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Pål Buhl-Mortensen (IMR) Margaret Dolan (NGU) Classification of seabed video observations as a basis to predict biotopes and ecotopes

Classification of seabed video observations as a basis to predict biotopes and ecotopes

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Classification of seabed video observations as a basis to predict biotopes and ecotopes. Pål Buhl-Mortensen (IMR) Margaret Dolan (NGU). Nature. - PowerPoint PPT Presentation

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Page 1: Classification of seabed video observations as a basis to predict biotopes and ecotopes

Pål Buhl-Mortensen (IMR)Margaret Dolan (NGU)

Classification of seabed video observations as a basis to predict biotopes and ecotopes

Page 2: Classification of seabed video observations as a basis to predict biotopes and ecotopes

Nature type (”Biotope”)a homogeneous type of nature, which embraces all plant and animal life and the environmental factors acting there.

Nature types are generally home to a unique combination of species that reflects the local environmental conditions.

http://www.artsdatabanken.no/

Nature is a general and scale-independent term referring to a specific area including the species living there and the environment surrounding them, or the environment alone

Nature

Page 3: Classification of seabed video observations as a basis to predict biotopes and ecotopes

ROV (Remotely Operated Vehicle)

Towed platforms/drop cameras

Manned submersibles

Video/camera-platforms

Page 4: Classification of seabed video observations as a basis to predict biotopes and ecotopes
Page 5: Classification of seabed video observations as a basis to predict biotopes and ecotopes

• Few existing habitat classes

• Deep sea less homogenous that perceived

• Many new nature types

• How to define?

Recognition/definiton of habitats and biotopes in deep water

Page 6: Classification of seabed video observations as a basis to predict biotopes and ecotopes
Page 7: Classification of seabed video observations as a basis to predict biotopes and ecotopes

Sponge habitats on sandy mud, Sand, Gravelly sand, Gravel, Moraine ridges

Fine mud, Seepages (Pockmark)*

Fine mud, Sand, Gravelly sand, Coral reef*, Seepages (Pockmark)*

Landscape/landscape element level Ecosystem level

Nature types in the MAREANO-areaIdentified with DCA of results from video analysis of 200 m sequencesanalysed in field (landscape level) and post cruise (ecosystem level)

Bank

Sedimentation-bassain

Shelf trough(marine valley)

Shelf break

Upper continental slope

Lower continental slope

Consolidated mud with large glass sponges, Fine deep sea mud, ++

Gravelly bottom with soft corals ++

Page 8: Classification of seabed video observations as a basis to predict biotopes and ecotopes

Quantifying image and video data

Page 9: Classification of seabed video observations as a basis to predict biotopes and ecotopes

Analysis of videotransects

100 m

Video transect divided into sequences:

Video-sequences: Average length = 12 m

Counting of individuals an colonies

Estimation of % coverage of bottomsubstrates and encrusting organisms

Quantification of human impact:

damaged organisms trawlmarks lost fishing gears garbage

Page 10: Classification of seabed video observations as a basis to predict biotopes and ecotopes

Recording of videoand additional data

Page 11: Classification of seabed video observations as a basis to predict biotopes and ecotopes

Video-signal

Stored data: Time

PositionDepth

AltitudePitch

Monitor

Semi-automaticalextraction

of data

Extracted data: Taxon, Abundance,Bottom type, Cover,

Position, Depth,Estimated area ….

Integration of stored dataand image analysis

Synchronization

Page 12: Classification of seabed video observations as a basis to predict biotopes and ecotopes
Page 13: Classification of seabed video observations as a basis to predict biotopes and ecotopes
Page 14: Classification of seabed video observations as a basis to predict biotopes and ecotopes

File Edit View Insert Format Tools HabiMap

Predefined species A (n)

Predefined species B (n)

Predefined species C (%)

N 7052000E 576000

Depth 150

New species A: ………….

New species B: ………….

New species C: ………….

Time 03:27:56

1 2 2-5 5-10 10-20 …….

1 2 2-5 5-10 10-20 …….

1 2 2-5 5-10 10-20 …….

1 2 2-5 5-10 10-20 …….

1 2 2-5 5-10 10-20 …….

1 2 2-5 5-10 10-20 …….

Sequence 56.5 m Area 112,7

Predefined bottomtype A

Predefined bottomtype B

New bottomtype: ………..

<10 10-25 ….

10-25 ….

10-25 ….

