A Comparison of Seafloor Classification Methods Through the Use ...

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Misc Info • Error Matrix Statistics July 17 th , 2006 MS Thesis Defense
Benthic Classification Benthic habitat: is a seafloor region that can be characterized using the physical gradients of several variables, including depth, substrate, slope, roughness, and morphology, (may also include oceanographic factors such as temperature, salinity, nutrient flux, and biogenic content). Habitat Classification Schemes By Scale Using Nautical charts and existing geological maps (Greene, 2005) Towards Process An idealized scheme to reflect influence of environmental conditions (Valentine et al., 2004) Habitat classes were categorized into either of four groups: • megahabitats (10’s of km’s – 1km) • mesohabitats (1km - 10’s of meters) • macrohabitats (10’s of meters to 1m) • microhabitats (1m to 1cm). A hierarchy of attributes can be associated with a single seafloor region Their scheme recognized the eight themes of: – seabed topography – sediment dynamics – texture – grain size – Roughness – fauna and flora – habitat association and usage – habitat recovery from disturbance Those themes are further attributed by a sequence of modifiers that address habitat characteristics in increasing levels of detail July 17 th , 2006 MS Thesis Defense
Page 1 and 2: A Comparison of Seafloor Classifica
Page 3 and 4: Background Timeline of Significant
Page 5 and 6: Motivation Study Goals Identify the
Page 7 and 8: Active Remote Sensing Multibeam Son
Page 9 and 10: Data Specifics: - Collected using U
Page 11 and 12: Thesis Outline • Introduction - B
Page 13 and 14: Benthic Terrain Modeler Classificat
Page 15 and 16: SIM Classification Surface Interpre
Page 17 and 18: Topographic Classification Results
Page 21 and 22: Quantitative Assessment Results Jul
Page 25 and 26: Textural Analysis Methods Backscatt
Page 27 and 28: Strengths Rocky Ridges, Low Relief
Page 29 and 30: Discussion Data Set • Topographic
Page 31 and 32: Backscatter Classification Discussi
Page 33 and 34: Conclusions Study Goals 2. How do y
Page 35: • Major Advisor: Chris Goldfinger
Page 39 and 40: What are the benefits to classifica
Page 41: 4. Can the texture of multibeam son

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