U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2008–5009

Evaluation of a Single-Beam Sonar System to Map Seagrass 

at Two Sites in Northern Puget Sound, Washington
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Evaluation of a Single-Beam Sonar System 

to Map Seagrass at Two Sites in Northern 

Puget Sound, Washington

By Andrew W. Stevens, Jessica R. Lacy, David P. Finlayson, and Guy Gelfenbaum

Scientific Investigations Report 2008-5009

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director 

U.S. Geological Survey, Reston, Virginia: 2008

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Suggested citation:

Stevens, A.W., Lacy, J.R., Finlayson, D.P., and Gelfenbaum, G., 2008, Evaluation of a single-beam sonar system to map 

seagrass at two sites in northern Puget Sound, Washington: U.S. Geological Survey Scientific Investigations Report 

2008-5009, 45 p.
iii

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

Methods...........................................................................................................................................................2

Study Sites and Survey Design ..........................................................................................................2

Equipment and Data Collection ..........................................................................................................2

Underwater Video Classification........................................................................................................4

Acoustic Data Analysis........................................................................................................................6

Bottom Detection and Plant Recognition in Single-Beam Data ..........................................6

Analyzing real-world data ..........................................................................................................7

Data Output...................................................................................................................................9

Results and Discussion...............................................................................................................................11

Underwater Video and Acoustic Data Comparison......................................................................11

Possession Point.................................................................................................................................15

Browns Bay..........................................................................................................................................18

Summary and Conclusions.........................................................................................................................28

Acknowledgements.....................................................................................................................................28

References....................................................................................................................................................28

Appendix A. Data collected at Possession Point ...................................................................................30

Appendix B. Data collected at Browns Bay............................................................................................36

Appendix C. Grain-size data from Possession Point..............................................................................39

Figures

1. Map of northern Puget Sound, Washington state................................................................................3

2. Detailed map of the two study sites: Possession Point and Browns Bay. .......................................4

3. Photographs showing the main components of the acoustic survey equipment...........................5

4. Photographs showing the main components of the underwater video system..............................6

5. Typical profile of acoustic backscatter in an area without seagrass...............................................7

6. Typical profile of acoustic backscatter in an area with seagrass.....................................................8

7. Classified acoustic data showing the bottom depth and top of vegetation canopy.......................8

8. Acoustic data and a corresponding still frame from the underwater video camera 

showing the typical acoustic signature of seagrass. ................................................................9

9. Acoustic data and a corresponding still frame from the underwater video camera 

showing the typical acoustic signature of red algae.................................................................9

10. Acoustic data and a corresponding still frame from the underwater video camera 

showing the typical acoustic signature of sea pens..................................................................9

11. Screen-shot of the GUI created for seagrass and depth classification.......................................10

12. Example acoustic backscatter image with black and white video stills containing no 

seagrass, dense seagrass, and patchy seagrass ....................................................................12

13. Numerical comparison of acoustic seagrass percent cover with underwater video data ......13

14. Mean acoustic seagrass percent cover compared with classified underwater video 

collected at Possession Point......................................................................................................14

15. Acoustic and underwater video data coverage for the Possession Point study site ................15
iv

16. Interpolated bathymetry from acoustic survey data at Possession Point ...................................16

17. Interpolated seabed slope at Possession Point ...............................................................................16

18. Interpolated bathymetric slope angle for the northern portion of the Possession 

Point study site................................................................................................................................17

19. Classified acoustic data showing seagrass percent cover at Possession Point .......................18

20. Seagrass percent cover at Possession Point interpolated from raw fractional 

ping data ..........................................................................................................................................18

21. Depth distribution of seagrass at Possession Point calculated from the interpolated 

grid of seagrass percent cover....................................................................................................19

22. Location of surface sediment samples and mean grain size from the Possession 

Point study site................................................................................................................................20

23. Classified underwater video data from the Possession Point study site .....................................21

24. Acoustic and underwater video data coverage for the Browns Bay study site.........................22

25. Interpolated bathymetry from acoustic data at Browns Bay.........................................................23

26. Classified acoustic data showing seagrass percent cover ...........................................................24

27. Interpolated seagrass percent cover at Browns Bay .....................................................................25

28. Depth distribution of seagrass at Browns Bay calculated from the interpolated grid of 

seagrass percent cover................................................................................................................26

29. Classified underwater video data from the Browns Bay study site..............................................27

A1. Planned lines for acoustic survey at Possession Point. Cross-shore lines 1-72 were 

spaced 50 m apart and lines 73-148 were spaced 25 m apart. Lines 149-163 

were oriented alongshore.........................................................................................................30

A2. Predicted tidal elevations for Seattle (47.6 °N, 122.3 °W) for January 30, 2007. The gray 

bar shows the time span when data were collected at the Possession Point 

study site ......................................................................................................................................34

A3. Predicted tidal elevations for Seattle (47.6 °N, 122.3 °W) for January 31, 2007. The gray 

bar shows the time span when data were collected at the Possession Point 

study site ......................................................................................................................................35

A4. Predicted tidal elevations for Seattle (47.6 °N, 122.3 °W) for February 1, 2007. The gray 

bar shows the time span when data were collected at the Possession Point 

study site ......................................................................................................................................35

B1. Planned lines for acoustic survey at Browns Bay. Cross-shore lines 1-123 were spaced 

roughly 15 m apart. Lines 124-126 were oriented alongshore.............................................36

B2. Predicted tidal elevations for Seattle (47.6 °N, 122.3 °W) for February 2, 2007. The gray 

bar shows the time span when data were collected at the Browns Bay study 

site.................................................................................................................................................38

C1. Sediment grain-size distribution (weight percent) for samples 1 through 12 from 

Possession Point. See table C1 for locations of samples. ...................................................41

C2. Sediment grain size distribution (weight percent) for samples 13 through HP-7 from 

Possession Point. See table C1 for locations of samples. ...................................................42

C3. Sediment grain size distribution (weight percent) for samples TN-1 through TS-9 from 

Possession Point. See table C1 for locations of samples. ...................................................43

C4. Sediment grain size distribution (weight percent) for samples G22-1A through AP-4 from 

Possession Point. See table C1 for locations of samples. ...................................................44

C5. Sediment grain size distribution (weight percent) for samples AP-5 through AP-9 from 

Possession Point. See table C1 for locations of samples. ...................................................45
v

Tables

1. Confusion matrix of acoustic versus underwater-video characterization of seagrass 

cover in the Possession Point study site ................................................................................11

A1. List of lines completed, elapsed time, and distance covered at the Possession Point 

study site, January 30, 2007 ......................................................................................................31

A2. List of lines completed, elapsed time, and distance covered at the Possession Point 

study site January 31, 2007 .......................................................................................................32

A3. List of lines completed, elapsed time, and distance covered at the Possession Point 

study site, February 1, 2007. Asterisks next to the line number indicate that 

underwater video was also collected.....................................................................................34

B1. List of lines completed, elapsed time, and distance covered in Browns Bay study 

area, February 2, 2007. Asterisks next to the line number indicate that underwater 

video was also collected...........................................................................................................37

C1. Locations and grain-size parameters for samples collected at Possession Point............ 39
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Abstract

Seagrass at two sites in northern Puget Sound, Possession Point and nearby Browns Bay, was mapped using both a 

single-beam sonar and underwater video camera. The acoustic 

and underwater video data were compared to evaluate the 

accuracy of acoustic estimates of seagrass cover. The accuracy 

of the acoustic method was calculated for three classifications 

of seagrass observed in underwater video: bare (no seagrass), 

patchy seagrass, and continuous seagrass. Acoustic and underwater video methods agreed in 92 percent and 74 percent of 

observations made in bare and continuous areas, respectively. 

However, in patchy seagrass, the agreement between acoustic 

and underwater video was poor (43 percent). The poor agreement between the two methods in areas with patchy seagrass is 

likely because the two instruments were not precisely colocated. 

The distribution of seagrass at the two sites differed 

both in overall percent vegetated and in the distribution of 

percent cover versus depth. On the basis of acoustic data, 

seagrass inhabited 0.29 km2 (19 percent of total area) at Possession Point and 0.043 km2 (5 percent of total area) at the 

Browns Bay study site. The depth distribution at the two sites 

was markedly different. Whereas the majority of seagrass at 

Possession Point occurred between -0.5 and -1.5 m MLLW, 

most seagrass at Browns Bay occurred at a greater depth, 

between -2.25 and -3.5 m MLLW. Further investigation of the 

anthropogenic and natural factors causing these differences in 

distribution is needed. 

Introduction

Seagrass is an important component of nearshore 

ecosystems that support many estuarine species, including a 

number of commercially important fisheries (Deegan, 2002). 

In Puget Sound, the most abundant, as well as the most ecologically important, seagrass is the eelgrass Zostera marina. 

The distribution of seagrass is controlled by light availability 

(Duarte, 1991), as well as by several physical, geological, 

and geochemical factors in the nearshore environment (Koch, 

2001). Many of the habitat requirements of seagrass can be 

disrupted by human activities, and loss of seagrass habitat can 

often be attributed to anthropogenic causes (Short and WyllieEcheverria, 1996). Recent worldwide losses in seagrass habitat 

have caused many government agencies and environmental 

groups to develop monitoring programs for this important 

coastal resource.

The need to monitor seagrass has led to the use of 

numerous methods for assessing its distribution and attributes, 

including land-based surveys, diving-based surveys, aerial 

photography, and underwater video (Duarte and Kirkman, 

2001). Recently, there has been extensive research into using 

acoustic devices, such as single-beam sonar (Sabol and others, 

2002), side-scan sonar (Pasqualini and others, 2000), multibeam sonar (Komatsu and others, 2003), and acoustic Doppler current profilers (Warren and Peterson, 2007) to quantify 

seagrass habitat. Underwater video data have been used 

extensively in monitoring programs to estimate areal coverage of subtidal aquatic vegetation in Puget Sound (Norris and 

others, 1997; Gaeckle and others, 2007). Underwater video 

data provide an unambiguous assessment of seagrass presence, 

but quantitative information other than presence or absence is 

very difficult to extract. Many acoustic devices, on the other 

hand, have the potential to determine relevant descriptors 

of seagrass habitat, including plant cover, plant height, and 

biovolume (area × percent cover × plant height) (for example, 

Thomas and others, 1990; Sabol and others, 2002; Warren and 

Peterson, 2007). Acoustic methods can be used in conditions 

that are challenging for collecting underwater video, such 

as in turbid estuaries where visibility is minimal. However, 

the accuracy of acoustically derived vegetation maps can be 

lower than those created with underwater video because of 

errors associated with interpreting and classifying acoustic 

data. With a few exceptions (for example, Winfield and others, 

2007), the accuracy of acoustically derived plant attributes is 

either not determined or not reported. 

