In Support of New York State Offshore Wind Energy presentation_1… · Robinson Willmott, J. C., G....

DIGITAL AERIAL BASELINE

SURVEY OF MARINE WILDLIFE In Support of New York State Offshore

Wind Energy

• OSW represents an essential renewable energy resource for NYS toward achieving its State Energy Plan targets including meeting half of NYS electricity with renewable resources.

• NYS goal is to advance OSW in a manner that is sensitive to environmental, maritime and social issues in a cost effective manner that maximizes environmental and economic benefits

NYS Offshore Wind Energy (OSW)

Development

• Baseline data on potential wildlife exposure of NY Offshore Planning Area (OPA)

• Quarterly aerial digital surveys for three years

• Sample 5-10% of OPA

• Image resolution 1-2 cm GSD

Aerial Digital Survey

Requirements and Objectives

• High Level Summary and Discussion of Approach

• Review and feedback on timing of seasonal surveys

• Feedback on distribution and format of data

• Identify other potential collaborators to leverage the work for increased value

Feedback and suggestions should be sent to:

[email protected]

Webinar Goals

mailto:[email protected]

Image Analysis

Simon Warford

Ann Pembroke Project Manager

Stuart Clough Technical Director

APEM

Julia Robinson Willmott Technical Director

Normandeau

Data Management/

Reporting

Data Collection David Campbell

Survey Design Mark Rehfisch

Survey Execution Tracy Shaw John McCarthy

Simon Warford

Stephanie McGovern

Lauren Hooton

Melinda Sweeny

Stephanie McGovern Laura Jervis

Adam Kent

Crissy Sutter

Robert Kenney

Karen Gilland

Greg Forcey

Crissy Sutter

Mark Rehfisch

Preproccess Image ID QA/QC

Christian Newman Subcontract Manager

• Normandeau/APEM Team Proposal Framework

• Known Wildlife Distributions in OPA

• Approach

– Camera Sensor

– Flight Planning and Survey Design

– Data Output

– Adaptive Methods Consideration

– Survey Timing

• Data Distribution

• Opportunities for collaboration

Outline

• Confidence

– Species Identification

– Geospatial Accuracy

– Statistically and Scientifically Defensible

• Flexibility

– Offshore Planning Area (OPA) vs Wind Energy Area (WEA)

– Design

• Forward Thinking

– Change Detection

– Extra Data

– Potential Other Uses

Normandeau/APEM Team Proposal

Framework

KNOWN WILDLIFE

DISTRIBUTIONS IN OPA

Northwest Atlantic Seabird Catalog by USGS, BOEM, available at NOAA New England Aquarium using the North Atlantic Right Whale Consortium MARCO Mid-Atlantic Ocean Data Portal

Data Sources: Spatial Data

Spatial: Birds

NY Offshore Planning Area

NY Wind Energy Area

Atlantic Offshore Seabird Dataset Catalog!( Roseate Tern

ROTE Probability

High

Low

Spatial: Roseate Tern


NY Wind Energy Area

Turtles Summer

Sightings Per Unit Effort

!( 1 - 100

!( 100 - 300

!( 300 - 800

!( 800 - 1,565

Relative Abundance

200 - 325

120 - 200

50 - 120

1 - 50

0


NY Wind Energy Area

Turtles Fall


!( 1 - 40

!( 40 - 90

!( 90 - 180

!( 180 - 300

Relative Abundance

60 - 118

40 - 60

20 - 40

1 - 20

0

Spatial: Turtles

Showing sperm whale, sei whale, right whale, minke whale, humpback whale, fin whale

Spatial: Marine Mammals

APPROACH

Key Advantages to 1.5 cm Resolution

• More accurate measurements of head to tail and wingspan lengths

• Greater confidence in identification based on measurements (e.g., Flying shearwaters and sitting alcids and gulls)

• Greater confidence in identification of terns, bill colour visible at 1.5 cm

• High quality imagery resulting in more accurate flight height calculation

• Increased identification rates for most species

• Increased chance of identifying specific individual mammals based on their unique patterning

