ACOUSTIC AND VISUAL SEABED CLASSIFICATION OF THE

Transcription

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
Digitally signed by Robert Roer
DN: cn=Robert Roer, o=UNCW,
ou=Graduate School and Research,
[email protected], c=US
Date: 2012.03.29 14:33:13 -04'00'
Dean, Graduate School
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 iii
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 iv
REFERENCES ............................................................................................................................. 75 APPENDIX ................................................................................................................................... 78 v
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 .......................................................................... 22 2. Distribution of visual habitats for assigned relief classes. ....................................................... 29 3. Unsupervised QTC Multiview™ acoustic classes with their relief class definitions and
benthic habitat definitions ............................................................................................................. 40 4. Chapman’s Reef supervised classification results. .................................................................. 44 5. North Study Area supervised classification results ................................................................. 57 x
LIST OF FIGURES
Figure
Page
1. Map of Oculina Habitat of Particular Concern (OHAPC) ......................................................... 4 2. Chapman's Reef bathymetry and ROV dive tracks ................................................................. 14 3. Chapman's Reef mosaic of backscatter intensity ..................................................................... 15 4. Bathymetry of the North Study Area with ROV dive tracks ................................................... 18 5. North Study Area mosaic of backscatter intensity ................................................................... 19 6. Flow Chart for Automated and Visual Classification. ............................................................. 21 7. Sample photo showing how the habitat digitizer was used to delineate benthic habitats........ 27 8. Selected bottom photographs of standing dead and live coral habitat ..................................... 30 9. Selected bottom photographs of coral rubble and gravelly sand habitat ................................. 32 10. Selected bottom photographs of low relief hard bottom habitat ............................................ 34 11. Selected bottom photographs of sandy habitat ...................................................................... 35 12. Selected bottom photographs of shell hash habitat ................................................................ 37 13. QTC Multiview™ unsupervised classification of Chapman’s Reef...................................... 39 14. Supervised points of Chapman’s Reef showing areas of the reef used to classify the five
supervised benthic habitats ........................................................................................................... 46 15. QTC Multiview™ supervised classification of Chapman’s Reef showing definition and
distribution of Classes 1-5 ............................................................................................................ 47 16. Profile of ROV Dive 1 showing relief change over dive duration with habitat classification
colors of supervised classification. ............................................................................................... 48 17. Profile of ROV Dive 2 showing relief change over dive duration with habitat classification
colors of supervised classification. ............................................................................................... 49 18. Profile of ROV Dive 4_2006 showing relief change over dive duration with habitat
classification colors of supervised classification.. ........................................................................ 50 19. Profile of ROV Dive 6 showing relief change over time with habitat classification colors of
supervised classification ............................................................................................................... 51 20. Profile of ROV Dive 8 showing relief change over time with habitat classification colors of
supervised classification ............................................................................................................... 52 21. QTC MultiviewTM supervised classification of bathymetry and backscatter over the North
Study Area showing definition and distribution of acoustic Classes 1-5 ..................................... 56 22. Profile of ROV Dive 4 showing relief change over dive duration with habitat classification
colors of supervised classification ................................................................................................ 58 23. Profile of ROV Dive 5 showing relief change over dive duration with habitat classification
of supervised classification ........................................................................................................... 59 xi
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).
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).
16
17
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.
18
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.
19
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).
20
Figure 6. Flow Chart for Automated and Visual Classification.
21
Algorithm
Basic Statistics
Function
Mean, Standard Deviation, Higher order moments to indicate acoustic
resistance and roughness
Quantile and
Measures the distribution of backscattered intensities at low resolution
Histogram
Fast Fourier
Used to find power spectra which describe statistical characteristics on many
Transforms (FFTs) resolution scales
Ratios based on Ratios of log-normalized power in various frequency bands that provide good
Power Spectra
discrimination for classifying images