Evaluation of a Single-Beam Sonar System to Map 

Seagrass at Two Sites in Northern Puget Sound, 

Washington

By Andrew W. Stevens, Jessica R. Lacy, David P. Finlayson, and Guy Gelfenbaum
2 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

In this report, we describe data-collection and analysis methods for characterizing seagrass distribution using a 

Biosonics single-beam sonar and their application to two sites 

in northern Puget Sound. Underwater video was collected 

synchronously with the acoustic data in a subset of the survey 

lines. We compare results from the two techniques to better 

understand the accuracy, benefits, and limitations of acoustic 

mapping of seagrass. Metadata for this report are available at 

http://walrus.wr.usgs.gov/infobank/b/b207ps/html/b-2-07-ps.

meta.html.

Methods

Study Sites and Survey Design

Two study sites in northern Puget Sound, Washington 

(fig. 1) were selected to conduct bathymetric and seagrass 

surveys using a single-beam sonar system and underwater 

video camera on U.S. Geological Survey Cruise B-2-07-PS. 

The two sites are separated by Possession Sound, a deep channel that connects the main body of Puget Sound to the south 

with Saratoga Pass and Skagit Bay to the north. The first study 

site, Possession Point, is located on Whidbey Island on the 

west side of Possession Sound (fig. 2). The Possession Point 

site is characterized by a wide range of conditions in both 

vegetation and bottom type. During a low-tide site visit, we 

observed regions populated with seagrass, regions populated 

with kelp, and unvegetated regions, as well as both continuous 

and patchy seagrass. Bottom substrates included grain sizes 

from boulders to sand, large bed forms, and ripples. Results 

of the acoustic mapping at Possession Point were used to site 

instruments in a study of the influence of seagrass on hydrodynamics and sediment transport during the spring of 2007.

A total of 163 sonar lines were planned to cover the 

Possession Point study site. The majority of planned lines, 

148, were oriented in the cross-shore direction, and 15 lines 

were along-shore (see appendix A, fig. A1). The cross-shore 

survey lines were followed from as close to shore as possible 

to a depth of roughly 20 m. In the southern and middle sections of the study site (lines 1-72), survey lines were spaced 

at 50-m intervals. In the northern section, lines 73-148 were 

spaced at 25-m intervals. The higher density of lines in the 

northern part of the study site was intended to better resolve 

bed forms that had been observed during previous site visits (J. 

Lacy, oral communication, 2007).

The second study site, Browns Bay, is located on the 

east side of Possession Sound, roughly 5 km to the south of 

the Possession Point study site. At Browns Bay, the inshore 

edge of the beach is bounded by a railroad grade and tracks, 

which extend below the high water line along much of the 

shoreline. The extent of seagrass coverage in the nearshore 

adjacent to the railroad grade is known to be very low. Browns 

Bay was selected as a site on the east shore of Puget Sound 

with some seagrass present. Because both tidal currents and 

exposure to southerly winds and waves are thought to be 

fairly similar for the northern part of the Possession Point 

and Browns Bay sites, we anticipated that the two sites might 

contribute to understanding the influence of factors other than 

hydrodynamics on seagrass distribution and health. In particular, the potential impact of shoreline armoring on nearshore 

habitat is a significant concern for environmental managers in 

Puget Sound.

For the Browns Bay study site, 126 lines were planned. 

The majority of lines, 123, were oriented cross-shore at 

roughly 15-m intervals, and another 3 lines were oriented in 

along-shore (see appendix B, fig. B1).

Equipment and Data Collection

The primary components of the acoustic survey equipment were a deck unit, a laptop computer, transducers, and a 

real-time kinematic global positioning system (GPS) (fig. 3). 

The transducers used in this survey were 199 kHz and 420 

kHz Biosonics DT-X series digital transducers with beam 

width of 6 degrees. The ping rate for both transducers was set 

to 5 Hz (200-ms intervals), and the duration of each pulse was 

0.4 ms and 0.1 ms for the 199 kHz and 420 kHz transducer, 

respectively. The operating range of both transducers was set 

to 20 m.

Control of the transducers and a real-time display of the 

output from the system was achieved through Biosonics acquisition software installed on a laptop personal computer (PC). 

The laptop PC was connected through an Ethernet cable to a 

deck unit that sends and receives signals from the transducers and integrates data from the echo sounder with available 

external sensors (in this case, the GPS). Return echoes from 

the transducers were digitized by a dedicated processor in the 

deck unit at 41.67 kHz, leading to an approximate vertical 

resolution of 1.8 cm. 

The horizontal and vertical positions of the transducers 

were determined using a real-time kinematic global positioning system (RTK GPS). The RTK GPS system consisted of 

a base station and a roving receiver. The base station was 

equipped with a Trimble 4400 receiver with a Trimble L1/

L2 antenna and a Pacific Crest 35-Watt radio transmitter. The 

rover unit consisted of a Trimble 4700 receiver, a Trimble 

Zepher Antenna and a Pacific Crest radio receiver. 

The base station was placed on a survey benchmark. 

Using the known location, the base station transmitted realtime corrections to the roving receiver (that is, the Trimble 

4700 receiver on the ship) using a radio transmitter. The 

manufacturer of the GPS equipment reports root mean square 

(RMS) accuracies of ± 3 cm plus 2 mm per km baseline length 

(distance from the base station) for horizontal positions and ± 

5 cm plus 2 mm per km baseline length for vertical positions 

(Trimble Navigation Unlimited, 1998). RTK-corrected GPS 

data were sent to the sonar deck unit at 1 Hz and incorporated 

directly into the acoustic data files.
Methods 3

Figure 1. Map of northern Puget Sound, Washington State. The detail area (red box) shows the location of the surveys conducted in 

this study (see fig. 2). DEM source was Finlayson and others, 2000.

123.0 ̊ W 122.5 ̊ W
4 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

Additionally, the RTK-corrected GPS signals were sent 

to a laptop computer mounted near the boat operator. During 

data collection, this computer was used in conjunction with 

the computer program Hypack Max to navigate along planned 

survey lines (fig. A1 and fig. B1). Once the boat (the R/V Jet2) 

was positioned at the start of a planned line, the recorder was 

turned on and acoustic backscatter data were collected while 

the boat navigated along the line. The typical operating speed 

during data collection was 4-5 knots.

Sediment samples were collected at Possession Point 

with a grab sampler from the R/V Jet2 or from the R/V Karluk

during a later site visit (U.S. Geological Survey Cruise, K-2-

07-PS). Several additional surface samples were collected during low-tide surveys. The grain-size distributions were determined using standard techniques. Wet samples were sieved 

to separate coarser particles from the silt and clay. The coarse 

fraction was dried, gravel (> 2 mm) was separated, and gravel 

fractions were determined with sieves. The sand fractions (2 

mm – 63 μm) were quantified using a 2-m settling tube and 

fine fractions (<63 μm) with a Beckman Coulter Model LS230 

laser diffraction particle analyzer.

An underwater video camera was used on selected survey lines to evaluate the accuracy of the acoustically derived 

seagrass maps. The underwater camera system consisted of a 

SeaViewer Camera and LED lights housed in a small metal 

frame (fig. 4). The live video signal could be viewed on a standard CRT monitor and was recorded directly to digital-8 tape. 

Time, date, location, and ship speed were determined using a 

Garmin GPSMAP 60Csx handheld GPS. The manufacturersupplied horizontal accuracy of the Garmin GPS is within 3 

m when receiving wide area augmentation system (WAAS) 

corrections.

The positional information from the GPS was outputted at 0.5 Hz and overlaid on the video using the Sea-Trak

GPS overlay system developed by SeaViewer Cameras. The 

GPS data were also directly integrated with the digital video 

recording using Red Hen Systems (RHS) VMS200 hardware. 

The VMS200 receives NMEA message strings from the GPS 

and encodes the information on the audio channel of the video 

tape. These data can later be read by RHS MediaMapper 

software to determine the ship’s position at the time the video 

was recorded. 

Underwater video data were collected in cross-shore 

transects in a similar manner as the acoustic data except that, 

in order to collect the best possible imagery of the sea floor, 

the ship’s speed was kept at a minimum (1-2 knots). This 

made navigation along the planned transects difficult because 

of winds and currents. Acoustic data were recorded at the 

same time as underwater video for a side-by-side comparison 

(see section on “Underwater Video and Acoustic Data Comparison,” below, for details).

Underwater Video Classification

The first step in the classification of underwater video 

was to create and export a file containing the positional 

information encoded on the audio track of the video recording 

using MediaMapper software. The exported spreadsheet contained latitude, longitude, and time at 2-s intervals. Next, the 

underwater video recording was reviewed and the presence of 

seagrass was determined for each 2-s segment of video tape. 

Each segment was classified into one of three categories: bare 

(no seagrass), patchy seagrass, or continuous seagrass. The 

categories were defined as follows with respect to each 2-s 

segment of video: bare—no seagrass observed; patchy—both 

seagrass and bare areas observed; continuous—fully vegetated 

with seagrass. As the video tape was reviewed, the seagrass 

habitat classification was added to the spreadsheet containing 

the positional information. A single person reviewed all of the 

underwater video and used consistent standards for classification in order to minimize differences due to personal judgment. 

Figure 2. Detailed map showing the two study sites, Possession 

Point and Browns Bay. The two sites are approximately 5 km 

apart, separated by Possession Sound. The base map is a mosaic 

of images taken by the Landsat satellite between 2000 and 2001.
Methods 5

Figure 3. Photographs showing the main components of the acoustic survey equipment.
6 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

Acoustic Data Analysis

Bottom Detection and Plant Recognition in 

Single-Beam Data 

A single-beam echo sounder works by emitting a 

short burst of sound (ping) towards the sea floor and recording reflected sound (echoes). The strength of reflected sound 

(backscatter or echo intensity) is recorded at a series of time 

intervals, resulting in a profile of acoustic backscatter versus 

time. The distance between the transducer and objects in the 

water (range) is calculated based on the speed of sound in sea 

water. As the ship navigates along survey lines, data from multiple pings are recorded, resulting in a two-dimensional picture 

of backscatter intensity.

