Optical and Acoustic Imagery in the Northeast BenthoPelagic Observatory NEBO to Support Fisheries an

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Optical and Acoustic Imagery in the Northeast
Bentho-Pelagic Observatory (NEBO) to Support
Fisheries and Ecosystems Managementhttp//habcam
.whoi.eduhttp//nebo.whoi.eduScott Gallager,
Richard Taylor, Amber York, Norman Vine, Larry
Mayer, Lakshman Prasad, Steve Lerner Woods
Hole Oceanographic InstitutionAdvanced Habitat
Imaging ConsortiumUniversity of New
HampshireLos Alamos National laboratoryGeoHab,
May 2009

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Who we are Scott Gallager, Amber York (WHOI
Biology Department) Steve Lerner Jonathan Howland
(WHOI Applied Ocean Physics Engineering) Hauke
Kit-Powell (WHOI Marine Policy Center) Richard
Taylor Norman Vine (Fishing Community,
Geo-Spatial Specialists) Arnie DeMello, Paul
Rosonina, F/V Kathy Marie Larry Mayer Yuri
Rzhanov (image processing) Amy Holt Cline (UNH)
Education and Outreach Coordinator (Center for
Coastal Ocean Mapping, University of New
Hampshire) Peter Auster (National Undersea
Research Center, University of Connecticut) Laksh
man Prasad (Los Alamos National Laboratory)
Kathryn Ford , Massachusetts Department of
Marine Fisheries,
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Partners and End Users Stellwagen Bank National
Marine Sanctuary, David Wiley, Science
Director New England Fishery Management Council,
Sally McGee, Chair Habitat Committee Massachuset
ts Coastal Zone Management Program, Leslie Ann
McGee, Director NOAA National Marine Fisheries
Service, Mike Fogarty, Thomas Noji, Robert Reid,
Vince Guida. Dvora Hart, Paul Rago United States
Geological Survey, Page Valentine, Walter
Barnhardt Gulf of Maine Census of Marine Life
(CoML), Lew Incze Gulf of Maine Mapping
Initiative, Tracy Hart Massachusetts Fishermans
Partnership, Olivia Free.
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Goals of NEBO

• (1) To establish six sentinel sites along
  northeast coastline
• quantify benthic community structure
• coupling between the water column and benthic
  community
• system change over time scales of days to years
• (2) To develop tools to integrate disparate
  fisheries-relevant data sets
• segment and classify seafloor targets and
  substrate
• visualize the results in near real-time
• plankton distributions (Video Plankton Recorder)
• water column measurements (e.g., NERACOOS,
  GoMOOS)
• (3) To establish metrics for quantifying change
• ecosystem dynamics
• benthic community structure
• organism abundance and size distributions
• diversity of a wide range of benthic and
  demersal taxa
• substrate composition
• quantify impact of NEBO data products on
  management practices
• (socio-economic modeling)