Main bottom type: Mud….78

Bottomtypes

Taxa

25-50

25-50

25-50

50-75

50-75

50-75

75-90

<10

<10

75-90

75-90

90-100

90-100

90-100

% cover

Counts and % cover

Start newsequence

Pause Stopregistration

Page 15: Classification of seabed video observations as a basis to predict biotopes and ecotopes

St DateTime E NDistance

Cumul dist

Depth

Mud

Sand

Sand with shells

Sandy mud

Pebble

Cobble

Boulder

Bedrock Species Value

Laserbeams

Field width

Comments

Laserbeams

R100VL116 11.04.07:11:11:47 840669.5 7807508.4 0.0 0 316 5 85 0 0 10 0 0 0 2.4 198.8 2.4

R100VL116 11.04.07:11:11:47 840669.5 7807508.4 0.0 0 316 5 85 0 0 10 0 0 0 Stichopus 1 2.4 198.8 2.4

R100VL116 11.04.07:11:11:47 840669.5 7807508.4 0.8 0.7901093 316 5 85 0 0 10 0 0 0 Cerianthidae 1 2.4 198.8 2.4

R100VL116 11.04.07:11:11:56 840669.5 7807507.6 0.0 0.7901093 317 5 85 0 0 10 0 0 0 Stichopus 1 2.4 198.8 2.4

R100VL116 11.04.07:11:11:56 840669.5 7807507.6 1.2 1.9698279 317 5 85 0 0 10 0 0 0 Teleostei 1 2.4 198.8 2.4

R100VL116 11.04.07:11:12:08 840669.4 7807506.4 0.0 1.9698279 317 5 85 0 0 10 0 0 0 Cerianthidae 1 2.4 198.8 2.4

R100VL116 11.04.07:11:12:08 840669.4 7807506.4 0.0 1.9698279 317 5 85 0 0 10 0 0 0 Stichopus 1 2.4 198.8 2.4

R100VL116 11.04.07:11:12:12 840669.4 7807506.4 0.0 1.9698279 317 5 85 0 0 10 0 0 0 Unidentified 1 2.4 198.8 2.4

R100VL116 11.04.07:11:12:12 840669.4 7807506.4 0.4 2.3594394 321 5 85 0 0 10 0 0 0 Unidentified 1 2.4 198.8 2.4

R100VL116 11.04.07:11:12:16 840669.3 7807506.0 0.8 3.1495477 318 5 90 0 0 5 0 0 0 1.4 340.7 1.4

R100VL116 11.04.07:11:12:23 840669.3 7807505.2 0.0 3.1495477 318 5 90 0 0 5 0 0 0 Teleostei 1 1.4 340.7 1.4

R100VL116 11.04.07:11:12:23 840669.3 7807505.2 0.0 3.1495477 318 5 90 0 0 5 0 0 0 Stichopus 1 1.4 340.7 1.4

R100VL116 11.04.07:11:12:23 840669.3 7807505.2 0.4 3.5391593 318 5 90 0 0 5 0 0 0 Stichopus 1 1.4 340.7 1.4

R100VL116 11.04.07:11:12:32 840669.3 7807504.9 1.3 4.8720789 319 5 90 0 0 5 0 0 0 muddy sand with lots of tubes

R100VL116 11.04.07:11:12:45 840669.1 7807503.5 3.0 7.8738224 319 10 90 0 0 0 0 0 0 2.4 198.8 2.4

R100VL116 11.04.07:11:13:47 840670.7 7807501.0 0.4 8.2351037 318 10 90 0 0 0 0 0 0 2 238.5

whole last section: no sightsediment turbulences 2

R100VL116 11.04.07:11:13:53 840671.1 7807501.1 2.0 10.233926 317 10 90 0 0 0 0 0 0 Stichopus 2

R100VL116 11.04.07:11:14:47 840672.8 7807502.0 7.9 18.137544 318 10 90 0 0 0 0 0 0 7 68.1

camera zoomed inwhole last section sediment turbulences 7

R100VL116 11.04.07:11:15:48 840673.0 7807509.9 3.7 21.832473 319 15 85 0 0 0 0 0 0 no sightsediment disturbance

R100VL116 11.04.07:11:16:05 840672.2 7807513.5 0.0 21.832473 318 15 85 0 0 0 0 0 0 4 119.3camera zoomed in 4

