ACOUSTIC AND VISUAL SEABED CLASSIFICATION OF THE OCULINA HABITAT AREA OF PARTICULAR CONCERN (OHAPC), EASTERN FLORIDA SHELF

Amanda M. Maness

A Thesis Submitted to the

University of North Carolina Wilmington in Partial Fulfillment of the Requirements for the Degree of

Master of Science

Department of Geography and Geology

University of North Carolina Wilmington

2011

Approved by

Advisory Committee

Joanne Halls Andy Shepard

Nancy Grindlay Chair

Accepted by

Dean, Graduate School

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This thesis has been prepared in a style and format consistent with the journal Continental Shelf

Research

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TABLE OF CONTENTS

ABSTRACT ................................................................................................................................... vi

ACKNOWLEDGMENTS ........................................................................................................... viii

DEDICATION ............................................................................................................................... ix

LIST OF TABLES .......................................................................................................................... x

LIST OF FIGURES ....................................................................................................................... xi

1. INTRODUCTION ...................................................................................................................... 1

1.1. Oculina Deep-Water Coral Ecosystem .................................................................. 1

1.2. OHAPC Management ............................................................................................ 2

1.3. Previous Studies ..................................................................................................... 6

2. OBJECTIVES ............................................................................................................................. 8

3. METHODS ............................................................................................................................... 10

3.1. Acoustic Surveys ................................................................................................. 10

3.2. Visual Surveys and Grab Samples ....................................................................... 11

3.3. Bathymetric and Backscatter Maps ..................................................................... 12

3.4. Study Sites ........................................................................................................... 13

3.4.1. Study Area I: Chapman’s Reef ....................................................................... 13

3.4.2. Study Area II: North Study Area .................................................................... 16

3.5. Acoustic Seabed Classification ............................................................................ 20

3.6. Visual Seabed Classification ................................................................................ 25

4. RESULTS ................................................................................................................................. 28

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4.1. Benthic Habitats Delineated by Visual Analysis ................................................. 28

4.1.1. Standing Dead and Live Coral Habitat ........................................................... 28

4.1.2. Coral Rubble and Gravelly Sand .................................................................... 31

4.1.3. Low Relief Hard Bottom Habitat ...................................................................... 33

4.1.4. Sand Habitat .................................................................................................... 33

4.1.5. Shell Hash ....................................................................................................... 36

4.2. Unsupervised Classification of Chapman's Reef ................................................. 38

4.3. Supervised Classification of Chapman's Reef ..................................................... 42

4.3.1 ROV Dive Profile Discussion .......................................................................... 54

4.4. Supervised Classification of North Study Area ................................................... 55

5. DISCUSSION ........................................................................................................................... 61

5.1. Unsupervised Classification Review ................................................................... 61

5.2. Influences on Automated Classification .............................................................. 62

5.2.1. Underlying Hardbottom .................................................................................. 63

5.2.2. Transition Zones ............................................................................................. 64

5.2.3. Limited Data ................................................................................................... 65

5.3. Supervised Classification – Chapman’s Reef ...................................................... 67

5.4. Supervised Classification – North Study Area .................................................... 69

6. CONCLUSIONS AND FUTURE DIRECTIONS ................................................................... 70

6.2 Classification Considerations .............................................................................. 73

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REFERENCES ............................................................................................................................. 75

APPENDIX ................................................................................................................................... 78

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ABSTRACT

This research focuses on an area offshore southeastern Florida where the deep-water

scleractinian coral Oculina varicosa resides in large bioherms up to 30 m in height. Due to the

remote location 34-60 km offshore and depths of 70-100 m, it is difficult to survey and monitor

these habitats. This area known as the Oculina Banks has been protected since 1984 against

harmful anthropogenic practices such as bottom trawling, dragging, long lines, fish traps, and

anchoring.

Providing management entities with the necessary data to effectively monitor the health

of the ecosystem is essential to protecting these highly sensitive marine ecosystems. In this

study, methods for classifying the seafloor habitats in the Oculina Banks were developed to

facilitate and streamline future seafloor mapping and habitat characterization endeavors in this

area. Both unsupervised and supervised automated seabed classification methods were utilized

and tested for accuracy with video and benthic photographs collected by a remotely operated

vehicle.

Two study areas were chosen within the Oculina Banks to evaluate the QTC Multiview

automated seabed classification software. Unsupervised and supervised classification methods

were applied to multibeam bathymetry and backscatter imagery collected over a high relief

bioherm known as Chapman’s Reef. The Chapman’s Reef unsupervised classification obtained

seven different acoustic classes and had an overall 62% accuracy with low-relief sand and high

relief, coral rubble and gravelly sand being the most accurately classified habitats. The

Chapman’s Reef supervised classification consisted of five different habitats with an overall 30%

accuracy. The supervised catalogue file from Chapman’s Reef was then applied to data from the

North Study Area and produced an overall 10% accuracy. The low accuracy of the supervised

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classification most likely resulted because the North Study Area has different bioherm

morphology and includes low relief topographic ‘fingers’ on the shelf edge that are not present in

the Chapman’s Reef study area. In addition to the automated classification, areas of live O.

varicosa coral were identified from visual surveys to determine where successful mounds were

growing and to document the distribution of these mounds on habitat maps.

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ACKNOWLEDGMENTS

I would like to express sincere gratitude to Dr. Nancy Grindlay for her guidance

and dedication during my research. I have been fortunate to learn from her and I greatly

appreciate her going the extra mile as an advisor. I would also like to thank my committee, Dr.

Joanne Halls and Andy Shepard for their guidance and assistance throughout my research

endeavors and graduate studies. I wish to thank Andy Shepard for his constant inspiration to the

importance of scientific studies such as this on the Oculina Banks. Special thanks to Dave Crist

who developed the habitat digitizer used to classify the benthic photographs in this study.

Several UNCW professors have significantly contributed their expertise and I would like to

thank Dr. Susan Simmons for statistics consulting and Dr. James Blum for help in creating some

of the graphs. John Reed of Harbor Branch Oceanographic has been a true mentor to me and the

deep-water coral community through over three decades of research on the Oculina Banks. I

have been inspired by his legacy of a true passion for marine science.

I wish to acknowledge Lance Horn, Glenn Taylor and Jeff Williams for their invaluable

help and excellent skills while assisting in the at sea portions of this research; and whom I am

proud to call my shipmates.

This research was supported by NOAA grant #5-1005, the Department of Geography and

Geology and the Center for Marine Science at the University of North Carolina Wilmington.

Additional thanks to the software companies of both Caris® and Quester Tangent™ for their

help and support. I also appreciate the team at Land Management Group, Inc. for being so

understanding and supportive to my graduate studies during my employment there.

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DEDICATION

I would like to dedicate this thesis to my parents and grandmother. Thank you for never

telling me that any mountain was too high. To John, my late stepfather, thank you for all your

encouragement.

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LIST OF TABLES

Table Page 1. QTC Multiview™ algorithms with functions .......................................................................... 222. Distribution of visual habitats for assigned relief classes. ....................................................... 293. Unsupervised QTC Multiview™ acoustic classes with their relief class definitions and benthic habitat definitions ............................................................................................................. 404. Chapman’s Reef supervised classification results. .................................................................. 445. North Study Area supervised classification results ................................................................. 57

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LIST OF FIGURES

Figure Page 1. Map of Oculina Habitat of Particular Concern (OHAPC) ......................................................... 42. Chapman's Reef bathymetry and ROV dive tracks ................................................................. 143. Chapman's Reef mosaic of backscatter intensity ..................................................................... 154. Bathymetry of the North Study Area with ROV dive tracks ................................................... 185. North Study Area mosaic of backscatter intensity ................................................................... 196. Flow Chart for Automated and Visual Classification. ............................................................. 217. Sample photo showing how the habitat digitizer was used to delineate benthic habitats ........ 278. Selected bottom photographs of standing dead and live coral habitat ..................................... 309. Selected bottom photographs of coral rubble and gravelly sand habitat ................................. 3210. Selected bottom photographs of low relief hard bottom habitat ............................................ 3411. Selected bottom photographs of sandy habitat ...................................................................... 3512. Selected bottom photographs of shell hash habitat ................................................................ 3713. QTC Multiview™ unsupervised classification of Chapman’s Reef ...................................... 3914. Supervised points of Chapman’s Reef showing areas of the reef used to classify the five supervised benthic habitats ........................................................................................................... 4615. QTC Multiview™ supervised classification of Chapman’s Reef showing definition and distribution of Classes 1-5 ............................................................................................................ 4716. Profile of ROV Dive 1 showing relief change over dive duration with habitat classification colors of supervised classification. ............................................................................................... 4817. Profile of ROV Dive 2 showing relief change over dive duration with habitat classification colors of supervised classification. ............................................................................................... 4918. Profile of ROV Dive 4_2006 showing relief change over dive duration with habitat classification colors of supervised classification.. ........................................................................ 5019. Profile of ROV Dive 6 showing relief change over time with habitat classification colors of supervised classification ............................................................................................................... 5120. Profile of ROV Dive 8 showing relief change over time with habitat classification colors of supervised classification ............................................................................................................... 5221. QTC MultiviewTM supervised classification of bathymetry and backscatter over the North Study Area showing definition and distribution of acoustic Classes 1-5 ..................................... 5622. Profile of ROV Dive 4 showing relief change over dive duration with habitat classification colors of supervised classification ................................................................................................ 5823. Profile of ROV Dive 5 showing relief change over dive duration with habitat classification of supervised classification ........................................................................................................... 59

1. INTRODUCTION

1.1. Oculina Deep-Water Coral Ecosystem

Deep-water coral bioherms constructed by the scleractinian ivory tree coral, Oculina

varicosa, stretch over 167 km (90 nm) along the shelf edge of eastern Florida at depths of 70-100

m. (Avent et al., 1977; Reed, 1980; Thompson and Gilliland, 1980). This section of the eastern

Florida shelf is the only known location where this azooxanthellate form of the facultatively

zooxanthellate O. varicosa constructs high relief bioherms, some reaching 30 m in height and

100 m wide (Reed, 2002). More shallow forms of Oculina grow as unconsolidated and isolated

colonies from 10 to 100 m depth in the Gulf of Mexico, Caribbean and Bermuda, and off the

southeast U.S. from North Carolina to Florida (Reed, 1980).

The Oculina Banks have been the focus of numerous geological and biological studies to

document the variety of fish and coral species, and seabed habitats found in the region

(Thompson and Gilliland, 1980; Reed and Hoskin, 1987; Brooke, 1998; Scanlon, 1999; Koenig

et al., 2000; Koenig et al., 2005; Reed et al. 2007; Harter et al. 2009). The delicate branches of

this "ivory tree" coral within the Oculina species provide a complex habitat for dense and diverse

communities of fish and invertebrates (Reed and Hoskin, 1987). The Oculina coral colonies

serve as spawning and nursery habitats for many fish including some commercially important

populations of grouper, snapper, amberjack and squid (Gilmore and Jones, 1992; Reed, 2002).

Biodiversity of the Oculina Banks ecosystem is, in fact, comparable to that of shallow

hermatypic reefs (Reed and Hoskin, 1987).