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Northeast Bentho-pelagic Observatory (NEBO)
Northeastern Regional Coastal Ocean Observing
System
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1. SBNMS Stellwagen Bank Marine Sanctuary 2.
CLAII Northeast Peak, Closed Area II, Habitat of
Particular Concern 3. CLAI Closed Area I 4.
WGSC Western Great South Channel, HAPC? 5.
NLSCA Nantucket Lightship Closed Area 6. ET
Hudson Canyon Closed Area and Elephant Trunk
Selection of 6 Sentinel Sites
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Bentho-pelagic coupling (highly simplified)
wind
CO2
Carbon pump
Convergent front
zooplankton
phytoplankton
fish
turbulence
detritus
Boundary layer shear stress
concentration deposition
anoxia
Bedform migration
Substrate composition
nutrients
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Thermal Fronts and the Distribution of
Scallops on Georges Bank
An example of strong bentho-pelagic
coupling adult scallops are most abundant
directly under persistent fronts where larvae
are concentrated
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Integration of pelagic ecosystem components
Video Plankton Recorder transect across Great
South Channel
abundance of Calanus finmarchicus
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with benthic components
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What is an Ecosystem Approach to Management? To
understand an ecosystem sufficiently well to
allow predicting change of one component in light
of change in another. Important holistic
concepts sustainable yield, species-specific
total allowable catch, recovery time, resilience
bentho-pelagic coupling (energy flow), habitat
functional value. oceanographic drivers
(nutrients, currents, fronts, eddies)
Variables species abundance, distribution,
substrate composition, water column properties
(temperature, salinity, nutrients, phyto-,
zooplankton). over time scales of
hours-months-years-decades
Indices patchiness indices, inter-species
and inter-substrate type relationships,
species diversity in relation to substrate at
multiple spatial scales, water column
processes, external impacts (storms, fishing,
oil spills, etc.) assess community composition
across habitat types and over time,
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How does an ecosystem respond to change?
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Methods Merge synoptically acquired optical and
acoustic benthic data with water column data
Kongsberg EM3002 300 kHz multibeam
175 kHz AST Synthetic Aperture Side scan VPR,
CTD, Fluorometer on Focus vehicle
Imagenix 600 kHz side scan
Stereo optical imaging
30m
3m
VPR, CTD, Fluorometer
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Integration of multi-resolution imaging
modalities
300 kHz Multibeam 300 m swath 2 m resolution
175 kHz synthetic aperture side scan, 200m
swath 5 cm resolution
600 kHz side scan 50 m swath 2 cm resolution
CCD 1 m swath 1 mm resolution
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0.68 inch fiber optic tow cable
Machine vision digital stereo video cameras
Fiber optic telemetry bottle
Benthos Altimiter
SBE 37 CTD
RDI 1200 kHz ADCP
2 Imagenix side scan sonars Teledyne Benthos C3D
side scan sonar
Strobe
Strobe
x4
2 - 3 m altitude typical
HABCAM Habitat Mapping Camera System
Stereo 2 Mpixel system
1m Field of View 5 10 frames per second gt50
overlap _at_ 5kts
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Stereo optics with Teledyne Benthos C3D 200 kHz
sidescan
Stereo camera and c3d
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Data Flow Wire Diagram
Field Operation
Real-Time Data Products
Multi-Resolution Synthesis Products
display
display
HabCam
species-specific abundance
image acquisition
patch dynamics
GIS acquisition
time, date, altitude, depth, lat, lon, roll,
pitch, yaw, temp, salinity, heading, speed
raw 16 bit tiff full res jpegs
habitat distribution
species-specific distribution
2TB/day
Predator-prey interactions
classification results
RAID array
Virtual Index card (VIEW) images metadata
diversity
QGIS-Mapserver Interface
RAID array
multi-species management models
manual classification
automated classification
real-time mosaics
population dynamics models
Image processing lightfield correction color
correction segmentation region of interest
extraction feature extraction
Index Card Viewer
ship navigation
adaptive sampling
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Data Flow Wire Diagram
Field Operation
Real-Time Data Products
Multi-Resolution Synthesis Products
display
display
HabCam
species-specific abundance
image acquisition
patch dynamics
GIS acquisition
time, date, altitude, depth, lat, lon, roll,
pitch, yaw, temp, salinity, heading, speed
raw 16 bit tiff full res jpegs
habitat distribution
species-specific distribution
2TB/day
Predator-prey interactions
classification results
RAID array
Virtual Index card (VIEW) images metadata
diversity
QGIS-Mapserver Interface
RAID array
multi-species management models
manual classification
automated classification
real-time mosaics
population dynamics models
Image processing lightfield correction color
correction segmentation region of interest
extraction feature extraction
Index Card Viewer
ship navigation
adaptive sampling
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Data Processing Architecture and Products
Data Processing Product Generation
Stage 0
Stage 1
Stage 2
Classification Manual Automated
JPG Images (3-10Hz)
Database Store
Species-Specific abundance, distribution
Vehicle Data Nav, Att, CTD
Species Richness Bio Diversity
Date, Time, Image, classification, target
density/image, binned density, morphology,
Substrate Composition Relationship to Species
Configuration Info Camera Calibration
DB Query
Patchiness Indices
KML Generator
User Data Accesss Visualization Tools Google
Earth Ossim Planet Map Server Matlab Web-based
Viewers
User Query By Image By Time By Location By
Classification By Substrate
Mapserver Interface
Mosaic Generator
Image Acoustic Data Fusion (TBD)
Models Interface (TBD) Population, Management
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Taxonomic Database
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Sampling Approach

• Conduct continuous imaging surveys
• 5 - 6 images/s _at_ 5 kts
• 500,000 images / day
• 250,000 m2/ day
• 2 Terabytes / day

1m
50 overlap to allow object registration for
mosaicing
1m
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Automated mosaicing Goal real-time with Yuri
Rzhanov and Larry Mayer (UNH)
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Elephant Trunk January 2006
The Challenge Automated segmentation and
classification
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Image acquisition Manual data extraction Image
test set Automated Image processing Light
field, color correction Segmentation ROI
extraction Feature extraction Classification
Texture energy analysis Fusion of images with
GIS Data Visualization Data Products Morphology
Diversity

• .
• strobed light ( 2 usec )
• eliminate motion artifacts
• homomorphic filtering
• (low pass filter)
• white balance correction
• to achieve true color
• adaptive homogeneity-directed demosaicing
• direct in situ measurements of
• optical water characteristics,
• linear application of Beer's Law

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Original image
Fine scale segmentation
Vectorized Image Segmentation raster to vector
Aggregate polygons to course scale
Frequency distribution of all polygon areas
Substrate grain size
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scallop