Acoustic data from a Biosonics DT-X series echo 

sounder were analyzed to determine the sea-floor bathymetry 

and the presence or absence of vegetation. The analysis is 

performed on each ping, and relies on distinct differences in 

the acoustic backscatter signal from vegetated and unvegetated 

surfaces. The signal-processing technique described here is 

based on an algorithm described in Biosonics (2004a) and 

Sabol and others (2002).

Before classification of the acoustic data, raw backscatter data were converted to target strength in decibels (dB) or 

volume scattering strength (in dB) using equations 4a and 4b 

in Biosonics (2004b). Both of these common acoustic quantities remove the effect of sound attenuation in seawater by 

applying a time-varied gain to the raw acoustic backscatter 

amplitude. The absorption, or attenuation coefficient (dB/m), 

was calculated using the equations given in Francios and Garrison (1982) and surface the temperature and salinity values 

measured during the survey.

A simple example illustrates the signal-processing technique in an area without seagrass (fig. 5). A profile of acoustic 

backscatter for one ping is shown on the right. The backscatter at that ping (450) is low (~-95 dB) and relatively uniform 

from 1 to 6.5 m from the transducer. At ~6.5-m range, a single 

sharp, narrow peak in acoustic backscatter occurs. This sharp, 

narrow peak is typical of a bare, unvegetated bottom. The 

bottom depth (z) is identified as the point of greatest positive 

slope associated with the largest peak in backscatter.

The profile of backscatter intensity is quite different 

at an earlier ping on the same transect (fig. 6). At ping 330, 

an area of low backscatter exists from 1 to ~4.5 m below the 

transducer. Below that, the backscatter increases, although 

more gradually than at ping 450, and stays high until 5.5-m 

range. Multiple local maxima within the broad region of 

Figure 4. Photographs showing the main components of the underwater video system. 
Methods 7

elevated backscatter are evident. These features are characteristic of a vegetated bottom. The top of the vegetation 

canopy (gz, green line) is defined as the first (that is, closest 

to the transducer ) value in backscatter intensity that exceeds 

a threshold value. The threshold value was typically between 

1 and 2.5 standard deviations above the mean backscatter 

intensity for each ping. As for ping 450, bottom depth (z) is 

the point of greatest positive slope associated with the largest 

peak in backscatter.

The first step of the classification algorithm is to determine the first point below the transducer where backscatter 

intensity exceeds a threshold value. Once the region of strong 

backscatter is identified, the location of the bottom is determined (the greatest rise in backscatter intensity). The region 

between the first elevated value and the greatest rise in backscatter intensity is considered the acoustic bottom envelope. 

If the height of the bottom envelope is greater than a predetermined value, the ping is classified as vegetated and vegetation 

height is calculated by subtracting the top of the vegetation 

canopy from the bottom depth. Otherwise, the ping is classified as unvegetated. The algorithm completes this assessment 

for each ping in an echogram, or backscatter record (fig. 7). 

Threshold values for determining the bottom location and the 

minimum bottom envelope height in the algorithm are determined for each survey line based on visual inspection of the 

acoustic data.

Analyzing Real-World Data

Although the basics of plant detection are quite simple 

in theory, there are several factors that make automated classification of vegetation challenging (see Sabol and others, 2002, 

for a list of challenges). For instance, a rapidly changing 

bottom depth in a localized area causes the bottom to appear 

thicker acoustically and may result in the site being classified 

as vegetated in a fully automated, unsupervised algorithm. 

Another major challenge for acoustic seagrass detection is that there can be objects on the sea floor (for example, 

other plant species, woody debris, or other organisms) that 

acoustically resemble seagrass. Oftentimes, a trained analyst 

will be able to distinguish these objects from seagrass, but 

an automated system will not (though complex techniques 

involving adaptive computer algorithms may be used in the 

future). For this study, underwater video was used to determine whether any such objects were present. Underwater 

video showed, in addition to seagrass (fig. 8), two habitat 

types that would have been classified incorrectly by an 

automated algorithm. The first was red algae growing mostly 

in rocky areas (fig. 9). The second was clusters of orange 

sea pens, Ptilosarcus gurneyi (T. D’Andrea, written comm., 

2007), a common megafauna found in the Puget Sound 

(Birkland, 1974)(fig. 10). Fortuitously, these potential targets 

did not overlap spatially with seagrass. Specifically, the red 

Figure 5. Acoustic results 

for a portion of line 59. A, 

Backscatter versus range. 

Blue line shows the location 

of ping 450. B, Backscatter 

versus range for ping 450 in a 

region without seagrass. The 

classified bottom depth (z) is 

shown by dotted red line. 
8 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

algae were observed in rocky areas to the south of the seagrass 

meadows, and sea pens were found in deeper water 

(> 8 m) than the seagrass. Once the acoustic characteristics 

and spatial distribution of these habitat types were established, 

a trained analyst was able to differentiate them from seagrass 

in the backscatter data without referring back to the video data 

(that is, the video data were not consulted during acoustic data 

processing).

In order to overcome some of the challenges of detecting vegetation acoustically, an algorithm with adjustable 

parameters was created that allowed for some subjective 

decision making by the data analyst. A graphical user interface 

(GUI) was developed using the computer program MATLAB to rapidly adjust several key parameters in conjunction 

with visual inspection of the echo-sounder data and resulting classification (fig. 11). Adjustable parameters included 

the maximum depth at which classification as vegetated was 

allowed and the minimum bottom-envelope width classified 

as vegetated. Changes in the classification parameters were 

applied to all pings from an acoustic data file (typically a 

survey line). This allowed the analyst to tune the parameters to 

correctly distinguish seagrass from other habitat types as well 

as to remove acoustic noise at the surface. Once the analyst 

was satisfied with the classification, it was exported as both a 

text file and a MATLAB data structure. 

Figure 6. Acoustic results 

for a portion of line 59. A, 

Backscatter versus range. 

Blue line shows the location 

of ping 330. B, Backscatter 

versus range for ping 330 in 

a region with vegetation. The 

classified bottom depth (z) 

is shown as dotted red line 

and the top of the vegetation 

canopy (gz) is shown in 

green. 

Figure 7. Classified acoustic data for a portion of line 59 showing 

the bottom depth (red line) and top of vegetation canopy (green).
Methods 9

Figure 8. Comparison between acoustic data and a still frame 

taken from the underwater video camera. A, Acoustic data from 

line 93 in the northern portion of Possession Point showing the 

typical acoustic signature of seagrass. The red arrow denotes 

the location of a still frame, B, taken from underwater video data 

showing seagrass.

Figure 9. Comparison between acoustic data and a still frame 

taken from the underwater video camera. A, Acoustic data from 

line 5 in the southern portion of Possession Point showing the 

typical acoustic signature of red algae. The red arrow denotes 

the location of a still frame, B, taken from underwater video data 

showing red algae.

Figure 10. Comparison between acoustic data and a still frame 

taken from the underwater video camera. A, Acoustic data from 

line 63 in the central portion of Possession Point showing the 

typical acoustic signature of sea pens. Note the elevated nearbottom backscatter at ranges greater than 8 m. The red arrow 

denotes the location of a still frame, B, taken from underwater 

video data showing sea pens.

Data Output 

After the classification was completed, the raw data 

output included x and y coordinates interpolated for each ping, 

raw bottom depth (range), vegetation presence or absence, and 

vegetation height. The raw bottom depths were corrected to 

the vertical datum NAVD88 by adding the GPS height to raw 

depths and subtracting the distance between the GPS antenna 

and transducer. The computer program VDatum (Spargo and 

others, 2006) was used to compute the difference between 

NAVD88 elevations and mean lower low water (MLLW). 

In this study, the survey area was small and a static offset of 
10 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

+0.605 cm was added to all NAVD88 elevations to convert to 

MLLW. Using the same system, Grossman and others (2007) 

calculated the root-mean-squared (RMS) error of vertical 

elevations (including error from the RTK-GPS, the sonar, and 

errors introduced during data processing) to be between 2.6 

and 9.8 cm.

Rather than outputting each ping individually, pings 

were grouped into packets of 10. For each group of 10 pings, 

mean x and y positions were calculated, as well as mean depth 

and seagrass height. The fraction of pings in each group classified as vegetated was reported as an estimate of percent cover. 

The final result of the processing was a data point reported 

every 2 s corresponding to a between-point horizontal distance 

of approximately 4-5 m, assuming a boat speed of 4-5 knots.

Smooth surfaces of bottom depth were generated using 

linear, Delaunay interpolation. However, using linear interpolation for seagrass, a feature that is inherently patchy in 

nature, can lead to errors (Guan and others, 1999; Valley and 

others, 2005) and a misleading representation of small-scale 

patchiness (Fonseca and others, 2002). Both Guan and others 

Figure 11. Screen shot of the graphical user interface (GUI) created for seagrass and depth classification. Adjustable 

parameters (edit boxes above) allow the signal-processing algorithm to be rapidly adjusted.
Results and Discussion 11

(1999) and Valley and others (2005) suggest kriging as the 

best interpolation method for seagrass attributes (that is, height 

and percent cover). Kriging is a procedure that uses a model 

of the observed spatial variability of a data set, or semivariogram, to interpolate between sample locations (Davis, 2002). 

For this study, interpolations of seagrass percent cover were 

performed with Surfer using kriging with a spherical model of 

the semivariogram.

Results and Discussion

Underwater Video and Acoustic Data 

Comparison

In this section we compare results from acoustic data 

and simultaneous underwater video transects in order to better 

understand the accuracy, benefits, and limitations of using 

acoustics to describe seagrass habitat.

In many cases, underwater video and acoustic data 

show remarkable agreement. For example, consider the 

qualitative differences in acoustic backscatter associated with 

changes in seagrass abundance observed in underwater video 

from survey line 123 in the Possession Point study site (fig. 

12). Bare and vegetated areas can be clearly distinguished in 

the acoustic data, and differences in seagrass percent cover 

appear to be evident as well (compare B and C). The acoustic data also appear to be sensitive to changes in plant height 

caused by the orientation of the plants in the water column 

(compare B and D). This indicates that estimates of plant 

height and biovolume from acoustic data should be used with 

extreme caution in areas subject to strong currents, as has been 

noted by Sabol and others (2002).

A quantitative comparison between acoustic data (fig. 