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Biodiversity and habitat structure, and their location under the west wall of the Gulf

Stream, make Oculina bioherms valuable for maintaining the ecosystem and essential fish

populations of the southeast continental shelf (Gilmore and Jones, 1992; Reed, 2002). The

potential destruction of the delicate coral mounds by mobile fishing gear prompted the South

Atlantic Fisheries Management Council (SAFMC) to designate portions of the Oculina Banks as

the first deep coral protected area off the U.S. in 1984, the Oculina Habitat Area of Particular

Concern (OHAPC). This action created the OHAPC where the use of all bottom trawls, bottom

long-lines, dredges, and fish traps are prohibited within the 314 km² (91 nm²) area (SAFMC,

1988) (Figure 1). Further actions in 1994 and 2000 expanded the area to 300 nm² (1029 km²)

and prohibited trawling, dredging, anchoring and other anthropogenic activities known to harm

the reefs. By 2004, additional legislation extended the prohibition of trawling indefinitely within

the OHAPC and required the size and configuration of the protected area to be reviewed within

three years and additional reviews in ten years to address research, monitoring, outreach and

enforcement strategies.

1.2. OHAPC Management

Legislation concerning the protection of ocean ecosystems and fisheries was bolstered

greatly by the first Magnuson-Stevens Act (MSA) in 1976. The Sustainable Fisheries Act of

1996 amended the MSA and directed the National Marine Fisheries Service (NMFS) to further

preserve and characterize fish habitats. The term “essential fish habitat” (EFH) is defined in

section 3(10) of the amendment as waters and substrate necessary to fish for spawning, breeding,

feeding or growth to maturity. Identifying ecologically sensitive zones in the oceans and

defining maximum sustainable yield of fish is imperative to delineating areas of EFH. Fisheries

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Figure 1. Map of Oculina Habitat of Particular Concern (OHAPC) (black polygon), and 20 m contour interval. The 2005 MBES survey coverage shown as areas of color-coded bathymetry. Study areas are outlined in red. Inset shows location of the OHAPC in relation to Florida coastline.

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management plans must include a description of the EFH for specific geographic areas

(Magnuson-Stevens, 1996). EFH that is judged to be particularly important to the long-term

productivity of populations of one or more managed species, or to be particularly vulnerable to

degradation, may be identified as “habitat areas of particular concern” (HAPC) to provide

additional focus for conservation efforts.

By 1984, concern was mounting over use of destructive fishing gear, including rock

shrimp trawls and scallop drags, in the vicinity of the Oculina Banks (Koenig et al., 2000).

Subsequently, the SAFMC established the first deep-sea coral protected areas in U.S. Atlantic

waters, the Oculina Habitat Area of Particular Concern (OHAPC), which prohibited use of all

bottom trawls, bottom long-lines, dredges, anchoring and fish traps within a 314 km² area of the

Oculina Banks (SAFMC, 1988). In 1994, prompted by continued regional decline in many

deep-water snapper/grouper species, the SAFMC further established a 10 year moratorium

prohibiting fishing for and retention of these species in the OHAPC, renaming the original

OHAPC as the Oculina Experimental Closed Area (OECA). This experimental closed area

served to identify its added purposes to conserve and strengthen reef fish stocks, and to promote

and facilitate scientific research. In 1998, the OHAPC was expanded beyond the OECA to

include a rock shrimp closed area, increasing the size and the fishing moratorium northward by

an additional 715 km² to a total of 1029 km² (300 nm²) (SAFMC Amendment 4, 1998a) (Figure

1). Also in 1998, a vessel monitoring system (VMS) for all rock shrimp vessels to monitor

compliance with the restricted area was mandated, and implemented in 2003 (SAFMC, 2002).

Surveillance of this particular fishery ensued because the trawling gear used in the rock shrimp

fishery was believed to be a vanguard in the destruction of the reefs (R. Chesler, personal

communication 2005). Evidence of continued decline in fish populations prompted the SAFMC

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to extend the fishing moratorium within the complete OHAPC indefinitely in 2004; and review

of its protected status and recent research is scheduled for 2014 (SAFMC, 2003).

In 2005, the SAFMC developed the first Oculina Evaluation Plan, which outlined plans

for mapping, research, monitoring, enforcement, and public outreach. As described in the

evaluation plan, more scientific data from mapping and monitoring the OHAPC is required to

give managers the knowledge to sustain the closed area. This information is necessary to set

practical and successful boundaries to maximize the desired results of restoring healthy coral

habitat and fish stocks (SAFMC, 2005).

1.3. Previous Studies

SAFMC management actions enhanced protection of the Oculina Banks ecosystem,

largely in response to undersea research efforts that began in 1970. Since 1970, research

supported by various institutions and government agencies studied a small portion of the

OHAPC and much less of the Oculina Banks habitat outside the reserve, including:

• Submersible dives collected between 1975-2001, 0.45 km² (0.13 nm²) (0.04% of

OHAPC) Total of 38 dives between 1975-77 and 16 Clelia dives in 2001 (Koenig et al.

2005; Reed et al. 2007)

• ROV dives (20 dives in 2003 and 9 in 2001) 0.69 km² (0.2 nm²) (0.07% of OHAPC)

(Harter et al. 2009) (8 dives in 2005 dives totaled 11.3nm/20.9km)

• 1995 USGS sidescan sonar survey, 384 km² (112 nm²) (37% of OHAPC) and a total of

65 sediment grab samples collected between 1995-1997 (Scanlon et al. 1999)

• 2002 Multibeam Echosounder (MBES) survey, 295 km² (86 nm²) (29% of OHAPC)

• 2005 MBES survey, 88.1km2 , (25.66nm2) (8.6% of OHAPC) (Figure 1)

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Collectively, all ROV and submersible dives combined since 1995 cover less than 0.11%

of the Oculina HAPC (Reed et al., 2005), whereas the MBES surveys covered 37.6% of the

OHAPC, and over 90% of the Oculina mounds in the OHAPC.

The first attempt to classify the OHAPC seabed was undertaken in 1995. A study by the

United States Geological Survey (USGS) mapped 180 km² of the OHAPC using a towed

sidescan sonar. Poor navigational control due to strong Gulf Stream currents and low resolution

of the sidescan mosaics resulted in a limited seabed classification. Remotely Operated Vehicle

(ROV) video, sediment grab samples and the sidescan mosaic were used to divide the seafloor

into three types (Scanlon et al. 1999). High relief/high backscatter (HR/HB) areas were found to

be present near the 80 m contour and often included peaks up to 30 m above the adjacent

seafloor. Visual confirmation of these peaks revealed that Oculina thickets most likely colonize

on carbonate outcrops and grow into high relief bioherms (Reed, 1980; Scanlon, 1999). The

rough and rocky terrain was found to support high biodiversity and serve as habitat for reef fish

to feed and spawn (Gilmore and Jones, 1992; Koenig et al., 2000). Low relief/high backscatter

(LR/HB) areas contained some rocky hardbottom compared to the HR/HB areas. Common to

the LR/HB areas is a thin veneer of sand covering rocky hardbottom and large amounts of

gravelly sand and shell hash on the seafloor (Reed, 2002; Scanlon et al., 1999). Last, the low

relief/low backscatter (LR/LB) areas were found to exist east and west of HR/HB and consisted

largely of muddy sands. Sediment comprised mostly of sand produces the low backscatter

characteristic of such terrain (Scanlon et al., 1999).

The effectiveness of the OHAPC's protected status should be constantly evaluated with

continued monitoring. Biological decline of economically important reef fish and many macro

invertebrate species inhabiting the live reefs has been documented by comparing modern counts

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to historical data (Reed, 1980; Koenig et al., 2000). Visual evidence captured from video taken

by submersibles and ROVs in 2001 and 2003 affirms that this area is still in decline in terms of

reef fish populations and live coral cover (Koenig et al., 2005; Reed et al., 2007).

Video footage taken from submersible dives in 1980 was compared to ROV video dives

taken in 1995 on Jeff’s Reef in the southern end of the OHAPC for percent composition of

species. Commercial fish populations had gone down over the 15 year period as evidenced by

red snapper going from 1.896% of observed species in 1980 to 0.5% in 1995. Gag grouper

populations also showed a decline from 4.774% in 1980 to 0.07% in 1995 (Koenig et al., 2000).

Additionally, an Oculina website created in 2003 is currently run from University of

North Carolina Wilmington server under the web address, www.uncw.edu/oculina (Manning,

2003). The website makes some of the past data from OHAPC acoustic surveys and dives

available via Web pages and an online ArcIMS® Oculina Geographic Information System

(OGIS). Content of the web-based OGIS include: multibeam bathymetry data, dive logs, habitat

classification information, fish catch data, NOS bathymetry data, and multimedia links to photos

and videos associated with the 2001 Clelia submersible and ROV dives done in 2003 and 2005.

A few products from this study will be integrated into the OGIS, and into the SAFMC regional

habitat GIS

(http://www.safmc.net/EcosystemManagement/EcosystemBoundaries/MappingandGISData/tabi

d/632/Default.aspx).

2. OBJECTIVES

As deep (> 50 m depth) marine protected areas, such as the OHAPC, are designated

worldwide, the need to develop efficient, reliable, and cost-effective methods to classify and

monitor seabed habitats will grow. Both management entities and scientists need detailed benthic

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habitat maps to make educated decisions concerning the future protection and monitoring of the

area. For example, determining the boundary of a reserve, location and description of sensitive

habitats and baseline data to provide a benchmark for future research are facilitated by benthic

habitat mapping.

Although the OHAPC has been studied since the late 1970’s, seafloor habitat maps

required for monitoring and management are lacking. In the OHAPC, the complexity of reefs

makes them difficult to map using towed sampling gear because the seafloor terrain and current

speed of the Gulf Stream causes navigation and maneuverability problems (Scanlon et al., 1999).

Furthermore, the extent, depth and variability and cost of required operations limits direct visual

documentation of the Oculina ecosystem. Quantitative methods using acoustic remote sensing

techniques such MBES bathymetry and backscatter data complement more expensive, human

hour-intensive classification methods using in situ observations. A few recent studies have

addressed the feasibility of automated classification of the seabed using MBES bathymetry and

backscatter data (for example, Grasmueck et al., 2007; Riegl, 2005; Anderson, 2002; Andrews,

2003; Freitas, 2003; Greene et al., 1999); none have been conducted in the OHAPC.

The overarching goal of this project is to develop and evaluate a benthic habitat

classification system derived from the automated classification of MBES data for the OHAPC.

To accomplish this goal the objectives of this project were to:

1) Use the automated seabed classification software, QTC Multiview™ to classify the

acoustic data of one known bioherm area within the OHAPC.

2) Use ROV video and digital still camera photos to classify the seabed in the same

representative area.

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3) Compare the results of the unsupervised classification with seabed classification by

video and digital camera.

4) On the basis of both visual and acoustic data classification, "train" the software and

perform a supervised classification on the original area and an additional representative area with

OHAPC.

Rapid, high-resolution habitat mapping may help delineate areas where coral is most

likely to succeed and assist in future restoration projects and research. Highlighting areas of live

successful coral mounds will give further credence to methods already employed for protection

of coral areas, and give scientists better guidance for recovery efforts. The picture of coral health

within the OHAPC can be further rendered by coupling locations of new coral mounds with

known current data and previous successful coral growth since the beginning of its protected

status.

