This dataset contains all the layers associated with U.S. Geological Survey (USGS) Great Lakes Coastal Wetland Restoration Assessment (GLCWRA) initiative for the Saginaw Bay Restoration Assessment (SBRA) which aims to identify and rank coastal areas with the greatest potential for wetland habitat restoration. Each layer has a unique contribution to the identification of restorable wetlands. The 7 parameters (Parameter 0: Mask, Parameter 1: Hydroperiod, Parameter 2: Wetland Soils, Parameter 3: Flowlines, Parameter 4: Conservation and Recreation Lands, Parameter 5: Impervious Surfaces, and Parameter 6: Land Use) and Index Composite directly correlate to areas that are recommended for restoration. The dikes, degree flowlines, culverts, study area and DEM datasets are supplemental layers that provide additional information around the priority rank values. All layers were produced in collaboration with the USGS Upper Midwest Water Science Center, USGS Great Lakes Science Center, and by the New College of Florida. For more information on these parameters, see the full data release documentation and the GLCWRA webpage: https://glcwra.wim.usgs.gov/.

The U.S. Geological Survey (USGS) created geospatial datasets of potential culvert locations along with flowlines connected toLake Huron as part of the Saginaw Bay Restoration Assessment (SBRA). The Degree Flowlines and Culverts datasets represent the flowline network and culverts in the SBRA study area. Both datasets will be served in the Great Lakes Wetlands Restoration Area mapping application [https://glcwra.wim.usgs.gov/]. The map-based user interface can be used by stakeholders to find potential areas for successful wetlands restoration. Each flowline was assigned a connectivity score describing its level of connectedness to Lake Huron. Low numbers represent fewer disconnections, such as culverts or road crossings, between the reach and the water body requiring no flow network modification to restore the area. For more information, see the full data release documentation and the GLCWRA webpage: https://glcwra.wim.usgs.gov/.

The U.S. Geological Survey (USGS) created geospatial datasets of potential culvert locations along with flowlines connected toLake Huron as part of the Saginaw Bay Restoration Assessment (SBRA). The Degree Flowlines and Culverts datasets represent the flowline network and culverts in the SBRA study area. Both datasets will be served in the Great Lakes Wetlands Restoration Area mapping application [https://glcwra.wim.usgs.gov/]. The map-based user interface can be used by stakeholders to find potential areas for successful wetlands restoration. Each flowline was assigned a connectivity score describing its level of connectedness to Lake Huron. Low numbers represent fewer disconnections, such as culverts or road crossings, between the reach and the water body requiring no flow network modification to restore the area. For more information, see the full data release documentation and the GLCWRA webpage: https://glcwra.wim.usgs.gov/.

This dataset represents the location of dikes within the Saginaw Bay Restoration Assessment (SBRA) study area. An ArcGIS model identified dikes as having a difference in elevation and slope above a certain threshold. See the full data release documentation and the GLCWRA webpage: https://glcwra.wim.usgs.gov/ for further information.

This dataset represents the location of dikes within the Saginaw Bay Restoration Assessment (SBRA) study area. An ArcGIS model identified dikes as having a difference in elevation and slope above a certain threshold. See the full data release documentation and the GLCWRA webpage: https://glcwra.wim.usgs.gov/ for further information.

This dataset contains all the layers associated with U.S. Geological Survey (USGS) Great Lakes Coastal Wetland Restoration Assessment (GLCWRA) initiative for the Connecting River Systems Restoration Assessment (CRSRA) which aims to identify and rank coastal areas with the greatest potential for wetland habitat restoration. Each layer has a unique contribution to the identification of restorable wetlands. The 7 parameters (Parameter 0: Mask, Parameter 1: Hydroperiod, Parameter 2: Wetland Soils, Parameter 3: Flowlines, Parameter 4: Conservation and Recreation Lands, Parameter 5: Impervious Surfaces, and Parameter 6: Land Use) and Index Composite directly correlate to areas that are recommended for restoration. The dikes, degree flowlines, culverts, study area and DEM datasets are supplemental layers that provide additional information around the priority rank values. All layers were produced in collaboration with the USGS Upper Midwest Water Science Center, USGS Great Lakes Science Center, and by the New College of Florida. For more information, see the full data release documentation and the GLCWRA webpage: https://glcwra.wim.usgs.gov/.