Key Advantages to 1.5 cm Resolution

• 1.5cm resolution is the same as a nickel at sea-surface

• More accurate measurements of head to tail and wingspan lengths

• Greater confidence in identification based on measurements (e.g., Flying shearwaters and sitting alcids and gulls)

• Greater confidence in identification of terns, bill colour visible at 1.5 cm

• High quality imagery resulting in more accurate flight height calculation

• Increased identification rates for most species

• Increased chance of identifying specific individual mammals based on their unique patterning

Bird Identification Rates: 2 cm v predicted 1.5 cm

Species Group / Species

% ID

(2cm)

% ID

(1.5cm)

Ducks

Black Scoter >85% >98%

Surf Scoter >75% >98%

White-winged Scoter >75% >98%

Red-breasted Merganser >80% >98%

Long-tailed Duck >85% >95%

Loons

Common Loon >90% >98%

Red-throated Loon >90% >98%

Petrels and Shearwaters

Wilson’s Storm Petrel >75% >85%

Great Shearwater >80% >90%

Cory’s Shearwater >80% >90%

Manx Shearwater >60% >80%

Audubon’s Shearwater >60% >80%

Sooty Shearwater >60% >80%

Jeagers and Skuas

Parasitic Jaeger >85% >95%

Pomarine Jaeger >85% >95%

Species Group / Species % ID

(2cm ) % ID

(1.5cm) Gulls and Terns

Great Black-backed Gull 100% 100%

Lesser Black-backed Gull 100% 100%

Herring Gull >90% >99%

Laughing Gull >85% >95%

Ring-billed Gull >50% >60%

Kittiwake >85% >95%

Bonaparte’s Gull >80% >90%

Caspian Tern >85% >95%

Royal Tern >85% >95%

Common Tern >60% >75%

Roseate Tern >60% >75%

Least Tern >60% >75%

Black Tern >60% >75%

Forster’s Tern >60% >75%

Black Skimmer >95% >95%

Alcids

Common Murre >95% Summer, <50% in Winter >98% Summer, <50% in Winter

Razorbill >90% Summer, <50% in Winter >99% in Summer, <50% in Winter

Dovekie >60% >90%

Atlantic Puffin >60% >90%

Bird Identification Rates: 2 cm v predicted 1.5 cm

• Twin Engine capable of flying less than 140 knots

• Proposed to fly at 1360 ft

– Safety

– Weather

– Resolution

– Coverage

Flight Planning

24

Low confidence – overlapping confidence intervals

High confidence – non-overlapping confidence intervals

Survey Design Confidence in Estimates

Without small confidence intervals it is impossible to assess if environmental change has had a significant impact on a population

- High confidence = small confidence intervals

- Low confidence = large confidence intervals

• OPA=43,650 km2

• 7% Transect of OPA (3073 km2)

584 m x 110 m ground sampling per image

• 10% Grid of WEA + 4 km buffer (128 km2)

330 m x 219 m ground sampling per image

• Rationale for Transect and Grid Design

Survey Design

Survey Design

Project Example: • Calculate the confidence intervals (and associated % coverage) of common scoter

abundance derived from grid and transect survey designs using Markov-Chain Monte Carlo simulations (MCMC) on data collected at Carmarthen Bay.

0

50000

100000

150000

200000

250000

300000

0 1 2 3 4 5 6 7 8 9 10

Sco

ter

spec

ies

esti

ma

te

Coverage (%)

Grid population estimate

Grid lower CL

Grid upper CL

Transect Population

estimateTransect lower CL

Survey Design: Confident Change Detection

Survey Design: Accommodating Glare

• Consideration will be given to:

• Sun angle (time of day / time of year)

• Sea state ( 4 or less aiming for 2 or less)

• Direction of flight in relation to the sun

• Camera angle

• Camera technicians continuously monitor the images collected for quality. And image acquisition will be stopped until suitable conditions occur