3. METHODS

3.1. Acoustic Surveys

Acoustic data used in this study were collected during a 2005 MBES mapping cruise. During

this cruise, a total of 88.1 km² in and outside the OHAPC were mapped using a pole-mounted

Kongsberg Simrad EM 3002 MBES, capable of surveying in water depths of 50-150 m where

the horizontal resolution of the system will vary from 1-3 m (including backscatter) and vertical

resolution of 1 cm. At 300 kHz, the EM 3002 operates with 160 individual 1.5 x 1.5 degree

beams per ping and a swath width of about 200 m in 50 m of water depth. Adjacent lines were

run with an overlap of approximately 30%. All of the MBES data were collected with Simrad

Seafloor Imaging Software (SIS) and logged in the Simrad .all format.

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Sound velocity corrections were made by making Conductivity Temperature Density

(CTD) casts roughly every four hours using a Seabird SB-19 SEACAT Profiler. Seawater

conductivity, temperature and density information of the water column were sampled at 4 Hz and

converted to ASCII files. These files were loaded into the Caris® sound velocity editor to create

a sound velocity profile of the water column. Additionally, a Valeport Mini SVS was used to

monitor the sound velocity at the water surface to detect any sudden changes in sound velocity.

3.2. Visual Surveys and Grab Samples

In October 2005, an ROV cruise aboard NASA’s M/V Liberty Star was conducted to

groundtruth the bathymetric maps in the two study areas. The M/V Liberty Star was equipped

with a dynamic positioning system and DGPS allowing geo-referencing of dive and grab

operations. A pole-mounted hydrophone recorded slant range positioning data for the ROV

relative to the host vessel; managed using HYPACK®. The ROV was equipped with a forward

looking video camera and a downward looking digital still camera that took photos roughly

every minute during the transect. Lasers with a separation of 10cm are mounted on the still

camera to provide scale for the downward looking still photographs with average coverage of 0.5

m-1 m². ROV dive numbers are not always numbered sequentially due to the order in which the

dives were conducted. ROV dives 1, 2, 4, 5, 6, and 8 were conducted in 2005; Dive 4_2006 was

conducted in 2006.

Visual inspection of the final bathymetry and backscatter images helped identify and

target both high relief areas and lower relief ledges in order to plan for ROV dive transects to

cross over diverse bottom types. Data collected for this study included seven video transects

totaling 20.9 km (11.3 nm) of linear footage and 737 digital still photographs of selected dive

sites within the OHAPC. The video records were geo-referenced using slant range GPS in

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HYPACK and timestamps on the video feed, yielding a maximum position error of 6 m on all

dives. Dive #4 in 2006 has a higher error of 9 m due to propeller cavitation interference with

slant range signals causing more sparse pings.

Additionally, 20 sediment samples using a Van Veen grab sampler were analyzed for

grain size and carbonate content in the sedimentology laboratory of the USGS at Woods Hole,

Massachusetts (Appendix A). Grain size was grouped into gravel, sand, and silty-sand for

comparison with image habitat assessments. NASA’s M/V Liberty Star was equipped with

dynamic positioning (DP) during maneuvering for grab samples, therefore the spatial error is less

than 5m.

3.3. Bathymetric and Backscatter Maps

The support vessel, UNCW’s R/V Cape Fear, was equipped with a NorthstarTM

Differential Geographic Positioning System (DGPS) with a lateral position error of

approximately 10 m and was used as the auxiliary GPS. Vessel movement (position, heave,

pitch, yaw and roll of vessel) was measured by the Ashtech DGPS (<30cm error) and Applanix

POS/MV system with an inertial motion unit mounted directly above the transducer and parsed

real-time into the MBES data. The raw swath bathymetry data were then converted and

processed to remove outliers using Caris® HIPS 5.4. Bathymetry and backscatter files were

corrected for tides using NOAA tide files merged post-processing.

The final products of the cruise included .all files in the Simrad format, geotiffs of the

bathymetry and backscatter base layers created in Caris® HIPS 5.4, XYZ files containing

latitude, longitude and depth and XYA files containing latitude, longitude and amplitude.

Bathymetry and backscatter were gridded using MB grid in MB Systems. Chapman’s Reef

study area had a grid interval of 1 m whereas the North Study Area was gridded using a 2 m

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interval. During data collection, a different mode of using all stacked pings on EM 3002 was

used for Chapman’s Reef study area whereas computer RAM capabilities did not allow for this

to be utilized during the rest of the survey, including within the North Study Area. Additionally,

bathymetric grids were used only for creating maps in GIS and surfaces in Fledermaus; the grids

were not utilized in the QTC MultiviewTM automated classification.

3.4. Study Sites

This study focused on two sites within the area mapped in 2005 (Figure 1). Each site is

roughly 1 nm² and was chosen so that all known benthic habitats within the OHAPC would be

represented in the analysis.

3.4.1. Study Area I: Chapman’s Reef

The southern site, Chapman's Reef, is a high-relief bioherm complex with live and dead

coral, and extensive dead coral rubble areas (Figure 2). The large isolated mounds such as

Chapman's Reef that comprise the Oculina Banks along the 80m isobath near the shelf break

consist of matrices of coral and sediments built on a base of lithified oölitic limestone ridges or

dunes (Macintyre and Milliman, 1970; Reed, 1980). Chapman's Reef is elongated in the E-W

direction with a base depth of 91m and maximum height of 54 m.

Highest backscatter signals correlate with areas of high relief (on the bioherm) to medium

relief such as the knolly terrain 1-2m in height directly north of the main mounds (Figure 3).

Low backscatter values occur in the areas adjacent to the main mounds but not directly north of

the mounds. This low backscatter signal is reflected in areas of fine grain softer sediment as

observed from grab samples analyzed as sand and ROV video observations. The high

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Figure 2. Chapman's Reef bathymetry and ROV dive tracks outlined in white. Dives 1, 2, 6 and 8 were completed in October 2005 and Dive 4 was completed in October 2006. All dives combined from 2005 and 2006 cover 6.7 nm of the seafloor. Grab samples taken in 2005 are represented as shapes: Circle = Gravel >10%, Square = Sand, Triangle = Silty Sand.

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Figure 3. Chapman's Reef mosaic of backscatter intensity with 4 m depth contours in white. Grab samples taken in 2005 are represented as shapes: Green Circle = Gravel >10%, Red Square = Sand, Blue Triangle = Silty Sand.

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backscatter region appears on the eastern and western ridge and some areas north of the ridges

and is associated with high-relief coral rubble and some hard bottom. The coral rubble areas are

observed from ROV video observations and sediment samples showing gravel >10% with high

carbonate composition over 90% (Appendix A and Figure 3).

3.4.2. Study Area II: North Study Area

The North Study Area, located 96 km north and 3 km shoreward of Chapman's Reef,

consists of more low-lying hard bottom pavement with lower relief knolls and flat bottom (Fig. 4

and 5). These low relief knolls are vertically smaller in size compared to the size of Chapman's

Reef and typically top out at 1 m in height. This low-lying hard bottom with some relief but

mostly covered in sand which is sometimes only a few cms thick was also seen in the 1995

USGS sidescan sonar images (Scanlon, 1999). Backscatter data from this area did not have as

much contrast between high and low backscatter values and covers the area west of the ledge. It

contains mostly low backscatter in the areas with sand that lie adjacent to the ledge areas that

have slightly more relief. The high backscatter areas that trend N-S and are spaced at ~200m

intervals within the North Study Area appear to be mostly noise reflected from the sonar at nadir.

However, near the ledge and knolly areas the backscatter shows higher intensity values

compared to the low backscatter areas through the middle of the study area (Figure 5).

17

18

Figure 4. Bathymetry of the North Study area with ROV dive tracks 4 and 5 in white. Grab samples taken in 2005 are represented as shapes: Circle = Gravel >10%, Square = Sand, and Diamond = No samples, possible hard bottom.

19

Figure 5. North Study Area mosaic of backscatter intensity with 4 m contours in white. Grab samples taken in 2005 are represented as shapes: Green Circle = Gravel >10%, Red Square = Sand, Yellow Diamond = no sample, possible hard bottom.

20

3.5. Acoustic Seabed Classification

A combination of quantitative and qualitative methods was used in the seabed

classification of the two study areas. A flow chart outlining the steps of the automated and visual

classification is shown in Figure 6.

Automated seabed classification of the processed bathymetry and backscatter imagery

was done using the software package QTC Multiview™ by Quester Tangent Corporation.

(Quester Tangent Corporation, 2004). QTC Multiview™ is able to compensate for grazing angle

and range artifacts before generating statistical features for patches of the seafloor from recorded

backscatter and depth data. These statistical features help group areas of similar backscatter

together to identify areas of continuity. Several algorithms are employed to produce the various

statistical features used in classifying bottom types (Table 1).

Of these algorithms, Grey Level Co-occurrence Matrices (GLCM) have proven to be the

most effective in seabed classification of sidescan sonar acoustic imagery (Blondel, 2000;

Huvenne et al., 2002). The human eye has been found less able to determine the differences in

image textures; therefore GLCMs have proven more precise than visual interpretation alone

(Huvenne et al., 2002). GLCMs measure the texture of an image, where texture can best be

explained as the spatial/statistical placement of different grey tones within a small region. By

looking at a small pixel region in an image, a GLCM can measure the spatial relationship

between pixels, and determine the probability a pixel with a given value will be adjacent to

another pixel at a given angle and distance with a different grey value (Haralick, 1979; Blondel,

1993; Blondel, 2000; Cochrane, 2002).

21

Figure 6. Flow Chart for Automated and Visual Classification.

22

Table 1. QTC Multiview™ algorithms with functions (Preston et al., 2004).

Algorithm Function

Basic Statistics Mean, Standard Deviation, Higher order moments to indicate acoustic resistance and roughness

Quantile and Histogram Measures the distribution of backscattered intensities at low resolution

Fast Fourier Transforms (FFTs)

Used to find power spectra which describe statistical characteristics on many resolution scales

Ratios based on Power Spectra

Ratios of log-normalized power in various frequency bands that provide good discrimination for classifying images

Grey Level Co-Occurrence Matrices (GLCM)

Describe amplitude changes over selected distances and directions in the image patch, and are used to determine smooth or rough texture

23

More than 25 textural indices can be derived from a GLCM (Haralick, 1979). QTC

Multiview™ uses seven indices: Correlation, Shade, Prominence, Contrast, Energy, Entropy,

and Homogeneity.

Once these statistical features are generated in QTC Multiview™, the information is

stored in a matrix with more than 130 dimensions. Most of the variance in large matrices such as

these exist in the first three principal components. Principal components analysis (PCA) is run

on the matrix in order to reduce the large matrix yet also retain over 90% of the variability

contained in the statistical features (Preston et al., 2004). Consequently, only these first three

components known as Q1, Q2, and Q3 are retained for the QTC Multiview™ clustering process

(Preston et al., 2004).

The unsupervised classification is first ascertained by the automated segmentation of

acoustic classes in QTC Multiview™. The optimal number of classes was determined using the

auto cluster engine in QTC Multiview™, which runs a simulated annealing K-means algorithm

on the seabed file to determine the best number of acoustically similar classes for the dataset

(Quester Tangent Corporation, 2004). A total of seven classes were generated in QTC

Multiview during the unsupervised classification.

