Marine Sanctuaries Conservation Series ONMS-07-05

U.S. Department of Commerce 

National Oceanic and Atmospheric Administration

National Ocean Service 

Office of Ocean and Coastal Resource Management 

Automated, objective texture 

segmentation of multibeam echosounder data - 

Seafloor survey and substrate maps from James 

Island to Ozette Lake, Washington Outer Coast

National Marine Sanctuary Program November 2007 
About the Marine Sanctuaries Conservation Series 

The National Oceanic and Atmospheric Administration’s National Ocean Service (NOS) administers the

National Marine Sanctuary Program (NMSP). Its mission is to identify, designate, protect and manage the 

ecological, recreational, research, educational, historical, and aesthetic resources and qualities of nationally

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resource management issues, and results of scientific research and monitoring projects. The series facilitates 

integration of natural sciences, socioeconomic and cultural sciences, education, and policy development to 

accomplish the diverse needs of NOAA’s resource protection mandate. 
Automated, objective texture 

segmentation of multibeam echosounder data - 

Seafloor survey and substrate maps from James Island to 

Ozette Lake, Washington Outer Coast 

Steven S. Intelmann1

, George R. Cutter2, and Jonathan D. Beaudoin3

1

Olympic Coast National Marine Sanctuary, NOAA 

2

Southwest Fisheries Science Center, NOAA 

3

Ocean Mapping Group, University of New Brunswick 

Silver Spring, Maryland

November 2007

U.S. Department of Commerce 

Carlos M. Gutierrez, Secretary 

National Oceanic and Atmospheric Administration 

VADM Conrad C. Lautenbacher, Jr. (USN-ret.) 

Under Secretary of Commerce for Oceans and Atmosphere

National Ocean Service 

John H. Dunnigan, Assistant Administrator 

National Marine Sanctuary Program

Daniel J. Basta, Director 
DISCLAIMER 

Report content does not necessarily reflect the views and policies of the National Marine 

Sanctuary Program or the National Oceanic and Atmospheric Administration, nor does 

the mention of trade names or commercial products constitute endorsement or 

recommendation for use. 

REPORT AVAILABILITY 

Electronic copies of this report are available from the National Marine Sanctuary

Program web site at www.sanctuaries.nos.noaa.gov. Hard copies are available from the 

following address: 

National Oceanic and Atmospheric Administration

 National Marine Sanctuary Program

 SSMC4, N/ORM62 

 1305 East-West Highway 

Silver Spring, MD 20910 

COVER

NOAAS Rainier survey launch. 

SUGGESTED CITATION

Intelmann, S.S., G.R. Cutter, J.D. Beaudoin 2007. Automated, objective texture 

segmentation of multibeam echosounder data - Seafloor survey and substrate maps from

James Island to Ozette Lake, Washington Outer Coast. Marine Sanctuaries Conservation 

Series MSD-07-05. U.S. Department of Commerce, National Oceanic and Atmospheric 

Administration, National Marine Sanctuary Program, Silver Spring, MD. 31 pp. 

CONTACT

Steven S. Intelmann 

Habitat Mapping Specialist 

NOAA/National Marine Sanctuary Program

N/ORM 6X26 

2725 Montlake Blvd East 

Seattle, WA 98112 

(206) 861-7603 

steve.intelmann@noaa.gov 
ABSTRACT 

Without knowledge of basic seafloor characteristics, the ability to address any number of 

critical marine and/or coastal management issues is diminished. For example, 

management and conservation of essential fish habitat (EFH), a requirement mandated by 

federally guided fishery management plans (FMPs), requires among other things a 

description of habitats for federally managed species. Although the list of attributes 

important to habitat are numerous, the ability to efficiently and effectively describe many, 

and especially at the scales required, does not exist with the tools currently available.

However, several characteristics of seafloor morphology are readily obtainable at 

multiple scales and can serve as useful descriptors of habitat. Recent advancements in 

acoustic technology, such as multibeam echosounding (MBES), can provide remote

indication of surficial sediment properties such as texture, hardness, or roughness, and 

further permit highly detailed renderings of seafloor morphology. With acoustic-based 

surveys providing a relatively efficient method for data acquisition, there exists a need for 

efficient and reproducible automated segmentation routines to process the data. Using 

MBES data collected by the Olympic Coast National Marine Sanctuary (OCNMS), and 

through a contracted seafloor survey, we expanded on the techniques of Cutter et al. 

(2003) to describe an objective repeatable process that uses parameterized local Fourier 

histogram (LFH) texture features to automate segmentation of surficial sediments from

acoustic imagery using a maximum likelihood decision rule. Sonar signatures and 

classification performance were evaluated using video imagery obtained from a towed 

camera sled. Segmented raster images were converted to polygon features and attributed 

using a hierarchical deep-water marine benthic classification scheme (Greene et al. 1999) 

for use in a geographical information system (GIS). 

KEY WORDS 

Benthic, habitat mapping, sediment classification, multibeam echosounder, local Fourier 

histogram texture features, essential fish habitat, Olympic Coast National Marine

Sanctuary 

i 
TABLE OF CONTENTS 

Topic Page

Abstract and Key Words......................................................................................... i 

Table of Contents.................................................................................................... ii 

List of Figures and Tables....................................................................................... iii 

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

Survey Area ............................................................................................................ 2 

Sonar Acquisition and Data Logging...................................................................... 3 

Sonar Data Processing ............................................................................................ 3 

Image Classification................................................................................................ 4 

Groundtruthing........................................................................................................ 7 

Discussion of Survey Results and Interpretation.................................................... 8 

Acknowledgments................................................................................................... 17 

References............................................................................................................... 17 

Appendix 1. Vessel Offsets..................................................................................... 21 

Appendix 2. Bathymetry Surface............................................................................ 28 

Appendix 3. Backscatter Imagery........................................................................... 29 

ii 
LIST OF FIGURES AND TABLES

Figure/Table Number and Title Page

Figure 1. Extent of 2002 (green) and 2003 (red) MBES survey effort near

Lapush, WA............................................................................................................ 2 

Figure 2. Backscatter mosaic showing areas of void data along nadir tracks. 