• Extra imagery is routinely collected to replace a few expected glint-affected images

Data Output: Geospatial Accuracy • Custom flight planning software pre-

programs the survey transects and grids

• System-specific, aircraft mounted GPS/GNSS systems ensure that surveys are flown as accurately as possible

• Automatic image acquisition over specified locations

• As data capture occurs, GPS data are automatically logged with each exposure including the xyz coordinate and heading of the camera at the point of capture

Data Output

• Identify wildlife to lowest possible taxonomic group

– Birds, marine mammals, turtles, sharks, rays, and fish

• Geo-rectified image snags with associated meta data including height and direction

• Other Anthropogenic Data- Boats

Camera

Surface Resolution

(known)

Bird Resolution

(Calculated from

average size)

Altitude of

Aircraft (known)

Bird Altitude

(calculated)

33

Data Output: Flight Height

• Individual wildlife are geo-

referenced

• Bearing is automatically

determined from head-tail

axis

• Extraction to GIS

• Rose diagrams produced for

defined areas

• Predominant flight/path

direction is detectable 34

Data Output: Flight/Path Direction

35

Data Output: Flight/Path Direction

• Goal Create best value for 3 year data set

• Do Year 1 and/or 2 data confirm historical hot spot and cold spots?

• Stratified by area and/or by target species

• More detailed data–sampling emphasis and/or resolution

Adaptive Methods Consideration

Northwest Atlantic Seabird Catalog by USGS, BOEM, available at NOAA

New England Aquarium using the North Atlantic Right Whale Consortium

MARCO Mid-Atlantic Ocean Data Portal

eBird

Data Sources: Temporal Data

MAR_APR JUL OCT_NOV

0

50

100

150

200

250

300

1 2 3 4 5 6 7 8 9 10 11 12

# an

imal

s

Beaked Whale (Mesoplodon sp.)

Beluga (Delphinapterus leucas)

Blainville's Beaked Whale (Mesoplodon densirostris)

Blue Whale (Balaenoptera musculus)

Bryde's Whale (Balaenoptera brydei)

Cuvier's Beaked Whale (Ziphius cavirostris)

Dwarf Sperm Whale (Kogia sima)

False Killer Whale (Pseudorca crassidens)

Gervais' Beaked Whale (Mesoplodon europaeus)

Killer Whale (Orcinus orca)

Long-finned Pilot Whale (Globicephala melas)

North Atlantic Right Whale (Eubalaena glacialis)

Northern Bottlenose Whale (Hyperoodon ampullatus)

Pygmy or Dwarf Sperm Whale (Kogia sp.)

Pygmy Sperm Whale (Kogia breviceps)

Sei Whale (Balaenoptera borealis)

Sowerby's Beaked Whale (Mesoplodon bidens)

Sperm Whale (Physeter macrocephalus)

True's Beaked Whale (Mesoplodon mirus)

Temporal: Marine Mammals

MAR_APR JUL OCT_NOV

0

500

1000

1500

2000

2500

3000

3500

4000

1 2 3 4 5 6 7 8 9 10 11 12

# an

imal

s

Fin Whale (Balaenoptera physalus)

Humpback Whale (Megaptera

novaeangliae)

Minke Whale (Balaenoptera

acutorostrata)

Pilot Whale (Globicephala sp.)

Marine Mammals

0

5000

10000

15000

20000

Winter

(Dec - Feb)

Spring

(Mar - May)

Summer

(Jun - Aug)

Fall

(Sep - Nov)


Turtles

MAR MAY NOV

Temporal: eBird

• Population Sensitivity

– ROST

– COSH

– AUSH

Robinson Willmott, J. C., G. Forcey, and A. Kent. 2013. The relative vulnerability of migratory bird species to offshore

wind energy projects on the Atlantic Outer Continental Shelf. OCS Study BOEM 2013-207.

