To download this dataset, click below:Zipped TIFF File: LC_FCD_RECLASS_2016.zip (2GB)The reclassified landcover dataset was derived from the 2016 landcover, one of the products available as part of the the LARIAC program.NOTE: The extent of the derived dataset only covers the area located within the County's flood control district. This raster dataset was combined with the County's parcel layer to produce a file geodatabase of impermeable and permeable areas by parcel for use by the County's Safe Clean Water program.Attributes0 = Permeable1 = ImpermeableThe 2016 landcover dataset was reclassified as follows:Tree Canopy - PermeableGrass/Shrubs - PermeableBare Soil - PermeableWater - PermeableBuildings - ImpermeableRoads/Railroads - ImpermeableOther Paved - ImpermeableTall Shrubs - PermeableFor more information, please contact Bowen Liang (bliang@dpw.lacounty.gov)

The Vermont Water Quality Standards (VTWQS) are rules intended to achieve the goals of the Vermont Surface Water Strategy, as well as the objective of the federal Clean Water Act which is to restore and maintain the chemical, physical, and biological integrity of the Nation's water. The classification of waters is in included in the VTWQS. The classification of all waters has been established by a combination of legislative acts and by classification or reclassification decisions issued by the Water Resources Board or Secretary pursuant to 10 V.S.A. � 1253. Those waters reclassified by the Secretary to Class A(1), A(2), or B(1) for any use shall include all waters within the entire watershed of the reclassified waters unless expressly provided otherwise in the rule. All waters above 2,500 feet altitude, National Geodetic Vertical Datum, are designated Class A(1) for all uses, unless specifically designated Class A(2) for use as a public water source. All waters at or below 2,500 feet altitude, National Geodetic Vertical Datum, are designated Class B(2) for all uses, unless specifically designated as Class A(1), A(2), or B(1) for any use.

NLCD 2019 - reclassification to suitable/unsuitable for alligator gar spawning - LouisianaSuitable: any low open vegetation classes: emergent herbaceous, agriculture, grassland, shrub/scrub Unsuitable: all other classesThis version of the information highlights "woody wetlands" and "barren" which are unlikely to provide suitable alligator gar spawning habitat. Used in conjunction with other layers to evaluate the accuracy of a statewide (Louisiana) assessment of habitat suitable for alligator gar spawning using the techniques described in Allen et. al 2020. Allen, Y., K. Kimmel, and G. Constant. 2020. Using Remote Sensing to Assess Alligator Gar Spawning Habitat Suitability in the Lower Mississippi River. North American Journal of Fisheries Management 40:580–594.

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the... Visit https://dataone.org/datasets/sha256%3A3e3f055bf6281f979484f847d0ed5eeb96143a369592149328c370fe5776742b for complete metadata about this dataset.

