Marine Sanctuaries Conservation Series ONMS-06-03

U.S. Department of Commerce 

National Oceanic and Atmospheric Administration

National Ocean Service 

Office of Ocean and Coastal Resource Management 

Normalization and Characterization of 

Multibeam Backscatter: 

Koitlah Point to Point of the Arches, 

Olympic Coast National Marine Sanctuary - 

 Survey HMPR-115-2004-03 

Office of National Marine Sanctuaries March 2006 
About the Marine Sanctuaries Conservation Series 

The National Oceanic and Atmospheric Administration’s Office of National Marine 

Sanctuaries (ONMS) administers the National Marine Sanctuary Program. Its mission is 

to identify, designate, protect and manage the ecological, recreational, research, 

educational, historical, and aesthetic resources and qualities of nationally significant 

coastal and marine areas. The existing marine sanctuaries differ widely in their natural 

and historical resources and include nearshore and open ocean areas ranging in size 

from less than one to over 5,000 square miles. Protected habitats include rocky coasts, 

kelp forests, coral reefs, sea grass beds, estuarine habitats, hard and soft bottom habitats, 

segments of whale migration routes, and shipwrecks. 

Because of considerable differences in settings, resources, and threats, each marine 

sanctuary has a tailored management plan. Conservation, education, research, 

monitoring and enforcement programs vary accordingly. The integration of these 

programs is fundamental to marine protected area management. The Marine 

Sanctuaries Conservation Series reflects and supports this integration by providing a 

forum for publication and discussion of the complex issues currently facing the National 

Marine Sanctuary Program. Topics of published reports vary substantially and may 

include descriptions of educational programs, discussions on 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. 
Normalization and Characterization of Multibeam Backscatter: 

Koitlah Point to Point of the Arches, Olympic Coast National 

Marine Sanctuary Survey - HMPR-115-2004-03 

Steven S. Intelmann1

, Jonathan Beaudoin2 and Guy R. Cochrane3

1

Olympic Coast National Marine Sanctuary, NOAA 

2

Ocean Mapping Group, University of New Brunswick 

3

Coastal and Marine Geology Program, USGS 

Silver Spring, Maryland

March 2006

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 

Office of National Marine Sanctuaries 

Daniel J. Basta, Director 
DISCLAIMER 

Report content does not necessarily reflect the views and policies of the Office of 

National Marine Sanctuaries 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 may be downloaded from the National Marine Sanctuaries 

Program web site at www.sanctuaries.nos.noaa.gov. Hard copies may be available from 

the following address:

National Oceanic and Atmospheric Administration

 Office of National Marine Sanctuaries

 SSMC4, N/ORM62 

 1305 East-West Highway 

Silver Spring, MD 20910 

COVER 

NOAA Ship RAINIER transiting between Sitka and Juneau, Alaska. The ship is designed 

and outfitted primarily for conducting hydrographic surveys in support of nautical 

charting. Photo credit Charles Doxley. 

SUGGESTED CITATION

Intelmann, S.S., J. Beaudoin, and G.R. Cochrane. 2006. Normalization and 

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

Coast National Marine Sanctuary - Survey HMPR-115-2004-03. Marine Sanctuaries 

Conservation Series MSD-06-03. U.S. Department of Commerce, National Oceanic and 

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

CONTACT

Steven S. Intelmann 

Habitat Mapping Specialist 

NOAA/National Marine Sanctuaries Program

N/ORM 6X26 

115 E. Railroad Avenue, Suite 301 

Port Angeles, WA 98362 

(360) 457-6622 X22 

steve.intelmann@noaa.gov
ABSTRACT 

Through a partnership between the National Oceanic and Atmospheric Administration’s 

(NOAA) Office of Coast Survey (OCS), NOAA’s National Marine Sanctuary Program

(NMSP), and NOAA’s Office of Marine and Aviation Operations (OMAO), high 

resolution bathymetry (HRB) was collected on various opportunistic occasions during the 

months of October from 2001-2004 in the Olympic Coast National Marine Sanctuary 

(OCNMS). These particular survey operations were conducted aboard the NOAA ship 

RAINIER using a variety of multibeam echosounders suitable for the various regions of 

the sanctuary that were surveyed. Backscatter was derived from the Reson shallow water 

multibeam echosounders using custom software developed by researchers at the 

University of New Brunswick (Fredericton, Canada), for an area in the OCNMS, near 

Cape Flattery from Koitlah Point to Point of the Arches, and mosaiced at 1-meter pixel 

resolution. This process of normalizing the backscatter imagery significantly reduced the 

post-processing validation efforts that are required for the characterization effort. 

Textural classification of the sonar imagery suggests that nearly 58 percent of the 

seafloor in this area is covered by soft substrates such as mud or silt, 19 percent of the 

area is comprised of mixed sediment including cobbles, pebbles, gravel and boulders 

mixed with soft substrate, and over 23 percent of the total area is characterized by hard, 

complex rocky bottom. Video from a towed camera sled, bathymetry data, sediment 

samples, and the backscatter have been integrated to describe geological and biological 

aspects of habitat. Polygon features have also been created and attributed with a 

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

data can be used with geographic information system (GIS) software for display, query, 

and analysis. 

