An environmental classification developed in conjunction with the NSW Marine Parks Authority Research Committee. For more information see: Breen D.A. and R.P. Avery. (2002). Broad-scale biodiversity assessment of the Manning Shelf marine bioregion. Draft final report for the NSW Marine Parks Authority. Copies of the report may be borrowed from the library: Environment Australia, GPO Box 787, Canberra ACT 2601 Australia. This coverage is intended for used in regional level marine conservation assessment. It was prepared using very low cost techniques (ie. unrectified API) and should not be relied upon for navigation purposes. This record describes the environmental classification of the four ocean ocean ecosystems classified by depth. The full study also describes classes for each of the five major estuary ecosystems, and nine habitat surrogates.

An environmental classification developed in conjunction with the NSW Marine Parks Authority Research Committee. For more information see: Breen D.A. and R.P. Avery. (2002). Broad-scale biodiversity assessment of the Manning Shelf marine bioregion. Draft final report for the NSW Marine Parks Authority. Copies of the report may be borrowed from the library: Environment Australia, GPO Box 787, Canberra ACT 2601 Australia. This coverage is intended for used in regional level marine conservation assessment. It was prepared using very low cost techniques (ie. unrectified API) and should not be relied upon for navigation purposes. This record describes the environmental classification of the four ocean ocean ecosystems classified by depth. The full study also describes classes for each of the five major estuary ecosystems, and nine habitat surrogates.

An environmental classification developed in conjunction with the NSW Marine Parks Authority Research Committee. For more information see: Breen D.A. and R.P. Avery. (2002). Broad-scale biodiversity assessment of the Manning Shelf marine bioregion. Draft final report for the NSW Marine Parks Authority. Copies of the report may be borrowed from the library: Environment Australia, GPO Box 787, Canberra ACT 2601 Australia. This coverage is intended for used in regional level marine conservation assessment. It was prepared using very low cost techniques (ie. unrectified API) and should not be relied upon for navigation purposes. This represents an historic dataset providing transparency on the 2002 marine park systematic planning process. This product is one of three related datasets used in the assessment process: "NSW Ocean Ecosystems 2002", "NSW Estuarine Ecosystems 2002" & "NSW Marine Habitats 2002" This record describes classes for each of the five major estuary ecosystems. The full study also describes the environmental classification of the four ocean ocean ecosystems classified by depth, and nine habitat surrogates.

These data were developed to support ecosystem-based management in the Humboldt Bay region. The focus of the mapping effort was on shallow water benthic habitats with particular concern for eelgrass meadows. The study area covers Arcata (North) Bay, Entrance Bay, South Bay, and the Eel River Delta in Humboldt County, California. Humboldt Bay is the largest estuary north of San Francisco Bay and represents a significant resource for the north coast region. Beginning in 2007, NOAA's Office for Coastal Management, in partnership with the California Sea Grant Program and other local organizations, initiated an ecosystem-based management project for the bay. A key component of this project was the establishment of subtidal habitat goals to guide long-term management and provide a framework for conservation efforts across the land-sea interface. The collection of imagery and subsequent delineation of benthic habitat were essential steps for developing and implementing ecosystem-based management in Humboldt Bay's subtidal zone. Collectively, these efforts establish an important and replicable data and information framework crucial for ecosystem-based coastal and marine conservation planning and implementation. The layers available within the data download include biotic, field_point_sample, geoform, and substrate. Partners: California Sea Grant, Humboldt State University, California State University, California Department of Fish and Game, California Coastal Conservancy, California Sea Grant, The Nature Conservancy, United States Fish and Wildlife Service, and United States Geological Survey

In 2006 and 2007 the NOAA Office for Coastal Management purchased services to process existing digital multi-spectral imagery (ADS-40) and create digital benthic habitat data from this imagery for selected Texas coastal bend bays.The Center worked cooperatively with the Texas Parks and Wildlife Department (TPWD) and the Texas A and M University Center for Coastal Studies to develop benthic habitat data, primarily Submerged Aquatic Vegetation(SAV) for several coastal bays. This data will support the state's recently adopted Seagrass Monitoring Program which calls for regional mapping of SAV for status and trends assessment. The Center, Texas A and M, and TPWD have coordinated on the requirements of this project. Original contact information: Contact Org: NOAA Office for Coastal Management Phone: 843-740-1202 Email: coastal.info@noaa.gov

These data provide a baseline inventory of submerged aquatic vegetation within Long Island's South Shore bays. The data were derived from conventional-color metric film diapositives obtained in June 2002 from the New York Department of State's Division of Coastal Resources. Benthic classifications follow the System for Classification of Habitats in Estuarine and Marine Environments (SCHEME). The study area spans approximately 443 square kilometers, extending from the west end of Long Beach Island in Nassau County eastward to Heady Creek at the east end of Shinnecock Bay in Suffolk County. The creation of this baseline inventory was a critical need identified in the Comprehensive Management Plan for the Long Island South Shore Estuary Reserve. Established following the state legislature's passage of the Long Island South Shore Estuary Reserve Act in 1993, the management plan aimed to protect and improve the estuary's ecosystem, enhance public access, and support sustainable economic activities. Ultimately, the goal was to sustain existing high-quality habitats and restore degraded areas to support the productivity of commercially and ecologically important estuarine species. The management plan also mandated a long-term monitoring program to evaluate progress toward estuarine resource improvement goals, building upon this foundational benthic habitat data. The layers available within the data download include biotic, geoform, and substrate. Partners: New York Department of State's Division of Coastal Resources