• Collision Sensitivity — ROST — HERG — Jaegers — BLKI

• Displacement Sensitivity

—ROST —ATPU —RAZO —RTLO —NOGA

Sensitive Species

Survey Timings: Winter

0

2

4

6

8

10

Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

ATPU

0

2

4

6

8

10

12


RAZO

0

200

400

600

800


HERG

0 10 20 30 40 50 60 70 80 90 100

Surf Scoter

Black Scoter

Red-breasted Merganser

Common Loon

Northern Fulmar

Sooty Shearwater

Northern Gannet

Red-necked Phalarope

Great Skua

Parasitic Jaeger

Common Murre

Razorbill

Black-legged Kittiwake

Little Gull

Ring-billed Gull

Iceland Gull

Glaucous Gull

Feb_Mar

Survey Timings: Spring

0

1

2

3

4

5

6

7


ROST

N.B. Also one LETE record in May

0 20 40 60 80 100

Surf Scoter

Black Scoter

Red-throated Loon

Northern Fulmar

Great Shearwater

Manx Shearwater

Wilson's Storm-petrel

Band-rumped Storm-petrel

Double-crested Cormorant


Great Skua

Parasitic Jaeger

Atlantic Puffin

Bonaparte's Gull

Ring-billed Gull

Iceland Gull

Glaucous Gull

Least Tern

Common Tern

Eastern Kingbird

Gray Catbird

Apr_May

Survey Timings: Summer

0 20 40 60 80 100

Common Loon

Northern Fulmar

Cory's Shearwater

Great Shearwater

Sooty Shearwater

Manx Shearwater

Audubon's Shearwater

Wilson's Storm-petrel

Leach's Storm-petrel

Band-rumped Storm-petrel

Northern Gannet


American Golden Plover

Semipalmated Plover

Sanderling


Red Phalarope

South Polar Skua

Pomarine Jaeger

Parasitic Jaeger

Laughing Gull

Ring-billed Gull

Herring Gull

Great Black-backed Gull

Sooty Tern

Bridled Tern

Roseate Tern

Common Tern

Arctic Tern

Barn Swallow

Jul_Aug

0

20

40

60

80

100


COSH

0

5

10

15

20


AUSH

Survey Timings: Fall

0 20 40 60 80 100

Canada Goose

Surf Scoter

Long-tailed Duck

Common Loon

Cory's Shearwater

Sooty Shearwater

Audubon's Shearwater

Leach's Storm-petrel



Great Skua

Pomarine Jaeger

Dovekie

Razorbill

Black-legged Kittiwake

Laughing Gull

Herring Gull

Lesser Black-backed Gull

Common Tern

Pine Siskin

Oct_Nov

0

500

1000


NOGA

0

10

20


RTLO

0

50

100


Jaegers and Skuas

0

200

400


BLKI

We propose

• WINTER Feb/Mar

• SPRING End Apr/May

• SUMMER End July/Aug

• FALL Oct/Nov

Collaborate with DEC marine mammal surveys

Survey Timing

Data Distribution AMAPPS program NOAA and USFWS

Northwest Atlantic Seabird Catalog

by USGS, BOEM, available at NOAA

North Atlantic Right Whale Consortium

MARCO Mid-Atlantic Data Portal

MA Marine Fisheries

NY DOS Geographic Information Gateway

Large Pelagics Research Center at UMass

• Dive and availability biases

• Beach or terrestrial colony counts

• Higher resolution for shorebirds

• Shark, Harbor Seal and Turtle Tags- tracking receivers

• Thermal surveys

• Other ideas?

Leveraging NYSERDA Data Collection

• Known Wildlife Distributions in OPA

• Approach

– Camera Sensor

– Flight Planning and Survey Design

– Data Output

– Adaptive Methods Consideration

– Survey Timing

• Data Distribution

• Leveraging NYSERDA Data Collection

Discussion

Feedback and suggestions should be sent to:

[email protected]

mailto:[email protected]

In Support of New York State Offshore Wind Energy presentation_1… · Robinson Willmott, J. C., G....

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