Using remote-sensed data for quantitative shallow water ...conference.co.nz/files/docs/shallow survey/presentations...Geoffroy Lamarche – Shallow Survey 2012 Using remote-sensed

Geoffroy Lamarche – Shallow Survey 2012

Using remote-sensed data for quantitative shallow water habitat mapping in New Zealand

with substantial contribution from :

Jean-Marie Augustin2, Xavier Lurton2, Vanessa Lucieer3, Scott Nodder1, Arne Pallentin1, Anne-Laure Verdier1

1: NIWA; 2: Ifremer, Brest, France; 3: University of Tasmania, Hobart

Friday 24 February 2012

Geoffroy Lamarche

National Institute of Water and Atmospheric Research

Wellington


Outline

I. Habitat Mapping - Rationale

II. The New Zealand Ocean Survey 2020 project

III. Segmentation & classification of biophysical datasets

IV. Quantitative use of Backscatter data


What is an Habitat? The natural environment of an organism [Oxford Dic]

Localized surroundings to which an organism, species, or community is specially adapted and which provides for all its needs. [Google]


Contribute to the sustainable management of critical ecosystems (Ecosystem-Based Management - EBM);

To support effective management of economic and biological resources;

To support scientific research as a foundation for sustainable management ;

To assess vast remote & isolated regions;

To increase certainty in decision making

Why Habitat Mapping?

http://www.forestandbird.org.nz

Defining the spatial domains of organisms, geology and environmental variables, that together constitute “habitat”


C o

n f I d

e n

c e

The Habitat Mapping

Conundrum Can we develop a global quantitative

procedure to routinely and objectively characterize seafloor substrate, habitat and biodiversity

using remotely sensed data ?

Physical surrogates

+

+

=


Habitat Mapping in New Zealand

Cook Strait : object-based BS image analysis

OS 2020 National initiative : Coastal

Bay of Island

170E

41S

36S

46S

51S

Subtropical Front

http://bathymetry.co.nz


OS 20/20 Bay of Islands

Fate of sediments & pollutants

Conserve and manage sustainably its ocean resources

Involvement of indigenous &

environmental groups.

OS 20/20 is to provide NZ with knowledge of its ocean territory to demonstrate its stewardship

and exercise its sovereign rights

Provide baseline for estimating impacts of uses on ecosystems ;

http://www.os2020.org.nz/

Seabed Mapping

Offshore EM300 Multibeam 50-200 m. 5m grid resolution.

Inner Bay of Islands EM3000D for > 10 m Sidescan in < 10 m Aerial Photographs for Shallow 1m grid resolution.

• 10 classes derived from Backscatter Strength

• Classes used to define Phase 2 sampling plan for Deep Towed

Imaging System (DTIS)


Direct sampling: Field teams - intertidal Coring - subtidal (incl by divers) Trawling - subtidal (fish, benthos)

Indirect sampling: Cameras (video/still; DTIS/BUV/Drop) Diver observations Multibeam/side-scan sonar/aerial photography

Sampling habitat & measuring biodiversity

-# taxa -# individuals -diversity indices


imaged Holocene sediment to bedrock

→ up to 30 m of sediment

High sedimentation (& gas)

SW NE

Very-high resolution seismic reflection (boomer) profiles

Grain size • Muddy sand dominates the shelf with

increasing mud towards the south • BOI is predominantly sandy mud, with up

to 90% mud in the inlets

Sediments: Carbonate content

• Highest carbonate contents (60-80%) in gravel / very coarse sand in areas of high

backscatter reflectivity.

Biodiversity

Land use

Mean annual sediment loads by land-use Flood events can greatly exceed mean loads

0

50

100

150

200

250

300

Pasture

(Cattle)

Pasture

(sheep)

Pasture

(sub-soil)

Native

(broadleaf)

Kanuka

(scrub)

Pine

(clear-fell)

Se

dim

en

t y

ield

(k

t/y

)

2600000 2605000 2610000 2615000 2620000 2625000 6645000

6650000

6655000

6660000

6665000

6670000

6675000

-31

-30.1

-29.2

-28.3

-27.4

-26.5 C18:0 ? 13 C ‰ C18:0 ? 13 C ‰

3.7

7.5

10

0.8

Mean Annual Discharge (m3/s)

Stable isotopes indicate 3 major inflows with Kerikeri Riv. plume isolated from Waitangi and Kawakawa river plumes


Pixel

Objects

*Limited with texture: ** e.g.: polarimetric, entropy, etc

Source: Daniel L. Civco, University of Connecticut

Parameters Pixel Object

Colour

Size -*

Shape -

Neighbors -

Hierarchy -

Sensor Specific** ~

Object-Based Image Analysis vs pixel-based segmentation (the human perception)

Segmentation and Classification

Integration of ecologically-significant biophysical variables to create classes

Lucieer, V.; Lamarche, G., 2011. Continental Shelf Research, 31: 1236-1247.


Image Segmentation

• Refers to the process of partitioning an image into multiple homogeneous regions

• Locate objects and boundaries (lines, curves, …) in images

• Spatial homogeneity plays the most important role in segmentation.