• Automated Image processing
• Segmentation
• Feature extraction
• Classification

skate
flounder
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Morphological features length, width,
area moments circularity Fourier
descriptors shape Template matching
(works well for scallops but slow) 20
elements
Feature Extraction
X
Region based features texture, color Gabor
wavelets standard error of matrix
16 orientations 24 scales color- RGB
space 1152 elements

unsupervised
supervised
Support Vector Machine Soft-margin, Gaussian RBF
kernel
Classification with cross validation
Discriminant Analysis
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Establish training sets for automated
classification using novel point and click
routine to sort through large image data base
over the web Available for Students Researche
rs Resource managers
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• 6 Sentinel Sites
• Imaged 2 million m2
• in each site so far
• Combination of RSA
• and NEBO cruises

SBNMS
CLAII
CLAI
WGSC
NLS
11/25/2008
ET
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SBNMS
Three examples 1. SBNMS Biodiversity 2. Scallops
in Elephant Trunk 3. Time series of an invasive
species in Closed Area II
CLAII
CLAI
WGSC
NLS
11/25/2008
ET
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Example Data Set Stellwagen Bank National Marine
Sanctuary
Seasonal sampling provides intra- and inter-
annual comparisons
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• 2 nm grids at 0.25 nm spacing typical
• experimenting with spirals to
• cover more area in less time

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Results for one transect across Tillies Bank
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Taxon Composition Stellwagen Bank National
Marine Sanctuary49 species
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West Ridge 65m
East Ridge 70m
Species Composition
Trench 130m
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Shannon-Wiener Diversity Index (10 m bins)
EAST RIDGE
WEST RIDGE
TRENCH
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Elephant Trunk Example data products
SBNMS
CLAII
CLAI
WGSC
NLS
11/25/2008
ET
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Strong patchiness makes discontinuous sampling
difficult
Scallop Abundance- Elephant Trunk 2007
Scallop concentration /m2
CV
cv
cumulative mean
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Power Spectrum of Abundance as a Function of
Spatial scale Elephant Trunk scallops
Patchiness index can be used for dynamic
sampling of scallop populations
Spatial Scale (m))
1000 900 800 700 600 500
400 300 200 100
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Understanding Patchiness Grain and Intensity
whelks
scallops
L(t)L(t)
L(t))
t (m)
t t(m))
320 m
Neighbor k analysis second-order moment k(t)
EN(t) / X Observed number of individuals within
distance t of any individual. L(t) EN(t) -
mean EN(t) of 999 iterations observed
expected under CSR
Astropecten
L(t))
t (m)
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Taxon composition in three sentinel sites
Distance (m)
10 Taxonomic groups gt180 species
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Substrate composition
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Regional scale cluster analysis of taxa vs.
substrate
all 6 test sites combined
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Local interactions that define EFH
Predatory/Prey Interactions
Predation of sea scallops by Asterias vulgaris
sea stars
Predation of sea scallops by Buccinum undatum
whelks
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Commensalism-
Red hake (Urophycis chuss) and sea scallops
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Metabiosis- Hermit crab (Pagurus spp.)
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Inquilinism and multualism- Cerianthids and red
fish (Sebastes
marinus)
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demersal fish/skate
Yellowtail flounder
Tile fish
Barndoor Skate
Cusk
Monkfish
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Codfish on Stellwagen Bank
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• Real-time data processing for adaptive sampling
• in patchy communities
• Minimize ship time
• Maximize statistical robustness of evaluating
  sample size
• Use simulations of population distributions to
  evaluate
• sample designs

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L 0.5
1 Km
L 0.3
1 Km
Poisson Clustering
1. Simulate parent points under CSR 2. For each
parent, determine offspring N (f) 3. Locate
each child relative to parent L (t) Neighbor-k
patchiness index
L 0.1
L 0.05
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Real-time adaptive sampling using knowledge of
community patchiness
Poisson simulated scallop distribution with
even allocation of sample grid spacing
yields convergence of mean density and CV
after 10 transects
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Poisson simulated scallop distribution with
allocation of sample grid spacing based on
Neighbor K calculated in real-time
yields convergence of mean density and CV
after 4 transects
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Summary of NEBO Data products Indices for use
in Ecosystem Based Management
1. Taxon- specific abundance plots of
macrobenthos and macrophytes 2. Spatial plots
of Species Diversity Indices (e.g.,
Shannon-Weiner) as a function of binning scale)
3. Species-specific patch size estimates using
point process statistics 4. Organism-substrate
association indices using cluster analysis 5.
Georeferenced species and substrate
distributional maps, 6. Water column properties-
hydrography, chlorophyll, and zooplankton
abundance in relation to habitat structure, 7.
Temporal information including seasonal and inter
annual variation in species and substrate
composition, organism-substrate associations,
water column properties, and food
availability, 8. Imagery data sets and mosaics
for each of the sentinel sites to be available
over the internet, 9. Estimates of annual
variation in larval/juvenile recruitment for
selected species, 10. Compile data to support
characterization and designation of EFH,
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BENTHIC ID GUIDE
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BENTHIC ID GUIDE