KEY WORDS 

Benthic, habitat mapping, sediment classification, multibeam backscatter normalization,

textural analysis, Olympic Coast National Marine Sanctuary, essential fish habitat, 

groundtruthing, accuracy assessment 

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.................................................................................................... 3 

Sonar Data Processing and Image Classification.................................................... 4 

Groundtruthing........................................................................................................ 8 

Survey Results and Interpretation........................................................................... 10 

Discussion and Conclusions ................................................................................... 14 

Acknowledgments................................................................................................... 16 

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

Appendix................................................................................................................. 20 

ii 
LIST OF FIGURES AND TABLES

Figure/Table Number and Title Page

Figure 1. Koitlah Point to Point of the Arches (Hydrographic Survey Sheet A) 

survey footprint shown with 50 m bathymetry contour.......................................... 2 

Figure 2. Survey launch and retrieval operations on the NOAA ship RAINIER.... 3 

Figure 3. Comparison of image mosaicing produced by commercially 

available software (left) with the same survey line processed using custom

software developed by the UNB OMG (right). Red line depicts coverage of 

bathymetric data, which is used as a stencil by OMG software ............................. 5 

Figure 4. Backscatter mosaic produced with OMG software for survey HMPR115-2004-03............................................................................................................ 7 

Figure 5. Video sled deployment for groundtruthing. ........................................... 8 

Figure 6. Video transects and sediment samples taken from the USSEABED 

project shown with survey footprint and bathymetry contours. ............................. 9 

Table 1. Data acquisition effort by year and survey launch. Acquisition time

refers to actual logged data derived from start and stop times in XTF headers. 

Linear kilometers of tracklines was calculated with NOAA HSTP Pydro

software................................................................................................................... 10

Table 2. Bottom hardness classified from HMPR-115-2004-03 survey data. 

Bottom induration codes (Greene et al. 1999) are provided by area in square 

meters, and area as percentage of total mapped area.............................................. 11 

Table 3. Habitat types classified from HMPR-115-2004-03 survey data. 

Habitat codes are provided per Greene et al. (1999) and are presented by area 

in square meters, and area as percentage of total mapped area. Code 

concatenation for slope, complexity, and microhabitat have been omitted from

table for brevity....................................................................................................... 11 

Table 4. Contingency matrix, accuracy totals, and Kappa statistics for 

accuracy assessment analysis.................................................................................. 12 

Figure 7. Classification of habitat for survey HMPR-115-2004-03. As listed 

in Table 3, codes in the image only indicate a habitat class concatenation of 

megahabitat, bottom induration, meso-scale identifier, and modifier id. Other 

fields have been omitted for brevity . ..................................................................... 13 

iii 
iv 
INTRODUCTION

Having an increased understanding of the distribution and abundance of sea floor 

substrates can be useful for supporting management, research, monitoring, and education 

within the national marine sanctuaries (Barr 2003) and can assist with a myriad of 

concerns ranging from submarine cable engineering projects to cultural resource 

identification and protection. Seabed characterization was recently described as 

providing an important contribution to the sustainable management of groundfish 

fisheries (Kassakian and Ostdahl 2005) and has been integral to the Pacific Coast 

groundfish management (PFMC 2004) for the past several years. 

Located off the Washington coast, the Olympic Coast National Marine Sanctuary 

(OCNMS) is a highly productive area of more than 2,400 nm2

 in size and supports an 

extensive fishery, yet information describing the distribution of benthic habitats for many 

of the groundfish species along the Washington coast is often sparse, especially at useful 

spatial scales (Wang 2005). In 2002, the OCNMS determined benthic habitat 

characterization as being a major program priority for the site, and has since devoted 

significant base resources toward gaining an increased understanding of the benthic 

environment. Although several sources of historic sediment grab sampling existed within 

the area (Venkatarathnam and McManus 1973; Nittrouer 1978; Sternberg 1986; Reid et 

al. 2001), it became evident that medium-scale acoustic geological sampling could better 

provide an effective means for describing the marine habitat (Greene et al. 1999; 

Valentine et al. 2003), and that technological innovations such as side scan sonar and 

multibeam sonar could better assist with delineating ocean floor substrates (Mitchell and 

Hughes Clark 1994; Auster et al. 1999; Cochrane and Lafferty 2002; Huvenne et al. 

2002; Dartnell and Gardner 2004). 

OCNMS has since undertaken various ship based acoustic (Intelmann and Cochrane 

2006) and air-borne optical remote-sensing surveys (Intelmann 2005) to provide 

information for delineating benthic habitats within its jurisdictional boundaries. Of 

particular note, through a partnership with the National Oceanic and Atmospheric 

Administration’s (NOAA) Office of Coast Survey (OCS), and NOAA’s Office of Marine 

and Aviation Operations (OMAO), high resolution bathymetry (HRB) was collected on 

various opportunistic occasions during the months of October from 2001-2004. These 

particular survey operations were conducted aboard the NOAA ship RAINIER using a 

variety of multibeam echosounders suitable for the various regions of the sanctuary that 

were surveyed. 

Backscatter was derived from the Reson shallow water multibeam echosounders using

custom software developed by researchers at the University of New Brunswick 

(Fredericton, Canada), for an area in the Olympic Coast National Marine Sanctuary 

(OCNMS), near Cape Flattery from Koitlah Point to Point of the Arches (Hydrographic 

Survey Sheet A), and mosaiced at 1-meter pixel resolution. Textural classification 

(Cochrane and Lafferty 2002) was used to classify the mosaic into three distinct bottom

types. Video from a towed camera sled, bathymetry data, sedimentary samples, and the 

backscatter have been integrated to describe geological and biological aspects of habitat. 

1 
Polygon features have also been created and attributed with a hierarchical marine benthic 

classification scheme (Greene et al. 1999), and an accuracy assessment was used to 

quantify classification performance. The data can be used with geographic information 

system (GIS) software for display, query, and analysis. 