Segmentation with LFH method using xyz data. Segmentation of backscatter 

using entropy and homogeneity derivatives. ......................................................... 4 

Figure 3. Schematic of data layers input into the Spatial Analyst maximum 

likelihood classification procedure. ........................................................................ 6 

Figure 4. Location of groundtruthing validation in relation to individual 

survey blocks........................................................................................................... 7 

Table 1. Survey effort statistics for HMPR-110-2002-04...................................... 8 

Table 2. Distribution of bottom hardness for each sonar mosaic classified from 

survey HMPR-110-2002-04.................................................................................... 8 

Figure 5. Seafloor substrate polygons, Ozette Lake to Carroll Island with 

bottom_id codes taken from Greene et al. (1999)................................................... 9 

Figure 6. Seafloor substrate polygons, Carroll Island to Cake Rock with 

bottom_id codes taken from Greene et al. (1999)................................................... 10 

Figure 7. Seafloor substrate polygons, Cake Rock to James Island with 

bottom_id codes taken from Greene et al. (1999)................................................... 11 

Figure 8. Example of homogenous bathymetry and unique backscatter signature 

as segmented by the modified LFH procedure. ...................................................... 12 

Figure 9. Example of data layers to the maximum likelihood classification routine 

(except C) with resulting segmentation output (C).. .............................................. 13 

Figure 10. Clipped section of area110_0204b, illustrating a geomorphic 

representation of seafloor substrates produced through the segmentation of 

acoustic MBES data. .............................................................................................. 14 

Figure 11. Example of rock features near Hand Rock and Cape Johnson.............. 15 

Figure 12. Example of refraction artifact encountered in the 2002 survey data..... 16 

iii 
Table 3. Frequency of sound velocity casts acquired each day during the 2002 

and 2003 surveys..................................................................................................... 17 

iv 
INTRODUCTION

In response to congressional amendments of the Magnuson-Stevens Fisheries 

Conservation Act, all fishery management plans (FMPs) are required to describe and 

identify essential fish habitat (EFH) for their respective fisheries (Public Law 104-297). 

As FMPs can encompass regions as expansive as the entire Pacific Coast, addressing this

mandate requires highly efficient methods for describing and characterizing various 

habitat attributes. Notably, the composition and texture of surficial sediments is widely 

recognized as being an important element of EFH, and plays a major role in determining 

the distribution and abundance of many groundfish species (Carlson and Straty 1981; 

Love et al. 1991; Stein et al. 1992; Krieger 1993; McConnaughey and Smith 2000). 

Recent advancements in acoustic technology, such as multibeam echosounding (MBES), 

can provide remote indication of surficial sediment properties and further permit highly 

detailed renderings of seafloor morphology across broad scales and in relatively short 

time as compared to traditional grab or core sampling. As such, MBES data-based 

seafloor maps have gained broad acceptance for providing a means to segment seafloors 

(Mayer et al. 1999; Todd et al. 1999; Kostylev et al. 2001; Dartnell and Gardner 2004), 

populate hierarchical marine classification schemes (Greene et al. 1999; Alee et al. 2000; 

Harney et al. 2006), and hold promise for informing the EFH designation process. 

With acoustic-based surveys providing a relatively efficient method for data acquisition, 

there exists a need for efficient and reproducible automated segmentation routines to 

process the data. Other work has described techniques using local Fourier histogram

features (Cutter et al. 2003), grey-scale covariance texture indices (Cochrane and Lafferty 

2002; Intelmann et al. 2006) and various statistical derivatives (Harney et al. 2006) to 

quantitatively segment acoustic seafloor imagery, yet these techniques vary in degree of 

reproducibility, robustness, and processing autonomy. 

Using MBES data collected by the Olympic Coast National Marine Sanctuary (OCNMS), 

and through a contracted seafloor survey, we expanded on the techniques of Cutter et al. 

(2003) to describe an objective repeatable process that uses parameterized local Fourier 

histogram texture features to automate segmentation of surficial sediments from multiple

types of acoustic imagery with a maximum likelihood decision rule. Video from a towed 

camera sled was integrated with sedimentary samples, backscatter, and the bathymetry 

data to describe geological and biological (where possible) aspects of habitat. Using a 

hierarchical deep-water marine benthic classification scheme (Greene et al. 1999), we 

then created and attributed polygon features for use in a geographical information system

(GIS). The report provides a description of the mapping and groundtruthing efforts, and 

technique and results of the automated segmentation procedure for each area surveyed in

2002 and 2003. 

1 
SURVEY AREA 

In 2002 and 2003 respectively, approximately 42 km2

 and 49 km2 of MBES-based 

seafloor mapping was conducted from the mouth of the Quileute River near Lapush to 

roughly Sand Point near Ozette Lake (Figure 1). Survey records were obtained from July 

27 – August 02 in 2002, and August 28 – September 25 of 2003. Water depths ranged 

between 0.5 and 35 meters throughout the survey area. 

Figure 1. Extent of 2002 (green) and 2003 (red) MBES survey effort near Lapush, 

WA. The survey was divided into 3 blocks (110_0204a, 110_0204b, and 

110_0204c) to reduce file size and to accommodate more efficient data archival and 

sharing. Large-scale inset provides context placement along the Washington

coastline and within the Sanctuary boundary. 

2 
SONAR ACQUISITION AND DATA LOGGING

The 2002 contract survey, awarded to the Seafloor Mapping Lab at California State 

University Monterey Bay (CSUMB), used the 9.8 m R/V MacGinitie as an acquisition 

platform while a 9.8 m survey launch provided by the NOAA Ship Rainier, and operated 

by OCNMS, was used to acquire the 2003 data. A Reson 8101 MBES with extended 

range projector was used on both vessels. The echosounder was hull-mounted on a

retractable flange for the 2003 survey but pole-mounted to the bow on the R/V 

MacGinitie. Vessel speed was targeted at 8 knots during acquisition. Sensor offsets and 

photos for each vessel are provided in Appendix 1. 