The Cooperative Land Cover Map is a project to develop an improved statewide land cover map from existing sources and expert review of aerial photography. The project is directly tied to a goal of Florida's State Wildlife Action Plan (SWAP) to represent Florida's diverse habitats in a spatially-explicit manner. The Cooperative Land Cover Map integrates 3 primary data types: 1) 6 million acres are derived from local or site-specific data sources, primarily on existing conservation lands. Most of these sources have a ground-truth or local knowledge component. We collected land cover and vegetation data from 37 existing sources. Each dataset was evaluated for consistency and quality and assigned a confidence category that determined how it was integrated into the final land cover map. 2) 1.4 million acres are derived from areas that FNAI ecologists reviewed with high resolution aerial photography. These areas were reviewed because other data indicated some potential for the presence of a focal community: scrub, scrubby flatwoods, sandhill, dry prairie, pine rockland, rockland hammock, upland pine or mesic flatwoods. 3) 3.2 million acres are represented by Florida Land Use Land Cover data from the FL Department of Environmental Protection and Water Management Districts (FLUCCS). The Cooperative Land Cover Map integrates data from the following years: NWFWMD: 2006 - 07 SRWMD: 2005 - 08 SJRWMD: 2004 SFWMD: 2004 SWFWMD: 2008 All data were crosswalked into the Florida Land Cover Classification System. This project was funded by a grant from FWC/Florida's Wildlife Legacy Initiative (Project 08009) to Florida Natural Areas Inventory. The current dataset is provided in 10m raster grid format.Changes from Version 1.1 to Version 2.3:CLC v2.3 includes updated Florida Land Use Land Cover for four water management districts as described above: NWFWMD, SJRWMD, SFWMD, SWFWMDCLC v2.3 incorporates major revisions to natural coastal land cover and natural communities potentially affected by sea level rise. These revisions were undertaken by FNAI as part of two projects: Re-evaluating Florida's Ecological Conservation Priorities in the Face of Sea Level Rise (funded by the Yale Mapping Framework for Biodiversity Conservation and Climate Adaptation) and Predicting and Mitigating the Effects of Sea-Level Rise and Land Use Changes on Imperiled Species and Natural communities in Florida (funded by an FWC State Wildlife Grant and The Kresge Foundation). FNAI also opportunistically revised natural communities as needed in the course of species habitat mapping work funded by the Florida Department of Environmental Protection. CLC v2.3 also includes several new site specific data sources: New or revised FNAI natural community maps for 13 conservation lands and 9 Florida Forever proposals; new Florida Park Service maps for 10 parks; Sarasota County Preserves Habitat Maps (with FNAI review); Sarasota County HCP Florida Scrub-Jay Habitat (with FNAI Review); Southwest Florida Scrub Working Group scrub polygons. Several corrections to the crosswalk of FLUCCS to FLCS were made, including review and reclassification of interior sand beaches that were originally crosswalked to beach dune, and reclassification of upland hardwood forest south of Lake Okeechobee to mesic hammock. Representation of state waters was expanded to include the NOAA Submerged Lands Act data for Florida.Changes from Version 2.3 to 3.0: All land classes underwent revisions to correct boundaries, mislabeled classes, and hard edges between classes. Vector data was compared against high resolution Digital Ortho Quarter Quads (DOQQ) and Google Earth imagery. Individual land cover classes were converted to .KML format for use in Google Earth. Errors identified through visual review were manually corrected. Statewide medium resolution (spatial resolution of 10 m) SPOT 5 images were available for remote sensing classification with the following spectral bands: near infrared, red, green and short wave infrared. The acquisition dates of SPOT images ranged between October, 2005 and October, 2010. Remote sensing classification was performed in Idrisi Taiga and ERDAS Imagine. Supervised and unsupervised classifications of each SPOT image were performed with the corrected polygon data as a guide. Further visual inspections of classified areas were conducted for consistency, errors, and edge matching between image footprints. CLC v3.0 now includes state wide Florida NAVTEQ transportation data. CLC v3.0 incorporates extensive revisions to scrub, scrubby flatwoods, mesic flatwoods, and upland pine classes. An additional class, scrub mangrove – 5252, was added to the crosswalk. Mangrove swamp was reviewed and reclassified to include areas of scrub mangrove. CLC v3.0 also includes additional revisions to sand beach, riverine sand bar, and beach dune previously misclassified as high intensity urban or extractive. CLC v3.0 excludes the Dry Tortugas and does not include some of the small keys between Key West and Marquesas.Changes from Version 3.0 to Version 3.1: CLC v3.1 includes several new site specific data sources: Revised FNAI natural community maps for 31 WMAs, and 6 Florida Forever areas or proposals. This data was either extracted from v2.3, or from more recent mapping efforts. Domains have been removed from the attribute table, and a class name field has been added for SITE and STATE level classes. The Dry Tortugas have been reincorporated. The geographic extent has been revised for the Coastal Upland and Dry Prairie classes. Rural Open and the Extractive classes underwent a more thorough reviewChanges from Version 3.1 to Version 3.2:CLC v3.2 includes several new site specific data sources: Revised FNAI natural community maps for 43 Florida Park Service lands, and 9 Florida Forever areas or proposals. This data is from 2014 - 2016 mapping efforts. SITE level class review: Wet Coniferous plantation (2450) from v2.3 has been included in v3.2. Non-Vegetated Wetland (2300), Urban Open Land (18211), Cropland/Pasture (18331), and High Pine and Scrub (1200) have undergone thorough review and reclassification where appropriate. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com.Changes from Version 3.2.5 to Version 3.3: The CLC v3.3 includes several new site specific data sources: Revised FNAI natural community maps for 14 FWC managed or co-managed lands, including 7 WMA and 7 WEA, 1 State Forest, 3 Hillsboro County managed areas, and 1 Florida Forever proposal. This data is from the 2017 – 2018 mapping efforts. Select sites and classes were included from the 2016 – 2017 NWFWMD (FLUCCS) dataset. M.C. Davis Conservation areas, 18331x agricultural classes underwent a thorough review and reclassification where appropriate. Prairie Mesic Hammock (1122) was reclassified to Prairie Hydric Hammock (22322) in the Everglades. All SITE level Tree Plantations (18333) were reclassified to Coniferous Plantations (183332). The addition of FWC Oyster Bar (5230) features. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com, including classification corrections to sites in T.M. Goodwin and Ocala National Forest. CLC v3.3 utilizes the updated The Florida Land Cover Classification System (2018), altering the following class names and numbers: Irrigated Row Crops (1833111), Wet Coniferous Plantations (1833321) (formerly 2450), Major Springs (4131) (formerly 3118). Mixed Hardwood-Coniferous Swamps (2240) (formerly Other Wetland Forested Mixed).Changes from Version 3.4 to Version 3.5: The CLC v3.5 includes several new site specific data sources: Revised FNAI natural community maps for 16 managed areas, and 10 Florida Forever Board of Trustees Projects (FFBOT) sites. This data is from the 2019 – 2020 mapping efforts. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. This version of the CLC is also the first to include land identified as Salt Flats (5241).Changes from Version 3.5 to 3.6: The CLC v3.6 includes several new site specific data sources: Revised FNAI natural community maps for 11 managed areas, and 24 Florida Forever Board of Trustees Projects (FFBOT) sites. This data is from the 2018 – 2022 mapping efforts. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com.Changes from Version 3.6 to 3.7: The CLC 3.7 includes several new site specific data sources: Revised FNAI natural community maps for 5 managed areas (2022-2023). Revised Palm Beach County Natural Areas data for Pine Glades Natural Area (2023). Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. In this version a few SITE level classifications are reclassified for the STATE level classification system. Mesic Flatwoods and Scrubby Flatwoods are classified as Dry Flatwoods at the STATE level. Upland Glade is classified as Barren, Sinkhole, and Outcrop Communities at the STATE level. Lastly Upland Pine is classified as High Pine and Scrub at the STATE level.