SURVEY AREA 

Approximately 110 km2

 of sea floor were surveyed on the Hydrographic Survey Sheet A, 

located in the general vicinity of Cape Flattery and bounded by coordinates 48o 14’42’’ 

N, 124o

 51’06’’W, and 48o 25’14’’ N, 124o37’50’’W (Figure 1). Survey work on Sheet 

A occurred from October 1-7, 2001; October 5-7, 2002; October 1-7, 2003; and October 

11-12, 2004. Water depth throughout the project area ranged from 0.5 to 201 meters. 

Figure 1. Koitlah Point to Point of the Arches (Hydrographic Survey Sheet 

 A) survey footprint, shown with 50m bathymetry contour. 

2 
SONAR ACQUISITION

To maximize data acquisition effort, the NOAA ship RAINIER, measuring 70m in length, 

was used to simultaneously deploy multiple 9.8m survey launches (Figure 2). In general, 

survey launches were deployed at 0800 and retrieved at 1630 hours. Operations were 

frequently shortened or extended in response to changing weather and/or sea conditions, 

which varied greatly by day and year. 

 

Each multibeam system was setup in a hullmounted configuration as described in Table 1. 

After 2003, survey launch RA4 was outfitted 

with both an Reson 8125 (455 kHz) and an Elac 

1180 (180 kHz) multibeam system. The 8125 is 

generally used to obtain full-bottom coverage in 

depths from 4-60 meters while the 1180 is used 

in depths from 50-300 meters, thus having the 

capability of both multibeam systems onboard 

increases the vessel’s effective range of 

operation. Reson sonar data were logged in 

Extended Triton Format (XTF) using Isis Sonar 

(Triton Imaging International) with the “FullNew” side scan beam forming technique. This 

“new” process 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 ultimately selected (Reson 

2003). Snippet packets were also logged from

the 8101 systems during the 2003 and 2004 

field operations but were not used in the 

mosaics since they were not captured in 2001 or 

2002. Snippets are an intensity time series 

derived by composition from each narrow 

receive beam used in bottom detection. Snippet 

imagery is limited to only the bathymetric 

sector and is thus lost if bottom detection fails. 

Multiple samples per beam produces a better 

signal-noise ratio and additionally results in no 

Figure 2. Survey launch and retrieval operations 

 aboard the NOAA ship RAINIER. 

multiple return artifacts. Elac sonar data were 

logged with HydroStar software. 

 

Vessel attitude and positioning for each of the launches was monitored with an TSS 

(Applanix) POS/MV 320 and logged in Isis Sonar. The POS/MV provides accurate 

attitude, heading, heave, position, and velocity data for each vessel. The ship’s (RA) 

attitude was recorded using a TSS Meridian Attitude and Heading Reference System

(MAHRS). The MAHRS is a heading reference instrument combined with a dynamic 

3 
motion sensor (DMS) to provide accurate heave, pitch and roll measurements. Aside 

from RA3, which was outfitted with an CSi MBX-2 differential GPS (DGPS) beacon, all 

other launches were equipped with Trimble Pro Beacon DGPS for survey line plan 

control. A Trimble DSM 212L DGPS was installed on the ship for line control and sonar 

positioning. Pro Beacon DGPS corrections were made using the Amphitrite Point base 

reference station, operating at 315 Khz in British Columbia, 

Canada. 

To make necessary sound velocity corrections, and for Elac 1050D beam steering, either 

Seabird Seacat SBE 19 or SBE 19plus CTD-profilers were used to acquire information 

about physical properties of the water column that dictate ray tracing, or essentially how 

sound will travel through the water. Water level observations were acquired from the 

Neah Bay tidal station 9443090 and applied with zoned corrections. Line planning and 

project control were accomplished through Hypack marine positioning and surveying 

software. Vessel speed was targeted at 8 knots during sonar acquisition. 

 

SONAR DATA PROCESSING AND IMAGE CLASSIFICATION 

Production of acoustic backscatter imagery was done with software tools developed by 

the Ocean Mapping Group (OMG), University of New Brunswick (Beaudoin et al. 2002). 

The software corrects for variations in acoustic source level, receiver gain, and pulse 

length, in addition to modeling/removing the effects of transmitter and receiver beam

patterns. The work described in Beaudoin (2002) only addresses variations in source 

level and receiver gain; the software has been upgraded to address the remaining 

aforementioned corrections since then. Additionally, the software performs an acrosstrack signal normalization to minimize the variations due to the angular response of the 

seafloor. Beam pattern models were used for beam pattern normalization since it was not 

logistically possible to measure the beam pattern of each launch directly. Further, the 

acoustic backscatter derived from the OMG software is an approximation because a 

calibration of the receiver was not performed and an estimate had to be used to convert 

receiver digital data to physical units of pressure. This approximation would not be an 

issue with classification performance since it would only introduce a bulk shift in the 

acoustic backscatter values across the entire survey area, leaving relative differences 

between sediment regimes unaffected. 

Commercially available software packages available for processing multibeam

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

seafloor assumption. Such assumption introduces positioning errors that grow with the 

seafloor’s deviation from an ideal flat surface. Thus geometric corrections were 

performed using the OMG software that allowed for the more accurate geo-registration of 

the acoustic backscatter; this is performed using the corresponding bathymetric profile 

derived from the multibeam system. Because no bathymetric data is collected beyond the 

angular sector of the multibeam system, OMG software does not geo-register the acoustic 

backscatter data beyond this point (red lines in Figure 3). 

4 
Figure 3. Comparison of image mosaicing produced by commercially available software (left) with the 

 same survey line processed using custom software developed by the UNB OMG (right). Red 

 line depicts coverage of bathymetric data, which is used as a stencil by OMG software. 