Sonar data were logged in Extended Triton Format (XTF) using Isis Sonar (Triton 

Imaging International) with the “Full-New” side scan beam forming technique, a process 

that yields less noisy output by combining the bathymetry beams into two side scan 

beams where adjacent pairs of beams are then averaged and the brightest points of the 

averaged beams are then ultimately used (Reson 2003). Vessel attitude and positioning 

for each of the launches was monitored with a TSS (Applanix) POS/MV 320 and logged

in Isis Sonar. Survey line control was accomplished through differential GPS (DGPS) 

using Hypack marine positioning and surveying software with sound velocity corrections

being made through use of Seabird SBE 19plus CTD profilers. Water level observations 

were acquired from the Neah Bay tidal station 9443090 and applied with zoned 

corrections. 

SONAR DATA PROCESSING 

Bathymetry data were cleaned of anomalies using Caris HIPS software, creating BASE 

(Bathymetry Associated with Statistical Error) surfaces for each of the three main survey 

blocks with the CUBE (Combined Uncertainty Bathymetric Estimator) method (Calder 

and Mayer 2003). A 5x5 surface interpolation with 12 nearest neighbors was used to fill

small data gaps. Accepted xyz values from the interpolated CUBE surface were 

converted to Arcview ascii grid format at 1-meter resolution using WGS84 UTM zone 10 

projection parameters. 

For best use in seafloor characterization, sonar echo strength data should be normalized 

to leave only the seafloor’s backscattering strength as the sole source of signal strength 

variation. Because commercial software packages currently available for processing 

acoustic backscatter perform only a rudimentary geo-registration through use of a flat 

seafloor assumption and additionally ignore variations in acoustic source level and

receiver gain, production of acoustic backscatter imagery was accomplished using

software tools developed by the Ocean Mapping Group (OMG), University of New 

Brunswick (Beaudoin et al. 2002). Three separate mosaics were created for each survey 

block from the RI_Theta, side scan, and snippet packets. However, only the side scan 

data was used in the classification process since the 2002 survey platform was not snippet 

enabled. The side scan backscatter imagery was mosaicked at 1-meter resolution and 

exported to Arcview ascii grid format. 

3 
IMAGE CLASSIFICATION

To date, OCNMS has used textural 

derivatives (i.e. homogeneity and entropy 

calculated from either multibeam

backscatter or side scan sonar data) and 

applied a supervised image classification

using a maximum likelihood decision 

rule to segment acoustic data into

discrete substrate types (Intelmann and

Cochrane 2006a, Intelmann et al. 2006, 

Intelmann and Cochrane 2006b, and 

Intelmann et al. 2007). Although this 

method provided objective results, 

substantial manual subjective editing was 

required to clean up poorly classified 

regions, such as near-nadir (Figure 2, 

black areas visible throughout inset C). 

A segmentation routine previously 

described by Cutter et al. (2003) offered 

an alternative method of autonomously 

classifying acoustic data using local 

Fourier histogram texture features, and 

relied solely on using bathymetry data to 

segment the imagery (Figure 2, inset B). 

Contrary to backscatter imagery,

calculating textural indices from 

bathymetry data avoids problems

associated with classifying the near-nadir 

backscatter artifacts (shown in Figure 2, 

inset A) since there can be continuous 

data coverage along nadir. 

However, in cases where roughness of 

the seafloor is uniform (i.e. uniformly 

flat or with identical textural pattern at all 

spatial scales), statistical roughness or 

textural properties calculated from

bathymetry data may not discriminate 

between facies. However, backscatter 

intensities in these areas sometimes 

indicate a unique acoustic signature. In 

other words, if the seafloor consists of 

flat mud or flat rock the bathymetry will 

Figure 2. Backscatter mosaic showing areas of

void data along nadir tracks (A). Segmentation 

with LFH method using xyz data (B). 

Segmentation of backscatter using entropy and

homogeneity derivatives (C). Note nadir

misclassification in C. 

4 
not indicate a difference and texture features or roughness measures do not provide 

enough information to differentiate bottom type in these cases. Through describing local 

spatial variation of grid cell values, we tried incorporating backscatter intensity into a 

segmentation rule along with LFH indices calculated from bathymetry data to produce a 

potentially more robust method for delineating in these unique instances. 

Using customized software, the texture procedure of Cutter et al. (2003) was modified by 

parameterizing the standard LFH feature vector. For the standard LFH, there are four 

component LFHs that comprise the complete LFH. Each of the component LFHs

represents the distribution of value coefficients from discrete Fourier transforms applied

to local grid cells. Instead of binning the local Fourier map (LFM) data to create a 32-

element LFH feature vector, we calculated the feature vector elements that represent the 

mean and standard deviation of each component LFH. This process reduces the complete 

LFH from a 32-element feature vector to an 8-element feature vector (i.e. LFH0_mean, 

LFH0_StD, LFH1_mean, LFH1_StD, LFH2_mean, LFH2_StD, LFH3_mean, 

LFH3_StD) and provides a more concise description of the texture feature. Reducing 

data dimensionality was a necessity due to the computational requirements associated 

with populating a covariance matrix of more than 20 vectors for maximum likelihood 

classification (also the maximum allowable number of input raster bands when using 

Arcview Spatial Analyst MLClassify), and subsequently classifying each data point of a

1-meter grid across 90 km2

 of seafloor. After the parameterized LFH texture feature 

vectors were calculated on a per cell basis, the LFH1, LFH2, and LFH3 indices were 

reformatted into Arcview ascii grid format. To reduce classification impacts related to

mean depth effects, LFH0 values were not used in the classification since it essentially

represents the mean value of the data series. 

Using video data from block 110_0204a, training classes were manually digitized in 

Arcview to define representative statistics for areas of four distinct backscatter signatures

which corresponded to rock outcrop (h), mixed sediment of boulders, cobbles and sand 

(m(bcs)), soft sand (s(s)), and soft sand and shell with waves (s(sq)). This four class

segmentation effectively corresponds to the bottom induration attribute described in 

Greene et al. (1999). To insure comparable results between blocks, the signature 

covariance matrix output from block 110_0204a was subsequently used to segment

blocks 110_0204b and 110_0204c. 

