

Hamilton, L.J., 2005. A Bibliography of Acoustic Seabed Classification, Defense Scienceand Technology Organization (DSTO), Pyrmont, NSW 2009. CRC for Coastal Zone,Estuary and Waterway Management, Australia.

Holbrook, B.V., Hrabik, T.R., Branstrator, D.K., Yule, D.L., Stockwell, J.D., 2006.Hydroacoustic estimation of zooplankton biomass at two shoal complexes in theApostle Islands region of Lake Superior. J. Great Lakes Res. 32 (4), 680–696.

Holtschlag, D.J., Koschik, J.A., Jan 2002. Flow distribution in selected branches ofSt. Clair and Detroit rivers. J. Great Lakes Res. 28 (3), 379–395.

Leroy, C.C., 1969. Development of simple equations for accurate and more realisticcalculations of the speed of sound in seawater. J. Acoust. Soc. Am. 46 (1), 216–226.

Manley, P.L., Manley, T.O., Hayo, K., Cronin, T., 2011. Small-scale lacustrine drifts in LakeChamplain, Vermont. J. Great Lakes Res. 38 (Supplement 1), 88–100.

Miller, Gerald S., 2003. Mysis vertical migration in Grand Traverse Bay, Lake Michigan, ob-served by an acoustic Doppler current profiler. J. Great Lakes Res. 29 (3), 427–435 (Jan).

Morse, B., Masoud, H., Bourel, C., 2003. Characteristics of ice in the St. Lawrence River.Can. J. Civ. Eng. 30 (4), 766–774.

O'Shea, J.M., Meadows, G., 2009. Evidence for early hunters beneath the Great Lakes.Proc. Natl. Acad. Sci. 106 (25), 10120–10123 (June).

Ocean Engineering Laboratory, 2006. Tawas Point cumulative impacts and recurve spitdynamics. Final Report.The University of Michigan (December).

SeaBeam, Inc., 2000. Multibeam Sonar Theory of Operation. L3 Communications SeaBeamInstruments.

182 G.A. Meadows / Journal of Great Lakes Research Supplement 39 (2013) 173–182

Seim, H.E., Edwards, C.R., 2007. Comparison of buoy-mounted and bottom-mooredADCP performance at Gray's Reef. J. Atmos. Ocean. Technol. 24.

Stockwell, J.D., Yule, D.L., Gorman, W.T., Isaac, E.J., Moore, S.A., 2006. Evaluation of Bot-tom trawls as compared to acoustics to assess adult lake herring (Coregonus artedi)abundance in Lake Superior. J. Great Lakes Res. 32 (2), 280–292.

Warner, D.M., Claramund, R.M., Janssen, J., Jude, J.D., Wattrus, N., 2009. Acoustic esti-mates of abundance and distribution of spawning lake trout on Sheboygan Reefin Lake Michigan. J. Great Lakes Res. 35 (1), 147–153.

Webb, P., Cotel, A., Meadows, L., 2009. Waves and eddies: effects on fish behavior andhabitat distribution. In: Domenici, P., Kapoor, B.G. (Eds.), Fish Locomotion: An Eco-Ethological Approach. Science Publishers (June).

Wilson, W.D., 1960. Equation for the speed of sound in sea water. J. Acoust. Soc. Am. 32(10), 1357.

Zhao, J., Rao, Y.R., Wassenaar, L.I., 2012. Numerical modeling of hydrodynamics andtracer dispersion during ice-free period in Lake Winnipeg. J. Great Lakes Res. 38(Suppl. 3), 147–157.