The process and need for normalizing multibeam backscatter data, as is possible with the 

OMG Reson processing routine, becomes paramount when a goal of data acquisition is to 

create classified habitat maps. The signal return strength from any sonar system is 

modulated by its propagation through the oceanic environment, its interaction with the 

seafloor, and by variable sonar system parameters. In order to be used for seafloor 

characterization, sonar echo strength data must be corrected for all variations, leaving the 

seafloor’s backscattering strength as the sole source of signal strength variation. 

Oceanographic variations are readily modeled and removed through time-varying gain 

functions that are routinely implemented in the sonar receiver hardware (as is the case 

with the Reson systems discussed herein). Signal strength variations due to sonar system

parameters such as source level, receiver gain, pulse length, and transmitter/receiver

beam patterns must then be addressed. Provided that sufficient documentation of the 

sonar system parameters is available, these variations can be removed from the return 

signal. The final hurdle involves normalizing the return signal to compensate for signal 

strength variation due to imaging geometry. Because multibeam sonars ensonify the 

seafloor over a large swath, signals returning from below the vessel are typically much 

stronger than signals returning from the outer portions of the swath, as evident by the 

white strip in the left image of Figure 3. This large variation must be normalized; 

otherwise seabed characterization algorithms will characterize the seabed below the 

5 
vessel as being different from the seabed away from the vessel. In comparing OMG 

processed results to those derived from commercially available software (as in Figure 3), 

it is obvious that seafloor angular response and beam pattern normalization is required for 

seabed characterization algorithms to produce desirable results. For example, whilst 

examining the data below the vessel track in Figure 3, characterization algorithms would 

classify the white strip as being of a different sediment type than the remainder of the 

data. Clearly, this would be incorrect and the analyst would spend increased time 

correcting the algorithm’s output. After processing and normalization using OMG 

software, the white strip no longer has the detrimental effect on the characterization 

algorithms, effectively leaving the analyst with much less post-processing validation 

work. 

Backscatter was processed only from the Reson multibeam systems because the Elac data 

format does not preserve the parameters necessary for signal normalization (e.g. source 

level, gain, pulse length, etc). Backscatter data were individually normalized through 

separate process routines for each survey launch by year. An image stretch was applied 

to individual mosaics to maximize use of grey scale bandwidth. Processed mosaics from

each survey launch were then imported to Imagine image processing software (Leica 

Geosystems) and merged into a single image file (Figure 4). The merged mosaic was 

then split into four geographic regions for more manageable image computation during 

the classification process. The four “regional” mosaics were converted to a raw binary 

format comprised of only grey-scale pixel intensities ranging from 0 to 255. 

 

Several studies (Skohr 1991; Blondel 1996) have found the use of grey-level alone for 

assigning classification codes to side scan sonar imagery as being inadequate, and other 

studies (Blondel 1996; Cochrane and Lafferty 2002; Huvenne et al. 2002; Intelmann and 

Cochrane 2006) have successfully used various textural indices to more effectively 

classify side scan sonar data, thus we used a second-order textural analysis (Cochrane and 

Lafferty 2002) on the raw binary images to differentiate bottom types from the 

backscatter imagery. Using a co-occurrence matrix approach provides an alternative for 

classifying acoustic imagery, and has been found to more effectively assess the spatial 

relationship of pixel intensities from remote sensing data (Haralick 1973; Blondel 1996). 

Indices for homogeneity and entropy were calculated for each regional mosaic, thereby

producing textural measures for roughness and organization. 

 

For each of the four regionally split images, the original backscatter data (or DN image), 

entropy index, and the homogeneity index images were all geo-referenced and stacked 

into a single Imagine file, creating a "pseudo" multi-spectral image file. Using 

knowledge gained from video data, training signatures were manually digitized in the 

Imagine classification module for areas representing soft, mixed, and hard bottom types 

(see Appendix). Soft bottom included mud, sand or silt. Mixed bottom included a 

combination of soft bottom and/or sand, shell hash, gravel, pebble, or cobble. Areas of 

boulders or rock outcrops were classified as hard bottom. Using a maximum likelihood 

decision rule, a supervised classification procedure was independently run on each of the 

four “pseudo” multi-spectral images. The output resulted in four separate classified 

thematic raster images that were converted back to grey scale and again exported as raw 

6 
binary files. Unclassified, misclassified (such as at nadir) or poorly classified data were 

manually reclassified in Adobe Photoshop by digital overlay and cross-referencing the 

thematic image with the original backscatter (DN) images. Each of the four cleaned grey 

Figure 4. Backscatter mosaic produced with OMG software for survey HMPR-115-2004- 

 03. 

7 
scale images were then imported back into Imagine and mosaiced into one classified 

image for the entire survey area. 

 

The MajorityFilter command in ArcInfo (ESRI) was then used to filter the image to 

reduce the number of polygon features and essentially smooth the data through a 3x3 

neighborhood analysis. The raster image was then converted to a polygon feature using 

ArcInfo. Video and sediment grab data were used as a validation tool to manually split 

polygons, where needed, and attach attributes describing macrohabitat according to 

Greene et al. (1999). Polygons features were manually added or removed where 

backscatter data quality was too poor to allow for reliable textural classification, such as 

acoustic shadows, or where features could be better discerned from overlay with a highresolution shaded digital terrain model created from the multibeam sounding data. 