Using Arcview Spatial Analyst, a maximum likelihood classification procedure was then 

used to segment the data into the four distinct classes using the LFH indices (with 

exception of LFH0), backscatter intensity, and a simple standard deviation surface 

calculated from the bathymetry data as raster input layers (Figure 3). The resulting 

output raster was converted to a feature polygon layer and attributed according to Greene 

et al. (1999). Micro-scale habitat features were added to the polygons in areas where 

video groundtruthing was conducted. 

5 
Figure 3. Schematic of data layers input into the Spatial Analyst maximum likelihood

classification procedure. The eight layers represent backscatter intensity, standard deviation 

surface calculated from bathymetry data, and 6 LFH indices corresponding to the mean and 

standard deviation feature vectors of LFH1, LFH2, and LFH3. The LFH0 magnitude was not

used in the classification since it basically represents the mean of the input data. Colors in the 

output grid correspond to rock outcrop (red), mixed substrate of boulders, cobble, and sand

(yellow), and soft sand (blue). Scalar indices have been draped over bathymetry data to

illustrate relief. 

6 
GROUNDTRUTHING

 A camera sled was deployed from the NOAA research vessel Tatoosh on September 5 

and 6, 2006 and September 4, 2007 to acquire underwater videography for assisting with 

sonar signature validation. The camera device was configured with a Deep Sea Power & 

Light SeaCam, SeaLite and dual SeaLasers, TriTech 200 kHz altimeter, and an Applied 

Acoustic micro beacon. Video was captured using a Sony GV-D1000 mini-DV recorder 

using a Sea-Trak GPS overlay to dub positioning information onto the video. Since no 

an effort to 

minimize

positional offs

from the vessel’

DGPS antenn

Due to sched

vessel 

availability

unfavorable sea 

conditions, 

limited video 

effort was 

accomp

during each year

Records 

extracted from

the usSEAB

project (Reid e

al. 2006) 

USBL positioning was available, the tow sled was drifted directly below the A-frame in 

et 

s

ae.

uled

and

lished 

.

ED

t

rovided 40

o

o

p

samples t

further describe

sedimentology 

within the survey

area. Vide

transects and

usSEABED

sample locations

are shown in 

Figure 4. 

Figure 4. Location of groundtruthing validation in relation to

survey blocks. Purple spheres define track lines of limited vi

individual 

deo 

confirmation, and black cross hairs indicate location of bottom samples 

extracted from the usSeabed database. 

7 
DISCUSSION OF SURVEY RESULTS AND INTERPRETATION

ith effort of both vessels, over 1,300 linear km of MBES data were acquired and nearly 

86 hours 1).

W

of actual logged sonar records were obtained throughout the area (Table

Table 1. Below are the survey effort statistics for HMPR-110-2002-04. Reson 8101 MBES data were

acquired aboard the R/V MacGinitie in 2002 (110_0204a) and NOAA Ship Rainier survey launch RA3 in

2003 (110_0204b and 110_0204c). Area is presented in square kilometers, length of linear track lines in

kilometers, and hours of actual logged sonar packets in hours, minutes, and seconds. 

Year Survey Dates Area (km2) Tracks (km) Hours (h:m:s) 

2002 July 27-Aug. 02 41.9 575.4 35:57:08 

2003 Aug. 28-Sept. 25 49.0 754.4 49:55:28 

Total 90.9 1,329.8 85:52:36 

Subs egme

The LFH segmentation utine resulted re than 75 percent of each block (Table 2,

igures 5-7) classified as unconsolidated sand and silt sediment, likely being of glacial

2002). The remainder of each block was nearly split between 

l,

resented in square meters (top value) and percentage of

trate S ntation

ro in mo

F

origin (Dragovich et al.

hard, exposed rock outcrop and a mixed substrate consisting of boulder, cobble, grave

and sand. Tabor and Cady (1978) suggest the cobble, gravel, and sands throughout this 

area are glacial deposits left over from the continental ice sheet. Several geologic maps 

(Tabor and Cady 1978; Rau 1979; Snavely et al. 1993; and Dragovich et al. 2002)

additionally describe the majority of rock outcrops throughout the survey area as being 

marine sedimentary rocks of sandstone granular conglomerate, although the flanks of 

Cake Rock and many of the offshore rocks north toward Cape Johnson have been defined 

as basalt (Tabor and Cady 1978). James Island has additionally been described as 

massive to thick bedded greywacke sandstone (Rau 1979). However, for the purpose of 

this survey report all segmented rock outcrops were simply classified as hard, exposed 

rock outcrop according to Greene et al. (1999). Polygon segmentation results are 

presented in Figures 5-7, and shown with the adjacent survey block to illustrate

classification continuity between blocks. 

Table 2. Distribution of bottom hardness for each sonar block classified from survey HMPR-110-2002-04. 

See Figure 1 for area locations. Bottom hardness codes are hard (h), mixed (m) and soft (s) – additional

class description classes provided above. Area is p

each individual mapped area (bottom bold value in the matrix).

Year Survey Block h m(bcs) s(s) s(sq)

2002 110_0204a 5,119,694 1,753,000

12.86 

32,901,169 

82.66 

31,447 

4.40 0.08 

2003 110_0204b 2,770,162

9.85 

3,932,410 

13.98 

21,412,001 

76.16 

0 

0.00 

2003 110_0204c 2,479,180

11.17 

2,184,399 

9.85 

17,524,119 

78.98 

0 

0.00 

8 
Figure 5. Seafloor substrate polygons, Ozette Lake to Carroll Island, with bottom_id codes taken from

Greene et al. (1999). Classing generated through maximum likelihood LFH segmentation with further

refinement from video observation. h=hard bottom; m(bcs)=mixed sediment of boulders, cobble and 

sand; s(s)=soft sandy bottom; s(sq)=soft sandy bottom with shell hash. 

9 
Figure 6. Seafloor substrate polygons, Carroll Island to Cake Rock, with bottom_id codes taken from

Greene et al. (1999). Classing generated through maximum likelihood LFH segmentation with further

refinement from video observation. h=hard bottom; m(bcs)=mixed sediment of boulders, cobble and 

sand; s(s)=soft sandy bottom; s(sq)=soft sandy bottom with shell hash. 