This webmap is a subset of Global Urban Density Footprint in 2020 Tile Image Layer. This layer represents an estimate of the footprint of urban settings in 2020. It is intended as a fast-drawing cartographic layer to augment base maps and to focus a map reader's attention on the location of human population. This layer is not intended for analysis. This layer was derived from the 2020 slice of the WorldPop Population Density 2000-2020 100m and 1km layers.Also see the Populated Footprint layer, which like this layer, is intended to provide a fast-drawing cartographic context for the footprint of total population.The following processing steps were used to produce this layer in ArcGIS Pro:1. Int tool (Spatial Analyst) to truncate double precision values; all values less than 0.99 become 0.2. Reclassify tool (Spatial Analyst) to set values 0 through 1499 to NoData (Null) and all other values become 1.3. Copy Raster tool with Output Coordinate System environment set to Web Mercator, bit depth to 1 bit, and NoData Value to 0.Source:WorldPop Population Density 2000-2020 100m, which is created from WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation. The DOI for the original WorldPop.org total population population data is 10.5258/SOTON/WP00645.

The High Priority Zones (HPZs) polygons for Franklin's bumble bee are a subset of geographic areas within the historic range of the species. The HPZs are areas where Franklin's bumble bee are more likely to occur, and within which actions (such as habitat management) and events (such as wildfire) are more likely to have affects on the species. The HPZs have been modelled based off of known historic Franklin's bumble bee occurrences, along with a reclassified National Land Cover Database (2019) layer. The High Priority Zones (HPZs) is fundamentally based on a cost distance analysis using historic Franklin's bumble bee observations as source points and a reclassified National Land Cover Database (2019) layer as the barrier surface. This method follows closely with that used for the rusty patched bumble bee. For a complete workflow process document and downloadable spatial files, see: https://ecos.fws.gov/ServCat/Reference/Profile/143332.