The output of the QTC Multiview™ classification process is an ASCII a file capable of

being input into ArcGIS® to display the number and spatial distribution of the different acoustic

classes. The ASCII file contains the following information for each record: latitude, longitude,

depth (m), acoustic class, Q1, Q2, Q3, class confidence (%), class probability (%), source vessel

or survey name, source dataset name, source date stamp, source time stamp, source FFV file ID,

source FFV file record index, source date stamp and time stamp for each record in the file. This

ASCII file is not a direct product of the bathymetric grids, but the acoustic classes as analyzed

24

from the raw MBES data. Using ArcGIS® Spatial Analyst, an inverse distance weighted raster

grid of the seabed ASCII file was created by interpolating between points to create a continuous

coverage of the supervised area.

The habitats paired with the seven unsupervised classes were assigned by comparing the

location of each acoustic class with the visual data from the ROV Dives. By reviewing the

dominant habitat within each acoustic class, a definition was given to the class. Some classes

existed in one or more relief classes, and were therefore segregated by relief class and habitats

identified for each relief class within an acoustic class. For example, Class 2 existed in low

relief, high relief and high relief off mound areas and therefore had three different habitats for

each relief class. Accuracy of the unsupervised classification of Chapman’s Reef was calculated

by comparing the acoustic data with the visual data from the ROV dives. Underwater video and

grab samples from each study area were used to create descriptors for the dominant benthic

habitat types within the study area; then assigned to the unsupervised acoustic classes produced

by QTC Multiview™. The final unsupervised benthic habitat map was tested against the ROV

footage collected in October 2005 and October 2006 to assign a visual classification to appear on

top of the multibeam bathymetry and QTC Multiview™ map.

In the supervised classification of bathymetry and backscatter over Chapman’s Reef, the

definition of the supervised classes was guided by the identification of habitats from the

unsupervised classification. As several of the unsupervised classes were distributed in all three

relief types, there was some duplication where a habitat would appear in more than one acoustic

class. Using the Trackplot Editor in QTC Multiview™, it was possible to define the areas within

the dataset to establish individual files for each desired class. At least 50 data points were used to

25

create a supervised class so that each class is represented by significant coverage (Karl Rhynas

of QTC™, personal communication, 2007).

Once the areas were designated for the supervised classification, files containing the

accepted points for each habitat were created and then merged together as one file. This merged

supervised file was then applied to the same dataset and a new classified seabed file was

exported from QTC Multiview™. The accuracy of the supervised classification was determined

using the ROV video and still photographs as was used with the unsupervised classification.

Each photo was tagged as correctly or incorrectly classified based on visual habitat matching of

the appropriate supervised acoustic class. Finally, the number correctly classified was divided

by the total number of photos for each class and a percent accuracy was produced.

3.6. Visual Seabed Classification

For this study, downward looking digital stills were most useful to quantitatively

document the change of seafloor habitats throughout the ROV dives. First, the digital stills were

used to identify the classes on the QTC Multiview™ classification map. A 6-m buffer (9 m for

Dive 4 in 2006) was put around each photo point in ArcGIS 9.1® to account for the maximum

positioning error of the ROV. These buffers were used to assign each benthic photograph with

an acoustic class from the QTC Multiview™ seabed file. Buffer zones of photos that contained

two or more different acoustic classes were discarded from the analysis (Kendall, 2005). Habitat

coverage of each photo was delineated using the habitat digitizer application yielding a

comprehensive table of percent habitat cover (Figure 7). ROV video records had continuous

seafloor coverage and were used as ancillary data to aid in the interpretation of benthic photos.

Then each photo was given a habitat descriptor based on the majority of bottom type

present such as sand, rubble, and hardbottom. Since the coral cover is the most important yet

26

most sparse bottom type, any presence of coral in the photo was automatically classified as coral

cover. Once visual classification was complete, whichever sediment type was dominant for all

the photos within each acoustic class determined the habitat identification given to the

unsupervised QTC Multiview™ acoustic classes.

27

Figure 7. Sample photo showing how the habitat digitizer was used to delineate benthic habitats by drawing polygons around like habitats. This photo, still #90 taken from ROV Dive 2, is approximately 61% sand and 37% standing dead and live coral outlined by blue and red polygons respectively. The habitat digitizer was created by Dave Crist at UNCW especially for this research. Two lasers appear on the photo as a reference scale, placed 10cm apart and can be seen on the SDLC section in the lower half of the photo.

28

4. RESULTS

4.1. Benthic Habitats Delineated by Visual Analysis

Digital still photographs and grab samples were used to interpret the substrate within the

two study areas in the OHAPC. The five main facies found to characterize the seafloor were: (1)

standing dead and live coral (SDLC), (2) coral rubble and gravelly sand (CRG), (3) low relief

hard bottom (LRHB), (4) sand (SA), (5) shell hash (SH). Each habitat was also assigned one of

the following relief classes: (1) high relief on mound areas (HR), (2) high relief off mound

(HROM), for areas off the main mound but with at least 0.5 m relief within 10 m of

georeferenced photo point, (3) low relief (LR), for areas with <0.5 m relief change. Table 2

shows the habitat classes with each relief class in which photos analyzed for this study were

observed.

4.1.1. Standing Dead and Live Coral Habitat

Photos were classified as standing dead or live coral no matter how much coverage of

live coral existed in the images. Documenting, mapping and monitoring live or dead structure

provided by O. varicosa is required in order to assess coral recruitment potential and fish habitat

quality. Both commercial fish species and their prey concentrate around structure on the Banks

(Koenig et al. 2005; Harter et al. 2009). The 1.6 cm/year growth rate of this coral (Reed, 1980)

means it will take 5-10 years to grow a small (1 m) colony. In order to gauge recovery and

effectiveness of protected status on the timetable desired by the SAFMC, monitoring the return

of these colonies is required. Live coral heads up to 1.5 m in height were observed near the east

and west flanks of the southern mound of Chapman's reef (Figure 8F).

29

Visual Habitat Classification

Coral Rubble and Gravelly Sand (CRG)

Low Relief Hard Bottom

(LRHB)

Sand (SA)

Standing Dead or Live Coral (SDLC)

Shell Hash (SH)

Relief Class

High Relief On Bioherm (HR)

X

High Relief Off Mound (HROM)

≥ 0.5m

X X

Low Relief (LR) X X X X X

Table 2. Distribution of visual habitats for assigned relief classes. Coral rubble and gravelly sand is the only habitat to exist in all three relief classes.

30

Figure 8. Selected bottom photographs of standing dead and live coral habitat taken from the Phantom II ROV in October 2005. Insert in upper right corner shows the locations of the photos relative to grab samples taken in 2005 and ROV dive transects. Grab samples are represented as shapes: Circle = Gravel >10%, Square = Sand, Triangle = Silty Sand. Standing dead and live coral habitats exist mostly away from the mounds in low relief areas or on medium relief knolls between 80-90 m depth. Laser separation on photographs is 10 cm.

31

Live coral heads were also observed in patches approximately 150 m and 550 m north of

the main mound on the eastern side of the knolls (Figures8 B, C and D), away from the high

relief mounds in areas of little to no slope.

4.1.2. Coral Rubble and Gravelly Sand

Coral rubble and gravelly sand covered the majority of Chapman's Reef while less

coverage of this habitat was seen in the northern study area. Most of this coverage was proximal

to the main reef complex or due north of the mounds. As the thickets of live coral were

subjected to mechanical destruction, the rubble was distributed on the mounds or stayed close to

Chapman’s Reef or was mobilized north of the reef from the predominant current speed and

direction of flow (Scanlon, 1999; Reed, 2002a). This habitat existed in all relief classes and

often was populated by gorgonians, sea urchins and sponges. Purple urchins (Arbacia

punctulata) were noticed on the southern mound of Chapman's Reef (Figure 9B) while the

northern mound was predominantly covered with sponges.

In some lower relief areas, the coral rubble consolidated to form a more complex

structure, sometimes with a few inches of relief (Figures 9A, D and E). Other low relief areas,

especially those north of the eastern ridge, were filled with finer sediments. This area is

predominantly coral rubble draped with sediment and mixed with gravel and some shell hash

(Figures 9 A, C and F). Coral rubble and gravelly sand habitats exist in both low and high relief

environments. This bottom type is usually near the mound itself, or in direct shot of current flow

from the mound, such as the large rubble field directly to the north of the mound. This habitat

has the largest depth range of 54-93 m and is often inhabited by urchins and other sessile

organisms in high relief areas. Laser separation on photographs is 10 cm.

32

Figure 9. Selected bottom photographs of coral rubble and gravelly sand habitat taken from the Phantom II ROV in October 2005. Insert on right side shows the locations of the photos relative to grab samples taken in 2005 and ROV dive transects. Grab samples are represented as shapes: Circle = Gravel >10%, Square = Sand, Triangle = Silty Sand.

33

4.1.3. Low Relief Hard Bottom Habitat

Hard bottom provides significant habitat to fish in the area such as short bigeye, tattlers,

bank sea bass and members of the snapper/grouper complex. Low relief hard bottom was more

difficult to detect and assign from images as a thin (few cms thick) veneer often covered hard

bottom areas, masking the underlying rock. A strong northward flowing current in the Gulf

Stream with velocities of 3-5 knots may distribute sediment to valleys of small knolls and atop

limestone hard bottom. (Hollister and Heezen, 1972; Scanlon et al, 1999). Often the only

evidence of possible underlying hard bottom would be sessile species such as sea whips, sea pens

and black coral; depth of burial was unable to be determined by photographs and video alone. In

these instances, photos were classified as whatever substrate existed right at the sediment-water

interface. In the Chapman's Reef complex, most hard bottom was seen on the northwest corner of

the southern reef (Figures 10A and B) while more coverage of this habitat was visible in the

North Study Area in the form of smaller knolls and rock ledges.

4.1.4. Sand Habitat

The vast majority of soft substrate is sand or sandy-silt located mostly in low relief areas

in depths of 82-93 m and sporadically between medium (1 m) relief knolls. South of Chapman's

Reef the sediments are visibly finer and little rubble is present with sparse debris (Figures 11A,

C and F). The softer sediments are found at least 50-100 m away from the reef complex mostly

in the east and west directions and between the isolated peaks of Chapman’s eastern ridge

(Scanlon, 1999) (Figure 11D) while the area north of the mound is composed predominantly of

coral rubble coverage. Farther north of the main mound and the rubble field, the sediments again

become finer as seen in Figures 10E and B. Open burrows seen in the still

34

Figure 10. Selected bottom photographs of low relief hard bottom habitat taken from the Phantom II ROV in October 2005. Insert on right side shows the locations of the photos relative to grab samples taken in 2005 and ROV dive transects. Grab samples are represented as shapes: Circle = Gravel >10%, Square = Sand, Triangle = Silty Sand. Low relief hard bottom habitats exist between 75-93 m depth and mostly near the mounds in lower relief areas or areas scoured out by currents tumbling over the 30 m mound.

35

Figure 11. Selected bottom photographs of sandy habitats taken from the Phantom II ROV in October 2005. Insert in upper right corner shows the locations of the photos relative to grab samples taken in 2005 and ROV dive transects. Grab samples are represented as shapes: Circle = Gravel >10%, Square = Sand, Triangle = Silty Sand. Sandy habitats exist mostly away from the mounds in low relief areas between 80-93 m depth. Laser separation on photographs is 10 cm.

36

photos mark areas of bioturbation (Figures 10A and C) and some biological growth of sea pens

and hydroids also existed on the flat sandy areas. Evidence of significant near-bottom currents

included sand waves seen clearly in ROV video footage (Figure 11A and F).