13A) and classified underwater video (fig. 13B) along line 123 

also shows broad agreement. The bare areas observed in the 

underwater video (white areas, in B) coincide with areas classified acoustically as unvegetated (0 percent cover, red line). 

On the other hand, continuous seagrass identified in the underwater video (black areas, in B) coincide with areas of high 

percent cover from the acoustic data (red line). In seagrass 

classified as patchy in the video (gray areas, in B), acoustically 

derived seagrass cover is highly variable, but mostly greater 

than zero. The classified underwater video data overlaid on a 

map of interpolated seagrass percent cover from acoustic data 

(fig. 13C) reveals a complex spatial distribution in this area 

that may account for some of the differences between the two 

techniques.

Comparison of acoustic seagrass percent cover versus 

underwater video for the entire Possession Point data set is 

summarized in figure 14. For each underwater video data 

point, the nearest acoustic data point was determined. Only 

points with GPS locations less than 3 m apart were used in the 

analysis (NOTE: This distance does not include the layback 

or the offset between underwater video camera and GPS). 

The acoustically derived percent cover estimates were binned 

according to the three video classes (B= bare, P = patchy, and 

C = continuous). Overall, mean acoustically derived percent 

cover in areas classified with the underwater video as bare, 

patchy, and continuous are 4, 65 and 93 percent, respectively 

(fig. 14A). Histograms for the three categories (fig. 14B-D) 

show that the acoustic technique is highly efficient at determining whether an area is bare or has continuous seagrass. 

However, within areas classified with the video as patchy 

seagrass, a wide variety of acoustic percent cover estimates 

were observed.

A confusion matrix (see, for example, Fielding and 

Bell, 1997) showing the number of correct and incorrect 

acoustic-based percent cover estimates and their associated 

accuracies is given in table 1. Out of 808 underwater video 

observations that were classified as bare, 740 (91.6 percent) 

were correctly classified with acoustics. Within areas classified as patchy seagrass in underwater video, the sonar classification was considered correct if it fell between 10 and 90 

percent cover. The acoustic cover estimates of patchy seagrass 

were correct in 186 out of 428 observations (43.5 percent). 

Finally, in areas observed to be continuous seagrass with 

underwater video, the acoustic data were correct in 155 out of 

203 observations (76.4 percent). 

One reason for the wide range of acoustic percent cover 

estimates in the patchy underwater video class (fig. 14B) is 

the definition used for classification of the underwater video 

data. For example, in a 2-s segment of underwater video, 1.8 

s was vegetated and 0.2 s was bare. This 2-s segment would 

Table 1. Confusion matrix of acoustic versus underwater-video characterization of seagrass cover in the Possession Point study 

site.

Underwater Video

Bare Patchy Continuous

Acoustic

Bare 740 75 4

Patchy 51 186 44

Continuous 17 167 155

Total 808 428 203

Accuracy (percent) 91.6 43.5 76.4
12 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

Figure 12. Acoustic backscatter data from line 123 (top left) with black and white video stills containing no seagrass (A, E), 

dense seagrass (B) and patchy seagrass (C,D). The same letters mark the location of the stills in the acoustic data.
Results and Discussion 13

be classified as patchy using the definitions of the classification scheme in this study. Meanwhile, the acoustic estimate 

of percent cover would be 90 percent. Now suppose that 0.2 s 

of video was sparsely vegetated and 1.8 s was bare. This too 

would be considered patchy seagrass, but the acoustic percent 

cover would only be as much as 10 percent. Possible solutions 

to this problem include adding more classification categories 

to the underwater video analysis or breaking the video into 

smaller units of 0.5 or 1 s. However, this example does not 

explain why so many points classified as patchy seagrass in 

the underwater video were observed to be either 0 or 100 percent cover with the acoustic technique.

Additional sources of error arising from the techniques 

used to collect and process the data contribute to the differences between underwater video and acoustic estimates of 

seagrass habitat, especially in areas with patchy seagrass. 

First, although acoustic and underwater video data were 

collected simultaneously, they were not precisely colocated. 

The echo sounder was operated from the starboard side of 

the ship, whereas the underwater video was deployed from 

the port side. Furthermore, layback between the underwater 

video camera tow-fish and the shipboard GPS recording the 

video camera position was occasionally observed during data 

collection. The layback increases with the depth of the video 

Figure 13. Comparison of acoustic and video classification along line 123 at the Possession Point study site. A, Classified acoustic 

backscatter data. B, Numerical comparison of acoustic percent cover with underwater video data. Red line is the acoustic seagrass 

percent cover. The video classification is represented with white (bare), gray (patchy), and black (continuous) areas. C, Map view of 

classified underwater video overlaid on grid of acoustic seagrass cover.
14 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

camera and the speed of the ship. Using calculations given in 

Gibbs and others, 2005, the layback of the camera could be as 

great as 15 m in water depths greater than 10 m. This problem 

is minimized because most of the seagrass habitat occurred 

in shallow areas where the video camera was just below the 

surface and very near the GPS. Nevertheless, uncertainties in 

the relative position between the underwater video and acoustic survey equipment likely contribute to the poor agreement 

between the two methods in patchy seagrass. 

Another limitation of the direct comparison is that the 

area of the sea floor sampled (the instrument footprint) may be 

different. Ideally for a direct comparison, the two techniques 

would be sampling a similar area of the sea floor. In this case, 

the unit area for both methods is based on a 2-s interval of 

continuous data (10 pings at 5 Hz are averaged for the acoustic 

data, 2 s of underwater video in between GPS fixes). However, 

the field of view changes for the video camera with the distance between the camera and the sea floor, and the area that 

is sampled by the sonar equipment changes with the distance 

between the transducer and the seabed (6 degree beam-angle). 

These differences may be significant when trying to quantitatively compare measurements of plant cover from the two 

techniques. 

Figure 14. Comparison of acoustic and video classification for different densities of seagrass. A, Mean acoustic seagrass percent 

cover (± 1 standard deviation, black lines, ± 95 percent confidence interval, red lines) compared with classified underwater video (B = 

bare, P = patchy and C = continuous) collected at Possession Point. B-D, Normalized histograms of acoustic percent cover of seagrass 

for each video classification group.
Results and Discussion 15

ming the fractional percent cover estimates in each grid cell 

multiplied by the grid cell area. At the Possession Point study 

area, seagrass covered roughly 0.29 km2

 (19 percent) of the 

total 1.51 km2 area surveyed. 

Both the total area of seagrass and percent seagrass 

cover are strongly affected by water depth (fig. 21). The 

observed seagrass habitat was limited to depths between 0.5 

and -4.5 m, though a very small amount was found in both 

shallower and deeper water. The most abundant and highest 

Figure 15. Acoustic (red lines) and underwater-video (cyan 

lines) data coverage for the Possession Point study site. Map 

projection is Washington State Plane North. 

Possession Point

Acoustic data were collected along 163 survey lines 

in the Possession Point study site between January 30 and 

February 1, 2007 (fig. 15). A total of 60 km of acoustic data 

was collected, with individual line lengths ranging from 33 

m to 3.7 km. A list of survey line number, filename, time of 

collection, as well as elapsed time and distance for each line 

collected at Possession Point is given in appendix A. Additionally, 4.6 km of underwater video data was collected along 19 

cross-shore survey lines throughout the study site (fig. 15). 

The bathymetric grid produced by the acoustic survey 

data (fig. 16) reveals a complex morphology with prominent 

changes from north to south. At the very northern end of the 

study site (north of 105.25 km northing), the low-tide terrace 

is narrow, with steep slopes very near the shoreline (fig. 17). 

From 105.25 to 103 km northing, the low-tide terrace is broad, 

reaching as much as ~300 m wide. North of 104.5 km northing, large shore-parallel bed forms (~30 m wavelength) are 

evident in the shaded bathymetry. The bed-form morphology 

is further elucidated by local changes in the direction of slope 

of the seabed (fig. 18). Whereas most of the bathymetry is 

sloped away from shore at roughly 45-60 degrees, the inshore 

sides of the bed forms are directed in the opposite direction. 

Farther south, between 103 and 102 km northing, the low-tide 

terrace is narrow and steep, with slope angles typically greater 

than 10 degrees. At the southern tip of Possession Point, the 

low-tide terrace again widens and flattens, stretching towards 

the south, where Possession Sound and Admiralty Inlet intersect.

The distribution of seagrass at the Possession Point 

study site derived from acoustic data is shown in figures 

19-21. The majority of seagrass at Possession Point is limited 

to the broad, shallow low-tide terrace region between 103 

and 105.25 km northing, though some seagrass extends as far 

south as 102.3 km northing. The cross-shore extent of seagrass 

appears to largely be controlled by local water depth, with 

seagrass mostly observed between 0.5 and -4.5 m MLLW. One 

stark contrast in this pattern is a gap in the seagrass between 

103.5 and 103.75 km northing. The sediment in this bare 

region is distinctly coarser (fig. 22), with a mean grain size as 

large as -2 phi. The coarser sediment may be the reason for the 

lack of seagrass (Koch, 2001). Grain size in phi units can be 

converted to grain size in millimeters using equation 1:

millimeter = 2 –phi (1)

The apparent spatial complexity of seagrass shown in 

figure 20 increases from south to north, coinciding with the 

increased presence of bed forms in the far northern portion of 

the study site. This patchiness could be indicative of increased 

wave and current energy in that area (Fonseca and Bell, 1998). 

However, the true complexity of the seagrass distribution is 

not fully resolved because of a transect spacing of 25 m. 

Using the interpolated grid of percent seagrass cover, 

the total aerial coverage of seagrass was estimated by sum-
16 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

percent cover of seagrass were found at water depths between 

-0.5 and -2.5 m, where as much as 50 percent of the total 

area was 90-100 percent seagrass cover. Below -2.5 m water 

depth, both the amount of total seagrass habitat and percent 

cover decrease exponentially. By -5 m water depth, seagrass 

habitat was found in only 2 percent of the area surveyed. The 

pronounced decline in seagrass habitat with depth is typically 

associated with light limitation (Duarte, 1991). In shallow 

water, between 0.5 and -1 m water depth, the meadow is characterized by a higher proportion of low percent seagrass cover. 

The seagrass in shallow water is not light limited, but growth 

is most likely limited by physical factors such as desiccation 

or increased wave energy reaching the bottom (Koch, 2001). 