This tile layer displays percent slope in the Bromley Hollow area of the Eastern Divide Insect & Disease Control management project being undertaken by the USFS in the Jefferson National Forest.Purpose:The data was included to provide additional environmental context for the user’s understanding of the project’s likely environmental impactsSource & Date:Downloaded from the Virginia Geographic Information Network's (VGIN's) Virginia GIS Clearinghouse on 3/31/2021. Data was collected in 2016.Processing:The slope was calculated from the 1-meter LIDAR-derived digital elevation model mosaic. The slope raster was reclassified, as shown below. ABRA published the reclassified raster to ArcGIS Online as a tile layer.Symbology:EDID Dismal Creek Slope0 - 5%: Gray5 - 10%: Dark Green10 - 15%: Med Green15 - 20%: Light Green20 - 25%: Yellow-Green25 - 30%: Yellow30 - 40%: Light Orange40 -50%: Dark Orange50 - 60%: Orange-Red> 60%: Dark Red

This tile layer, UCR_Project_Area_Slopes, provides the slope steepness within the boundaries of the Upper Cheat River project, proposed by the U.S. Forest Service in the Monongahela National Forest of West Virginia. Purpose:This data was included to provide additional environmental context for the user’s understanding of the project’s likely environmental impacts.Source & Date:The data was downloaded from the WV Elevation and LIDAR Download Tool, hosted by the West Virginia GIS Technical Center. The data was collected in 2018, and downloaded on 7/20/2021 from (DEM_Mosaic_FEMA_2019-19_Tucker-Randolph_WV_1m_UTM17).Processing:The slope was calculated from the 1-meter LIDAR-derived digital elevation model. The slope model was reclassified, as shown below. ABRA published the reclassified mosaic to ArcGIS Online as a tile layer.Symbology:Project Area Slopes (%):0-10%: dark green10-20%: light green20-30%: yellow30-40%: orange40-50%: red>50%: brown

Open the Data Resource: https://gis.chesapeakebay.net/ags/rest/services/InterGIT/HUC12_Rest_Composite/MapServer To develop this data resource, Chesapeake Bay Program Goal Implementation Teams (GITs) were asked to identify data layers that represented resources that reflected important geographic areas to conserve or restore based on the goals and outcomes of the 2014 Chesapeake Bay Watershed Agreement. There are several considerations for mapping geographic areas, including:

Focus on places most important for living resources (fish, wildlife and people); Identify areas to focus restoration and conservation activities; Consider threats from land and climate change; and Identify areas where multiple partners are already working or consider priorities.

Data was assembled from a wide variety of sources and reclassified according to values for restoration based on GIT-specific criteria. Data layers were reclassified into 30-m grids and combined into composite restoration suitability rankings. The 30-meter grids were aggregated to USGS HUC-12 boundaries to give a composite watershed restoration score. Finally, these watershed scores were ranked from high to low to establish quintile classes within each jurisdiction. The Cross-GIT HUC-12 Restoration Composite complements the Cross-GIT HUC-12 Conservaiton Composite: https://gis.chesapeakebay.net/ags/rest/services/InterGIT/HUC12_Cons_Composite/MapServer