Individual line cleaning and subset editing of the bathymetry data was accomplished 

using Hips software (Caris). Additional area-based edits were made with Fledermaus 

(Interactive Visualization Systems) to insure more thorough data cleaning. The 

bathymetry data was used to create two separate grids representing seafloor slope and 

complexity, the latter through a standard deviation neighborhood analysis (3x3). Both 

the slope and complexity grids were converted to polygon features and reclassified 

according to Greene et al. (1999), although classes were modified to better represent the 

physical characteristics of this particular survey area. Slope and complexity values were 

then filtered and joined with the bottom polygon feature through a UNION process.

GROUNDTRUTHING

In August 2005, the NOAA vessel Tatoosh was used to tow a Taras camera sled (Figure 

5) to acquire underwater videography for validating the sonar imagery. The tow sled was 

configured with a Deep Sea Power & Light SeaCam, SeaLite, and dual SeaLasers 

(mounted with 10cm spacing), TriTech 200 kHz altimeter, and an Applied Acoustic 

micro beacon. The video was 

sent through Falmat Xtreme

Green video cable and captured 

with a Sony GV-D1000 mini-D

recorder. A Sea-Trak GPS 

overlay was used to dub 

positioning information onto the 

video. 

V

Figure 5. Video sled deployment for groundtruthing. 

Since no acoustic positioning 

was available, the tow sled was 

drifted directly below the Aframe to minimize positional 

offset from the vessel’s DGPS 

antennae. In instances where 

significant currents were 

8 
encountered, effort was made to actively tow the sled with the current to reduce layback. 

The USSEABED project, a database providing information on sediment and rock 

distributions in the waters off the United States (Reid et al. 2001), was also queried and 

provided 94 samples to describe sedimentology within the survey area. Video transects 

and USSEABED sample locations are shown in Figure 6. 

An accuracy assessment of the texture classification was accomplished by converting the 

navigation data from the video sled into an xyz text file, where z represents bottom

hardness (hard, mixed, soft). The texture classification image was then compared to the 

video observations to calculate overall accuracy and Kappa statistics (Table 4). 

Figure 6. Video transects and sediment samples taken from the 

 USSEABED project shown with survey foot print 

 selected and bathymetry contours.

 

9 
SURVEY RESULTS AND INTERPRETATION

Over 98 km2

 near Cape Flattery, from Koitlah Point to Point of the Arches, were 

surveyed with Reson SeaBat multibeam echosounders, the remainder using L-3 Seabeam

Elac multibeam systems (Table 1). Approximately 945 linear km of track lines were 

collected throughout the effort, yielding more than 119 hours of logged sonar data. An 

additional twenty hours of video were captured as groundtruthing validation. 

Table 1. Data acquisition effort by year and survey launch. Acquisition time refers to actual logged data, 

derived from start and stop times in XTF headers. Linear kilometers of track lines was calculated 

with NOAA HSTP’s Pydro software. 

Year Survey Launch MB Sounder

1 Acq. Time Linear Km Area2

2001 RA1 8101 21:59:08 151.8 27.1

RA6 8101 18:25:19 203.0

2002 RA4 8125 11:16:08 83.3 19.4

RA5 8101 10:36:41 86.8

2003 RA3 8101 15:11:42 123.2 42.6

RA4 8125 14:56:16 97.8

RA5 8101 17:52:13 130.6

2004 RA4 1180 2:28:59 18.7 20.4

RA5 8101 2:45:05 18.6

 RA 1050D 3:46:29 30.8 

Total 119:18:00 944.6 109.5 1

Multibeam systems used for the survey were either a Reson SeaBat 8101, Reson SeaBat 8125, L-3 

Seabeam Elac 1050D, or L-3 Seabeam Elac 1180. 2

Area calculations are presented as a combination of 

survey platforms for each year. 

Megahabitat (Greene et al. 1999) was defined as continental shelf throughout the entire 

survey area. Textural classification of the sonar imagery suggests that nearly 58 percent 

of the seafloor in the survey area near Cape Flattery, from Koitlah Point to Point of the 

Arches, is covered by soft substrates such as mud or silt, 19 percent of the area is 

comprised of mixed sediment including cobbles, pebbles, gravel and boulders mixed with 

soft substrate, and over 23 percent of the total area is characterized by hard complex 

rocky bottom (Table 2). Based on an accuracy assessment report of bottom hardness, 

overall classification accuracy was 86.11 percent with an overall Kappa statistic of 

0.7596 (Table 4). 

10 
Towed camera and visual observations at low tide indicate the majority of the hard rocky 

outcrops found in the nearshore resemble those same middle-upper Eocene marine 

sedimentary rocks identified directly onshore (Dragovich et al. 2002). Several geologic 

forces were evident in the sonar imagery including areas of tilted, differentially eroded 

bedrock. The two parallel, northwest-trending fault strands located offshore of Makah 

Bay are further evidence of the anticlinal folding and thrust faulting that occur in this 

geologic collision zone (McCrory et al. 2004). Multiple areas were characterized as 

having large distinct sediment waves (Table 3). One particularly notable area of 

sediment waves identified in the video imagery, located approximately 1 km offshore of 

Chibahdehl Rocks, consisted of large mega-ripples likely attributed to the high tidal 

currents that occur in that particular region of the Strait of Juan de Fuca. 

Table 2. Bottom hardness classified from HMPR-115-2004-03 survey data. Bottom induration codes 

(Greene et al. 1999) are provided by area in square meters, and area as percentage of total mapped

area. 

Bottom_ID Descriptor Square m Percentage

h Hard 22,900,464 23.2

m mixed 18,670,687 18.9

s Soft 57,143,375 57.9

Table 3. Habitat types classified from HMPR-115-2004-03 survey data. Habitat codes are provided per 

Greene et al. (1999) and are presented by area in square meters, and area as percentage of total 

mapped area. Code concatenation for slope, complexity, and microhabitat have been omitted from

table for brevity. 