10 
Figure 7. Seafloor substrate polygons, Cake Rock to James Island, with bottom_id codes taken from

Greene et al. (1999). Classing generated through maximum likelihood LFH segmentation with further

refinement from video observation. h=hard bottom; m(bcs)=mixed sediment of boulders, cobble and 

sand; s(s)=soft sandy bottom; s(sq)=soft sandy bottom with shell hash. 

11 
A processed bathymetry surface and backscatter mosaics are additionally presented in 

Appendices 2 and 3, respectively. By modifying the LFH method introduced by Cutter et 

al. (2003) and additionally incorporating backscatter intensity, we were successful at

segmenting adjacent areas with homogenous roughness into unique classes. For

example, Figure 8 shows a flat region of the seafloor with minimal deviation in 

roughness, evident in the standard deviation surface shown at left. Within this same flat 

homogenous area, a unique backscatter signature (delineated by the red outline) exists in 

the plate shown at right. Video groundtruthing further revealed this particular area to 

consist of sand waves mixed with shell hash transitioning into a soft sand field. Adding 

backscatter intensity as a textural descriptor into the maximum likelihood rule increased 

our effectiveness at delineating these particular substrate types in other areas. 

Although minimal groundtruthing was captured due to vessel availability further 

constrained by weather conditions, the limited video data indicated the modified LFH 

procedure could successfully segm

y 

is

utcrops may have become

buried in sand, and vice versa, thus potentially complicating validation. 

Figure 8. Example of homogenous bathymetry and unique backscatter signature as segmented by

the modified LFH procedure. Standard deviation bathymetry surface at left and backscatter intensity

at right. Video groundtruthing (light green track) revealed area defined in red as sand and shell hash

with waves, transitioning into a soft sand field (blue track line). Large boulders (purple) and rock

outcrop (pink) were also observed in video collected nearby. 

ent the imagery into four classes that broadly represent 

istinct substrate conditions. It should be noted, however, that ground conditions ma

ave changed during the 4 year period that span the acoustic and video surveys. It

ntirely conceivable, for example, that previously exposed o

d

h

e

re

12 
13 

Figure 9. Example of data layers to the maximum likelihood classification routine (except C) 

with resulting segmentation output (C). Individual extractions shown include backscatter mosaic 

(A); standard deviation surface (3x3 window) calculated from multibeam bathymetry (B); 

LFH1_mean (D); LFH1_StD. (E); LFH2_mean (F); LFH2_StD. (G); LFH3_mean (H); 

LFH3_StD. (J). Segmentation results in plate C correspond to soft sand (blue), mixed sediment of 

boulder, cobble and sand (red), and exposed rock outcrop (yellow). 
By creating mean and standard deviation feature vectors for each of the 4 feature 

magnitudes, the LFH dimensionality was reduced from a 32-element vector to an 8-

element vector while maintaining the ability to successfully segment the data and with

crippling computers during computation. The textural variation maintained by the mean 

and standard deviation LFH magnitudes can be seen in Figure 9. 

The technique avoided the subjective manual editing that OCNMS has experienced in the 

past when using grey-scale covariance indices as a classification method. The LFH 

texture indices further provided an objective means for incorporating bathym

characteristics without having to subjectively define classes based on slope or complexity 

criteria. Exchanging the standard deviation bathymetry input layer with one calculated 

from a rugosity index may provide even more robust results, especially along flat and 

sloping interfaces. Future work will focus on the effectiveness of calculating LF

indices from side scan sonar data alone, and investigate the ability of the techniqu

segment imagery without associated bathymetry data. 

The ability to remotely define EFH could benefit from output produced by this efficient, 

reproducible, and robust means of segmenting MBES data by defining numerous seafloor

substrate characteristics important to federally managed species of concern. Although 

geomorphic properties of the seafloor can now be remotely characterized with so

degree of relative efficiency (Figure 10) they provide only a piece of the EFH puzzle.

more adequately describe EFH, additional sampling protocol is needed to gain a be

understanding of the many other physical and environmental processes (besides seafloor 

substrate) that are important to the biota (McConnaughey et al. 2007). Having data 

sources to populate a multitude of variables may provide better proxies for ultim

modeling distribution of commercially important species. 

out 

etry 

H 

e to 

me 

 To

tter

ately 

Figure 10. Clipped section of area110_0204b, illustrating a geomorphic representation of seafloor 

su produced through the LFH texture segmentation of acoustic MBES data. Scalar Bottom_Id 

v soft

s

bstrates 

alues from Greene et al. (1999) are draped over the associated bathymetry data showing areas of

and (light blue), mixed substrate of boulders, cobbles, and sand (yellow), and rock outcrop (red). 

14 
Bathymetry Data 

Previous knowledge of seafloor in this area was not well documented. In fact, the

nautical chart covering the survey area (18480) is one the of the smallest scale charts 

(1:176,000) released along the west coast with previously charted soundings throughout 

this particular survey area being estimated from partial bottom coverage surveys obtained 

etween 1900-1939. These full bottom coverage surveys conducted in 2002 and 2003 

The bathymetry data collected in 2002 (block 110_0204a) suffered from sound velocity 

refraction problems (Figure 12) especially evident in the most western lines where water 

depths were greatest and sediments consisted of the soft alluvial sand characteristic of 

lower backscatter strength. As accurate depth estimates depend greatly on reduced sound 

f the

3

ey

ata

cquisition during the 2002 survey and for each 1.7 hours of surveying in 2003 (Table 3). 

b

identified numerous uncharted features and/or potential chart misrepresentations and will 

be forwarded to NOAAs Office of Coast Survey for further scrutiny. A few interesting 

rock features identified are presented in Figure 11. 