Figure 16. Interpolated bathymetry from acoustic survey data at 

Possession Point. The grid resolution is 5 m and contour interval is 

4 m. Vertical datum is MLLW. 

Figure 17. Interpolated seabed slope at Possession Point. The 

grid resolution is 5 m.
Results and Discussion 17

Figure 18. Interpolated bathymetric slope angle for the northern portion of the Possession Point study site (top). 

Generally, the slope of the seabed is to the southeast (green colors), but shore-parallel bed forms with landward sides 

facing northwest (pink) are also evident. The grid resolution is 5 m. The red line (A-B) shows the location of acoustic 

data in the bottom panel. The bathymetric cross-section (thin red line, bottom panel) shows large-scale bed forms with 

wavelengths of 30-60 m.
18 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

Figure 19. Classified acoustic data for percent cover of 

seagrass.

The classified underwater video data showed a similar 

pattern as the acoustic data, with most of the seagrass habitat 

concentrated in the northern portion of the study site (fig. 23). 

Of the 2,317 individual video segments, 1,456 (63 percent) 

were classified as being bare, 515 (22 percent) were classified 

as patchy seagrass, and the remaining 346 (15 percent) were 

continuous seagrass. 

Figure 20. Percent cover of seagrass at Possession Point 

interpolated from raw fractional ping data shown in figure 19. Grid 

resolution is 5 m. Bathymetric contour interval is 1 m.

Browns Bay

Acoustic data were collected along 65 lines in the 

Browns Bay study site on February 2, 2007 (fig. 24). A total 

of 18 km of acoustic data was collected, with individual line 

lengths ranging from 73 m to 3.1 km. A list of line number, 

filename, time of collection, as well as elapsed time and dis-
Results and Discussion 19

tance for each line collected at Browns Bay is given in appendix B. Additionally, 2.4 km of underwater video data were 

collected in 12 cross-shore transects throughout Browns Bay.

The bathymetric grid of Browns Bay generated from 

the acoustic survey data (fig. 25) shows a relatively simple 

morphology compared to that at Possession Point, although 

the width of the shallow low-tide terrace does vary somewhat 

from north to south. No large-scale bed forms were evident in 

the bathymetric data. 

The seagrass in Browns Bay is mainly distributed in 

four distinct beds (figs. 26 and 27). Very little seagrass was 

observed at the extreme north and south ends of the Browns 

Bay study site. The total areal coverage of seagrass was 

estimated by summing the fractional percent cover estimates 

at each location multiplied by the area in each grid cell. At the 

Browns Bay study area, seagrass covered roughly 0.043 km2

(5 percent) of the total 0.84 km2 area surveyed. 

The presence and percent cover of seagrass are strongly 

affected by water depth (fig. 28). Seagrass was mainly limited 

to depths ranging from -1.75 to -6 m, though a very small 

amount was found in both shallower and deeper water. No 

seagrass occurs in depths shallower than -0.25 m. The most 

abundant seagrass habitat in terms of areal coverage occurred 

at a depth of -1.25 m MLLW. However, a large percentage 

of seagrass habitat at that depth was sparse (between 1 and 

10 percent). Higher percent cover seagrass occupied a larger 

portion of the area at greater depths (-2.5 to -3.5 m). The depth 

distribution is shifted into deeper water than at Possession 

Point. 

The underwater video data show very little continuous seagrass in the Browns Bay study site (fig. 29). Of the 

1078 individual data points, 881 (82 percent) were classified 

as bare, 177 (16 percent) were classified as patchy seagrass 

habitat, and the remaining 20 (2 percent) were continuous 

seagrass. 

Figure 21. Depth distribution of seagrass at Possession Point calculated from the interpolated grid of seagrass 

percent cover. The total length of the bar represents the total area surveyed in each 0.25-m depth interval. Colors 

indicate the distribution of seagrass percent cover in each depth interval.
20 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

Figure 22. Locations of surface-sediment samples (left panel) and their mean grain sizes in phi units (right panel) along the Possession 

Point study site. The colors of the markers corresponds to mean grain size in both plots. Phi units can be converted to millimeters using 

equation 1.
Results and Discussion 21

Figure 23. Classified underwater video data from the Possession 

Point study site. Bathymetric contour interval is 1 m.
22 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

Figure 24. Acoustic (red lines) and underwater video (cyan lines) data coverage for the Browns Bay 

study site. Map Projection is Washington State Plane North.
Results and Discussion 23

Figure 25. Interpolated bathymetry from acoustic data at Browns Bay. The grid resolution is 3 m and 

bathymetric contour interval is 4 m. Vertical datum is MLLW.
24 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

Figure 26. Classified acoustic data for percent cover of seagrass. 
Results and Discussion 25

Figure 27. Percent cover of seagrass at Browns Bay interpolated from raw fractional ping data 

shown in figure 26. Grid resolution is 3 m. Bathymetric contour interval is 1 m.
26 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

Figure 28. Depth distribution of seagrass at Browns Bay calculated from the interpolated grid of seagrass percent cover. The total 

length of the bar represents the total area surveyed in each 0.25-m depth interval. Colors scale indicate the distribution of seagrass 

percent cover in each depth interval.
Results and Discussion 27

Figure 29. Seagrass habitat from classified underwater video transects collected from the Browns 

Bay study site. Bathymetric contour interval is 1 m.
28 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

Summary and Conclusions

This study has shown that a single-beam acoustic system can accurately map seagrass and has demonstrated some 

of the strengths and weaknesses of the method. Classification 

of single-beam acoustic data for seagrass was simple, fast, 

and intuitive relative to other mapping techniques. However, 

in areas where more than one type of vegetation was present, 

underwater video or some other form of ground-truth data 

was needed in order to accurately interpret the acoustic data. 

We conclude that classification algorithms should not be fully 

automated, but rather should be supervised in order to properly 

adjust relevant parameters according to site-specific needs and 

to separate other vegetation types from seagrass during data 

processing. 

Comparison of underwater video and acoustic data collected synchronously at Possession Point showed that classification of the acoustic data was highly accurate for determining the presence or absence of seagrass. For video segments 

classified as bare, patchy seagrass, and continuous seagrass, 

the accuracy of the acoustic classification was 92, 42, and 76 

percent, respectively. We believe that the low accuracy measured in patchy areas is due in part to the fact that the video 

and acoustic equipment were not precisely co-located. Specifically, the underwater video camera and sonar were operated 

from different sides of the survey vessel during data collection. 

Acoustic methods have a much greater potential for measuring 

percent cover than video, because the high spatial resolution 

of the sampling lends itself to quantitative analysis. Another 

benefit of acoustic methods is the ability to survey in turbid 

areas, where visibility is minimal. 

Seagrass distributions at two sites in northern Puget 

Sound, Possession Point and Browns Bay, were quantified 

using both acoustics and underwater video. Acoustic mapping revealed extensive seagrass meadows in the northern 

two-thirds of the Possession Point site. Seagrass occupied a 

depth range of 1 to -5.5 m MLLW, with the greatest area of > 

70 percent cover occurring between -0.5 and -1.5 m MLLW. 

Patchiness of the meadow increased in the northern part of the 

area, where there were large sand dunes. A bare patch in the 

middle of the densely vegetated area was observed, coinciding with a region of substrate with much larger particle size. 

Seagrass coverage in Browns Bay was relatively sparse. The 

depth range was 0 to -7 m MLLW, with greatest area of > 70 

percent cover occurring between -2.25 and -3.5 m MLLW. 

Tidal currents and exposure to southerly winds and waves are 

thought to be fairly similar at Possession Point and Browns 

Bay. A major difference between the two sites is the railroad 

grade and tracks that extend below the high water line along 

much of the shoreline at Browns Bay. Human alteration of 

nearshore processes is one of many possible factors influencing the seagrass distributions at Possession Point and Browns 

Bay. Further investigation into why the distribution of seagrass 

at these two seemingly similar sites is so different may shed 

light on the effects of human alteration of nearshore processes 

on seagrass throughout Puget Sound. 

Acknowledgments

The authors acknowledge several groups and individuals without whom this study would not have been possible. 

First and foremost, we would like to thank several people at 

the USGS Columbia River Research Lab in Cook, Washington. Marty Liedtke was instrumental in facilitating the loan of 

both the Biosonics system and a boat for the work presented 

in this report. In addition, we borrowed one of her technicians, 

Collin Smith, who ably navigated the boat during the surveys. 

Tom Batt shared his knowledge of the Biosonics system during the testing phase of this work. The cooperation between 

the Biological Resource Division facility in Cook and our 

group was tremendous. 

Andrew Schwartz of the Washington State Department 

of Ecology helped install the benchmark and operated the GPS 

equipment for the surveys. Art Pratt at the Possession Point 

Waterfront Park went out of his way to help keep the boat 

ramp clear of sand so that we could launch and retrieve our 

boat each day. He also provided storage for the boat and trailer 

during the surveys. Mike Burger at Biosonics provided last 

minute help troubleshooting the acoustic gear. Gerry Hatcher 

of the USGS in Santa Cruz, California, was a tremendous 

help in setting up the video and sonar systems during tests 

conducted at the Columbia River Research Lab. Tom Reiss, 

also of the USGS in Santa Cruz, installed and surveyed the 

benchmark that was used in this study. 

This report was improved tremendously by excellent 

reviews from Ann Gibbs (USGS), Ted DeWitt (U.S. Environmental Protection Agency) and Jeff Gaeckle (Washington 

State Department of Natural Resources). We thank all our 

friends and colleagues for their support during this effort. 

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Valley, R.D., Drake, M.T., and Anderson, C.S., 2005, Evaluation of alternative interpolation techniques for the mapping 

of remotely submerged vegetation abundance: Aquatic 

Botany, v. 81, p. 13-25. 

Warren, J.D., and Peterson, B. J., 2007, Use of a 600-kHz 

acoustic Doppler current profiler to measure estuarine bottom type, relative abundance of submerged aquatic vegetation, and eelgrass canopy height: Estuarine, Coastal and 

Shelf Science, v. 72, p. 53-62. 

Winfield, I.J., Onoufriou, C., O’Connell, M.J., Godlewska, M., 

Ward, R.M., Brown, A.F., and Yallop, M.L., 2007, Assessment in two shallow lakes of a hydroacoustic system for 

surveying aquatic macrophytes: Hydrobiologia, v. 584, p. 