R100VL116 11.04.07:11:16:05 840672.2 7807513.5 0.9 22.747898 318 15 85 0 0 0 0 0 0 Unidentified 2 4 119.3camera zoomed in 4

R100VL116 11.04.07:11:16:09 840672.0 7807514.4 3.5 26.223316 318 15 85 0 0 0 0 0 0 Unidentified 1

R100VL116 11.04.07:11:16:25 840672.3 7807517.9 1.9 28.098207 318 15 85 0 0 0 0 0 0 Unidentified 2

R100VL116 11.04.07:11:16:34 840672.6 7807519.7 0.0 28.098207 318 15 85 0 0 0 0 0 0camera zoomed in during the whole transect

R100VL116 11.04.07:11:16:34 840672.6 7807519.7 1.1 29.208444 318 15 85 0 0 0 0 0 0 Kophobelemnon 1

R100VL116 11.04.07:11:16:38 840672.7 7807520.8 1.7 30.884585 318 15 85 0 0 0 0 0 0 Unidentified 1

R100VL116 11.04.07:11:16:44 840672.9 7807522.5 1.1 31.994822 318 15 85 0 0 0 0 0 0 Unidentified 1

R100VL116 11.04.07:11:16:47 840673.0 7807523.6 0.6 32.549939 318 15 85 0 0 0 0 0 0 6 79.5 6

Page 16: Classification of seabed video observations as a basis to predict biotopes and ecotopes

Actiniaria dark

Actiniaria pink

Actiniaria red

Actiniaria violet

Actiniaria white

Amphipoda cave

Antho dichotoma

Aplysilla sulfurea Ascidia sp

Ascidia virginea

Asterias rubens Asteroidea Axinella

100_1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00100_2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00100_3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.31 0.00100_4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00100_5 0.24 0.00 0.00 0.00 0.00 0.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00101_1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.23 0.00101_2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00101_3 0.00 0.00 0.19 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00101_4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.19 0.00101_5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.53 0.00102_1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00102_2 0.49 0.97 0.00 0.00 0.00 0.00 0.00 1.22 0.00 0.00 0.00 0.00 0.00102_3 0.37 0.37 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.37 0.00102_4 0.00 0.65 0.00 0.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.33 0.00102_5 0.25 0.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00103_1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.13 0.00103_2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.68 0.00103_3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.33 0.00 0.00 1.80 0.00103_4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.81 0.00103_5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.83 0.00103_6 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.17 0.00 0.00 1.02 0.00103_7 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.23 0.00104_1 0.00 0.00 0.00 0.00 0.00 0.00 0.20 0.00 0.00 0.00 0.00 0.00 0.20104_2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00104_3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00104_4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00104_5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.22 0.00 0.00 0.00 0.43 0.22105_1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.60 0.00 0.00 0.00 2.39 0.00105_2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.49 0.00 0.00 0.00 15.49 0.00105_3 0.00 0.00 0.22 0.00 0.00 0.00 0.00 2.87 0.00 0.00 0.00 11.71 0.00105_4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.67 0.00 0.00 0.00 3.33 0.00105_5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.71 0.00 0.00 0.00 9.03 0.00106_1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00106_2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.21 0.21

Page 17: Classification of seabed video observations as a basis to predict biotopes and ecotopes

- Depth

- Terrain- Slope- Aspect- Curvature- Rugosity- ++

- Backscatter

Variables derived from multibeam echosouder(naturetype indicators)

Page 18: Classification of seabed video observations as a basis to predict biotopes and ecotopes

General procedure for characterization and prediction of habitats

1)Multivariate analysis of species data from bottom video-surveys to find groups

of locations that are similar with respect to composition of species.

2)Identification of environmental variables (e.g. Depth, surface sediment

composition, topography, etc) that best explain the composition of species

identified on video records.

3)Comparison of the explanatory ability of variables derived from the MBE data-

set and parameters collected by means of visual inspection.

4)”Supervised” GIS analysis for classification with full areal coverage (Maxent).

5)Presentation of the general biodiversity of habitats based on species

composition in samples collected with different bottom sampling gears.