Figure 11. Rock features near Hand Rock (A) and offshore of Cape Johnson (B). 

speed errors, significant time was spent attempting post-processing “correction” o

002 data using the Caris refraction editor. There were no refraction artifacts in the 200

rvey, likely because sound speed profiles were more frequently measured each surv

ay. On average, sound speed profiles were collected for each 3.7 hours of d

2

su

d

a

15 
Since the Reson 8101 MBES is not a flat-head transducer, its barrel-like physical shape 

determines initial transmit geometry during each ping cycle. This particular type of 

transducer, therefore, assumes the correct sound departure angle already exists at time of 

transmit and any subsequent error in sound velocity measurements will translate into 

additional errors in the estimated depth values (Cartwright and Hughes Clarke 2002). As 

fresh water input from the Quileute River near Lapush can impact salinity in this area, 

and thus sound refraction, it is evident that more frequent sound velocity casts become

critical to minimizing refraction artifacts in these types of areas. 

Figure 12. Example of refraction artifact encountered in the 2002 survey data with overlap of adjacent

lines shown in inset A, and a depth cross-section of these same two lines illustrated in inset B. The 

result of refraction in this area is evident by the approximate half meter “false rise” in the middle of the

insets where the two outer edges of each line overlap. Additional refraction is illustrated in unedited 

data (C) as the apparent “upward bending” of the outer swath edges. Inset D shows the result of postprocessing refraction editing of this same swath. 

16 
Table 3. Frequency of sound velocity (sv) casts acquired each day during the 2002 and 2003 surveys. 

Hours surveyed (h:m:s) refers to the amount of logged MBES data collected in a given day. 

Survey 

Group 

Survey 

Year 

Julian 

Day 

 # of Daily 

 SV Casts 

Hours 

Surveyed 

Hours Surveying

Per Cast 

CSUMB 2002 209 1 2:03:35 2.1 

 210 2 5:29:27 2.7

 211 2 5:21:41 2.7 

 212 2 7:01:52 3.5 

 213 2 7:33:28 3.8 

 214 2 8:27:05 4.2 

OCNMS 2003 237 1 0:59:38 1.0 

 240 2 5:32:43 2.8 

 241 3 6:32:21 2.2 

 248 4 3:44:25 0.9 

 251 3 5:11:54 1.7 

 258 5 5:31:46 1.1 

 259 3 3:54:35 1.3 

 260 3 3:42:56 1.2 

 265 2 3:37:42 1.8 

 266 2 5:01:56 2.5 

 267 2 4:37:57 2.3 

ACKNOWLEDGMENTS

The authors would like to thank the NOAA Ship Rainier for access to the survey launch in 

2003, and David Kirner, Andy Palmer and Wally Pierce for safely skippering the vessel 

in a challenging near shore environment. Additional thanks to David Kirner and Mike 

lee, R.J. M. Dethier, D. Brown, L.F. Deegan, R.G. Ford, T.F. Hourigan, J. Maragos, C. 

Schoch, K. Sealey, R. Twilley, M.P. Weinstein, and M. Yaklovich. 2000. U.S.

Marine and estuarine ecosystem and habitat classification system. NOAA Tech. 

Memo. NMFS-F/SPO-43. 

eaudoin, J., Hughes Clarke, J.E., Van den Ameele, E. and Gardner, J., 2002, Geometric 

and radiometric correction of multibeam backscatter derived from Reson 8101 

systems: Canadian Hydrographic Conference 2002 Proceedings (CDROM), 

Toronto, Canada. 

alder, B.R. and L.A. Mayer. 2003. Automatic processing of high-rate, high-density 

multibeam echosounder data. Geochem. 4(6):1048-1064. 

evine for skippering the R/V Tatoosh during groundtruthing efforts. 

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L

A

B

C

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Cutter, G.R. Jr., Y. Rzhanov, L.A. Mayer. 2003. Automat

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2006a. Benthic Ha

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MSD-06-01. U.S. Department of Commerce, National Oceanic and Atmospheric 

Administration, Marine Sanctuaries Division, Silver Spring, MD. 22pp. 

Intelmann, S.S. and G.R. Cochrane. 2006b. Olympic Coast National Marine Sanctuary 

Habitat Mapping: Survey report and classification of side scan sonar data from

surveys HMPR-114-2004-02 and HMPR-116-2005-01. Marine Sanctuaries

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Oceanic and Atmo

Silver Spring, MD. 35 pp. 

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 MSD-07-01. U.S. Department of 

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Sanctuaries Conse

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nn, S.S., G.R. Cochrane, C. E. Bowlby, M

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M

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Rau, W.W. 1979. Geologic map in the vicinity of the lower Bogachiel and Hoh River 

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19 
Reid, J.A., J.M. Reid, C.J. Jenkins, M. Zimmermann, S.J. Williams, and M.E. Field. 

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Stein, D

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Snavely, P.D., Jr., N.S. MacLeod, and A.R. Niem. 1993. Geologic Map of the Cape 

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R.W., and W.M. Cady. 1978. Geologic map of the Olympic Peninsula, 

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Todd, B.J., G.B.J. Fader, R.C. Courtney, R.A. Pickrill. 1999. Quaternary geolog

surficial sediment processes; Browns Bank, Scotian Shelf; base

20 
APPENDIX

Appen

R/V Ma

HIPSVesselConfig Version="2.0">

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<En

 <Entry X="1.300000" Y="-4.100000"/> 

 <Entry X="1.300000" Y="2.400000"/> 

 <Entry X="0.000000" Y="4.100000"/> 

 <Entry X="-1.300000" Y="2.400000"/> 

<Entry X="-1.300000" Y="-4.100000"/>

 </PlanCoordinates> 

 <ProfileCoordinates> 

 <Entry Y="-4.100000" Z="0.750000"/> 

 <Entry Y="-4.100000" Z="-0.750000"/> 

 <Entry Y="2.400000" Z="-0.750000"/> 

 <Entry Y="4.100000" Z="0.750000"/> 

 <Entry Y="-4.100000" Z="0.750000"/> 

 </ProfileCoordinates> 

 <RP Length="4.100000" Width="1.300000" Height="0.750000"/> 

 </VesselShape> 

 <DepthSensor> 

 <TimeStamp value="2002-200 00:00:00">

 <Latency value="0.000000"/> 

 <SensorClass value="Swath"/> 

 <TransducerEntries> 

 <Transducer Number="1" Model="sb8101"> 

dix 1. Vessel Offsets 

cGinitie 

<?xml version="1.0"?>

<

 <VesselShape> 

nCoordinates>

try X="-1.300000" Y="-4.100000"/>

21 
 <Offsets X="0.000000" Y="5.890000" Z="1.000000" Latency="0.000000"/> 

 <MountAngle Pitch="-6.700000" Roll="0.800000" Azimuth="1.500000"/> 

</Transducer>

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002-200 00:00:00">

e="0.000000"/>

e="(null)"/>

Z="0.030000"/>

002-200 00:00:00">

4"/>

0:00:00">

es"/>

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</DepthSensor>

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<Latency valu

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<Offsets X="-0.060000" Y="0.000000"