The Cooperative Land Cover Map is a project to develop an improved statewide land cover map from existing sources and expert review of aerial photography. The project is directly tied to a goal of Florida's State Wildlife Action Plan (SWAP) to represent Florida's diverse habitats in a spatially-explicit manner. The Cooperative Land Cover Map integrates 3 primary data types: 1) 6 million acres are derived from local or site-specific data sources, primarily on existing conservation lands. Most of these sources have a ground-truth or local knowledge component. We collected land cover and vegetation data from 37 existing sources. Each dataset was evaluated for consistency and quality and assigned a confidence category that determined how it was integrated into the final land cover map. 2) 1.4 million acres are derived from areas that FNAI ecologists reviewed with high resolution aerial photography. These areas were reviewed because other data indicated some potential for the presence of a focal community: scrub, scrubby flatwoods, sandhill, dry prairie, pine rockland, rockland hammock, upland pine or mesic flatwoods. 3) 3.2 million acres are represented by Florida Land Use Land Cover data from the FL Department of Environmental Protection and Water Management Districts (FLUCCS). The Cooperative Land Cover Map integrates data from the following years: NWFWMD: 2006 - 07 SRWMD: 2005 - 08 SJRWMD: 2004 SFWMD: 2004 SWFWMD: 2008 All data were crosswalked into the Florida Land Cover Classification System. This project was funded by a grant from FWC/Florida's Wildlife Legacy Initiative (Project 08009) to Florida Natural Areas Inventory. The current dataset is provided in 10m raster grid format.Changes from Version 1.1 to Version 2.3:CLC v2.3 includes updated Florida Land Use Land Cover for four water management districts as described above: NWFWMD, SJRWMD, SFWMD, SWFWMDCLC v2.3 incorporates major revisions to natural coastal land cover and natural communities potentially affected by sea level rise. These revisions were undertaken by FNAI as part of two projects: Re-evaluating Florida's Ecological Conservation Priorities in the Face of Sea Level Rise (funded by the Yale Mapping Framework for Biodiversity Conservation and Climate Adaptation) and Predicting and Mitigating the Effects of Sea-Level Rise and Land Use Changes on Imperiled Species and Natural communities in Florida (funded by an FWC State Wildlife Grant and The Kresge Foundation). FNAI also opportunistically revised natural communities as needed in the course of species habitat mapping work funded by the Florida Department of Environmental Protection. CLC v2.3 also includes several new site specific data sources: New or revised FNAI natural community maps for 13 conservation lands and 9 Florida Forever proposals; new Florida Park Service maps for 10 parks; Sarasota County Preserves Habitat Maps (with FNAI review); Sarasota County HCP Florida Scrub-Jay Habitat (with FNAI Review); Southwest Florida Scrub Working Group scrub polygons. Several corrections to the crosswalk of FLUCCS to FLCS were made, including review and reclassification of interior sand beaches that were originally crosswalked to beach dune, and reclassification of upland hardwood forest south of Lake Okeechobee to mesic hammock. Representation of state waters was expanded to include the NOAA Submerged Lands Act data for Florida.Changes from Version 2.3 to 3.0: All land classes underwent revisions to correct boundaries, mislabeled classes, and hard edges between classes. Vector data was compared against high resolution Digital Ortho Quarter Quads (DOQQ) and Google Earth imagery. Individual land cover classes were converted to .KML format for use in Google Earth. Errors identified through visual review were manually corrected. Statewide medium resolution (spatial resolution of 10 m) SPOT 5 images were available for remote sensing classification with the following spectral bands: near infrared, red, green and short wave infrared. The acquisition dates of SPOT images ranged between October, 2005 and October, 2010. Remote sensing classification was performed in Idrisi Taiga and ERDAS Imagine. Supervised and unsupervised classifications of each SPOT image were performed with the corrected polygon data as a guide. Further visual inspections of classified areas were conducted for consistency, errors, and edge matching between image footprints. CLC v3.0 now includes state wide Florida NAVTEQ transportation data. CLC v3.0 incorporates extensive revisions to scrub, scrubby flatwoods, mesic flatwoods, and upland pine classes. An additional class, scrub mangrove – 5252, was added to the crosswalk. Mangrove swamp was reviewed and reclassified to include areas of scrub mangrove. CLC v3.0 also includes additional revisions to sand beach, riverine sand bar, and beach dune previously misclassified as high intensity urban or extractive. CLC v3.0 excludes the Dry Tortugas and does not include some of the small keys between Key West and Marquesas.Changes from Version 3.0 to Version 3.1: CLC v3.1 includes several new site specific data sources: Revised FNAI natural community maps for 31 WMAs, and 6 Florida Forever areas or proposals. This data was either extracted from v2.3, or from more recent mapping efforts. Domains have been removed from the attribute table, and a class name field has been added for SITE and STATE level classes. The Dry Tortugas have been reincorporated. The geographic extent has been revised for the Coastal Upland and Dry Prairie classes. Rural Open and the Extractive classes underwent a more thorough reviewChanges from Version 3.1 to Version 3.2:CLC v3.2 includes several new site specific data sources: Revised FNAI natural community maps for 43 Florida Park Service lands, and 9 Florida Forever areas or proposals. This data is from 2014 - 2016 mapping efforts. SITE level class review: Wet Coniferous plantation (2450) from v2.3 has been included in v3.2. Non-Vegetated Wetland (2300), Urban Open Land (18211), Cropland/Pasture (18331), and High Pine and Scrub (1200) have undergone thorough review and reclassification where appropriate. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com.Changes from Version 3.2.5 to Version 3.3: The CLC v3.3 includes several new site specific data sources: Revised FNAI natural community maps for 14 FWC managed or co-managed lands, including 7 WMA and 7 WEA, 1 State Forest, 3 Hillsboro County managed areas, and 1 Florida Forever proposal. This data is from the 2017 – 2018 mapping efforts. Select sites and classes were included from the 2016 – 2017 NWFWMD (FLUCCS) dataset. M.C. Davis Conservation areas, 18331x agricultural classes underwent a thorough review and reclassification where appropriate. Prairie Mesic Hammock (1122) was reclassified to Prairie Hydric Hammock (22322) in the Everglades. All SITE level Tree Plantations (18333) were reclassified to Coniferous Plantations (183332). The addition of FWC Oyster Bar (5230) features. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com, including classification corrections to sites in T.M. Goodwin and Ocala National Forest. CLC v3.3 utilizes the updated The Florida Land Cover Classification System (2018), altering the following class names and numbers: Irrigated Row Crops (1833111), Wet Coniferous Plantations (1833321) (formerly 2450), Major Springs (4131) (formerly 3118). Mixed Hardwood-Coniferous Swamps (2240) (formerly Other Wetland Forested Mixed).Changes from Version 3.4 to Version 3.5: The CLC v3.5 includes several new site specific data sources: Revised FNAI natural community maps for 16 managed areas, and 10 Florida Forever Board of Trustees Projects (FFBOT) sites. This data is from the 2019 – 2020 mapping efforts. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. This version of the CLC is also the first to include land identified as Salt Flats (5241).Changes from Version 3.5 to 3.6: The CLC v3.6 includes several new site specific data sources: Revised FNAI natural community maps for 11 managed areas, and 24 Florida Forever Board of Trustees Projects (FFBOT) sites. This data is from the 2018 – 2022 mapping efforts. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com.Changes from Version 3.6 to 3.7: The CLC 3.7 includes several new site specific data sources: Revised FNAI natural community maps for 5 managed areas (2022-2023). Revised Palm Beach County Natural Areas data for Pine Glades Natural Area (2023). Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. In this version a few SITE level classifications are reclassified for the STATE level classification system. Mesic Flatwoods and Scrubby Flatwoods are classified as Dry Flatwoods at the STATE level. Upland Glade is classified as Barren, Sinkhole, and Outcrop Communities at the STATE level. Lastly Upland Pine is classified as High Pine and Scrub at the STATE level.