Habitat Code Descriptor Square m Percentage

Ss_u Shelf soft unconsolidated 52,287,128.6 53.97 

She Shelf hard exposure (bedrock) 19,642,359.7 19.90 

Sm Shelf mixed 18,360,151.9 18.60

Ss Shelf soft 4,853,227.9 4.92

Shd_d Shelf hard deformed differential erosion 3,225,211.0 3.27 

Smw_r Shelf mixed waves ripples 283,816.8 0.29 

Sh Shelf hard 32,893.2 0.03

Smw Shelf mixed sediment waves 26,718.5 0.03 

Ssw Shelf soft waves 3018.2 <0.01 

11 
Table 4. Contingency matrix, accuracy totals, and Kappa Statistics for accuracy assessment analysis. 

Error Matrix

Reference Data

Classified Data no_data M h s 

no_data 0 1 0 0

m 0 15658 802 2094

h 0 427 7035 584

s 0 16 274 3333 

Column Total 0 16102 8111 6011 

Accuracy Totals

Class 

Name 

Reference 

Totals 

Classified 

Totals 

Number 

Correct 

Producers 

Accuracy 

Users 

Accuracy 

No_Data 0 1 0 -------- --------

m 16102 18554 15658 97.24% 84.39%

h 8111 8046 7035 86.73% 87.43%

s 6011 3623 3333 55.45% 92.00%

Totals 30224 30224 26026

Overall Accuracy 86.11%

Kappa Statistics

Overall Kappa 0.7596 Class Name Kappa 

1 0.6659

2 0.8203

3 0.9001

Microhabitat and presence of certain biologic attributes were also populated into the 

polygon features, but were strictly limited to areas where video groundtruthing occurred 

and where the sea floor was clearly visible in the footage. Figure 7 provides a graphical 

representation of the habitat characterization including the codes for microhabitat and 

biologic attributes. 

12 
Figure 7. Habitat classification for survey HMPR-115-2004-03. As listed in Table 3, codes 

in the image only indicate a habitat class concatenation of megahabitat, bottom induration, 

meso-scale identifier, and modifier id. Other fields have been omitted for brevity. 

13 
DISCUSSION AND CONCLUSIONS 

The assistance provided by NOAA’s OCS, Hydrographic Survey Division, permitted 

OCNMS to sporadically acquire a significant amount of hydrographic survey data over a 

period of four years, at minimal cost to the program. The OCS is responsible for 

creating, maintaining, and updating nautical charts of U.S. waters, with critical survey 

areas being determined from a variety of factors including marine traffic patterns and 

inadequacy of prior surveys. The combined priority for creating contemporary highresolution sea floor maps of the nation’s marine sanctuaries, and the need for updating 

the nautical charts along the western coast of Washington, created a unique opportunity 

for an intra-agency cooperation within NOAA between the NMSP, OCS, and OMAO. 

The expertise and capabilities of the NOAA ship RAINIER proved to be an extremely 

valuable asset for advancing the sanctuary program’s characterization efforts. 

Additional sea days by the RAINIER, her sister ship FAIRWEATHER, or the HI’IALAKAI

would be of great value to the OCNMS’ continued high priority habitat mapping 

program. Furthermore, a regular commitment by OMAO to support the entire NMSP’s 

mapping efforts through the fleet allocation process would help to accomplish NOAA’s 

ecosystem mission goal, which specifically states mapping and characterizing previously 

uncharted habitats as being a key foci in the 5-year research plan (NOAA 2005). 

Continued OMAO support would also provide critical support for NOAA’s overall 

mission (NOAA 2005). Even though the overall assets of the RAINIER are not entirely 

compatible with the mapping goals of the OCNMS, as the multibeam system installed on 

the ship itself is not capable of preserving the parameters necessary for signal 

normalization (e.g. source level, gain, pulse length, etc.), the systems available on the 

various launches were capable of providing useful data for creating quality benthic 

habitat maps. However, even the multibeam data acquired by the ship’s lower frequency 

Elac multibeam systems provided useful data for creating digital terrain models. Such 

data, for example, can be extremely valuable as a reconnaissance tool for deep-water side 

scan surveys, especially in the canyon areas (Intelmann and Cochrane 2006). The deepwater multibeam systems installed on the NOAA Ships FAIRWEATHER and 

HI’IALAKAI, in addition to those installed on their survey launches, are all capable of 

capturing the necessary parameters for normalizing backscatter data, and as such, those 

platforms could potentially provide even more useful data for characterization efforts 

than the RAINIER. The sanctuary’s continued high priority for describing benthic 

habitats and the close physical home port locality of the NOAA hydrographic survey 

vessels, especially the RAINIER, provide a prime opportunity for cross program

collaboration that should be utilized to an even greater degree. 

As previously stated, the process and need for normalizing multibeam backscatter data, as 

is possible with the OMG Reson processing routine, becomes paramount when a goal of 

data acquisition is to create classified habitat maps. A benefit of using backscatter, as

opposed to traditional side scan imagery, for seafloor characterization is the generally 

more precise positioning achievable through a hull-mounted configuration. The data are 

also precisely geo-coordinated with the multibeam sounding data and thus avoid the 

14 
various challenges associated with the geo-positioning of devices such as towed side scan 

sonar. However, a drawback of multibeam backscatter is that because the instrument is 

hull-mounted, image resolution will typically decline with increasing water depths. 