111-119.
30 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

Appendix A. Data Collected at Possession Point 

Appendix A provides a list of all acoustic and underwater video data collected at Possession Point between January 30 

and February 1, 2007. The location of planned lines is given in figure A1. The line numbers, acoustic data filenames, collection 

dates and line lengths collected each day are provided in tables A1 through A3. The tide heights during data collection for each 

day are shown in figures A2 through A4.

Figure A1. Planned lines for acoustic survey at Possession Point. Cross-shore lines 

1-72 were spaced 50 m apart and lines 73-148 were spaced 25 m apart. Lines 149-163 

were oriented alongshore.
Appendix A. Data Collected at Possession Point 31

Table A1. List of lines completed, elapsed time, and distance covered at the Possession Point study site, January 30, 

2007—Continued.

Line Filename Time Collected (GMT) Elapsed Time (min) Total Length (m) 

42 20070130_165012.dt4 1/30/2007 16:50 0.8 68.2

43 20070130_165314.dt4 1/30/2007 16:53 0.9 77.1

44 20070130_165510.dt4 1/30/2007 16:55 0.8 67.5

45 20070130_165712.dt4 1/30/2007 16:57 0.7 69.0

46 030_001.dt4 1/30/2007 17:12 0.9 60.2

47 030_002.dt4 1/30/2007 17:14 1.0 64.1

48 030_003.dt4 1/30/2007 17:15 0.9 48.2

49 030_004.dt4 1/30/2007 17:18 1.3 84.6

50 030_005.dt4 1/30/2007 17:20 1.6 117.1

51 030_006.dt4 1/30/2007 17:23 2.1 164.9

52 030_007.dt4 1/30/2007 17:25 2.2 171.2

53 030_008.dt4 1/30/2007 17:29 2.5 196.5

54 030_009.dt4 1/30/2007 17:32 2.4 202.4

55 030_010.dt4 1/30/2007 17:36 0.8 70.2

 55a 030_011.dt4 1/30/2007 17:40 2.6 217.0

56 030_012.dt4 1/30/2007 17:44 2.4 238.6

57 030_013.dt4 1/30/2007 17:47 3.1 258.8

58 030_014.dt4 1/30/2007 17:51 2.9 288.2

59 030_015.dt4 1/30/2007 17:55 3.2 285.1

60 030_016.dt4 1/30/2007 17:59 1.9 190.8

 60a 20070130_100803.dt4 1/30/2007 18:08 2.8 316.1

61 20070130_101123.dt4 1/30/2007 18:11 3.4 336.5

62 20070130_101533.dt4 1/30/2007 18:15 3.5 354.1

63 20070130_101939.dt4 1/30/2007 18:19 3.7 382.5

64 20070130_102422.dt4 1/30/2007 18:24 4.0 420.5

65 20070130_102914.dt4 1/30/2007 18:29 4.4 450.0

66 20070130_103422.dt4 1/30/2007 18:34 4.3 459.6

67 20070130_104002.dt4 1/30/2007 18:40 4.8 476.6

68 20070130_104600.dt4 1/30/2007 18:46 4.7 482.1

69 20070130_105128.dt4 1/30/2007 18:51 5.3 523.8

70 20070130_105745.dt4 1/30/2007 18:57 5.8 522.4

71 20070130_110428.dt4 1/30/2007 19:04 5.2 526.5

72 20070130_111040.dt4 1/30/2007 19:10 6.1 519.1

77 20070130_115648.dt4 1/30/2007 19:56 1.6 157.9

79 20070130_120014.dt4 1/30/2007 20:00 2.3 210.1

81 20070130_120516.dt4 1/30/2007 20:05 2.6 256.2

83 20070130_120837.dt4 1/30/2007 20:08 3.6 355.9

85 20070130_121304.dt4 1/30/2007 20:13 3.7 382.4

87 20070130_121727.dt4 1/30/2007 20:17 4.0 416.9

89 20070130_122255.dt4 1/30/2007 20:23 5.1 506.6

91 20070130_122838.dt4 1/30/2007 20:28 5.0 504.8

93 20070130_123540.dt4 1/30/2007 20:35 5.5 487.8

95 20070130_124141.dt4 1/30/2007 20:41 5.3 472.8

97 20070130_124914.dt4 1/30/2007 20:49 5.1 354.3

99 20070130_125626.dt4 1/30/2007 20:56 6.0 415.8

101 20070130_131004.dt4 1/30/2007 21:10 6.3 409.8

103 20070130_131712.dt4 1/30/2007 21:17 6.0 420.2

105 20070130_132537.dt4 1/30/2007 21:25 6.0 431.1

107 20070130_133231.dt4 1/30/2007 21:32 6.5 447.6

109 20070130_134113.dt4 1/30/2007 21:41 6.4 450.0

Table A1. List of lines completed, elapsed time, and distance covered at the Possession Point study site, January 30, 2007.
32 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

Table A1. List of lines completed, elapsed time, and distance covered at the Possession Point study site, January 30, 

2007—Continued.

Line Filename Time Collected (GMT) Elapsed Time (min) Total Length (m) 

111 20070130_135120.dt4 1/30/2007 21:51 6.6 439.7

113 20070130_135934.dt4 1/30/2007 21:59 6.6 454.7

115 20070130_140646.dt4 1/30/2007 22:06 7.1 473.8

117 20070130_141522.dt4 1/30/2007 22:15 7.5 473.1

119 20070130_142345.dt4 1/30/2007 22:23 7.7 476.1

121 20070130_143339.dt4 1/30/2007 22:33 7.2 438.0

123 20070130_144159.dt4 1/30/2007 22:41 6.6 433.9

125 20070130_145054.dt4 1/30/2007 22:51 8.3 449.4

127 20070130_150019.dt4 1/30/2007 23:00 6.7 440.3

129 20070130_150928.dt4 1/30/2007 23:09 6.8 409.4

131 20070130_151727.dt4 1/30/2007 23:17 5.4 384.9

133 20070130_152510.dt4 1/30/2007 23:25 5.7 338.9

Table A2. List of lines completed, elapsed time, and distance covered at the Possession Point study site January 31, 

2007—Continued.

Line Filename Time Collected (GMT) Elapsed Time (min) Total Length (m) 

135 20070131_090923.dt4 1/31/2007 17:11 4.6 278.7

137 20070131_091441.dt4 1/31/2007 17:14 2.0 232.0

139 20070131_091954.dt4 1/31/2007 17:20 2.1 193.5

141 20070131_092250.dt4 1/31/2007 17:22 1.1 119.7

143 20070131_092644.dt4 1/31/2007 17:26 0.7 69.4

145 20070131_092821.dt4 1/31/2007 17:28 0.4 32.7

147 20070131_093039.dt4 1/31/2007 17:30 0.8 55.2

152 20070131_093505.dt4 1/31/2007 17:35 12.9 1,537.3

76 20070131_095105.dt4 1/31/2007 17:51 2.2 256.2

78 20070131_095409.dt4 1/31/2007 17:54 2.4 234.1

80 20070131_095745.dt4 1/31/2007 17:57 2.2 284.4

82 20070131_100029.dt4 1/31/2007 18:00 2.6 320.9

84 20070131_100351.dt4 1/31/2007 18:03 2.7 344.3

86 20070131_100710.dt4 1/31/2007 18:07 3.5 439.8

88 20070131_101127.dt4 1/31/2007 18:11 3.8 468.0

90 20070131_101551.dt4 1/31/2007 18:15 3.8 468.3

92 20070131_102106.dt4 1/31/2007 18:21 4.0 453.7

94 20070131_102541.dt4 1/31/2007 18:25 3.8 458.6

96 20070131_103045.dt4 1/31/2007 18:30 3.7 426.5

98 20070131_103513.dt4 1/31/2007 18:35 3.4 408.3

100 20070131_103957.dt4 1/31/2007 18:39 3.4 402.7

102 20070131_104402.dt4 1/31/2007 18:44 3.2 392.3

104 20070131_104827.dt4 1/31/2007 18:48 3.1 388.4

106 20070131_105217.dt4 1/31/2007 18:52 3.3 396.6

108 20070131_105628.dt4 1/31/2007 18:56 3.3 414.1

110 20070131_110022.dt4 1/31/2007 19:00 3.6 432.9

112 20070131_110653.dt4 1/31/2007 19:06 3.3 414.5

114 20070131_111054.dt4 1/31/2007 19:10 3.9 461.2

116 20070131_111557.dt4 1/31/2007 19:15 3.7 442.0

118 20070131_112017.dt4 1/31/2007 19:20 3.9 458.5

120 20070131_113305.dt4 1/31/2007 19:33 3.9 455.7

Table A2. List of lines completed, elapsed time, and distance covered at the Possession Point study site January 31, 2007.
Appendix A. Data Collected at Possession Point 33

Table A2. List of lines completed, elapsed time, and distance covered at the Possession Point study site January 31, 

2007—Continued.

Line Filename Time Collected (GMT) Elapsed Time (min) Total Length (m) 

