gov.noaa.nmfs.inport:78461 eng UTF8 dataset Office for Coastal Management resourceProvider 2025-12-09T14:08:12 ISO 19115-2 Geographic Information - Metadata Part 2 Extensions for imagery and gridded data ISO 19115-2:2009(E) geometryOnly curve EPSG::4269 Eastern Lake Erie Benthic Habitat 2020 2026-01-15 publication NOAA/NMFS/EDM 78461 https://www.fisheries.noaa.gov/inport/item/78461 WWW:LINK-1.0-http--link Full Metadata Record View the complete metadata record on InPort for more information about this dataset. information mapDigital 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. NOAA Office for Coastal Management completed NOAA Office for Coastal Management (843) 740-1202 2234 South Hobson Ave Charleston SC 29405-2413 coastal.info@noaa.gov https://coast.noaa.gov WWW:LINK-1.0-http--link NOAA Office for Coastal Management Website NOAA Office for Coastal Management Home Page information pointOfContact notPlanned EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > BENTHIC EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > COASTAL theme Global Change Master Directory (GCMD) Science Keywords 17.0 benthic biotic coastal seafloor substrate theme Great Lakes New York Ohio Pennsylvania United States of America place DOC/NOAA/NOS/OCM > Office of Coastal Management, National Ocean Service, NOAA, U.S. Department of Commerce dataCentre Global Change Master Directory (GCMD) Data Center Keywords 2017-04-24 publication 8.5 Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA) https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords GCMD Keyword Forum Page Global Change Master Directory (GCMD). 2025. GCMD Keywords, Version 22. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords information custodian MarineCadastre project InPort otherRestrictions Cite As: Office for Coastal Management, [Date of Access]: Eastern Lake Erie Benthic Habitat 2020 [Data Date Range], https://www.fisheries.noaa.gov/inport/item/78461. NOAA provides no warranty, nor accepts any liability occurring from any incomplete, incorrect, or misleading data, or from any incorrect, incomplete, or misleading use of the data. It is the responsibility of the user to determine whether or not the data is suitable for the intended purpose. otherRestrictions Use Constraints: 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. otherRestrictions Distribution Liability: 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. unclassified NOAA Data Management Plan (DMP) NOAA/NMFS/EDM 78461 https://www.fisheries.noaa.gov/inportserve/waf/noaa/nos/ocm/dmp/pdf/78461.pdf WWW:LINK-1.0-http--link NOAA Data Management Plan (DMP) NOAA Data Management Plan for this record on InPort. information crossReference vector eng; US biota environment -80.612613 -78.823837 41.937287 42.91887 | Currentness: Publication Date 2020 Bureau Code: 006:48, Program Code: 006:055 https://marinecadastre.gov/downloads/data/benthic/eastern_lake_erie_2020.zip WWW:LINK-1.0-http--link https://marinecadastre.gov/downloads/data/benthic/eastern_lake_erie_2020.zip download https://benthic-data-noaa.hub.arcgis.com/ WWW:LINK-1.0-http--link https://benthic-data-noaa.hub.arcgis.com/ NOAA Benthic Data Hub Registry download dataset Accuracy Untested Horizontal Positional Accuracy Compiled to meet 10 meters horizontal accuracy at 95% confidence level Completeness Report Untested Conceptual Consistency These data are logically consistent 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 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 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 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 Standardized common design elements, cleaned up field content, repaired geometry, and projected to NAD83. 2025-08-01T00:00:00 Source Contribution: Ancillary ecological information USGS Nonindigenous Aquatic Species and BIodiversity Serving Our Nation (BISON) databases Source Contribution: 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 Great Lakes Aquatic Habitat Framework (GLAHF) Source Contribution: Sample locations and sediment compositions Index of Marine and Lacustrine Geological Samples (IMLGS) Source Contribution: Used to clip DEMs to remove subaerial areas from the AOIs NOAA Continually Updated Shoreline Product (CUSP) Source Contribution: Ancillary ecological information NOAA Great Lakes Aquatic Nonindigenous Species Information System (GLANSIS) Source Contribution: 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 Great Lakes Hardened Shoreline Classification Source Contribution: Served as the base digital elevation models (DEMs) NOAA OCM Lidar Source Contribution: To view and classify shallow benthic substrates; however, water depth and clarity often inhibited using the NAIP imagery for direct substrate classification National Agriculture Imagery Program (NAIP)