• Objects or segments are formed because of their spatial correlation, not just because of their thematic similarity

2D feature space shows

that on Brightness and

Max difference the

classes separate well


Classification

• Classes have an identifiable and consistent relationship from a combination of different physical parameters

• Unsupervised classification do not attach meaningful labels to the classes

Need to ground truth the classes

Habitat surrogate (proxy): %Gravel %mud %sand Log of slope

Other possible habitat/biodiversity physical surrogates • % Carbonate • Primary productivity • Seafloor temperature • Sheer bed stress • probability of ground shaking • current velocity •…


Membership Result for each class

Uncertainty layer for entire image

Fuzzy C Means

Hard class map of Class Location

Quantifying uncertainty and progressive

transition from one class to the other


Validation • Both maps detect continental shelf in water depths < 120 m as one class • Classes 1 & 2 gravel & sand with Class 2 small to moderate-sized bed forms. • Classes 3 and 4 silt and mud • Canyon floors well delineated, reflects bedforms and coarse-grained sediment • Neither approach separate many classes in the SE • 1 dominant class in trough is coherent with homogeneous seabed

Aim: develop a simple robust model that quantifies (parameterises) the angular response of the BS

θ

Modeling the BS Angular Response

Lamarche, G.; Lurton, X.; Verdier, A.-L.; Augustin, J.-M., 2011, Continental Shelf Research, 31: S93-S109.

Backscatter Strength Angular Response

A functional model aimed at:

- Fitting a variety of BS(θ) shapes - Depicting the dominant physical

processes - Quantitative BS description - Avoiding detailed modelling - Robustness and simplicity

The Generic Seafloor Acoustic Backscatter model (GSAB)

θ

BS(θ) = 10 log[ A.exp(-θ²/2B²) + C. cosDθ + E.exp(-θ²/2F²) ]


3 physically significant components: Specular – Intermediate – Lambert

A

B

C

D

E

F

BS(θ) = 10 log[ A.exp(-θ²/2B²) + C. cosDθ + E.exp(-θ²/2F²) ]

C = Lambert Law Reference

sediment volume heterogeneities

D = Lambert Law Decrement (=2)

A : Specular Level

high for soft & smooth sediment

B : Specular Lobe width

Linked to seafloor roughness;

E: Transitory Regime Level (dB)

F: Transitory Lobe Width (°)

Backscatter Strength Angular Response


BS(q) classification 8 homogeneous reference areas selected from BS level and texture in Cook Strait. e.g., sandwaves, flanks, smooth, roughed, shallow, deep…


BS(q) classification

40˚ -40˚

One profile for each 8 areas.

Substrate Characterisation

BS Parameterization (A, B, C,… & BS40°)


Classes BS Angular Profiles

Profiles have distinct shapes, relate to the grain size, volume heterogeneity & seafloor roughness.

Classes 1 & 3 ~ sand, higher specular amplitude (class 3) suggests stronger interface roughness.

Class 2 ~ gravel or high volume heterogeneity.

Class 4 ~ mud with underlying sediments

High BS

Low BS


Conclusions Biodiversity mapping can be undertaken using biophysical relationship

to create maps of unsampled biodiversity on heterogeneous, difficult to sample features - OS2020 proved a successful integration of remote & direct sampling of biodiversity over a variety of environments

OS2020 showed requirement for continued monitoring to establish baselines, determine rates of change, and improve land-use & offshore resource management practices

Unsupervised classification is suitable to characterise habitats at multiple scales with ability to quantify uncertainties but there is a need to use other surrogates (seafloor velocity, disturbance, primary productivity) & validate classes

Backscatter Strength is a suitable tool to Qualitatively and Quantitatively characterise seafloor substrate but data processing is complex and requires good instrument calibration


Backscatter Strength (BS) angular response

BS

(d

B)

Mud

-20

-30

Incidence Angle

60° 0 60°

Sand

Gravel -10

Fluid sediments

Specular + volume Rock/coarse sedmts

Interface roughness

A

GR SL

SH

DR

EL

(BS)

TL TL

DT


Sandwaves detection

Amplitude 3 - 7 dB

Amplitude < 1 m

Range 46-65°

BS variation is an excellent descriptor of sandwave

Better than bathymetry data (altitude or angle)

The BS variation over sediment-wave cannot be explained by the incidence angle alone it is controled by sediment type variation


Habitat Mapping

• Defining the spatial domains of organisms, geology and environmental variables, that together constitute “habitat” from the perceptions of what organisms use as individual species or assemblages;

~5 km ~50 km ~500 km

• The classification and characterization of seabed benthos and substrate;

Snelder et al., 2005


Habitat Mapping Programmes Worldwide

Canada’s National Marine Mapping Strategy Marine Biodiversity Hub, Australia Framework for Mapping European Seabed Habitats (MESH) MAREANO programme, Norway Coastal & Marine Ecological Classification Standard (USA) California Seafloor Mapping Program (CSMP)

CERF Habitat Mapping Surveys

~5 km

Tatuteranga Marine Reserve Substrate map

Documents

Using remote-sensed data for quantitative shallow water ...conference.co.nz/files/docs/shallow survey/presentations...Geoffroy Lamarche – Shallow Survey 2012 Using remote-sensed