4.1.5. Shell Hash

The distribution of shell hash habitat is mostly located north of Chapman’s eastern ridge (Figure

12A-F). This area also contains dominant coverage of coral rubble and gravelly sand and exists

as a mixed habitat of rubble and shell hash. Additionally, the density of shell hash in benthic

photos does vary when seen in sand areas or mixed when with coral rubble. Photographs were

classified as shell hash if more than 50% of the photo had visible shell fragments or fully intact

bivalve shells and minimal presence of coral rubble pieces. Shell hash habitats exist in low relief

and high relief off mound areas in depths of 79-93 m. Although the area north of the eastern

ridge does have shell hash, it still primarily contains coral rubble. Often during dive transects,

habitats would change very quickly from shell hash to rubble. Knoll areas north of the eastern

ridge were characterized by patchy habitat distribution at the scale of meters, and boundaries

between habitat patches were not well defined. Several species of marine life were seen on the

shell hash habitat including: squid, spotted snake eel, scorpion fish (Figure 12D), rock shrimp,

sea anemones, sea urchins and sea whips.

37

Figure 12. Selected bottom photographs of shell hash habitat taken from the Phantom II ROV in October 2005 and October 2006. Insert in upper right corner shows the locations of the photos relative to grab samples taken in 2005 and ROV dive transects. Grab samples are represented as shapes: Circle = Gravel >10%, Square = Sand, Triangle = Silty Sand. Shell hash habitats exist mostly north of the mounds in low relief areas between 79-93 m depth. Laser separation on photographs is 10 cm.

38

4.2. Unsupervised Classification of Chapman's Reef

The unsupervised classification of bathymetry and backscatter over Chapman's reef

revealed seven distinct acoustic classes within the reef complex (Figure 13). The identity given

to the unsupervised classes was based on the dominant habitat type seen in the ROV video and

photos. The accuracy of each class with its relief component is shown in Table 3. Each class

will be presented in order of spatial coverage in the unsupervised dataset. Beginning with the

largest aerial coverage, Class 7 covered over 45% of the dataset and existed in high relief and

low relief areas. Of the three grab samples taken within Class 7, two were defined as sand and

silty sand and the other was gravel >10%. Class 7 existed predominantly (82%) in low relief

areas and had an equal number of photographs classified as sandy sediments and low relief coral

rubble and gravelly sand. Although the photo records showed a similarity between these two

habitats, a definition had to be made in order to proceed with the accuracy analysis. Due to this

duplicity, a habitat definition for low relief Class 7 was finalized by referencing the video

records and grab samples to determine that Class 7 existed mostly in sandy/softer sediment

habitats although the photo records showed both sand and coral rubble in low relief areas.

Spatial distribution of Class 7 shows it to lay farthest away from the mounds in flat seafloor

environments where the sediment appears to get finer with greater distance from the reef

complex. Class 7 has little to no presence in the rubble fields directly north of the reef complex

and small numbers present on the ridges.

Class 2 existed in all three relief classes and covered 40% of the entire Chapman’s Reef

survey area. Contrary to Class 7, Class 2 appeared mostly in the high relief areas and directly

north of the reef complex. The low relief component, defined as shell hash, was also a

significant part of Class 2, although it is important to note that 34% of the low relief photographs

39

Figure 13. QTC Multiview™ unsupervised classification of Chapman’s Reef showing seven distinct acoustic classes. White lines represent 2005 and 2006 ROV dive transects. Grab samples taken in 2005 are represented as shapes: Circle = Gravel >10%, Square = Sand, Triangle = Silty Sand.

40

QTC Class Relief Class

Habitat Class

# Photos Correct

# Photos Missed

# Photos Total % Accuracy

1 LR SA 7 0 7 100% 2 HR CRG 32 1 33 97% 2 HROM CRG 19 6 25 76% 2 LR SH 39 40 79 50% 3 LR SA 4 6 10 40% 3 HROM CRG 3 2 5 60% 4 LR SA 5 5 10 50% 4 HR CRG 1 0 1 100% 5 HROM CRG 1 3 4 25% 5 HR CRG 1 0 1 100% 6 LR CRG 2 0 2 100% 6 HR CRG 7 0 7 100% 6 HROM CRG 3 0 3 100% 7 HR CRG 8 1 9 89% 7 LR SA 21 29 50 42%

TOTAL 153 93 246 62%

Table 3. Unsupervised QTC Multiview™ acoustic classes with their relief class definitions and benthic habitat definitions. Habitat assignments were determined based on the majority or benthic habitat seen in the benthic photographs within each class. All photos were then analyzed for accuracy by determining correct or incorrect classification. Finally, a percent accuracy was calculated by dividing the number correct by the total number of photos used for each acoustic class.

41

were tagged as coral rubble and gravelly sand. All three grab samples taken within Class 2 were

defined as gravel >10%.

Class 4 covered 6% of the dataset and existed in high and low relief areas and appears to

be a transition zone meaning it contains a mixture of different habitats. Low relief sand was seen

in five of the ten photos associated with Class 4 yielding 50% accuracy. No grab samples were

taken in Class 4. The low relief distribution of sand in Class 4 was largely seen in ROV Dive 6

on the western edge of the rubble fields north of the eastern ridge. Smaller aggregations of Class

4 appeared on the eastern side of the reef and also up against the southern part of the eastern

ridge and up into high relief areas as coral rubble and gravelly sand. The high relief component

of Class 4, with only one photograph, yielded 100% accuracy (Table 3).

Class 6 spread over 3% of Chapman’s Reef in mostly high relief coral rubble and

gravelly sand, and the one grab sample within Class 6 was gravel >10%. Class 6 existed entirely

in the valleys between the two mounds of the eastern ridge, although not directly on the

pinnacles and low relief areas were approximately 20 m from the base of the mound.

Class 1 covered 2% of the dataset and was all low relief sand located south of the eastern

ridge and interspersed with Class 7. There were no grab samples located within Class 1 areas.

Class 3 covered 2% of Chapman’s Reef study area and covered two habitats equally, low

relief sand and coral rubble. This class also appears to be a transition zone similar to Class 4.

Low relief sand was found to dominate the video records within Class 3 located south of the

eastern ridge and closer to the bioherm than Class 1.

Class 5 had the lowest spatial coverage with just fewer than 2% of the dataset. It

occurred mostly on the western flank of the eastern ridge and also on the eastern edge of the

knoll area directly north of the north-facing eastern ridge. Only four photos were taken in Class

42

5 areas; however, each represented different habitats, so Class 5 is a possible transition zone with

a few diverse habitats such as live coral, coral rubble and sand.

A chi² analysis was performed on the acoustic classes from the unsupervised

classification and the photographic data to determine if a statistically significant relationship

existed between the habitats assigned to each class. The results showed a significant relationship

between the acoustic classes and visual classes, (chi-square = 89.3921, 18 d.f., P= <0.0001)

although relief class was not part of the chi2 analysis. Once a statistically significant relationship

was confirmed, then an accuracy analysis was pursued to further quantify the effectiveness of the

unsupervised classification. High relief coral rubble and gravelly sand was the most accurately

classified habitat with 97% accuracy and existed in the high relief regions.

4.3. Supervised Classification of Chapman's Reef

The supervised classification was based on the dominant habitats observed in the

photographs and video, as well as the habitats in the unsupervised classification. Several factors

did affect how the classes were grouped and defined for supervision. Firstly, sand and shell hash

were grouped together for the training of the supervised classification. This action was based on

several other previous studies where softbottom was grouped together in acoustic classification

(Huhnerbach et al., 2008; Vertino et al., 2010; Kendall et al., 2005) or sediments with more

varying grain sizes were separated. Secondly, another factor affecting supervision was the

objective to find areas of standing dead or live coral and determine an acoustic class for these

environments. The SDLC areas did not seem to exist in only one unsupervised class, nor did the

ROV dives have more coverage of these areas to make a definitive determination on how the

SDLC areas were acoustically represented.

43

Therefore, the ROV video data were used to pinpoint the more dense areas of live coral,

and these were used in the supervision of the SDLC class. As for differences in relief, SDLC

was not assigned a relief class due to the limited amount of areas that could be utilized for

supervision. The main facies on Chapman’s Reef were: (1) standing dead and live coral

(SDLC), (2) coral rubble and gravelly sand (CRG), (3) low relief hard bottom (LRHB), (4) sand

(SA), (5) shell hash (SH). Of these identified visually, and those identified acoustically from

Table 3, five habitats were chosen for supervision based on the objectives of the study and the

coverage of the habitats as seen from ROV data. The five habitats used in the supervision were:

(1) Standing dead and live coral, (2, 3, and 4) Coral rubble and gravelly sand (HR, HROM and

LR), and Sand/shell hash.

The CRG habitat was broken down by relief class for the supervision since it occurred in

many of the ROV photos and was represented by several of the records in the unsupervised

classification (Table 3). The low relief hardbottom habitat was not used in the supervision since

there was not enough ROV footage and benthic photographs to represent this habitat. The

supervised dataset was defined by using 4.4% of the data points from the unsupervised

classification coupled with habitat information from 245 benthic photographs. To further assure

an accurate supervision, points were selected for supervision only if the photograph and video

showed the same habitat. Of the total 14,536 data points created in the unsupervised

classification, 634 records (4.4% of total) were used to assign the five classes to supervise the

Chapman’s Reef habitat map (Figure 14). When compared to the visual classification, the

supervised classification of Chapman’s Reef yielded an overall accuracy of 30% (Table 4).

44

QTC Class Relief Class Habitat Class

# Photos Correct

# Photos Missed

# Photos Total % Accuracy

1 HROM CRG 12 48 60 20%

2 SDLC 3 12 15 20%

3 LR CRG 11 72 83 13%

4 SA 25 17 42 60%

5 HR CRG 24 21 45 53%

TOTAL 75 170 `245 30%

Table 4. Chapman’s Reef supervised classification results. Table shows acoustic class, relief class, habitat, and how many photographs for each class were correctly classified. A total of 245 photographs were analyzed yielding an overall supervised classification accuracy of 30%.

45

Class 1 defined as high relief off mound coral rubble and gravelly sand (CRG-HROM)

was 20% accurately classified and covered 23% of the Chapman’s Reef study area (Figure 15).

Class 1 existed predominantly in the knoll area directly north of the eastern ridge with a few

appearances north of the western ridge. All three grab samples within Class 1 were classified as

Gravel >10% (OB05-07, OB05-10, and OB06-16). The habitat profiles for ROV Dives 1 and 2

show strong aggregation of Class 1 on the knolls north of the eastern ridge (Figures 16 and 17).

The Dive 4_2006 profile shows the same results in weaker abundance (Figure 18).

Class 2 was supervised as standing dead and live coral (SDLC) and yielded 20%

accuracy and covered approximately 2% of Chapman’s Reef study area. Class 2 was spread

sparsely throughout the study area but did show a small aggregation in the right flank of the

eastern ridge where the video records showed the largest live coral coverage. There were no

grab samples taken within Class 2. The habitat profiles of ROV Dive 1 show this habitat to exist

largely on the southeast flank of the eastern ridge, and where the dive and photo classification

agree (Figure 16). ROV Dive 2 shows Class 1 and 2 intermingled in the area north of the reef

complex which does reflect the diverse composition of habitats in the knoll area (Figure 17).