Grey Level
Describe amplitude changes over selected distances and directions in the
Co-Occurrence
image patch, and are used to determine smooth or rough texture
Matrices (GLCM)
Table 1. QTC Multiview™ algorithms with functions (Preston et al., 2004).
22
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
23
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
24
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
25
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.
26
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.
27
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).
28
Visual Habitat
Classification
Coral Rubble
and Gravelly
Sand (CRG)
Relief
Class
High Relief On
Bioherm (HR)
X
High Relief Off
Mound (HROM)
≥ 0.5m
X
Low Relief (LR)
X
Low Relief
Hard Bottom
(LRHB)
Sand
(SA)
Standing
Dead or Live
Coral (SDLC)
Shell
Hash
(SH)
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.
29
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.
30
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.
31
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.
32
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
33
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.
34
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.
35
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.
36
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.
37
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
38
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.
39
Relief
Class
QTC 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.
40
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
41
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.
42
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).
43
QTC Class Relief Class
1
HROM
2
3
LR
4
5
HR
Habitat
Class
# Photos
Correct
# Photos
Missed
# Photos
Total
% Accuracy
CRG
12
48
60
20%
SDLC
3
12
15
20%
CRG
11
72
83
13%
SA
25
17
42
60%
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%.
44
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).
45
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.
46
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.
47
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.
48
transect.
49
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.
50
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.
51
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.
52
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
53
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
54
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.
55
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.
56
QTC Class
1
HROM
2
3
LR
4
5
# Photos
Correct
# Photos
Missed
# Photos Total
% Accuracy
CRG
2
4
6
33%
SDLC
1
95
96
1%
CRG
1
4
5
20%
SA
8
2
10
80%
CRG
NA
NA
NA
NA
TOTAL
12
105
117
10%
Relief Class Habitat Class
HR
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%.
57
transect.
58
classification of supervised classification; S and N indicate the south and north ends of the
transect.
59
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 (OBO5-21Gravel>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
60
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
61
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
62
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.
63
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
64
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
65
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
66
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
67
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 CRGHROM 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
68
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
69
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 deepwater 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
70
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.
71
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
72
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. Highresolution 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
73
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.
74
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77
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.
78
Sediment Samples for Chapman's Reef and North Study Area
Oculina Habitat Area of Particular Concern (OHAPC)
Depth
Sample
%
%
%
%
%
Verbal
(m)
Wt. (g)
Sand
Gravel
Silt
Clay
Carbonate
Equivalent
37.4
84.5
7.74
6.64
1.12
82.23
SAND
79.6
36.32
68.87
5.46
21.7
3.91
65.19
SILTY
SAND
-79.9798
77.4
35.66
88.46
4.34
5.64
1.57
84.46
SAND
27.6023
-79.9809
77.4
40.11
87.74
5.51
4.72
2.04
85.52
SAND
27.6085
-79.9838
74.4
35.65
66.51
1.6
25.0
6.88
54.52
27.6061
-79.9742
82.9
36.21
79.92
11.02
7.28
1.77
85.19
27.6074
-79.9774
82.6
30.08
59.87
28.35
9.05
2.73
85.31
27.6039
-79.9733
82.3
40.84
84.22
11.91
3.2
0.67
92.77
27.6092
-79.9773
82.6
34.83
70.36
22.51
5.67
1.47
91.92
27.6092
-79.9741
83.5
40
72.74
20.08
5.93
1.25
90.93
27.6127
-79.9741
82.3
31.45
72.68
10.3
13.5
3.48
80.91
27.6125
-79.9772
81.1
34.24
65.33
25.21
7.89
1.57
87.79
27.6120
-79.9811
77.4
39.86
82.08
11.59
5.01
1.31
91.97
27.6038
-79.9769
64.3
23.73
40.38
37.74
17.6
4.22
78.9
27.6049
-79.9780
85.3
29.98
45.06
25.62
22.9
5
6.38
78.79
28.4980
-80.0305
79.2
31.69
78.3
16.09
4.3
1.3
93.06
28.4931
-80.0306
75.3
29.77
77.05
13.43
7.8
1.72
89.37
28.4862
-80.0258
82.3
36.22
78.29
16.03
4.54
1.14
93.93
28.4928
-80.0259
82.9
36.33
91.81
6.27
1.52
0.4
95.72
Lat.
Long
27.5990
-79.9735
81.7
27.6003
-79.9767
27.6004
28.4861
-80.0306
77.4
NA
NA
NA
79
NA
NA
NA
SILTY
SAND
GRAVEL
> 10%
GRAVEL
> 10%
GRAVEL
> 10%
GRAVEL
> 10%
GRAVEL
> 10%
GRAVEL
> 10%
GRAVEL
> 10%
GRAVEL
> 10%
GRAVEL
> 10%
GRAVEL
> 10%
GRAVEL
> 10%
GRAVEL
> 10%
GRAVEL
> 10%
SAND
No
Sample/pos
sible
hardbottom
Sample
ID
OB0501
OB0502
OB0503
OB0504
OB0505
OB0506
OB0507
OB0508
OB0510
OB0511
OB0512
OB0513
OB0514
OB0515
OB0516
OB0519
OB0520
OB0521B
OB0522
OBO5NS

ACOUSTIC AND VISUAL SEABED CLASSIFICATION OF THE

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