Benthic habitat mapping: a synopsis of methodologies and approaches. Dr. Craig Brown University of Ulster

Transcription

1 Benthic habitat mapping: a synopsis of methodologies and approaches Dr. Craig Brown University of Ulster

2 Technological advances in remote sensing

3 Insitu sampling Improving technology Single beam acoustics/video Multibeam sonar Point sampling (e.g. grabs/video) Single beam acoustic surveys (e.g. RoxAnn), or video/diver transects 100% swathe coverage (e.g. sidescan sonar or multibeam sonar)

4 What is habitat mapping? Habitat mapping is defined by the MESH Project to be: Plotting the distribution and extent of habitats to create a map with complete coverage of the seabed showing distinct boundaries separating adjacent habitats. No absolute accepted methodology and unlikely there will be for all applications

5 Habitat mapping in Europe

6 What s required? TRADITIONAL RESEARCH DISCIPLINES GEOPHYSICS GEOLOGY BIOLOGY HYDROGRAPHY G1 G3 B1 B2 Etc Salinity Bathymetry Acoustic data Backscatter Subbottom seismic G2 G4 G5 Point sample data (cores and grabs) B3 Point sample data (grabs, underwater video etc.) Temperature Current speed Modelled and measured hydrographic data at a spatial scale relevant to habitats BENTHIC HABITAT MAPPING Spatial integration of data sets through a wide variety of approaches usually involving some form of modelling: e.g. datadriven models/rulebased models/expert knowledge/mixed models/habitat suitability modelling etc. SEABED HABITAT MAPS

7 Habitat delineation Effective benthic habitat mapping relies on the ability to separate one habitat type from another. Scale and resolution are crucial factors

8 Remote sensing for ecological study (spatial ecology) Terrestrial spatial data Example from the NI countryside survey

9 Remote sensing for ecological study (spatial ecology) Terrestrial spatial data Marine spatial data Identification of spatial patterns in species, community and habitat distributions Estimation of habitat coverage, frequency, fragmentation, human impacts Identification of priority habitats Identify and establish protected areas Monitoring change Example from the NI countryside survey

10 Remote sensing for ecological study: backscatter data Data cleaning, mosaic production and image enhancement Old commercial Processed 5 bit greyscale New commercial Processed 8 bit greyscale PRISM Processed 16 bit greyscale

11 Linking biology to acoustics: Sidescan sonar A B C D Hamon grab Brown et al. (2002) Estuarine, Coastal and Shelf Science 17: Brown et al. (2004) JMBA 84: m beam trawl

12 Linking biology to acoustics: Sidescan sonar Inshore Habitat A north Habitat B Habitat C Habitat D Offshore Brown et al. (2002) Estuarine, Coastal and Shelf Science 17: Brown et al. (2004) JMBA 84:

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14 Backscatter and grain size Collier and Brown (2005) Marine Geology 214:

15 Unsupervised classification Raster images of interpolated Mean, Stdev and Median (gridded at 50m) Median Mean Stdev Brown and Collier, (2008) Estuarine Coastal and Shelf Science 78: Composite image and cluster map generated from above images

16 Unsupervised classification Automated unsupervised classification The crude automated classification applied to this data 4 groundtypes: set achieved a 78% accuracy at predicting benthic Hard, habitat reflective based ground on underwater video data over a difficult (red) site with few clear boundaries between acoustic facies High intermediate backscatter (yellow) Soft, nonreflective ground <30m (blue) Soft, nonreflective ground >30m (bluehatched) Brown and Collier, (2008) Estuarine Coastal and Shelf Science 78:

17 The advantages of MBES data Acoustic surveys

18 Backscatter processing and (semi)automated classification: Automated classification routines (A) (B) (C) Single beam AGDS e.g. RoxAnn, QTC View Relatively simple data cleaning and processing Extremely complex data cleaning and processing! Acoustic segmentation θ Swathe AGDS e.g. QTC Multiview Classes Acoustic facies map

19 Stanton 4 Celtic Explorer EM1002 Multibeam system McGonigle, Brown, Quinn and Grabowski (2009) Estuarine Coastal and Shelf Science

20 Stanton 4 Habitats (video data) (Multivariate trends from grab and stills data very fuzzy ) SS.SMu.CFiMu.SpnMeg SS.SCS.OCS.GlapThy A Sublittoral bioturbated mud with burrowing megafauna B Sublittoral coarse sand with cobbles. Polycheates and squat lobster SS.SMU.ISaMu.MelMagThy CR.HCR.DpSp.PhaAxi A* Sublittoral muddysand with polychaete dominated community C Highenergy circalittoral rock (boulders) with sponge communities SS.SSA.CFiSa.EpusOborApri CR.HCR.DpSp.PhaAxi A** Sublittoral sand with Ophiura sp. and Echinocyamus pusillus D Highenergy circalittoral rock with sponge communities

21 Stanton 4 Acoustic/Habitat relationship Total A 6 [2.9] 10 [4.9] 185 [91.5] 1 [0.5] 202 [100] A* 4 [44.4] 5 [55.5] 9 [100] A** 3 [4.4] 3 [4.4] 43 [64.1] 18 [26.8] 67 [100] B 21 [16.4] 11 [8.5] 96 [75] 128 [100] C 73 [69.5] 12 [11.4] 1 [0.9] 19 [18.1] 105 [100] D 10 [6.8] 105 [71.4] 2 [1.3] 29 [19.7] 1 [0.6] 147 [100] Frequency [% contribution] McGonigle, Brown, Quinn and Grabowski (2009) Estuarine Coastal and Shelf Science

22 Stanton 4 Habitat map McGonigle, Brown, Quinn and Grabowski (2009) Estuarine Coastal and Shelf Science

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