Inherent transducer design and the ability to tow a side scan sonar closer to the seafloor 

usually results in more pronounced image textural properties, which are important for 

calculating various textural indices, such as homogeneity and entropy as used in our 

characterization process. Also, since multibeam swath coverage depends directly on 

water depth, data acquisition productivity will decrease in shallower water (Kamoshita et 

al. 2005). Survey productivity in shallow water can be increased by removing the 

angular dependence of multibeam, as the case with side scan sonar or interferometric 

bathymetric methods. 

The accuracy assessment matrix provides useful information pertaining to the textural 

classification performance; however, in this survey the calculated statistics are probably 

lower than reality due to the lack of precise acoustic positioning on the towed camera 

sled and possible changes in the seafloor sediment regime between the time of acoustic 

imaging and the time of video groundtruthing. In some areas nearly four years separated 

the two independent survey efforts. With the highly variable tidal currents existing 

across some of the survey area it is highly probable that sediments shifted over time, 

thereby introducing error not necessarily attributable to the classification performance. 

Moreover, a reliable accuracy assessment should be based on the best possible reference 

data. In our groundtruthing survey, the reference data were derived from a video sled 

using positioning based on the vessel rather than the actual video sled itself as no acoustic 

tracking system was available. In this survey, reference data will have a minimum 6 

meter error because that was the DGPS antennae offset from the A-frame that served as 

the camera sled tow point. Further, the assumed position of the reference data would be 

more accurate in areas that experienced less influence from the current and in the areas of 

shallower water throughout the survey area, where less cable was deployed, ultimately 

translating to less potential layback error. 

In examining the error matrix (Table 4), it is important to assess the diagonal cells, as

they indicate the observations classified correctly according to the reference data (i.e.,

video groundtruthing). Observations deviating from the diagonal indicate misclassified 

data for each category. The overall classification accuracy statistic is useful information 

but should be interpreted with caution because it simply describes the number of 

incorrect observations divided by the number correct. The user’s accuracy essentially

provides a measure for classification performance in the field by class, and the producer’s 

accuracy depicts how accurately the analyst classified the image by class. The Kappa 

statistic provides a more powerful multivariate descriptor, incorporating both the 

diagonal observations (as used in the overall accuracy) and the off diagonal observations. 

The Kappa statistic essentially indicates the proportionate reduction in error generated by 

the classification process as opposed to a completely random generated classification 

(Leica Geosystems 2003). For this work, the overall Kappa statistic of 0.7596 implies 

that the classification process is avoiding nearly 76 percent of the errors that would be 

associated with a completely random classification process (Congalton 1991). 

15 
ACKNOWLEDGEMENTS 

The authors would like to thank the crew of the NOAA ship RAINIER for their excellent 

performance with data acquisition and cursory line cleaning of the multibeam sounding 

data; Andy Palmer and Mike Levine for skippering the R/V Tatoosh and assistance with 

the groundtruthing survey; and Rich Littleton for assisting with the video sled 

deployment. The manuscript benefited from additional review by CDR Guy Noll, 

NOAA Corps, Commanding Officer, NOAA ship RAINIER, and edits by Kathy Dalton. 

16 
REFERENCES 

Auster, P.J. Michalapoulos, R. Robertson, P.C. Valentine, K. Joy, and V. Cross. 1999. 

Use of acoustic methods for classification and monitoring of seafloor habitat 

Complexity: description of approaches. Pages 186-197 in: N.W. Munro and 

J.H.M. Wilison (eds.) Linking Protected Areas With Working Landscapes. 

Sciense and Management of Protected Areas Association, Wolfsville, Nova 

Scotia. 

Barr, B. 2003. US Geological Survey NOAA/National Marine Sanctuary Program’s 

seabed mapping initiative. 2002-2003 National Marine Sanctuary Annual Report. 

Beaudoin, 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. 

Blondel. P. 1996. Segmentation of the Mid-Atlantic Ridge south of the Azores, based on 

acoustic classification of TOBI data. In: MacLeod, C.J. Tyler, P.A. Walker, C.L. 

(Eds.), Tectonic, Magmatic, Hydrothermal and Biological Segmentation of MidOcean Ridges. Geological Society Special Publication No. 118. Boulder, CO. pp 

17-28. 

Cochrane, G.R., and K.D. Lafferty. 2002. Use of acoustic classification of sidescan sonar 

data for mapping benthic habitat in the Northern Channel Islands, California. 

Continental Shelf Research 22: 683-690. 

Congalton, R. 1991. A review of assessing the accuracy of classifications of remotely 

sensed data. Remote Sensing of Environment 37: 35-46. 

Dartnell, P., and J.V. Gardner. 2004. Predicting seafloor facies from multibeam 

bathymetry and backscatter data. Photogrammetric Engineering & Remote 

Sensing. 70(9): 1081-1091. 

Dragovich J.D., R.L. Logan, H.W Schasse, T.J. Walsh, W.S. Lingley Jr., D.K. Norman, 

W.J. Gerstel, T.J. Lapen, J.E. Schuster, and K.D. Meyers. 2002. Geologic map 

of Washington-Northwest Quadrant. Washington Division of Geology and Earth 

Resources. Geologic Map GM-50. 

Greene, H.G., M.M. Yoklavich, R.M. Starr, V.M. O’Connell, W.W. Wakefield, D.E. 

Sullivan,J.E. McRea, Jr., G.M. Cailliet. 1999. A classification scheme for deep 

seafloor habitats. Oceanologica Acta. 22(6):663.

Haralick, R.M., K. Shanmugan, and R. Dinstein. 1973. Textural features for image 

classification. IEEE Transactions Systems, Man, and Cybernetics SMC3, 610-

621. 

