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

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Using remote-sensed data for quantitative shallow water habitat mapping in New Zealand Friday 24 February 2012 Geoffroy Lamarche National Institute of Water and Atmospheric Research Wellington with

1 Using remote-sensed data for quantitative shallow water habitat mapping in New Zealand Friday 24 February 2012 Geoffroy Lamarche National Institute of Water and Atmospheric Research Wellington with substantial contribution from : Jean-Marie Augustin 2, Xavier Lurton 2, Vanessa Lucieer 3, Scott Nodder 1, Arne Pallentin 1, Anne-Laure Verdier 1 1: NIWA; 2: Ifremer, Brest, France; 3: University of Tasmania, Hobart

2 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

3 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]

to the sustainable management of critical ecosystems (Ecosystem-Based Management - EBM); To support effective management of

4 Why Habitat Mapping? Defining the spatial domains of organisms, geology and environmental variables, that together constitute habitat 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

5 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

6 Habitat Mapping in New Zealand OS 2020 National initiative : Coastal Bay of Island 36S 41S 46S Subtropical Front Cook Strait : objectbased BS image analysis 51S 170E

OS 20/20 Bay of Islands OS 20/20 is to provide NZ with

stewardship and exercise its sovereign rights Conserve and

estimating impacts of uses on ecosystems ; Fate of sediments &

7 OS 20/20 Bay of Islands OS 20/20 is to provide NZ with knowledge of its ocean territory to demonstrate its stewardship and exercise its sovereign rights Conserve and manage sustainably its ocean resources Provide baseline for estimating impacts of uses on ecosystems ; Fate of sediments & pollutants Involvement of indigenous & environmental groups.

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

Photographs for Shallow 1m grid resolution.

8 Offshore EM300 Multibeam m. 5m grid resolution. Inner Bay of Islands EM3000D for > 10 m Sidescan in < 10 m Aerial Photographs for Shallow 1m grid resolution. Seabed Mapping 10 classes derived from Backscatter Strength Classes used to define Phase 2 sampling plan for Deep Towed Imaging System (DTIS)

9 Sampling habitat & measuring biodiversity 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 -# taxa -# individuals -diversity indices

10 Very-high resolution seismic reflection (boomer) profiles imaged Holocene sediment to bedrock up to 30 m of sediment High sedimentation (& gas) SW NE

11 Sediments: Carbonate content Highest carbonate contents (60-80%) in gravel / very coarse sand in areas of high backscatter reflectivity. 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 Biodiversity

Sediment yield (kt/y) Stable isotopes indicate 3 major inflows with Kerikeri Riv.

7 6675000 6670000 6665000 Mean Annual Discharge (m 3 /s) C18:0? 13 C -26.5-27.

5 6650000 6645000 2600000 2605000 2610000 2615000 2620000 2625000 10 0.8-28.3-29.2-30.

12 Sediment yield (kt/y) Stable isotopes indicate 3 major inflows with Kerikeri Riv. plume isolated from Waitangi and Kawakawa river plumes Mean Annual Discharge (m 3 /s) C18:0? 13 C Land use Pasture (Cattle) Pasture (sheep) Pasture (sub-soil) Native (broadleaf) Kanuka (scrub) Pine (clear-fell) Mean annual sediment loads by land-use Flood events can greatly exceed mean loads

** e.g.: polarimetric, entropy, etc Source: Daniel L.

13 Segmentation and Classification Object-Based Image Analysis vs pixel-based segmentation (the human perception) Parameters Pixel Object Colour Size -* Shape - Neighbors - Hierarchy - Sensor Specific** ~ Pixel *Limited with texture: ** e.g.: polarimetric, entropy, etc Source: Daniel L. Civco, University of Connecticut Integration of ecologically-significant biophysical variables to create classes Objects Lucieer, V.; Lamarche, G., Continental Shelf Research, 31:

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

14 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

15 Classification 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 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

16 Fuzzy C Means Quantifying uncertainty and progressive transition from one class to the other Membership Result for each class Hard class map of Class Location Uncertainty layer for entire image

Classes 3 and 4 silt and mud Canyon floors well delineated, reflects bedforms and

17 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

response of the BS θ Lamarche, G.; Lurton, X.; Verdier, A.

18 Modeling the BS Angular Response Aim: develop a simple robust model that quantifies (parameterises) the angular response of the BS θ Lamarche, G.; Lurton, X.; Verdier, A.-L.; Augustin, J.-M., 2011, Continental Shelf Research, 31: S93-S109.

19 Backscatter Strength Angular Response BS(θ) = 10 log[ A.exp(-θ²/2B²) + C. cos D θ + E.exp(-θ²/2F²) ] θ 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)

20 Backscatter Strength Angular Response BS(θ) = 10 log[ A.exp(-θ²/2B²) + C. cos D θ + E.exp(-θ²/2F²) ] A B E F C D 3 physically significant components: Specular Intermediate Lambert A : Specular Level high for soft & smooth sediment B : Specular Lobe width Linked to seafloor roughness; C = Lambert Law Reference sediment volume heterogeneities D = Lambert Law Decrement (=2) E: Transitory Regime Level (db) F: Transitory Lobe Width ( )

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

22 BS(q) classification One profile for each 8 areas. BS Parameterization (A, B, C, & BS40 ) Substrate Characterisation

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

23 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

24 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

26 BS (db) Backscatter Strength (BS) angular response D T SL TL (BS) A SH DR EL TL G R Gravel Sand Mud -30 Fluid sediments Specular + volume Rock/coarse sedmts Interface roughness Incidence Angle

27 Sandwaves detection BS variation is an excellent descriptor of sandwave Better than bathymetry data (altitude or angle) Amplitude < 1 m Amplitude 3-7 db Range The BS variation over sediment-wave cannot be explained by the incidence angle alone it is controled by sediment type variation

variables, that together constitute habitat from the perceptions of what

28 Habitat Mapping The classification and characterization of seabed benthos and substrate; 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 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

29 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 Tatuteranga Marine Reserve Substrate map ~5 km

MBES BS calibration on reference patch areas using singlebeam calibrated data

MBES BS calibration on reference patch areas using singlebeam calibrated data Southampton, U.K. Brussels, Belgium NOC 3 FPS Economy 2, Continental Shelf Service D. Eleftherakis 1, X. Lurton 1, J.M. Augustin