78461 Data Set Published / External 79420 Benthic Project Completed 2026-01-15 These data represent benthic habitats in a coastal area of eastern Lake Erie. The area extends approximately 200 kilometers between Conneaut, Ohio, and Buffalo, New York, and covers approximately 289 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 and imagery, CMECS Substrate Component classifications at DEM boundaries were linearly extrapolated across gaps in DEM coverage. The layers available within the data download include area, biotic, confidence, and substrate. Partners: Coastal States Organization To support the inventory of benthic habitat classification, extent, and health pursuant to the National Environmental Policy Act, the Rivers and Harbors Act, and the Coastal Zone Management Act. Bureau Code: 006:48, Program Code: 006:055 Theme Global Change Master Directory (GCMD) Science Keywords EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > BENTHIC Theme Global Change Master Directory (GCMD) Science Keywords EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > COASTAL Theme ISO 19115 Topic Category biota Theme ISO 19115 Topic Category environment Theme benthic Theme biotic Theme coastal Theme seafloor Theme substrate Spatial Great Lakes Spatial New York Spatial Ohio Spatial Pennsylvania Spatial United States of America Office for Coastal Management Charleston SC Data Set None Planned Map (digital) Area: area_name, area_sq_km, area_sq_m Biotic: biotic_setting, biotic_class, biotic_subclass, biotic_group, biotic_community, biotic_modifier, cooccurrence_element, description Confidence: confidence Substrate: substrate_origin, substrate_class, substrate_subclass, substrate_group, substrate_subgroup, cooccurrence_element, description Any conclusions drawn from the analysis of this information are not the responsibility of NOAA, the Office for Coastal Management or its partners. All content is licensed under the CC0 1.0 Universal public domain dedication. NOAA Office for Coastal Management Point of Contact 2025-12 Organization NOAA Office for Coastal Management NOAA/OCM coastal.info@noaa.gov 2234 South Hobson Ave Charleston SC 29405-2413 (843) 740-1202 https://coast.noaa.gov NOAA Office for Coastal Management Home Page Online Resource Publication Date -80.612613 -78.823837 42.91887 41.937287 Discrete 2020 No Yes No No No Geometry Only Yes Geographic 2D EPSG:4269 NAD83 North American Datum 1983 GRS 1980 6378137 298.257222101 1 Geodetic Latitude Lat degree north 1 Geodetic Latitude Lat degree north 2 Geodetic Longitude Lon degree east 2 Geodetic Longitude Lon degree east Custom Unclassified These data were created for planning and management. Users should be aware of temporal changes that may have occurred since the collection of original source data sets. https://benthic-data-noaa.hub.arcgis.com/ NOAA Benthic Data Hub Registry https://marinecadastre.gov/downloads/data/benthic/eastern_lake_erie_2020.zip Untested Compiled to meet 10 meters horizontal accuracy at 95% confidence level Untested These data are logically consistent USGS Nonindigenous Aquatic Species and BIodiversity Serving Our Nation (BISON) databases Ancillary ecological information Great Lakes Aquatic Habitat Framework (GLAHF) Geospatial data and descriptions compiled from multiple sources, including peer reviewed publications, U.S. Army Corps of Engineers shoreline material studies, and benthic sampling or video data Index of Marine and Lacustrine Geological Samples (IMLGS) Sample locations and sediment compositions NOAA Continually Updated Shoreline Product (CUSP) Used to clip DEMs to remove subaerial areas from the AOIs NOAA Great Lakes Aquatic Nonindigenous Species Information System (GLANSIS) Ancillary ecological information NOAA OCM Great Lakes Hardened Shoreline Classification Shoreline segments classified by type and condition, used to identify coastal deposit types which in some cases were extrapolated offshore to interpret benthic geomorphology. These data also provided the locations of anthropogenic material (e.g. riprap, seawalls). NOAA OCM Lidar Served as the base digital elevation models (DEMs) National Agriculture Imagery Program (NAIP) To view and classify shallow benthic substrates; however, water depth and clarity often inhibited using the NAIP imagery for direct substrate classification 1 Digital Elevation Models (DEMs) The DEMs were clipped to the spatial extent of the project AOI, and then clipped by the current NOAA Continually Updated Shoreline Product (CUSP) to remove subaerial areas from the AOI. Using Benthic Terrain Modeler and Spatial Analyst tools within Environmental Research Systems Institute (ESRI) ArcGIS software, geomorphometric derivatives were calculated for each DEM, including slope, aspect, curvature, rugosity (ratio of surface area to planar area), terrain ruggedness (Vector Ruggedness Measure), and shaded relief. The derivative layers were imported into Trimble eCognition software for object-based image segmentation. The segmentation process was an iterative procedure