ROV Dive 6 (Figure 19) showed the left side of the eastern ridge to have less change in

relief/less topographic complexity than the right side as seen in ROV Dive 1 (Figure 16); this

difference is seen in the classification where there are less amounts of SDLC and HROM-CRG

habitats (Figure 14).

46

Figure 14. Supervised points of Chapman’s Reef showing areas of the reef used to classify the five supervised benthic habitats. Of the total dataset, 4.4% was used for the supervised classification. The dark green points show the entire unsupervised area overlaid on the backscatter mosaic.

47

Figure 15. QTC Multiview™ supervised classification of Chapman’s Reef showing definition and distribution of Classes 1-5. White lines represent 2005 and 2006 ROV dive transects. Grab samples taken in 2005 are represented as shapes: Circle = Gravel >10%, Square = Sand, Triangle = Silty Sand.

48

Figure 16. Profile of ROV Dive 1 showing relief change over dive duration with habitat classification colors of supervised classification; S and N indicate the south and north ends of the transect.

49

Figure 17. Profile of ROV Dive 2 showing relief change over dive duration with habitat classification colors of supervised classification; S and N indicate the south and north ends of the transect.

50

Figure 18. Profile of ROV Dive 4_2006 showing relief change over dive duration with habitat classification colors of supervised classification. Bathymetric data points were fewer on this dive due to slant range and ship positioning difficulties when ROV traversed over the bioherm. S and N indicate the south and north ends of the transect.

51

Figure 19. Profile of ROV Dive 6 showing relief change over time with habitat classification colors of supervised classification; S and N indicate the south and north ends of the transect.

52

Figure 20. Profile of ROV Dive 8 showing relief change over time with habitat classification colors of supervised classification; S and N indicate the south and north ends of the transect.

53

Class 3 defined as low relief coral rubble and gravelly sand (CRG-LR) was 13%

accurate and covered approximately 43% of the study area. This class spread over all low relief

areas of the entire Chapman’s Reef study area but appeared more sparsely in the knoll area

directly north of the eastern ridge. Three grab samples were taken within Class 3, two of which

were gravel >10% (OB05-06 and OB05-11) and one was classified as sand (OB05-03). The

habitat profiles of ROV Dive 6 show the strongest representation of Class 3 as this dive traverses

over the area where low relief bathymetric structure dominates (Figure 19).

Class 4 was identified in the supervision as sandy habitat (SA) and yielded 60% accuracy

and covered approximately 22% of Chapman’s Reef study area. It existed mostly south of both

the eastern and western ridges with a small aggregation of data points at the far northeastern end

of the knoll area. There were two grab samples taken within Class 4 (OB05-02 and OB05-05,

both classified as silty sand). The habitat profile of ROV Dive 1 also supports this habitat

classification by showing sandy areas designated as Class 4 at the beginning of the dive and

midway through the dive as the ROV track shows sandy areas flanking the eastern ridge (Figure

16).

Supervised Class 5 defined as high relief coral rubble and gravelly sand (CRG-HR)

covered approximately 10% of the dataset and yielded 53% accuracy. Class 5 existed

predominantly on the high relief mounds and had more sparse coverage in the knoll area north of

the eastern ridge. There was one grab sample taken within Class 5 (OB05-15 - Gravel >10%).

The high relief distribution of Class 5 is best represented in the habitat profiles of ROV Dives 1

and 2 than the other ROV dives covering the high relief area (Figure 16 and 17). The video did

show the eastern ridge of Chapman’s Reef to consist mostly of coral rubble and gravelly sand

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and this is strongly reflected in both the photo and video classification with a strong correlation

to the acoustic classification seen in the resulting accuracy (Table 4).

4.3.1 ROV Dive Profile Discussion

ROV Dive 1 was run from north to south on the eastern edge of Chapman’s eastern ridge.

The change in relief over time of ROV Dive 1 can be seen in Figure 16. Although ROV Dive 1

did not go directly over the bioherms within Chapman’s Reef, the range of habitats were

apparent and changed throughout the dive (Figure 16). The beginning of the dive is fairly flat

despite small areas of live coral patches. Habitats change from sand and shell hash to live coral

and finally, the middle to end of the dive is predominantly coral rubble as the end of the dive

terminates in the north end of the knoll area. Some small patches of live Oculina coral were seen

near the end of the dive in the far northeast of the knoll area north of the eastern ridge. The dive

profile of ROV Dive 2 showed strong aggregation of Class 4 in the low relief areas south of the

eastern ridge where many photos were correctly classified as sand with an overall 60% accuracy.

Class 5 also showed a strong aggregation between the two bioherms on the eastern ridge where

CRG-HR habitat was correctly classified in this section. Class 5 did not appear on the tops of

the bioherms themselves but did show sparse distribution on the northern bioherm. The rest of

ROV Dive 2 shows a scattering of habitats but perhaps correctly placed are many instances of

Class 1 (CRG-HROM) seen in the knoll area north of the eastern ridge. This same occurrence is

seen on ROV Dive 1, Dive 4_2006 and Dive 8 where large sections of Class 1 appear in the

knoll area north of the eastern ridge. Once ROV Dive 2 moves farther north away from the knoll

area and the seabed becomes more flat, another section of Class 4 appears where two

photographs were correctly classified as sand, but two photographs in this area also showed

SDLC habitat and that could be reflective of the short sections of other classes where acoustic

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signatures are showing some variation. ROV Dive 6 profile is a good example of how the low

relief habitats could be too similar acoustically to differentiate where a big section of the dive is

shown as Class 3 (CRG-LR) but visually classified as sand and shell hash. Most photos within

this dive were incorrectly classified but the aggregation of Class 5 on the small part of the

bioherm the dive traverses is of note since it is seen on several of the ROV dive profiles (Dive

4_2006 and higher relief section of Dive 1).

4.4. Supervised Classification of North Study Area

To test the applicability of the benthic habitat classification developed in Chapman's Reef

Study Area for the entire OHAPC, the same supervised classification of the bathymetry and

backscatter performed on Chapman’s Reef was applied to the North Study Area. Just as in the

Chapman’s Reef dataset, the supervised classification of the North Study Area consisted of the

five supervised acoustic classes (Figure 21). A total of 117 photos were analyzed within the

northern study area yielding an overall 10% accuracy (Table 5).

Class 1 defined as high relief off mound coral rubble and gravelly sand (CRG-HROM)

was classified with 33% accuracy and covered 9% of the North Study Area (Figure 22). Class 1

was found mostly in the eastern half of the study area and does appear to correlate with the

higher relief topographic ‘fingers’ of the continental shelf, which are oriented in a NWN-SES

direction with depths ranging from 70-77m. The habitat profiles for ROV Dives 4 and 5 show

strong aggregation of Class 1 on the eastern side of the study area near the ‘fingers’ of the

continental shelf (Figures 22 and 23). Class 1 did not have any grab samples and two photos

correctly classified from a total of six.

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Figure 21. QTC MultiviewTM supervised classification of bathymetry and backscatter over the North Study Area showing definition and distribution of acoustic Classes 1 thru 5. White lines represent 2005 ROV dive transects. Grab samples taken in 2005 are represented as shapes: Circle = Gravel >10%, Square = Sand, Diamond = No sample/possible hardbottom.

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QTC Class Relief Class Habitat Class # Photos Correct

# Photos Missed # Photos Total % Accuracy

1 HROM CRG 2 4 6 33%

2 SDLC 1 95 96 1%

3 LR CRG 1 4 5 20%

4 SA 8 2 10 80%

5 HR CRG NA NA NA NA

TOTAL 12 105 117 10%

Table 5. North Study Area supervised classification results. Table shows acoustic class, relief class, habitat, and how many photographs for each class were correctly classified. A total of 117 photographs were analyzed for this dataset yielding an overall supervised classification accuracy of 10%.

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Figure 22. Profile of ROV Dive 4 showing relief change over dive duration with habitat classification colors of supervised classification; S and N indicate the south and north ends of the transect.

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Figure 23. Profile of ROV Dive 5 showing relief change over dive duration with habitat classification of supervised classification; S and N indicate the south and north ends of the transect.

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Class 2 defined as standing dead or live coral (SDLC) was classified with only 1%

accuracy yet covered 43% of the North Study Area (Figure 21). There were three grab samples

taken within Class 2 (OBO5-19-Gravel>10%, OBO5-20-Gravel>10%, and OBO5-NS-no

sample/ possible hard bottom). Class 2 took up almost the entire western half of the North Study

Area and the majority of ROV Dive 4 in shallower water depths. Its presence in deeper water

occurred with sparse coverage in the eastern section of the study area.

Class 3 was identified by coral rubble and gravelly sand in low relief areas (CRG-LR)

covering 36% of the study area. A 20% accuracy was achieved for Class 3 with one correct

photo from a total of five. Two grab samples were taken within Class 3 areas (OBO5-21-

Gravel>10%, and OBO5-22-Sand) and only sample #21 agreed with the supervised

classification. The other four photos within Class 3 were identified as sand (3) and low relief

hard bottom (1).

Class 4 was defined as sand/shell hash in the supervised classification and comprised

approximately 10% of the North Study Area. Although making up a small portion of the dataset,

Class 4 had the highest accuracy of 80% with eight photos correctly classified out of a total of

ten (Table 5). Although most of ROV Dive 4 was visually classified as sand, only six of the

photos within ROV Dive 4 were correctly classified in the northern side of the study area and

existed in depth of 75-90m. Class 4 also appears to exist in the deeper valleys adjacent to the

NWN trending continental shelf edge ‘fingers’. No grab samples were taken within Class 4.

Class 5 was defined as high relief coral rubble and gravelly sand (CRG-HR) and did not

appear in the supervised classification of the North Study area. This was expected as Class 5

was assigned in high relief areas only during the Chapman’s classification. The North Study

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Area does not contain any mounds as seen at the Chapman’s Reef site. Therefore the supervised

classification in this study area was correct in that a high relief acoustic signature was not found.

5. DISCUSSION

5.1. Unsupervised Classification Review

The Chapman’s Reef unsupervised classification resulted in a 62% accuracy and showed

the best results in the CRG habitats of all three relief classes, LR, HR and HROM. Additionally,

low relief sand was also classified well, as expected from previous studies being able to

differentiate between hardbottom and softbottom areas (Kendall, 2005). Previous research had

stronger correlation results when only differentiating between hardbottom and softbottom areas

such as flat sand, rippled sand and colonized hardbottom (Cochrane and Lafferty, 2002; Kendall,

2005). When additional classes are introduced, the results include higher accuracy when the

classification is accompanied by aerial imagery to enhance acoustic data and in situ data (Riegl,

2005). In deeper areas where aerial imagery is not useful, additional classes have been difficult

to delineate in deep-water coral reef habitats (Huhnerbach, 2008). Results for Chapman’s Reef

attempt to enhance previous studies where only the distribution of three habitat classes was

identified and no accuracy results were tested (Scanlon et al., 1999). The level of acoustic

diversity within the automated classification dataset is more evident compared to the visual

interpretation of sidescan data (Scanlon et al., 1999). Deep-water habitat classification typically

does not have high accuracy values as additional groundtruthing data is hard to acquire, time

consuming, and cannot be enhanced by aerial photography.

