A map of known Reefs and Shoals in the Bay of Green Bay (Lake Michigan, USA) drafted by the Nature Conservancy. Data from: Goodyear Spawning fish atlas (https://www.arcgis.com/home/item.html?id=8e951782d20340708ced556274a18941) Known reef point locations were compiled from various data sources including the U.S. Geographic Naming Information System (GNIS), Ontario Ministry of Natural Resources and Forestry Lake Huron known reef locations (also in Environment Canada's Environmental Sensitivity Index), U.S. Fish and Wildlife Service reports to the Great Lakes Fishery Commission, U.S. Geological Survey northern Lake Michigan LiDAR collection, and published manuscripts from Edsall and Jude.The Great Lakes Aquatic Habitat Framework (GLAHF) project has been funded by the Great Lakes Fishery Trust and led by Dr. Catherine Riseng, PI, at the University of Michigan School of Natural Resources and Environment, with partners from Michigan Department of Natural Resources-Institute for Fisheries Research, NOAA Great Lakes Environmental Research Laboratory, International Joint Commission, Michigan State University, The Nature Conservancy, Ontario Ministry of Natural Resources, University of Minnesota-Duluth, U.S. Fish & Wildlife Service, U.S. Geological Survey and many collaborating partners in both the USA and Canada. More information about this project can be found at http://ifr.snre.umich.edu/projects/glahf/.NOAA Nautical Maps: The NOAA_RNC MapService provides a seamless collarless mosaic of the NOAA Raster Nautical Charts. Source charts are updated once per month. This map service is not to be used for navigation.Please note, reef polygons were digitized to approximate size and location and do not represent the entire extent of each reef.

Draft list of Lower Fox River/Green Bay Area of Concern Management Action List Project Boundaries. Draft overview of project areas and a summary of USFWS Green Bay Fish and Wildlife Conservation Office's AIS early detection team's fisheries monitoring data from 2016-2018. Used primarily for the Green Bay AOC fish and wildlife advisory team's project planning purposes.Last updated 06/30/2020

gl_Goodyear_Spawning_Atlasselection_CopyFeatures

Known reef point locations were compiled from various data sources including the U.S. Geographic Naming Information System (GNIS), Ontario Ministry of Natural Resources and Forestry Lake Huron known reef locations (also in Environment Canada's Environmental Sensitivity Index), U.S. Fish and Wildlife Service reports to the Great Lakes Fishery Commission, U.S. Geological Survey norrthern Lake Michigan LiDAR collection, and published manuscripts from Edsall and Jude.

This eelgrass layer includes the maximum extent of eelgrass beds that have been surveyed in the San Francisco Bay shown in green. It was created by merging the Bay-wide eelgrass surveys conducted by Merkel & Associates, Inc. (Merkel) in 2003, 2009, 2014, and a Richardson Bay survey conducted by Merkel in 2019. Merkel has granted permission for public use of these data. These eelgrass surveys represent the best available data on comprehensive eelgrass extent throughout San Francisco Bay in 2021 and are developed using a combination of acoustic and aerial surveys and site-specific ground truthing. This layer may be used as a reference to determine potential direct and indirect impacts to eelgrass habitat from dredging projects. These data do not replace the need for site-specific eelgrass surveys.Data from the 2003, 2009, and 2014 eelgrass surveys and associated Merkel reports which include information on mapping methodology are available for download on the San Francisco Estuary Institute’s (SFEI) website. Methods for creating this layer are as follows:Downloaded the Merkel Baywide Eelgrass Surveys for 2003, 2009, and 2014 from SFEI and combined into a single layer. Obtained original Richardson Bay 2019 eelgrass survey data from Merkel. Loaded all layers into ArcGIS Pro © ESRI and re-projected all data to the NAD 1983 UTM Zone 10N coordinate system. Ran union of both the SFEI and Richardson Bay 2019 layers. Merged features to create one single attribute table for eelgrass cover from all survey years. Removed extraneous columns in the attribute table, recalculated area fields based on new extent, and applied symbology.

Special Wetland Inventory Study Areas includes those surface waters in Wisconsin that are hydrologically connected to ecologically significant coastal wetlands of the Great Lakes or federal or state designated study areas. These are waters in areas identified in a special wetland inventory study (SWIS) under Chapter NR 103.04, Wis. Adm. Code. A Special Wetland Inventory Study exists for the area bordering the bay of Green Bay.

This web-based application was created by BCDC to support the Long Term Management Strategy for the Placement of Dredged Material in the San Francisco Bay Region (LTMS) program and the National Marine Fisheries Service’s 2011 LTMS Programmatic Essential Fish Habitat (EFH) consultation. The web application can assist project planners in identifying potential impacts of dredging projects in San Francisco Bay to eelgrass based on the LTMS EFH consultation. Once inside the application, click on the “about” button to learn more about assessing impacts and make sure to refer to the EFH consultation linked above for more specific information. Layers in this application include: 1) the maximum extent of eelgrass beds that have been surveyed in San Francisco Bay shown in green; 2) a 45-meter growth buffer for potential bed expansion shown in blue; 3) Polygons demonstrating where dredging occurs within San Francisco Bay; and 4) a 250-meter turbidity buffer around dredging footprints. The eelgrass survey data used in this web application represents the best available data on comprehensive eelgrass extent throughout San Francisco Bay as of 2021. The original eelgrass survey data were developed by Merkel & Associates, Inc. (Merkel) using a combination of acoustic and aerial surveys and site-specific ground truthing. This web application may be used to determine potential direct and indirect impacts to eelgrass habitat from dredging projects as described in the LTMS EFH consultation. These data do not replace the need for site-specific eelgrass surveys as directed by the regulatory and resource agencies.Data from the 2003, 2009, and 2014 baywide eelgrass surveys and associated Merkel reports, which include information on mapping methodology, are available for download on the San Francisco Estuary Institute’s (SFEI) website. Data from a Richardson Bay survey conducted by Merkel in 2019 is also included in this application. For further information on methods used here please enter the application by clicking “View Application” on the right, then click the “…” next to each layer, and then select “Show item details" in the drop-down menu for each individual layer.