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 <PitchSensor> 

 <TimeStamp value="2002-200 0

 <Latency value="0.000000"/> 

<ApplyFlag value="Y

22 
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 </TimeStamp> 

 </PitchSensor> 

 <RollSensor> 

 <TimeStamp value="2002-200 00:00:00">

="0.000000"/>

lue="Yes"/>

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0 00:00:00">

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.000000" Y="5.890000" Z="1.000000" X2="0.000000"

2="0.000000"/>

00000" Azimuth="0.000000"

0" Azimuth2="0.000000"/> 

ion>

lue="2002-200 00:00:00">

00" Y="5.890000" Z="0.070000" X2="0.000000"

0"/>

.040000" Y="4.710000" Z="3.240000"

Z2="0.000000"/>

atency="0.000000"/>

viation>

plitude="5.000000" Heave="0.050000" 

0" PitchStablized="0.000000"/> 

00"/>

" Navigation="0.010000" Gyro="0.010000"

10000" Roll="0.010000"/>

00000" Surface="0.500000"/> 

0" Zoning="0.200000"/>

ent Gyro="0.000000" Pitch="0.000000" Roll="0.000000"/> 

ed="0.030000" Loading="0.050000" Draft="0.100000" 

0000"/>

<Latency value

<ApplyFlag va

<Offsets Roll

 </TimeStamp> 

 </RollSensor> 

 <SVPSensor> 

 <TimeStamp value="2002-20

<Latency value=

<DualHead val

<Offsets X="0

Y2="0.000000" Z

 <MountAngle Pitch="0.000000" Roll="0.0

Pitch2="0.000000" Roll2="0.00000

 </TimeStamp> 

 </SVPSensor> 

<TPEConfigurat

 <TimeStamp va

 <Comment value="ss intelmann"/> 

<Latency value="0.000000"/>

 <Offsets> 

 <MRUtoTransducer X="0.0600

Y2="0.000000" Z2="0.00000

<NavigationToTransducer X="-0

X2="0.000000" Y2="0.000000"

 <Transducer Roll="0.000000" Roll2="0.000000"/> 

<Navigation L

 </Offsets> 

<StandardDe

 <Motion Gyro="0.020000" HeavePercAm

Roll="0.050000" Pitch="0.05000

<Position Navigation="0.7000

 <Timing Transducer="0.010000

Heave="0.010000" Pitch="0.0

 <SoundVelocity Measured="0.5

 <Tide Measured="0.01000

 <Offsets X="0.020000" Y="0.020000" Z="0.020000"/> 

<MRUAlignm

<Vessel Spe

DeltaDraft="0.01

 </StandardDeviation> 

 </TimeStamp> 

23 
24 

 </TPEConfiguration> 

 <WaterlineHeight> 

 <TimeStamp value="2002-200 00:00:00">

e="0.000000"/>

value="0.000000"/>

/>

ig> 

="-1.000000"/>

="1.000000"/>

<Latency valu

 <WaterLine

 <ApplyFlag value="Yes"/> 

 <StdDev Waterline="0.000000"

 </TimeStamp> 

 </WaterlineHeight> 

</HIPSVesselConf

NOAAS Rainier RA3 Survey Launch

<?xml version="1.0"?>

<HIPSVesselConfig Version="2.0"> 

<VesselShape>

 <PlanCoordinates> 

 <Entry X="-1.500000" Y="-4.500000"/> 

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000" Width="1.500000" Height="1.000000"/> 

0:00">

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tries>

ber="1" StartBeam="1" Model="sb8101"> 

0000" Y="0.330000" Z="0.680000" Latency="0.000000"/> 

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 <GyroSensor> 

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 <Latency value="0.000000"/> 

 <ApplyFlag value="No"/> 

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 <HeaveSensor> 

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<Comment value="(n

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25 
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"(null)"/>

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t>

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 <Ellipse value="WG84"/> 

<Offsets X="0.00

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 <TimeStamp v

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 <ApplyFlag value="Yes"/> 

 <Offsets Pitch="0.000000"/> 

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 <RollSensor> 

 <TimeStamp value="2003-223 00:00:00"> 

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 <ApplyFlag value="Yes"/> 

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 <SVPSensor> 

 <TimeStamp value="2003-223 00:00:00"> 

<Comment value

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 <WaterlineHeigh

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<Latency value="0.000000"/>

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26 
 <StdDev Waterline="0.000000"/>

lue="2003-223 00:00:00">

e="ss intelmann"/> 

lue="0.000000"/>

26000" Y="0.146000" Z="0.398000" X2="0.000000"

.189000" Y="-0.506000" Z="3.399000"

0" Z2="0.000000"/>

oll2="0.000000"/>

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ation>

ro="0.020000" HeavePercAmplitude="5.000000" Heave="0.050000" 

Pitch="0.020000" PitchStablized="0.000000"/> 

0" Navigation="0.001000" Gyro="0.001000"

000" Roll="0.001000"/>

0.500000" Surface="0.500000"/> 

ed="0.010000" Zoning="0.200000"/>

0.020000" Y="0.020000" Z="0.020000"/>

nment Gyro="0.000000" Pitch="0.000000" Roll="0.000000"/> 

30000" Draft="0.050000"

ion>

nfig> 

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<Comment valu

<Latency va

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<NavigationToTransducer X="1

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<Transducer Roll="0.000000" R

<Navigation Latency="0.0

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Roll="0.020000"

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<MRUAlig

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DeltaDraft="0.010000"/> 

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27 
Appendix 2. Bathymetry Surface

Appendix 2. Shaded relief surface generated from both 2002 and 2003 surveys overlain with 

-20 (black) and -30m (blue) contours.