17 
Huvenne, V.A.I., Ph. Blondel, and J.P. Henriet. 2002. Textural analyses of sidescan sonar 

imagery from two mound provinces in the Porcupine Seabight. Marine Geology 

(189):323-341. 

Intelmann, S.S. 2005. Hydrographic and topographic lidar acquisition. Northwest Coast, 

Washington. Neah Bay to Cape Alava. Unpublished Data. 

Intelmann, S.S. and G.R. Cochrane. 2006. Benthic Habitat Mapping in the Olympic 

Coast National Marine Sanctuary: Classification of side scan sonar data from

survey HMPR-108-2002-01: Version I. Marine Sanctuaries Conservation Series 

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

Administration, Marine Sanctuaries Division, Silver Spring, MD. 13pp. 

Kamoshita, T., Y. Sato, and T. Komatsu. 2005. Hydro-acoustic survey scheme for seabottom ecology mapping. Seatechnology 46(6): 39-43. 

Kassakian J. and M. Ostdahl. 2005. Memorandum to Washington State Policy Working 

Group. Improving the sustainability of fisheries off Washington's outer coast 

through benthic habitat mapping and characterization. November 3, 2005. 

Leica Geosystems GIS & Mapping, LLC. 2003. Erdas Field Guide. 7th Edition. Atlanta, 

Georgia. 261-262. 

McCrory, P.A., S.C. Wolf, S.S. Intelmann, W.W. Danforth, R.J. Weldon, J.L. Blair. 

2004. Quaternary tectonism in a collision zone, Northwest Washington. Eos 

Trans. AGU, 85(47), Fall Meet. Suppl., Abstract T33C-1391. 

Mitchell, N.C., and J.E. Hughes Clark. 1994. Classification of seafloor geology using 

multibeam sonar data from the Scotian shelf. Marine Geology 121: 143-160. 

Nittrouer, C.A., 1978. The process of detrital sediment accumulation in a continental 

shelf environment: an examination of the Washington shelf: PhD Thesis, 

University of Washington, Seattle, WA. 243p. 

 

NOAA 2005. Research in NOAA: Toward understanding and predicting earth’s 

environment. A five-year plan: fiscal years 2005-2009. US Department of 

Commerce NOAA. January 2005. 60pp. 

NOAA 2005. New priorities for the 21st century – NOAA’s strategic plan. Updated for 

FY 2006-FY2011. US Department of Commerce NOAA. April 2005. 24 pp. 

Pacific Fishery Management Council, 2004. Pacific coast groundfish fishery 

management plan for the California, Oregon, and Washington groundfish fishery 

as amended through Amendment 17. Pacific Fishery Management Council. 

Portland, OR. 145 pp. 

18 
Reid, J. A., Jenkins, C., Field, M. E., Gardner, J. V. and Box, C. E. 2001. USSEABED: 

defining the surficial geology of the continental shelf through data integration and 

fuzzy set theory. Geological Society of America Annual Meeting, Boston, MA. 

Abstracts with Programs 33:A106. 

Reson 2003. SeaBat 8101 Multibeam echosounder system operator’s manual. Version 

3.01. 

Skohr, M.E. 1991. Evaluation of second-order texture parameters for sea ice 

classification from radar images. Journal of Geophysical Research. 96: 10625-

10640. 

Sternberg, R.W. 1986. Transport and accumulation of river-derived sediment on the 

Washington continental shelf, USA. Journal of the Geological Society of 

London, v. 143, p. 945-956. 

Valentine, P.C., G.R. Cochrane, and K.M. Scanlon. 2003. Mapping the seabed and 

habitats in National Marine Sanctuaries. Marine Technology Society. 37(1): 10-

17. 

Venkatarathnam, K., and McManus, D.A. 1973. Origin and distribution of sands and 

gravels on the northern continental shelf off Washington. Journal of Sedimentary 

Petrology, v. 43, p. 799-811. 

Wang, S.S-E. 2005. Groundfish habitat associations from video survey with a 

submersible off the Washington Coast. Master’s Thesis. University of 

Washington. 110pp. 

19 
APPENDIX 

Still frames extracted from video for representative areas classified as soft bottom

20 
Still frames extracted from video for representative areas classified as mixed bottom.

21 
Still frames extracted from video for representative areas classified as hard bottom.

22 
ONMS CONSERVATION SERIES PUBLICATIONS 

To date, the following reports have been published in the Marine Sanctuaries 

Conservation Series. All publications are available on the Office of National Marine 

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

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) 

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) 

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 the Stellwagen Bank National Marine Sanctuary and the St.

Lawrence River Estuary (MSD-05-1) 

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 Research Workshop, Seattle, 

Washington (MSD-01-04) 

Workshop on Marine Mammal Research & Monitoring in the National Marine Sanctuaries (MSD01-03) 

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

Distribution and Sighting Frequency of Reef Fishes in the Florida Keys National Marine 

Sanctuary (MSD-01-1) 

Flower Garden Banks National Marine Sanctuary: A Rapid Assessment of Coral, Fish, and Algae 

Using the AGRRA Protocol (MSD-00-3) 

The Economic Contribution of Whalewatching to Regional Economies: Perspectives From Two 

National Marine Sanctuaries (MSD-00-2) 

Olympic Coast National Marine Sanctuary Area to be Avoided Education and Monitoring

Program (MSD-00-1) 

Multi-species and Multi-interest Management: an Ecosystem Approach to Market Squid (Loligo

opalescens) Harvest in California (MSD-99-1)