122 20070131_113808.dt4 1/31/2007 19:38 4.1 460.5

124 20070131_114307.dt4 1/31/2007 19:43 4.0 434.1

126 20070131_114751.dt4 1/31/2007 19:47 3.9 410.4

128 20070131_115323.dt4 1/31/2007 19:53 4.1 409.9

130 20070131_115814.dt4 1/31/2007 19:58 3.6 395.2

132 20070131_120419.dt4 1/31/2007 20:04 3.8 357.6

134 20070131_120942.dt4 1/31/2007 20:09 2.6 309.1

136 20070131_121536.dt4 1/31/2007 20:15 2.3 243.4

138 20070131_121822.dt4 1/31/2007 20:18 2.1 234.1

140 20070131_122238.dt4 1/31/2007 20:22 1.3 127.6

142 20070131_122430.dt4 1/31/2007 20:24 1.0 115.4

 142a 20070131_122738.dt4 1/31/2007 20:27 0.9 95.4

144 20070131_122945.dt4 1/31/2007 20:29 0.8 67.1

146 20070131_123115.dt4 1/31/2007 20:31 0.7 66.6

151 20070131_123402.dt4 1/31/2007 20:34 30.0 3,703.0

 151a 20070131_130402.dt4 1/31/2007 21:04 7.1 911.1

5 20070131_131645.dt4 1/31/2007 21:16 8.0 833.4

6 20070131_132536.dt4 1/31/2007 21:25 5.6 756.7

7 20070131_133205.dt4 1/31/2007 21:32 7.6 750.7

8 20070131_134030.dt4 1/31/2007 21:40 5.5 727.3

9 20070131_134701.dt4 1/31/2007 21:47 7.4 755.2

10 20070131_135514.dt4 1/31/2007 21:55 5.1 680.6

11 20070131_140137.dt4 1/31/2007 22:01 6.9 698.1

12 20070131_140916.dt4 1/31/2007 22:09 2.4 324.1

17 20070131_141920.dt4 1/31/2007 22:19 1.2 134.3

18 20070131_142122.dt4 1/31/2007 22:21 1.0 113.3

19 20070131_142326.dt4 1/31/2007 22:23 2.5 260.2

20 20070131_142713.dt4 1/31/2007 22:27 2.1 250.8

21 20070131_143038.dt4 1/31/2007 22:30 3.0 310.8

22 20070131_143421.dt4 1/31/2007 22:34 1.0 120.4

23 20070131_143702.dt4 1/31/2007 22:37 0.6 53.1

24 20070131_143830.dt4 1/31/2007 22:38 0.6 44.4

27 20070131_144056.dt4 1/31/2007 22:40 0.4 42.9

28 20070131_144152.dt4 1/31/2007 22:41 0.6 37.9

29 20070131_144322.dt4 1/31/2007 22:43 0.8 84.2

30 20070131_144458.dt4 1/31/2007 22:44 1.1 111.6

31 20070131_144650.dt4 1/31/2007 22:47 1.7 118.9

32 20070131_144907.dt4 1/31/2007 22:49 0.6 42.5

33 20070131_145056.dt4 1/31/2007 22:51 0.7 48.6

34 20070131_145217.dt4 1/31/2007 22:52 0.7 46.8

35 20070131_145346.dt4 1/31/2007 22:54 1.0 64.8

36 20070131_145531.dt4 1/31/2007 22:55 0.6 54.9

37 20070131_145655.dt4 1/31/2007 22:57 0.8 69.0

38 20070131_145824.dt4 1/31/2007 22:58 0.8 70.0

39 20070131_145955.dt4 1/31/2007 23:00 1.2 98.9

40 20070131_150150.dt4 1/31/2007 23:01 1.0 97.6

41 20070131_150350.dt4 1/31/2007 23:03 0.9 85.8

150 20070131_150612.dt4 1/31/2007 23:06 8.8 1,219.2

149 20070131_151534.dt4 1/31/2007 23:15 10.7 1,130.9
34 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

Table A3. List of lines completed, elapsed time, and distance covered at the Possession Point study site, February 1, 2007. Asterisks 

next to the line number indicate that underwater video was also collected.

Line Filename Time Collected (GMT) Elapsed Time (min) Total Length (m) 

150a 20070201_085716.dt4 2/1/2007 16:57 10.7 520.4

150b 20070201_090805.dt4 2/1/2007 17:08 25.7 3,416.0

 132v* 20070201_094656.dt4 2/1/2007 17:46 7.3 395.6

 123v* 20070201_100013.dt4 2/1/2007 18:00 6.2 329.3

 113v* 20070201_101052.dt4 2/1/2007 18:10 4.1 284.9

103v* 20070201_101936.dt4 2/1/2007 18:19 4.2 231.7

 93v* 20070201_102805.dt4 2/1/2007 18:28 3.9 207.0

 83v* 20070201_103610.dt4 2/1/2007 18:36 4.9 221.1

 73v* 20070201_104453.dt4 2/1/2007 18:44 6.9 327.0

 63v* 20070201_105539.dt4 2/1/2007 18:55 5.6 279.2

 53v* 20070201_110728.dt4 2/1/2007 19:07 2.4 98.1

 43v* 20070201_111426.dt4 2/1/2007 19:14 1.5 68.3

 44v* 20070201_111726.dt4 2/1/2007 19:17 1.1 45.1

 33v* 20070201_112319.dt4 2/1/2007 19:23 1.3 44.5

 23va* 20070201_112457.dt4 2/1/2007 19:24 6.7 728.7

 23v* 20070201_113734.dt4 2/1/2007 19:37 1.1 98.2

23v2* 20070201_113844.dt4 2/1/2007 19:38 2.4 160.9

 13v* 20070201_114638.dt4 2/1/2007 19:46 6.3 378.8

 5v* 20070201_122337.dt4 2/1/2007 20:23 6.2 370.0

 18v* 20070201_123630.dt4 2/1/2007 20:36 5.5 281.6

149a 20070201_124629.dt4 2/1/2007 20:46 10.4 1,083.0

 48v* 20070201_125945.dt4 2/1/2007 20:59 1.6 59.9

Figure A2. Predicted tidal elevations for Seattle (47.6 °N, 122.3 °W) for January 30, 2007. The gray bar shows the time 

span when data were collected at the Possession Point study site. 
Appendix A. Data Collected at Possession Point 35

Figure A3. Predicted tidal elevations for Seattle (47.6 °N, 122.3 °W) for January 31, 2007. The gray bar shows the time 

span when data were collected at the Possession Point study site.

Figure A4. Predicted tidal elevations for Seattle (47.6 °N, 122.3 °W) for February 1, 2007. The gray bar shows the time 

span when data were collected at the Possession Point study site. 
36 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

Appendix B. Data Collected at Browns Bay 

Appendix B provides a list of all acoustic data collected at Browns Bay on February 2, 2007. The locations of planned 

lines is shown in figure B1. The line numbers, acoustic data filenames, collection dates, and line lengths are provided in table 

B1. The tide height during data collection at Browns Bay is shown in figure B2.0

Figure B1. Planned lines for acoustic survey at Browns Bay. Cross-shore lines 1-123 were spaced roughly 15 m 

apart. Lines 124-126 were oriented alongshore.
Appendix B. Data Collected at Browns Bay 37

Table B1. List of lines completed, elapsed time, and distance covered in Browns Bay study area, February 2, 2007. Asterisks next to 

the line number indicate that underwater video was also collected—Continued. 

Line Filename Date Collected Elapsed Time (min) Total Length (m) 

1 20070202_114339.dt4 2/2/2007 19:43 2.3 268.8

3 20070202_114103.dt4 2/2/2007 19:41 1.9 248.5

 5* 20070202_113617.dt4 2/2/2007 19:36 4.1 257.7

7 20070202_113356.dt4 2/2/2007 19:33 1.7 215.0

9 20070202_113130.dt4 2/2/2007 19:31 1.5 192.1

11 20070202_112909.dt4 2/2/2007 19:29 1.7 220.1

13 20070202_112631.dt4 2/2/2007 19:26 1.9 221.1

15 20070202_112340.dt4 2/2/2007 19:23 2.3 246.2

17 20070202_112032.dt4 2/2/2007 19:20 2.2 256.1

 18* 20070202_131751.dt4 2/2/2007 21:17 6.2 270.3

19 20070202_111735.dt4 2/2/2007 19:17 2.3 277.9

21 20070202_111427.dt4 2/2/2007 19:14 2.2 263.4

23 20070202_111159.dt4 2/2/2007 19:11 2.1 255.0

25 20070202_110914.dt4 2/2/2007 19:09 2.1 243.1

27 20070202_110656.dt4 2/2/2007 19:06 1.6 199.7

 32* 20070202_105844.dt4 2/2/2007 18:58 6.1 291.5

34 20070202_105544.dt4 2/2/2007 18:55 2.2 263.0

36 20070202_105234.dt4 2/2/2007 18:52 2.2 243.0

 37* 20070202_130617.dt4 2/2/2007 21:06 6.9 283.9

38 20070202_105009.dt4 2/2/2007 18:50 1.8 212.5

40 20070202_104731.dt4 2/2/2007 18:47 1.8 197.2

42 20070202_103903.dt4 2/2/2007 18:39 1.6 181.7

44 20070202_103657.dt4 2/2/2007 18:36 1.3 159.3

 46* 20070202_103327.dt4 2/2/2007 18:33 2.4 125.4

48 20070202_103114.dt4 2/2/2007 18:31 1.1 132.4

50 20070202_102928.dt4 2/2/2007 18:29 1.0 98.1

54 20070202_102611.dt4 2/2/2007 18:26 0.9 117.8

 55* 20070202_125917.dt4 2/2/2007 20:59 2.8 136.5

56 20070202_102415.dt4 2/2/2007 18:24 0.9 126.5

58 20070202_102007.dt4 2/2/2007 18:20 1.3 167.8

60 20070202_101818.dt4 2/2/2007 18:18 1.2 163.4

62 20070202_101607.dt4 2/2/2007 18:16 1.3 172.6

64 20070202_101420.dt4 2/2/2007 18:14 1.2 160.7

 66* 20070202_100952.dt4 2/2/2007 18:09 3.6 174.0

68 20070202_100732.dt4 2/2/2007 18:07 1.4 184.2

70 20070202_100408.dt4 2/2/2007 18:04 0.8 106.7

72 20070202_100408.dt4 2/2/2007 18:04 0.8 106.7

74 20070202_100230.dt4 2/2/2007 18:02 0.9 113.3

 75* 20070202_125136.dt4 2/2/2007 20:51 2.5 119.9

76 20070202_100100.dt4 2/2/2007 18:00 0.9 120.9

78 20070202_095855.dt4 2/2/2007 17:58 1.2 142.2

80 20070202_095626.dt4 2/2/2007 17:56 0.6 73.0

82 20070202_095506.dt4 2/2/2007 17:55 0.7 84.8

84 20070202_095226.dt4 2/2/2007 17:52 1.7 204.9

 86* 20070202_094743.dt4 2/2/2007 17:47 3.6 209.2

88 20070202_094458.dt4 2/2/2007 17:44 1.6 186.2

93 20070202_094145.dt4 2/2/2007 17:41 1.7 205.5

95 20070202_093935.dt4 2/2/2007 17:39 1.5 192.9

 96* 20070202_124135.dt4 2/2/2007 20:41 4.8 225.0

Table B1. List of lines completed, elapsed time, and distance covered in Browns Bay study area, February 2, 2007. Asterisks next to 

the line number indicate that underwater video was also collected. 
38 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

Table B1. List of lines completed, elapsed time, and distance covered in Browns Bay study area, February 2, 2007. Asterisks next to 

the line number indicate that underwater video was also collected—Continued. 