Page 19: Classification of seabed video observations as a basis to predict biotopes and ecotopes

Tromsøflaket

MAREANO mapping areas

Barents Sea

Norwegian Sea

Page 20: Classification of seabed video observations as a basis to predict biotopes and ecotopes

Shallow banks (50-200m)

Trench with strong currents (200-300m)

Continental slope (1500-2000m)

Continental slope (1000-1500m)

Andøy Hola

Bleiksdjupet Canyon

Page 21: Classification of seabed video observations as a basis to predict biotopes and ecotopes
Page 22: Classification of seabed video observations as a basis to predict biotopes and ecotopes
Page 23: Classification of seabed video observations as a basis to predict biotopes and ecotopes
Page 24: Classification of seabed video observations as a basis to predict biotopes and ecotopes

Grovinndeling av marine landskap

Surveyed locations 2008

Page 25: Classification of seabed video observations as a basis to predict biotopes and ecotopes

0

400

200

600

800

2400

Fjord/coast

Shelf trough

BankShelf terrace

Shelf trough fan

Slope terrace

Upper slope

Mid and lower

slope

CanyonDeep-sea plain

MudSandy mud

SandGravelly sandSandy gravel

GravelBoulderBedrock

Consolidated mud

Dep

th (m

)

0.00.20.4

0.6

0.81.0

Bott

om ty

pes

(freq

uenc

y of

occ

urre

nce)

NAW (>0,5 °C)

NSAIW (-0,5 - 0,5 °C)

NSDW (-0,5 - 1,5 °C)

NCW

Wat

erm

asse

s0

Num

ber o

f tax

a

51015202530354045505560

Page 26: Classification of seabed video observations as a basis to predict biotopes and ecotopes

Landscape

195

196

197

198

199200

201

202

203

204

205

206207

208

209210

211

212213

215

217

218

219

220

221222

223

224225

226

227

228

229

230 231232

233

234

235

236

237

238

239240

241

242

243

244

245

246

247

248

249250

251

252

253

254255

256

257

258259

260

261

262

263

264

265

266

267

268

269

270

271

Depth

Gravel

Gravelly

MudSand

Sandy mud

Axis 1

Axi

s 2

Fjord/Coast

Shelf trench

Fan

Bank

Shelf terracee

Slope terrace

Upper slope

Lower slope

Canyon

Deep sea plain

Lower slopeUpper slope

Shelf break

Shelf trench

Bank

DCA of field observations, cruise 2008104 june 2008

Clasification of locations based on DCA groups

Page 27: Classification of seabed video observations as a basis to predict biotopes and ecotopes

DCA hele egga 09 2

Axis 1

Axi

s 2

Type

12345678

Page 28: Classification of seabed video observations as a basis to predict biotopes and ecotopes
Page 29: Classification of seabed video observations as a basis to predict biotopes and ecotopes

DCA T II April

Axis 1

Axi

s 2

CoralGravelSandSandy mudMud

Coral4 Gravel

3 Sand

1 Mud5 GravellySandy mud

2 Sandy mud

Page 30: Classification of seabed video observations as a basis to predict biotopes and ecotopes

Tromsøflaket

Page 31: Classification of seabed video observations as a basis to predict biotopes and ecotopes

DCA, 200m video sequenses

Page 32: Classification of seabed video observations as a basis to predict biotopes and ecotopes

Pelosina Asbestopluma. Calcareous foraminifera. Aplysilla. Geodia

Aphrodite, Stylocordyla

Brachiopoda. PhakelliaPolymastia. Antho

Bolocera. Sticopus

Fine-grained mudSandy mud

SandGravelly sand

Morainic rocky ridges Bouldery bottom

2 1

34

56

Page 33: Classification of seabed video observations as a basis to predict biotopes and ecotopes

Nature type prediction on Tromsøflaket, western partUsing same predictors as for the eastern part

1 – Mud in bassain, with pockmarks2 – Sandy mud, with iceberg ploughmarks3 – Sand/mud, on level bottoms4 – Gravelly and sandy sediments in gently sloping areas5 – Gravelly and sandy sediments, with iceberg ploughmarks6 – Gravelly and sandy sediments, with boulder on morainic ridges

Page 34: Classification of seabed video observations as a basis to predict biotopes and ecotopes
Page 35: Classification of seabed video observations as a basis to predict biotopes and ecotopes
Page 36: Classification of seabed video observations as a basis to predict biotopes and ecotopes
Page 37: Classification of seabed video observations as a basis to predict biotopes and ecotopes

Prediction in ”new” marine landscapes

Sources to ecosystem variation:-Terrain and landscape-Watermasses (temperature, salinity, etc)-Sediment composition-Bottom currents-Nutrient supply

Different regions often have different environmental conditions

MBE-derived predictors may change ”meaning”