The dataset used to test the accuracy of the Chapman’s Reef unsupervised classification

consisted of 246 total photos for a reef complex area approximately 0.6nm². Although there are

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weaknesses in this classification as a whole, there appears a significant agreement in the

classification of the high relief reef sections where the high relief portion of Class 2 resulted in

97% accuracy with 32 photos correctly classified. The unsupervised classification also showed

strong success with Class 1 in the low relief sand areas with seven of seven photos correctly

classified as low relief sand which was strongly supported by the video seen in ROV Dive 1

(Figure 16). Class 7 showed the weaknesses of this classification where the low relief areas

resulted in 42% accuracy.

The high relief off mound subset of Class 5 was defined as coral rubble and gravelly sand

and had only 25% accuracy. This was the lowest accuracy calculation within the unsupervised

dataset. And again, the possibility of Class 3 being a transition zone is most likely the

determination to make as well as Class 5 due to the variety of habitats seen in the photo

coverage. Although few photo records were taken within Class 5, habitats given to Class 5

photographs include live coral, coral rubble and shell hash. There was an apparent sampling bias

in Classes 4, 5 and 6 ( in all relief classes) where one or all classes showed 100% accuracy, but

these classes were represented by minimal visual data points.

5.2. Influences on Automated Classification

The evaluation of an automated classification depends highly on the data input into the

classifier and the groundtruthed data to support an automated unsupervised or supervised

classification. Based on review of the accuracy and the dive profiles along with the spatial

distribution of the classes, an examination can be conducted on: 1) possibilities as to why the

accuracy of the classification was not higher, 2) what influenced the low accuracy of

classification for certain areas based on the supervision, 3) survey equipment or data limitations,

and 4) knowledge of seabed habitats in the study areas and how their distribution may affect

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acoustic data. Conclusions can be drawn for what these influences could be and how to better

prepare for future habitat classification within the OHAPC.

5.2.1. Underlying Hardbottom

Errors in the classification may be attributed to the relationship between the sediment

dynamics and underlying geology of the area. A strong and predominant northward current in

the North Atlantic Gulf Stream flowing upwards of 3-5 knots at the surface and 0.5 knots at the

sediment water interface can distribute this sediment in valleys of small knolls and atop flat

limestone pavement and appear as soft bottom (Scanlon, 1999). These current conditions can

effectively create a false 'soft bottom’, which can be deceptive in classifying video and photo

frames as only the surface condition is seen. The 300 kHz transducer on the EM-3002 used for

the 2005 survey penetrates the sediment water interface less than 2cm. Therefore, if the sand

veneer was greater than 2cm thick, then the backscatter signal returned was truly a signal of

unconsolidated substrate and not that of harder composition (Anderson, 2008; Lurton, 2002).

The USGS 1995 sidescan survey reported a low relief/low backscatter environment adjacent to

the isolated peaks within the OHAPC and found the sediments to consist of sand and muddy

sand (Scanlon, 1999). On the other hand, seawhips and black coral as well as other sessile

organisms were visible in some of these questionable areas covered by sand although it was

impossible to know what substrate they were attached to and how deep their holdfasts were

buried. Without additional tools other than a video camera and sparse grab samples, such as

shallow seismic profiling, it is difficult to tell how deeply buried the holdfasts of sessile

organisms were, and to what surface they are attached. For questionable shots (true sand or

hardbottom with veneer), these possible sand veneers were classified as either sand or shell hash

cover, if no underlying hard bottom was visible.

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5.2.2. Transition Zones

The unsupervised classification was designed to determine areas of acoustic diversity in

the Chapman’s Reef study area. As seen from the ROV video footage and documented in

previous studies, these habitats often change or overlap at small scales forming a transition zone

between one habitat and the next. These transition zones are common in the marine environment

and characterized by a mixture of two or more habitats rather than a discrete line of change

between one habitat and another. Transition zones within the study area could be caused by

strong current action upon the Oculina varicosa coral itself where rubble pieces are transported

and deposited in the direction of the current. The failure of the automated unsupervised

classification in Class 7 is strong evidence that a patchy environment is difficult to delineate due

to the convergence of several different bottom types. Additionally, transition zones will differ

acoustically from the signatures of the single classes by themselves, therefore making it hard to

pinpoint signatures as each transition zone will vary spatially and may not have similar

signatures over an entire study area or habitat map area. Within the Q-space of the automated

classification software, data from one acoustic class can overlap with one or more other acoustic

classes, adding variability within the classes. These areas can be labeled as transition zones and

are expected due to the composition of the seafloor and other dynamics which do not occur with

fixed boundaries (Anderson, 2002). For example, as ROV dives progress over the Chapman’s

Reef study area, coral rubble areas do not abruptly disappear once the ROV is away from the

main reef complex. Rubble can be seen scattered in close proximity adjacent to the reef as well

as in flat sandy bottom areas some distance away; this can make delineating a specific coral

rubble area difficult as the density of rubble will change as distance from the main reef complex

increases. The density of the coral rubble will also change with areas farther away from the

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bioherm having sand and shell hash mixed in with the rubble and areas closer to the bioherm will

have complete rubble coverage. This can be seen with the distribution of unsupervised Classes

2-5. Class 2 appears in areas where the CRG habitat is the most dense, such as on the bioherms

and directly north or adjacent of the bioherms. Class 3 appears on the perimeter of Class2, and

continues with Classes 4 and 5 as distance away from the bioherm and knoll area increase. With

this in mind, the supervised classification was adjusted to include coral rubble and gravelly sand

habitats for all three relief classes (low relief, high relief, and high relief off mound) since the

CRG habitat appeared in many parts of the Chapman’s Reef study area. Class 7, the most

abundant class, exists in the outer fringes of the study area away from the eastern and western

ridges and therefore contains a mixture of the coral rubble from the reef complex as well as the

flat and sandy outer edges more characteristic of the seabed as distance from the reef increases.

5.2.3. Limited Data

Enhancing the groundtruthed dataset with additional benthic photographs is required to

more effectively define a dominant habitat type for the acoustic classes. For the unsupervised

classification, Classes 3-6 had minimal coverage and as seen on Table 3, had very few benthic

photographs to use in accuracy analysis or defining the acoustic class with a habitat. This did not

occur with Classes 2 and 7 since both took up large areas of the dataset, and therefore had more

ROV data coverage. It is difficult to produce a visual classification with one dataset and try to

keep part of it separate for an accuracy analysis, especially if some of the photos had to be

disqualified due to visibility, or too much distance from supervised classification points. Earlier

studies have done separate transects used solely for testing a classification, however, ship time

limitations and weather conditions offshore can make acquiring more data impossible (Kendall

et. al, 2005). The ROV dives were carefully planned to cover as many different habitat types as

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possible, but the coverage of some acoustic classes was sparse and segregated such that targeting

those areas was difficult considering navigation obstacles. It would also be beneficial to have

more benthic photographs, especially in the areas used for calibration of the supervised dataset.

However, using more calibration points could cause further variability problems within the

acoustic classes, rather more visual data could provide better certainty of the areas used and

could result in stronger accuracy. Knowledge of habitats in the study area needs to be viewed

acoustically since some habitats, even in transition zones, may have acoustic signatures of more

than one class. Confidence values within the supervised dataset can reflect some of the areas

where assigning a class was difficult.

As observed from the ROV video, habitats change quickly over short distances and make

picking supervision points subject to the size of the area and the distance between benthic

photographs. One goal of this study was to determine if automated seabed classification would

prove useful for such a large protected area. Due to the differences of habitat types over this

large area, it is clear that the use of automated classification systems would work better when

applied to similar habitats within the OHAPC. Reduced accuracy occurred in this study when

the supervised classification was applied to an area too dissimilar from the area where the

classification was created. If similar areas were classified and tested separately for accuracy, a

more useful and accurate habitat map could be created by merging the classifications produced

from smaller sections of the OHAPC.

The resolution and pixel size of the data is also a factor in finding live coral heads that

may be <1m in diameter. While large bioherms have long been documented within the OHAPC,

recent studies have shown smaller live coral heads where the Oculina varicosa coral is beginning

to repopulate. Repopulation in certain areas, especially in flat sections adjacent to large bioherms

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such as Chapman’s Reef has been documented in the 2005 ROV Dives. Further MBES work

should focus on getting equipment where the smallest pixel size will be sufficient to identify

these small coral heads and aid in future groundtruthing efforts. Larger pixel size/lower

resolution can limit the topographic complexity and backscatter seen in MBES data.

5.3. Supervised Classification – Chapman’s Reef

Some previous studies have had difficulty with supervised classification results,

especially in deep-water coral reef environments such as Chapman’s Reef (Huhnerbach et al.,

2008). Transition zones within deep-water coral areas are often given an acoustic class

identification of a transition zone because a visual class cannot be determined from the limited

groundtruthing data. Additionally, there can often be more than one transition zone with its own

acoustic signature appearing in the same dataset (Anderson, 2002; Huhnerbach, 2008).

The supervised classification of Chapman's Reef revealed low average success rate of

30%. Reduced agreement may have resulted from several possibilities such as supervision sites,

transition zones, and unnatural distribution of habitats. Success of a classifier depends upon the

variability within the dataset as well as the consistency of the supervision information

(Anderson, 2002), including quality and interpretation of imagery. The number of supervision

points used from the original unsupervised area totaled 634 out of 14,536, just over 4% of the

entire dataset. As advised by QTC MultiviewTM personnel, success could not be guaranteed by

using more supervision points, and may lead to even more inconsistencies within the dataset due

to higher variability within the supervision data.

Reed (2000) noted that the ivory tree coral structure was fragile enough to be broken and

transported by strong currents, and mechanical destruction by fishing gear may also mobilize

coral pieces. Therefore, some softbottom areas are littered with coral rubble pieces and can

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provide an acoustic signal independent from that of dense coral rubble or sand alone.

Additionally, previous research in Chapman’s Reef as well as other locations within the OHAPC

has shown a decrease in standing dead and live coral coupled with an increase of unconsolidated

coral rubble coverage indicating damage by means of mechanical destruction from fishing gear

(Reed, 2007). Previous sidescan sonar mapping within the OHAPC has not included accuracy

calculations based on groundtruth data as only three habitats were identified and an interpretive

geologic map was produced, delineating habitats by backscatter intensity alone. The

unsupervised classification certainly had better accuracy (62% vs. 30% of supervised

classification); however a supervised classification is usually more tailored to the dataset and can

account for previous knowledge of the study site as supervision is usually carried out by

scientists familiar with the given area.

Additionally, supervised classes can be designed to account for relief such as the CRG-

HROM class, when the unsupervised classification had to break the seven classes up with regard

to relief before determining accuracy. This can be seen in the CRG-HR Class where the high

relief component of the CRG Class was correctly not assigned anywhere in the North Study Area

dataset as no bioherms were present. Supervised classification is more helpful in reviewing

acoustic signatures and what may be causing the differences since it involves intense study of

groundtruth data prior to execution of the supervision. Scientists can review certain areas within

the dataset, including transition zones, and account for the presence of these types of

environments prior to supervision. Alternately, during an unsupervised classification, classes are

divided up based on depth and acoustic diversity alone, and erroneous classes may be identified

that can skew the results when incorporating groundtruth data. For example, the unsupervised

classification of Chapman’s Reef had five classes with CRG-HR components. Other studies

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involving habitat mapping of deep-water coral reef areas have experienced difficulties in

producing higher accuracy results (e.g., Freitas et al., 2003; Riegl, 2005; Huhnerbach, 2008;

Anderson, 2002). In many cases habitat maps from acoustic data and visual data are presented

without accuracy calculations to test the final habitat map (Dolan et al., 2008; Vertino et al.,

2010).