Line Filename Date Collected Elapsed Time (min) Total Length (m) 

97 20070202_093704.dt4 2/2/2007 17:37 1.8 218.0

99 20070202_093448.dt4 2/2/2007 17:34 1.5 193.2

101 20070202_093152.dt4 2/2/2007 17:31 1.9 226.0

103 20070202_092922.dt4 2/2/2007 17:29 1.8 216.6

 105* 20070202_092509.dt4 2/2/2007 17:25 3.4 217.4

107 20070202_092235.dt4 2/2/2007 17:22 1.8 200.5

109 20070202_092017.dt4 2/2/2007 17:20 1.7 188.4

111 20070202_091802.dt4 2/2/2007 17:18 1.6 182.4

113 20070202_091558.dt4 2/2/2007 17:15 1.3 142.1

115 20070202_091405.dt4 2/2/2007 17:14 1.0 118.3

117 20070202_091108.dt4 2/2/2007 17:11 0.9 106.4

119 20070202_090713.dt4 2/2/2007 17:07 1.1 110.5

 120* 20070202_123304.dt4 2/2/2007 20:33 2.4 111.7

121 20070202_090353.dt4 2/2/2007 17:03 0.8 87.9

123 20070202_090116.dt4 2/2/2007 17:01 1.6 148.9

SP1 20070202_115451.dt4 2/2/2007 19:54 27.6 3103.7

SP2 20070202_132847.dt4 2/2/2007 21:28 22.1 3,088.8

Figure B2. Predicted tidal elevations for Seattle (47.6 °N, 122.3 °W) for February 2, 2007. The gray bar shows the time 

span when data were collected at the Browns Bay study site.
Appendix C. Grain-Size Data from Possession Point

Appendix C contains grain-size data collected at Possession Point between February 1 and April 20, 2007. Sample locations as well as 

provided in table C1. Sediment distributions for each sample are shown in figures C1 through C5. Statistical parameters including mean, variaskewness and kurtosis were calculated using the graphical technique of Inman (1952).

Table C1. Locations and grain-size parameters for samples collected at Possession Point. —Continued.

Station ID Collection Date Latitude (deg N) Longitude (deg W) Gravel % Sand % Silt % Clay 

%

Mean 

(phi) Mean (mm) Variance Std. Dev. 

1 2/1/2007 22:19 47.9118 122.3752 0 100 0 0 2.07 0.24 0.21 0.46 

2 2/1/2007 22:21 47.9117 122.3752 0 100 0 0 2.12 0.23 0.23 0.48 

3 2/1/2007 22:27 47.9137 122.3752 0 100 0 0 2.48 0.18 0.18 0.42 

4 2/1/2007 22:35 47.9163 122.3753 0 100 0 0 2.47 0.18 0.42 0.65 

5 2/1/2007 22:36 47.9164 122.3753 0 100 0 0 2.59 0.17 0.41 0.64 

6 2/1/2007 22:39 47.9184 122.3753 0 93.6 5.1 1.3 2.55 0.17 1.69 1.3 

7 2/1/2007 22:43 47.9207 122.375 0 100 0 0 2.17 0.22 0.3 0.55 

8 2/1/2007 22:45 47.9228 122.3741 0 100 0 0 2.29 0.2 0.16 0.4 

9 2/1/2007 22:48 47.925 122.3729 0 100 0 0 2.15 0.23 0.12 0.35 

10 2/1/2007 23:02 47.9272 122.3711 0 100 0 0 2.21 0.22 0.22 0.46 

11 2/1/2007 23:05 47.9287 122.3689 0 100 0 0 2.15 0.23 0.08 0.28 

12 2/1/2007 23:09 47.9304 122.3666 0 100 0 0 2.11 0.23 0.12 0.35 

13 2/1/2007 23:12 47.9317 122.3641 0 100 0 0 2.05 0.24 0.08 0.28 

14 2/1/2007 23:17 47.9333 122.3617 0 100 0 0 2.05 0.24 0.08 0.28 

15 2/1/2007 23:19 47.9355 122.3599 0 100 0 0 1.97 0.25 0.11 0.33 

16 2/1/2007 23:28 47.9376 122.3582 0 100 0 0 2.34 0.2 0.29 0.54 

17 2/1/2007 23:41 47.9093 122.3756 0 98.1 1.9 0 2.14 0.23 0.28 0.53 

HP-1 2/28/2007 18:17 47.9339 122.3652 0 96.4 2.7 0.9 2.55 0.17 1.04 1.02 

HP-2 2/28/2007 18:20 47.9335 122.3647 0 100 0 0 1.63 0.32 0.27 0.52 

HP-3 2/28/2007 18:26 47.9332 122.3637 0 100 0 0 1.63 0.32 0.21 0.46 

HP-4 2/28/2007 18:31 47.9327 122.3623 0 100 0 0 1.93 0.26 0.09 0.31 

HP-5 2/28/2007 18:33 47.9323 122.3623 0 100 0 0 2.05 0.24 0.1 0.31 

HP-6 2/28/2007 18:35 47.9319 122.362 0 100 0 0 2.24 0.21 0.16 0.4 

HP-7 2/28/2007 18:37 47.9317 122.3613 0 100 0 0 2.31 0.2 0.22 0.47 

TN-1 2/28/2007 18:47 47.9221 122.3761 0 71 23.9 5.2 3.76 0.07 3.71 1.93 

TN-3 2/28/2007 19:00 47.9217 122.374 84.3 15.7 0 0 -2.75 6.75 3.73 1.93 

TN-4 2/28/2007 19:04 47.9217 122.3734 0 100 0 0 2.03 0.24 0.35 0.59 

TN-5 2/28/2007 19:06 47.9213 122.373 0 100 0 0 2.02 0.25 0.36 0.6 

TS-1 2/28/2007 19:11 47.9203 122.3762 57.5 41.8 0.6 0 -0.97 1.95 6.37 2.52 

Table C1. Locations and grain-size parameters for samples collected at Possession Point. 
Table C1. Locations and grain-size parameters for samples collected at Possession Point. —Continued. Station ID Collection Date Latitude (deg N) Longitude (deg W) Gravel % Sand % Silt % Clay % Mean (phi) Mean (mm) Variance Std. Dev. 

TS-2 2/28/2007 19:13 47.9201 122.3758 0 94.8 3.8 1.4 2.7 0.15 1.35 1.16 

TS-4 2/28/2007 19:14 47.9202 122.3755 0 92.9 5.3 1.8 2.67 0.16 1.66 1.29 

TS-5 2/28/2007 19:17 47.9203 122.3751 0 96.9 2.2 0.8 2.32 0.2 1.06 1.03 

TS-6 2/28/2007 19:19 47.9201 122.3745 0 100 0 0 2.04 0.24 0.3 0.55 

TS-7 2/28/2007 19:22 47.92 122.3737 0 100 0 0 1.79 0.29 0.34 0.58 

TS-8 2/28/2007 19:24 47.9202 122.3731 0 100 0 0 1.8 0.29 0.45 0.67 

TS-9 2/28/2007 19:27 47.9201 122.3739 17.8 82.2 0 0 0.89 0.54 3.84 1.96 

G22-1A 3/22/2007 15:47 47.9042 122.3778 13.2 86.8 0 0 1.11 0.46 2.67 1.63 

G22-1B 3/22/2007 15:47 47.9042 122.3778 41 59 0 0 -0.34 1.27 5.61 2.37 

G22-2A 3/23/2007 16:10 47.9219 122.3747 62.3 37.7 0 0 -1.37 2.59 4.7 2.17 

G22-2B 3/23/2007 16:10 47.9219 122.3747 53.6 46.4 0 0 -0.91 1.88 4.32 2.08 

G22-3A 3/24/2007 16:24 47.9203 122.3752 0 95.5 3.3 1.2 2.52 0.17 1.22 1.1 

G22-3B 3/24/2007 16:24 47.9203 122.3752 0 96 2.9 1.1 2.53 0.17 1.09 1.05 

G22-4 3/25/2007 16:38 47.933 122.3632 0 100 0 0 1.68 0.31 0.15 0.39 

G22-5 3/26/2007 16:47 47.9332 122.3641 0 100 0 0 2.04 0.24 0.15 0.38 

AP-1 4/19/2007 20:25 47.9334 122.3638 0 100 0 0 1.77 0.29 0.15 0.39 

AP-2 4/19/2007 20:30 47.9334 122.3635 0 100 0 0 1.82 0.28 0.16 0.4 

AP-3 4/19/2007 20:40 47.9337 122.3645 0 100 0 0 1.77 0.29 0.17 0.41 

AP-4 4/19/2007 20:45 47.9337 122.3642 0 95.8 3 1.3 2.59 0.17 1.25 1.12 

AP-5 4/19/2007 20:52 47.9342 122.3634 0 100 0 0 1.64 0.32 0.14 0.37 

AP-6 4/20/2007 20:30 47.9218 122.3742 22.5 77.5 0 0 0.74 0.6 4.37 2.09 

AP-7 4/20/2007 20:33 47.9214 122.3744 32.1 67.9 0 0 -0.15 1.11 5.24 2.29 

AP-8 4/20/2007 20:40 47.9218 122.3745 27.5 72.5 0 0 0.23 0.85 3.3 1.82 

AP-9 4/20/2007 21:15 47.9201 122.3759 5.7 86.3 6.2 1.8 2.61 0.16 3.61 1.9 
Appendix C. Grain-Size Data from Possession Point 41

Figure C1. Sediment grain-size distribution (weight percent) for samples 1 through 12 from Possession Point. See 

table C1 for locations of samples.
42 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

Figure C2. Sediment grain size distribution (weight percent) for samples 13 through HP-7 from Possession Point. See 

table C1 for locations of samples.
Appendix C. Grain-Size Data from Possession Point 43

Figure C3. Sediment grain size distribution (weight percent) for samples TN-1 through TS-9 from Possession Point. 

See table C1 for locations of samples.
44 Evaluation of a Single-Beam Sonar System to Map Seagrass in Northern Puget Sound, Washington

Figure C4. Sediment grain size distribution (weight percent) for samples G22-1A through AP-4 from Possession Point. 

See table C1 for locations of samples.
Appendix C. Grain-Size Data from Possession Point 45

Figure C5. Sediment grain size distribution (weight percent) for samples AP-5 through AP-9 from Possession Point. 

See table C1 for locations of samples.
Stevens and Others—Evaluation of a Single-Beam Sonar System to Map Seagrass at Two Sites in Puget Sound—Scientific Investigations Report 2008–5009