This dataset contains all the layers associated with U.S. Geological Survey (USGS) Great Lakes Coastal Wetland Restoration Assessment (GLCWRA) initiative for the Connecting River Systems Restoration Assessment (CRSRA) which aims to identify and rank coastal areas with the greatest potential for wetland habitat restoration. Each layer has a unique contribution to the identification of restorable wetlands. The 7 parameters (Parameter 0: Mask, Parameter 1: Hydroperiod, Parameter 2: Wetland Soils, Parameter 3: Flowlines, Parameter 4: Conservation and Recreation Lands, Parameter 5: Impervious Surfaces, and Parameter 6: Land Use) and Index Composite directly correlate to areas that are recommended for restoration. The dikes, degree flowlines, culverts, study area and DEM datasets are supplemental layers that provide additional information around the priority rank values. All layers were produced in collaboration with the USGS Upper Midwest Water Science Center, USGS Great Lakes Science Center, and by the New College of Florida. For more information, see the full data release documentation and the GLCWRA webpage: https://glcwra.wim.usgs.gov/.

This dataset contains all the layers associated with U.S. Geological Survey (USGS) Great Lakes Coastal Wetland Restoration Assessment (GLCWRA) initiative for the Western Lake Erie Restoration Assessment (WLERA) aims to identify and rank coastal areas with the greatest potential for wetland habitat restoration. Each of the 10-meter raster layers in this dataset has a unique contribution to the identification of restorable wetlands. The 7 parameters (Parameter 0: Mask, Parameter 1: Hydroperiod, Parameter 2: Wetland Soils, Parameter 3: Flowlines, Parameter 4: Conservation and Recreation Lands, Parameter 5: Impervious Surfaces, and Parameter 6: Land Use) and Index Composite directly correlate to areas that are recommended for restoration. The dikes, degree flowlines, culverts, study area and DEM datasets are supplemental layers that provide additional information around the priority rank values. All layers were produced in collaboration with the USGS Upper Midwest Water Science Center, USGS Great Lakes Science Center, and by the New College of Florida. For more information on these parameters, see the full data release documentation and the GLCWRA webpage: https://glcwra.wim.usgs.gov/.

This dataset contains all the layers associated with U.S. Geological Survey (USGS) Great Lakes Coastal Wetland Restoration Assessment (GLCWRA) initiative for the Western Lake Erie Restoration Assessment (WLERA) aims to identify and rank coastal areas with the greatest potential for wetland habitat restoration. Each of the 10-meter raster layers in this dataset has a unique contribution to the identification of restorable wetlands. The 7 parameters (Parameter 0: Mask, Parameter 1: Hydroperiod, Parameter 2: Wetland Soils, Parameter 3: Flowlines, Parameter 4: Conservation and Recreation Lands, Parameter 5: Impervious Surfaces, and Parameter 6: Land Use) and Index Composite directly correlate to areas that are recommended for restoration. The dikes, degree flowlines, culverts, study area and DEM datasets are supplemental layers that provide additional information around the priority rank values. All layers were produced in collaboration with the USGS Upper Midwest Water Science Center, USGS Great Lakes Science Center, and by the New College of Florida. For more information on these parameters, see the full data release documentation and the GLCWRA webpage: https://glcwra.wim.usgs.gov/.

This dataset is part of the U.S. Geological Survey (USGS) Great Lakes Coastal Wetland Restoration Assessment (GLCWRA) initiative. These data represent the location of dikes within the Upper Peninsula Restoration Assessment (UPRA) study area. An ArcGIS model (Python script) identified dikes as having a difference in elevation above a certain threshold. If the elevation difference was below a certain threshold, the area was not considered a dike. However, if the difference in elevation between two points was significantly high, then the area was marked as a dike. Areas continuous with each other were considered part of the same dike. Data underwent quality control (QC) procedures by having Subject Matter Experts and those familiar with the area examine the data output, comparing the proposed dike locations to aerial imagery, flowline data, and the Digital Elevation Model (DEM). Dikes that appeared to be false positives were deleted from the dataset. Please refer to the process steps and https://glcwra.wim.usgs.gov/ for further explanation on the methods. The GLCWRA initiative identifies coastal wetland areas that have the greatest habitat restoration potential. The data model uses seven parameters to identify and rank wetland restoration areas, resulting in a composite index raster that can be used by ecological managers and planners to assist with the selection of wetland restoration sites. The parameters are Parameter 0: Mask Parameter 1: Hydroperiod Parameter 2: Wetland Soils Parameter 3: Flowlines Parameter 4: Conservation and Recreation Lands Parameter 5: Impervious Surfaces Parameter 6: Land Use (represents developed areas without impervious surfaces but high societal value) The ancillary data include dikes, degree flowlines, study area and culverts. These data layers are put through an ecological model, which results in a composite restoration index of ranked restoration areas.