1. Thoughts on Algorthmic Approaches to Reveal Marine Geomorphology as a Proxy for Habitat Presentation IT13B-01 2010 Ocean Sciences Meeting Portland, OR Presentation IT13B-01 2010 Ocean Sciences Meeting Portland, OR Dawn Wright Dept. of Geosciences, Oregon State University Will Heyman Department of Geography, Texas A&M University Dawn Wright Dept. of Geosciences, Oregon State University Will Heyman Department of Geography, Texas A&M University

2. Primary Data Acquisition: Visual Hawaii Undersea Research Lab NOAA CRED

3. Primary Data Acquisition: “Shallow” Multibeam sonar, 200 m and shallower Ikonos, shoreline to 15 m Portable, pole-mounted EM3000 Ikonos satellite (Image from SatMagazine) R/V Acoustic Habitat Investigator w/ RESON 8101, NOAA

4. Primary Data Acquisition: “Deep” Multibeam sonar, regional scale, 200 m and deeper Image from Lost City Expedition (2003)

5. Algorithmic Approaches Almost always quantitative, usually automatic or semi-automatic… Allow the user to refine the classification at certain stages in the process based on visual observation… Subject to artifacts, but more repeatable, less expensive, and with resolution limited only by the source data…

6. Algorithmic Approaches What are the major tried and true algorithmic approaches for producing classifications? What should be the habitat classification categories? …for a particular region (shallow vs. deep) … sensor (satellite, acoustics at different resolutions and in the subsurface)? Is it feasible to move toward a standardization of algorithmic seafloor classification approaches?

7. Broad scale Fine scale Shape: Bathymetric Position Index (from TPI, Jones et al., 2000; Weiss, 2001; Iampietro & Kvitek, 2002)

8. Emily Lundblad, OSU Thesis Zone and Structure Flow Chart

9. Structure Classification Decision Tree Emily Lundblad, OrSt M.S. Thesis

10. http://dusk.geo.orst.edu/djl/samoa Benthic Terrain Modeler

11. Ecosystem-Based Mgmt Tools Network www.ebmtools.org Ecosystem-Based Mgmt Tools Network www.ebmtools.org

12. Roughness: Rugosity Measure of how rough or bumpy a surface is, how convoluted and complex Ratio of surface area to planar area Graphics courtesy of Jeff Jenness, Jenness Enterprises, and Pat Iampietro, CSU-MB Surface area based on elevations of 8 neighbors 3D view of grid on the leftCenter pts of 9 cells connected To make 8 triangles Portions of 8 triangles overlapping center cell used for surface area

13. Benthic Complexity Ardron and Wallace, in Wright and Scholz, Place Matters: Geospatial Tools for Marine Science… 2005

14. Benthic Complexity Ardron and Wallace, in Wright and Scholz, Place Matters: Geospatial Tools for Marine Science… 2005

15. Ecological Habitat Modeling GLM, GAM, classification/regression trees, etc. Ecological Habitat Modeling GLM, GAM, classification/regression trees, etc. Iampietro, Kvitek et al., Marine Geodesy, 2008

16. Bayesian Approaches Simons and Snellen, Applied Acoustics, 2009

17. Classification Schemes: CMECS Coastal and Marine Ecological Classification Standard Classification Schemes: CMECS Coastal and Marine Ecological Classification Standard Madden et al., NatureServe, NOAA

18. EUNIS eunis.eea.europa.net EUNIS eunis.eea.europa.net EEA/European Environmental Information Observation Network

19. EUSeaMap EUNIS classification is a common language for habitat. Propose modifications to EUNIS where appropriate (Baltic or Med) EUSeaMap EUNIS classification is a common language for habitat. Propose modifications to EUNIS where appropriate (Baltic or Med) Natalie Coltman, JNCC, UK, www.jncc.gov.uk/EUSeaMap

20. EUSeaMap Methodology Natalie Coltman, JNCC, UK, www.jncc.gov.uk/EUSeaMap Habitat Biological zone depth, light, disturbanceEnergytides, wavesSubstratesediment, rock High energy circalittoral rock Circalittoral zone High energyRock O2/POC/C hl Ice cover

21. Geoscience Australia Heap, Nichol et al., AAG, 2008

22. geohab.org

23. marinecoastalgis.net

24. How best to move forward with marine habitat mapping & modelling? Stay abreast of more than one community (e.g., GeoHab, EUSeaMap, etc.) Entrain more than one specialist (marine ecologists, geologists, physical oceanographers, GISers) Decision tree or matrix depending on scale, species (e.g., CMECS, EUNIS) Standard classification dictionaries, generic shallow-water dataset for all, w/testing tools (interdisciplinary technical working groups) “Semantic interoperability” of classification? What should be on the resulting maps? Concluding Thoughts

25. Related papers: http://marinecoastalgis.net/aag08 More info: dawn@dusk.geo.orst.edu Thank you…