designed to optimize segmentation results while minimizing redundant data layers. A sensitivity analysis was performed to identify the most appropriate combinations of DEM derivatives and segmentation parameters. The best result, determined qualitatively as the most natural segmentation of the DEMs, was attained using the slope, rugosity, ruggedness, curvature, and shaded relief layers, a segmentation scale parameter of 5, a color parameter of 0.2, and a smoothness parameter of 0.5. After segmentation, an additional operation was performed to remove all segmentation polygons containing less than 35 pixels (105 square meters), in accordance with the project Minimum Mapping Unit (MMU) of 100 square meters. The segmentation polygons served as the spatial framework for developing the benthic habitat maps. 2020-01-01T00:00:00 2 Substrate The substrate data were based on cross walking existing substrate data to the CMECS hierarchy as well as expert interpretation using available source data. A significant amount of manual geologic interpretation and classification was required to correlate benthic geomorphology as observed in the DEMs and derivative layers to the substrate types listed in the Great Lakes Aquatic Habitat Framework (GLAHF) substrate data. In some areas, high-resolution (1 meter) aerial imagery acquired by the National Agriculture Imagery Program (NAIP) provided a direct window to view and classify shallow benthic substrates; however, water depth and clarity often inhibited using the NAIP imagery for direct substrate classification. Ancillary substrate information was provided by the Index of Marine and Lacustrine Geological Samples (IMLGS), which provided sample locations and sediment compositions. However, IMLGS data are sparse within the Green Bay study area. NOAA OCM's Great Lakes Hardened Shoreline Classification data set, which consists of shoreline segments classified by type and condition, was also used to identify 1) coastal deposit types which in some cases were extrapolated offshore to interpret benthic geomorphology and 2) provide the locations of anthropogenic material (e.g. riprap, seawalls). The location of underwater anthropogenic structures was provided by NOAA nautical chart products. Anthropogenic structures were often digitized manually from the DEM and included exposed pipelines, water intakes and cribs, water/sewer outfalls, and vessel wrecks. Valuable information regarding the location and spatial extent of benthic bedrock material was provided by Holcombe et al. (2005), "Lake-Floor Geomorphology of Lake Erie" Areas that were not able to be interpreted were mapped as "Unknown Benthic Habitat". 2020-01-01T00:00:00 3 Biotic The biotic data were primarily based on crosswalking existing ecological data to the CMECS hierarchy. The Michigan Tech Research Institute (MTRI) provided information about the spatial distribution and abundance of nuisance Cladophora algae, subaquatic vegetation, and invasive Phragmites marshes. The MTRI maps were generalized and transferred directly to segmentation polygons using zonal statistical functions in ESRI ArcGIS software. Ancillary ecological information was provided by the USGS' Nonindigenous Aquatic Species and BIodiversity Serving Our Nation (BISON) databases, NOAA's Great Lakes Aquatic Nonindigenous Species Information System (GLANSIS), and GLAHF's Benthos database, which was compiled from multiple sources including the Environmental Protection Agency (EPA), NOAA's Great Lakes Environmental Research Laboratory (GLERL), and NOAA's Cooperative Science and Monitoring Initiative (CSMI). These data sets contain point observations of specific species. These databases were queried for observations of invasive Dreissena mussels, which were transferred directly to the segmentation polygons. Areas that were not able to be interpreted were mapped as "Unknown Benthic Habitat". 2020-01-01T00:00:00 4 Confidence The DEMs provided geomorphometric details which could often be related directly to substrate types using professional geologic judgment. Mapping confidence was higher in areas where the AOIs were spatially coincident with the topobathymetry DEMs, and was lower in areas where the AOIs were not covered by the DEMs. In these areas of lower confidence, object-based segmentation was not possible, therefore substrate and biotic data were manually interpreted from available source and/or ancillary data. In some cases, and when supported by geologic judgment and/or available imagery sources, substrate classifications at the edge of DEM coverage were linearly extrapolated across DEM coverage gaps and boundaries. Users are encouraged to view the habitat maps and confidence maps together. 2020-01-01T00:00:00 5 Standardized common design elements, cleaned up field content, repaired geometry, and projected to NAD83. 2025-08-01T00:00:00 gov.noaa.nmfs.inport:78461 Jesse Brass 2025-10-21T15:12:10 Jesse Brass 2025-12-09T14:08:12 2025-12-09 Office for Coastal Management OCM 1002 Public No No 2025-12-09 1 Year 2026-12-09