5.4. Supervised Classification – North Study Area

The supervised classification of the North Study Area revealed an average success rate of

10%. The north study area is not an Oculina bioherm complex like Chapman’s Reef and did not

have as large of a depth range. Although Macintyre and Milliman (1970) found that these

Oculina mounds were situated up the Florida coast from Fort Pierce to Daytona along the 80m

isobath line, the same type of bioherm structure was not observed in the North Study Area,

although similar bottom types aside from a large bioherm structure were expected based on

previous research. Class 2 of the supervised classification corresponded to hard bottom areas

and can be supported by the grab sample OBO5-NS. Class 1 had an accuracy of 33% and

covered 9% of the study area. Areas of HROM CRG mostly appeared in the eastern side of the

study area where the ROV dives had no coverage, however based on the results of Scanlon et al.

(1999), the north-south trending sections seen in the supervised classification are similar to those

found on the 1995 USGS survey. During the USGS survey they were classified as low

relief/high backscatter areas where much of the area was covered with gravelly sand and

carbonate contents >90% a few cms thick or more. Additionally, the north-south ‘streaks’ were

suggested to be caused by movement due to strong bottom currents (Scanlon, 1999). The

location of these north-south streaks could be similar to those found during the USGS survey,

especially given their location close to the shelf break where current dynamics create a unique

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environment for sediment transport over narrow shelf environments. The resolution of the

MBES backscatter is not as high as that of sidescan sonars, but in finding similar north-south

streaks would not necessarily suggest an error, but could be the recognition of similar features

found in 1995. However, the supervised classification of Class 2 as standing dead or live coral

(SDLC) seemed to cover most of the western side of the North Study Area where evidence of

live reefs or relict reefs were not seen in the video records or photo records. Class 2 was

represented by the highest number of photos as seen in Table 5, however, had the weakest

accuracy of only 1%. This evidence shows that the supervised classification improperly

delineated most of the study area (43%) to be standing dead or live coral when most of Class 2

was visually classified as low relief sand which could be a thin layer overlying hardbottom and

could cause the acoustic signal to be more similar to that of the SDLC in Chapman’s Reef. The

results of this class in particular, suggest that the supervised dataset was applied to an area that

was not similar enough to Chapman’s Reef. Therefore the supervised classification of

Chapman’s Reef itself appears to have worked better and shows particular strengths in

delineating soft bottom habitats such as sand and shell hash. This supervised classification may

prove more successful if applied to another reef complex similar to that of Chapman’s Reef.

6. CONCLUSIONS AND FUTURE DIRECTIONS

More cost-efficient, less labor-intensive methods of habitat mapping remote and deep-

water marine protected areas are needed. For example, the recently designated shelf-edge

marine protected areas off the southeast US are mostly unmapped. The Oculina Banks habitats

have not yet been mapped to an extent that enables understanding of the impacts protected status

on fish and coral habitat, especially recovery of live coral colonies. In this study, automated

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acoustic classification was applied to multibeam bathymetry and backscatter imagery collected

in two study areas within the OHAPC. The unsupervised classification of Chapman’s Reef area

highlighted the acoustic diversity of the dataset. When compared to ROV photo and video

records, which also documented the existence of several transition zones and complexity of

habitats, the unsupervised classification yielded an accuracy of 62%.

The supervised classification of Chapman’s Reef had a lower accuracy rate (30%) than

the unsupervised classification. This could be due to several transition zones within the dataset

and limited groundtruth data to support supervision of areas where there is a mixture of different

habitats. The existence of these habitat mixtures is also attributed to anthropogenic fishing

influences and not only natural occurrences such as current dynamics. The Chapman’s Reef

study area may be different than most previous research on deep-water coral environments due to

significant anthropogenic impacts by destructive fishing gear. Anecdotal evidence suggests that

heavy gears (drags, bars) were towed over the Oculina Banks to create clear paths for shrimp

nets (Thompson, pers. comm.). This fishing practice conducted over many years before the area

became protected likely increased the amount and spread of coral rubble in areas adjacent to the

main bioherm, and may also account for the presence of so many transition zones and acoustic

habitats over the study areas.

Further difficulty was observed in the supervised classification of the North Study Area

when the supervised classification catalogue file of Chapman’s Reef was applied to an area

where the habitats did not exist in similar environments. This resulted in a 10% accuracy for the

supervised classification of the North Study Area. The North Study Area was quite different

from Chapman’s Reef as it was not a high relief bioherm and also did not have any known

history of the types of anthropogenic influences on habitat distribution such as Chapman’s Reef.

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This research shows that density of groundtruthing data in dynamic environments should also be

increased and supervised classification catalogue files should only be applied to more similar

habitats within the OHAPC.

The main body of the Chapman’s Reef mound was found to only consist of standing dead

Oculina coral and rubble, however, maneuvering the ROV over these large structures in the Gulf

Stream current made it difficult to fully image the bioherm and certainly impacted positioning

for benthic photos. Adjacent to the mound was mostly low relief coral rubble and gravelly sand

although some smaller knolls were found north of the reef. Just 150 m south of Chapman's

eastern edge, live Oculina colonies were observed on flat sandy bottom or possibly low relief

limestone with sand veneer cover; these heads were on average 1 m in height and sometimes

occurred as a continuous cover of individual colonies. Based on the estimated growth rate of

1.6cm/yr (Reed, 1981), these were likely 50-100 years old. This discovery points out the need to

map and protect areas around the large bioherms, as these may also be valuable coral habitats.

6.1. Future Seafloor Mapping Considerations

The SAFMC has scheduled a review of the OHAPC protected status and recent research

to be held in 2014. This research has resulted in several future considerations for additional

habitat mapping within the protected area as well as considerations for future management and

protection (SAFMC, 2003). Combining acoustic MBES data with visual data is a viable

approach for habitat mapping the OHAPC. However, considering the scale of patchiness in

relief and habitat type, pixel size of the mapping technology should be decreased. This may

require deployment of higher frequency acoustic sounders or deep-towed bathymetry and

sidescan sonar as autonomous underwater vehicle (AUV) surveys in this area are highly

susceptible to Gulf Stream current dynamics and have proven difficult. Echosounders towed

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closer to the seafloor would aid in getting sub- meter resolution data in order to pinpoint the

smaller live Oculina varicosa coral heads seen in the 2005 ROV footage. In order to maximize

the data from a deep-towed system, the direction of survey may have to be carried out in one

direction to minimize the current affects on the towed instruments. Additionally, detection of

areas with underlying hardbottom that may be covered by a thin veneer of sediment would be

helpful with any further automated acoustic classification due to sonar penetration depths. High-

resolution sub-bottom seismic equipment could be utilized simultaneously with other acoustic

equipment and without interference. The advantages of using this equipment within the OHAPC

have yet to be discovered and could prove useful to enriching classification and producing

habitat maps. Giving more attention to areas adjacent to large bioherms is important based on

observations of new coral heads in these areas. Providing protection for these areas as well as

targeting them with groundtruthing efforts would greatly enhance any habitat maps to point out

younger, successful coral colonies.

6.2 Classification Considerations

The results of this research have also pointed to the need for separate supervised

catalogue files for different areas within the OHAPC such as bioherms and shelf breaks. It is the

conclusion of this study that one supervised catalogue will not be able to accurately classify the

entire OHAPC, but catalogues used for specific habitats could prove more successful.

It is important to tailor habitat mapping to areas of primary concern, especially regarding

the amount of groundtruthing needed to verify acoustic classification. For example, since the

majority of live coral found on Chapman’s Reef in the 2005 ROV survey existed adjacent to

bioherms, ROV dives could be planned to cover similar areas. For more homogenous areas, less

groundtruthing would be required to confirm habitats and more focus could be directed towards

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areas of higher acoustic variability. Conducting the groundtruthing surveys after acoustic

surveys and subsequent unsupervised classification will allow for ROV efforts to be made in the

most appropriate areas to support supervised classification in the future.

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APPENDIX

Sediment analysis from samples taken in 2005 on Chapman’s Reef and North Study Area

analyzed by USGS Woods Hole, MA. Verbal equivalent descriptions are taken from Shepard,

1954 nomenclature.

79

Sediment Samples for Chapman's Reef and North Study Area Oculina Habitat Area of Particular Concern (OHAPC)

Lat. Long Depth Sample % % % % % Verbal Sample

ID (m) Wt. (g) Sand Gravel Silt Clay Carbonate Equivalent

27.5990 -79.9735 81.7 37.4 84.5 7.74 6.64 1.12 82.23 SAND OB05-01

27.6003 -79.9767 79.6 36.32 68.87 5.46 21.7 3.91 65.19 SILTY SAND

OB05-02

27.6004 -79.9798 77.4 35.66 88.46 4.34 5.64 1.57 84.46 SAND OB05-03

27.6023 -79.9809 77.4 40.11 87.74 5.51 4.72 2.04 85.52 SAND OB05-04

27.6085 -79.9838 74.4 35.65 66.51 1.6 25.0 6.88 54.52 SILTY SAND

OB05-05

27.6061 -79.9742 82.9 36.21 79.92 11.02 7.28 1.77 85.19 GRAVEL > 10%

OB05-06

27.6074 -79.9774 82.6 30.08 59.87 28.35 9.05 2.73 85.31 GRAVEL > 10%

OB05-07

27.6039 -79.9733 82.3 40.84 84.22 11.91 3.2 0.67 92.77 GRAVEL > 10%

OB05-08

27.6092 -79.9773 82.6 34.83 70.36 22.51 5.67 1.47 91.92 GRAVEL > 10%

OB05-10

27.6092 -79.9741 83.5 40 72.74 20.08 5.93 1.25 90.93 GRAVEL > 10%

OB05-11

27.6127 -79.9741 82.3 31.45 72.68 10.3 13.5 3.48 80.91 GRAVEL > 10%

OB05-12

27.6125 -79.9772 81.1 34.24 65.33 25.21 7.89 1.57 87.79 GRAVEL > 10%

OB05-13

27.6120 -79.9811 77.4 39.86 82.08 11.59 5.01 1.31 91.97 GRAVEL > 10%

OB05-14

27.6038 -79.9769 64.3 23.73 40.38 37.74 17.6 4.22 78.9 GRAVEL > 10%

OB05-15

27.6049 -79.9780 85.3 29.98 45.06 25.62 22.95 6.38 78.79 GRAVEL

> 10% OB05-16

28.4980 -80.0305 79.2 31.69 78.3 16.09 4.3 1.3 93.06 GRAVEL > 10%

OB05-19

28.4931 -80.0306 75.3 29.77 77.05 13.43 7.8 1.72 89.37 GRAVEL > 10%

OB05-20

28.4862 -80.0258 82.3 36.22 78.29 16.03 4.54 1.14 93.93 GRAVEL > 10%

OB05-21B

28.4928 -80.0259 82.9 36.33 91.81 6.27 1.52 0.4 95.72 SAND OB05-22

28.4861 -80.0306 77.4 NA NA NA NA NA NA

No Sample/possible hardbottom

OBO5-NS