Special Wetland Inventory Study Streams includes those surface waters in Wisconsin that are hydrologically connected to ecologically significant coastal wetlands of the Great Lakes or federal or state designated study areas. These are waters in areas identified in a special wetland inventory study (SWIS) under Chapter NR 103.04, Wis. Adm. Code. A Special Wetland Inventory Study exists for the area bordering the bay of Green Bay.

This data resource is a layer in a map service. To download it, please go to the "Layers" section of this page and click the name of the dataset. This will open a new page that features a download button. Open the Map Service: https://gis.chesapeakebay.net/ags/rest/services/ChesapeakeProgress/cpSustainable_Schools_2021/MapServer This Chesapeake Bay Program indicator of progress toward the Sustainable Schools Outcome shows certified sustainable public and charter schools in the Chesapeake Bay watershed. Certified sustainable schools include public and charter schools that have been recognized as sustainable by the following programs: U.S. Green Ribbon Schools, National Wildlife Federation Eco-Schools USA (Bronze, Silver and Green Flag status), Maryland Green Schools, Pennsylvania Pathways to Green Schools and Virginia Naturally Schools.

Combined Statistical AreasThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays Combined Statistical Areas (CSA) in the United States. Per the USCB, "CSAs are defined by the Office of Management and Budget (OMB) and consist of two or more adjacent Core Based Statistical Areas (CBSAs) that have significant employment interchanges. The CBSAs that combine to create a CSA retain separate identities within the larger CSA. Because CSAs represent groupings of CBSAs, they should not be ranked or compared with individual CBSAs."Green Bay-Shawano, WIData currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Combined Statistical Areas) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 74 (Series Information for Combined Statistical Area (CSA) National TIGER/Line Shapefiles, Current)OGC API Features Link: (Combined Statistical Areas - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: Combined Statistical Areas Map (March 2020)For feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

Microsoft recently released a free set of deep learning generated building footprints covering the United States of America. As part of that project Microsoft shared 8 million digitized building footprints with height information used for training the Deep Learning Algorithm. This map layer includes all buildings with height information for the original training set that can be used in scene viewer and ArcGIS pro to create simple 3D representations of buildings. Learn more about the Microsoft Project at the Announcement Blog or the raw data is available at Github.Click see Microsoft Building Layers in ArcGIS Online.Digitized building footprint by State and City

Alabama Greater Phoenix City, Mobile, and Montgomery

Arizona Tucson

Arkansas Little Rock with 5 buildings just across the river from Memphis

California Bakersfield, Fresno, Modesto, Santa Barbara, Sacramento, Stockton, Calaveras County, San Fran & bay area south to San Jose and north to Cloverdale

Colorado Interior of Denver

Connecticut Enfield and Windsor Locks

Delaware Dover

Florida Tampa, Clearwater, St. Petersburg, Orlando, Daytona Beach, Jacksonville and Gainesville

Georgia Columbus, Atlanta, and Augusta

Illinois East St. Louis, downtown area, Springfield, Champaign and Urbana

Indiana Indianapolis downtown and Jeffersonville downtown

Iowa Des Moines

Kansas Topeka

Kentucky Louisville downtown, Covington and Newport

Louisiana Shreveport, Baton Rouge and center of New Orleans

Maine Augusta and Portland

Maryland Baltimore

Massachusetts Boston, South Attleboro, commercial area in Seekonk, and Springfield

Michigan Downtown Detroit

Minnesota Downtown Minneapolis

Mississippi Biloxi and Gulfport

Missouri Downtown St. Louis, Jefferson City and Springfield

Nebraska Lincoln

Nevada Carson City, Reno and Los Vegas

New Hampshire Concord

New Jersey Camden and downtown Jersey City

New Mexico Albuquerque and Santa Fe

New York Syracuse and Manhattan

North Carolina Greensboro, Durham, and Raleigh

North Dakota Bismarck

Ohio Downtown Cleveland, downtown Cincinnati, and downtown Columbus

Oklahoma Downtown Tulsa and downtown Oklahoma City

Oregon Portland

Pennsylvania Downtown Pittsburgh, Harrisburg, and Philadelphia

Rhode Island The greater Providence area

South Carolina Greensville, downtown Augsta, greater Columbia area and greater Charleston area

South Dakota greater Pierre area

Tennessee Memphis and Nashville

Texas Lubbock, Longview, part of Fort Worth, Austin, downtown Houston, and Corpus Christi

Utah Salt Lake City downtown

Virginia Richmond

Washington Greater Seattle area to Tacoma to the south and Marysville to the north

Wisconsin Green Bay, downtown Milwaukee and Madison

Wyoming Cheyenne