These data represent benthic habitats in a coastal area near Green Bay, Wisconsin. The area extends approximately 91 kilometers between Suamico, Wisconsin, and Menominee, Michigan, and covers approximately 64 square kilometers. Benthic biota and substrates were classified using the Coastal and Marine Ecological Classification Standard (CMECS). The classification process involved object-based image segmentation of lidar bathymetry, cross-referencing geospatial data with the CMECS hierarchy, and expert interpretation. Biotic and substrate components were classified to the CMECS group or community level, with additional detail provided by co-occurring elements and modifiers. Mapping confidence was higher in areas with Digital Elevation Models (DEMs), as geomorphometric details from digital elevation models could often be directly linked to substrate types through professional geologic judgment. In areas lacking DEM coverage, object-based segmentation was not possible, leading to lower confidence and requiring manual interpretation of substrate and biotic data from available source and ancillary data. In some instances, with support from geologic judgment or imagery, CMECS Substrate Component classifications at DEM boundaries were linearly extrapolated across gaps in DEM coverage. The layers available within the data download include biotic, confidence, and substrate. Partners: Coastal States Organization

In order to design a representative network of high seas marine protected areas (MPAs), an acceptable scheme is required to classify the benthic bioregions of the oceans. Given the lack of sufficient biological information to accomplish this task, we used a multivariate statistical method with 6 biophysical variables (depth, seabed slope, sediment thickness, primary production, bottom water dissolved oxygen and bottom temperature) to objectively classify the ocean floor into 11 different categories, comprised of 53,713 separate polygons, that we have termed "seascapes". Validation of the seascape classification was carried out by comparing the seascapes with an existing map of seafloor geomorphology, and by GIS analysis of the number of separate polygons and perimeter/area ratio. We conclude that seascapes, derived using a multivariate statistical approach, are biophysically meaningful subdivisions of the ocean floor and can be expected to contain different biological associations, in as much as different geomorphological units do the same. Our study illustrates how the identification of potential sites for high seas marine protected areas can be accomplished by GIS analysis of seafloor geomorphic and seascape classification maps. Using this approach, maps of seascape and geomorphic heterogeneity were generated in which heterogeneity hot-spots identify themselves as MPA candidates. The use of computer-aided mapping tools removes subjectivity in the MPA design process and provides greater confidence to stakeholders that an unbiased result has been achieved.

In order to design a representative network of high seas marine protected areas (MPAs), an acceptable scheme is required to classify the benthic bioregions of the oceans. Given the lack of sufficient biological information to accomplish this task, we used a multivariate statistical method with 6 biophysical variables (depth, seabed slope, sediment thickness, primary production, bottom water dissolved oxygen and bottom temperature) to objectively classify the ocean floor into 11 different categories, comprised of 53,713 separate polygons, that we have termed "seascapes". Validation of the seascape classification was carried out by comparing the seascapes with an existing map of seafloor geomorphology, and by GIS analysis of the number of separate polygons and perimeter/area ratio. We conclude that seascapes, derived using a multivariate statistical approach, are biophysically meaningful subdivisions of the ocean floor and can be expected to contain different biological associations, in as much as different geomorphological units do the same. Our study illustrates how the identification of potential sites for high seas marine protected areas can be accomplished by GIS analysis of seafloor geomorphic and seascape classification maps. Using this approach, maps of seascape and geomorphic heterogeneity were generated in which heterogeneity hot-spots identify themselves as MPA candidates. The use of computer-aided mapping tools removes subjectivity in the MPA design process and provides greater confidence to stakeholders that an unbiased result has been achieved.

In order to design a representative network of high seas marine protected areas (MPAs), an acceptable scheme is required to classify the benthic bioregions of the oceans. Given the lack of sufficient biological information to accomplish this task, we used a multivariate statistical method with 6 biophysical variables (depth, seabed slope, sediment thickness, primary production, bottom water dissolved oxygen and bottom temperature) to objectively classify the ocean floor into 11 different categories, comprised of 53,713 separate polygons, that we have termed "seascapes". Validation of the seascape classification was carried out by comparing the seascapes with an existing map of seafloor geomorphology, and by GIS analysis of the number of separate polygons and perimeter/area ratio. We conclude that seascapes, derived using a multivariate statistical approach, are biophysically meaningful subdivisions of the ocean floor and can be expected to contain different biological associations, in as much as different geomorphological units do the same. Our study illustrates how the identification of potential sites for high seas marine protected areas can be accomplished by GIS analysis of seafloor geomorphic and seascape classification maps. Using this approach, maps of seascape and geomorphic heterogeneity were generated in which heterogeneity hot-spots identify themselves as MPA candidates. The use of computer-aided mapping tools removes subjectivity in the MPA design process and provides greater confidence to stakeholders that an unbiased result has been achieved.