28 
Appendix 3. Backscatter Imagery 

Appendix 3a. Backscatter mosaic of survey block 110_0204a. See Figure 1 for perspective of survey

locations in relation to OCNMS boundary. 

29 
30 

Appendix 3b. Backscatter mosaic of survey block 110_0204b. See Figure 1 for perspective of survey

locations in relation to OCNMS boundary. 
Appendix 3c. Backscatter mosaic of survey block 110_0204c. See Figure 1 for perspective of survey

locations in relation to OCNMS boundary. 

31 
ONMS CONSERVATION SERIES PUBLICATIONS 

To date, the following reports have been published in the Marine Sanctuaries Conservation Series. All

publications are available on the National Marine Sanctuary Program website

(http://www.sanctuaries.noaa.gov/). 

Observations of Deep Coral and Sponge Assemblages in Olympic Coast National Marine Sanctuary,

Washington. Cruise Report: NOAA Ship McArthur II Cruise AR06-06/07 (NMSP-07-04) 

A Bioregional Classification of the Continental Shelf of Northeastern North America for Conservation 

Analysis and Planning Based on Representation (NMSP-07-03) 

M/V WELLWOOD Coral Reef Restoration Monitoring Report Monitoring Events 2004-2006, Florida Keys

National Marine Sanctuary, Monroe County, Florida (NMSP-07-02) 

Survey report of NOAA Ship McArthur II cruises AR-04-04, AR-05-05 and AR-06-03: Habitat classification

of side scan sonar imagery in support of deep-sea coral/sponge explorations at the Olympic Coast National 

Marine Sanctuary (NMSP-07-01) 

2002 - 03 Florida Keys National Marine Sanctuary Science Report: An Ecosystem Report Card After Five 

Years of Marine Zoning (NMSP-06-12) 

Habitat Mapping Effort at the Olympic Coast National Marine Sanctuary - Current Status and Future Needs

(NMSP-06-11) 

M/V CONNECTED Coral Reef Restoration Monitoring Report Monitoring Events 2004-2005 Florida Keys

National Marine Sanctuary Monroe County, Florida (NMSP-06-010) 

M/V JACQUELYN L Coral Reef Restoration Monitoring Report Monitoring Events 2004-2005 Florida Keys

National Marine Sanctuary Monroe County, Florida (NMSP-06-09) 

M/V WAVE WALKER Coral Reef Restoration Baseline Monitoring Report - 2004 Florida Keys National 

Marine Sanctuary Monroe County, Florida (NMSP-06-08) 

 

Olympic Coast National Marine Sanctuary Habitat Mapping: Survey report and classification of side scan

sonar data from surveys HMPR-114-2004-02 and HMPR-116-2005-01 (NMSP-06-07) 

A Pilot Study of Hogfish (Lachnolaimus maximus Walbaum 1792) Movement in the Conch Reef Research 

Only Area (Northern Florida Keys) (NMSP-06-06) 

Comments on Hydrographic and Topographic LIDAR Acquisition and Merging with Multibeam Sounding 

Data Acquired in the Olympic Coast National Marine Sanctuary (ONMS-06-05) 

Conservation Science in NOAA's National Marine Sanctuaries: Description and Recent Accomplishments 

(ONMS-06-04) 

Normalization and characterization of multibeam backscatter: Koitlah Point to Point of the Arches, Olympic 

Coast National Marine Sanctuary - Survey HMPR-115-2004-03 (ONMS-06-03) 

Developing Alternatives for Optimal Representation of Seafloor Habitats and Associated Communities in 

Stellwagen Bank National Marine Sanctuary (ONMS-06-02) 

 

Benthic Habitat Mapping in the Olympic Coast National Marine Sanctuary (ONMS-06-01) 

Channel Islands Deep Water Monitoring Plan Development Workshop Report (ONMS-05-05) 

 

32 

Movement of yellowtail snapper (Ocyurus chrysurus Block 1790) and black grouper (Mycteroperca bonaci

Poey 1860) in the northern Florida Keys National Marine Sanctuary as determined by acoustic telemetry

(MSD-05-4) 
The Impacts of Coastal Protection Structures in California's Monterey Bay National Marine Sanctuary

(MSD-05-3

e Stellwagen Bank National Marine Sanctuary and the St. Lawrence River

Estuary (MSD-05-1) 

,

ch Workshop, Seattle, 

istribution and Sighting Frequency of Reef Fishes in the Florida Keys National Marine Sanctuary (MSDlower Garden Banks National Marine Sanctuary: A Rapid Assessment of Coral, Fish, and Algae Using the 

ives From Two National 

 Monitoring Program (MSD-

 Approach to Market Squid (Loligo opalescens) 

)

An annotated bibliography of diet studies of fish of the southeast United States and Gray's Reef National 

Marine Sanctuary (MSD-05-2) 

Noise Levels and Sources in th

Biogeographic Analysis of the Tortugas Ecological Reserve (MSD-04-1) 

A Review of the Ecological Effectiveness of Subtidal Marine Reserves in Central California (MSD-04-2

MSD-04-3) 

 

Pre-Construction Coral Survey of the M/V Wellwood Grounding Site (MSD-03-1) 

Olympic Coast National Marine Sanctuary: Proceedings of the 1998 Resear

Washington (MSD-01-04) 

Workshop on Marine Mammal Research & Monitoring in the National Marine Sanctuaries (MSD-01-03) 

A Review of Marine Zones in the Monterey Bay National Marine Sanctuary (MSD-01-2) 

D

01-1) 

F

AGRRA Protocol (MSD-00-3) 

The Economic Contribution of Whalewatching to Regional Economies: Perspect

Marine Sanctuaries (MSD-00-2) 

Olympic Coast National Marine Sanctuary Area to be Avoided Education and

00-1) 

Multi-species and Multi-interest Management: an Ecosystem

Harvest in California (MSD-99-1) 

33