GEBCO’s gridded bathymetric data sets are global terrain models for ocean and land. This map shows the areas where changes in elevation/depth have occurred between GEBCO Gridded products. Change values are represented in meters. This particular layer shows the absolute value of the difference (in meters) between GEBCO 2021 and GEBCO 2022 gridded data products. The equation used to compute change is abs(GEBCO 2022 - GEBCO 2021). Then a reclass is applied to reclassify all pixels that are equal to zero as "No Data", helping further emphasize the locations where change has occurred. GEBCO aims to provide the most authoritative, publicly available bathymetry data sets for the world’s oceans.More Information about GEBCO: https://www.gebco.net/Dataset attribution for products used to create this layer:GEBCO Compilation Group (2021) GEBCO 2021 Grid (doi:10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f)GEBCO Compilation Group (2022) GEBCO_2022 Grid (doi:10.5285/e0f0bb80-ab44-2739-e053-6c86abc0289c)For more GEBCO related layers and maps please visit the GEBCO ArcGIS Online Group.

Reason for Selection This indicator represents aquatic connectivity between fresh and salt water in Atlantic drainages. It incorporates both physical barriers to connectivity and indirect barriers related to habitat quality. It also promotes consistency with the priorities of the Atlantic Coast Fish Habitat Partnership. Input Data

Atlantic Coast Fish Habitat Partnership (ACFHP) Fish Habitat Conservation Area Mapping and Prioritization Project: South Atlantic and Mid-Atlantic Diadromous Analysis
Base Blueprint 2022 extent
Southeast Blueprint 2023 extent

Mapping Steps

Convert the South Atlantic Diadromous Analysis from vector to 30 m raster using the FINALSCORE field.
Convert the Mid-Atlantic Diadromous Analysis from vector to 30 m raster using the TotalPoints field.
Combine the above rasters using the ArcPy Spatial Analyst Cell Statistics “MAX” function.
Reclassify the above raster into 8 classes, seen in the final indicator values below.
Clip to the spatial extent of Base Blueprint 2022.
As a final step, clip to the spatial extent of Southeast Blueprint 2023. 

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator values Indicator values are assigned as follows: 8 = Final score of 80 (areas of excellent fish habitat) 7 = Final score of 70 (areas of excellent fish habitat) 6 = Final score of 60 (restoration opportunity areas) 5 = Final score of 50 (restoration opportunity areas) 4 = Final score of 40 (restoration opportunity areas) 3 = Final score of 30 (restoration opportunity areas) 2 = Final score of 20 (restoration opportunity areas) 1 = Final score of 10 (degraded areas of opportunity) 0 = Final score of 0 (degraded areas of opportunity) Known Issues

  This indicator under and overrepresents migratory fish habitat in some areas. The South Atlantic and Mid-Atlantic Diadromous Analysis did not include fish presence and fishing data because of inconsistent sampling methods across the study area and because this data was unavailable in many shallow water habitats.

Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov). Literature Cited Martin, Erik, Kat Hoenke, and Lisa Havel. Atlantic Coast Fish Habitat Partnership. Fish Habitat Conservation Area Mapping and Prioritization Project: A Prioritization of Atlantic Coastal, Estuarine, and Diadromous Fish Habitats for Conservation. August 2020. [https://www.atlanticfishhabitat.org/wp-content/uploads/2020/08/ACFHP-Mapping-and-Prioritization-Final-Report.pdf].

This tile layer, UEER_Slopes_1m, provides the slope steepness within the boundaries of the Upper Elk River Project, proposed by the U.S. Forest Service in the Monongahela National Forest of West Virginia.Purpose:The data was included to provide additional environmental context for the user’s understanding of the project’s likely environmental impacts.Source & Date:The data was downloaded from the WV Elevation and LIDAR Download Tool, hosted by the West Virginia GIS Technical Center. The data was collected in 2018, and downloaded on 7/20/2021 from (DEM_Mosaic_FEMA_2019-19_Tucker-Randolph_WV_1m_UTM17) and (DEM_Mosaic_FEMA_2016_WV_East_1m_UTM17)Processing:The slope was calculated from the 1-meter LIDAR-derived digital elevation models from two LIDAR projects – FEMA 2016 WV East, and FEMA 2018-19 Tucker-Randolph WV. The slope model was reclassified, as shown below. ABRA published the reclassified mosaic to ArcGIS Online as a tile layer.Symbology:Project Area Slopes (%):0-10%: dark green10-20%: light green20-30%: yellow30-40%: orange40-50%: red>50%: brownMore information can be found on ABRA’s project description page, hosted by the National Forest Integrity Project. Additional detailed information is available on the USFS project page.

Historic flowways in Lee County, Florida represent historic paths of surface water conveyance. This dataset was developed from reclassification of the soils layer and aerial photo interpretation.A flowway for the purpose of this analysis is generally described as the lowest "pathway" allowing for the successive conveyance of surface waters. These areas were delineated by visual interpretation of the spatial differences between that shown on the 2005 aerials and that determined from the categorical reclassification of the soils layer.See associated layers, FlowwayArrows and FlowwaysHistoricConnections

BY USING THIS WEBSITE OR THE CONTENT THEREIN, YOU AGREE TO THE TERMS OF USE. A spatial representation of land use. The polygons contained in this feature class were derived from the Oakland County Tax Parcel feature class. Each parcel was categorized by its land use. When a parcel has multiple land uses, the dominant land use is shown.

Assessing records and orthophotography were the main sources used to attribute each tax parcel with land use information. The data was collected in 2002. Key attributes are the land use and key pin (Sidwell number). Land Use stores the Land Use description for each parcel. The Key Pin is the unique Parcel Identification Number (Pin) used to link the parcel to the parcel attributes which are stored and maintained in Oakland County Land Records.The 2002 version of land use was created using the 2001 version as a primary source. It was assumed that if the Parcel Identification Number and the Use Code were equal in both years, then the land use did not change. Thus, only the parcels where a change occured had to be assigned a land use.The development process consists of three basic steps. First, parcels with use codes that are assumed to relate to a single land use are categorized as such. Second, parcels with a use code that is assumed to relate to more than one land use are manually classified using orthophotography and ownership as a reference. Lastly, tax parcels, right-of-way, and hydrography are unioned to create a single land use dataset.Assumptions:Any parcel classified as vacant may be an accessory use to an adjacent, commonly owned, improved parcel. In this event, the vacant parcel is reclassified as the use of the adjacent, commonly owned, improved parcel. It should be noted, however, that parcels with use codes of RV, SV, and LV, that represent a single family vacant use, are exempt from this assumption.Parcels with an Equalization use code of RV, SV, or LV may include uses to be reclassified as Vacant or Recreation & Conservation (due to subdivision open space). These parcels are not manually checked.
Queries are conducted to search for those parcels that are subdivision open space. The following strings are queried from the Owner1 and Owner2 fields: "*own*" (unknown, homeowner), "ass*" (association, ass'n, etc), and "park."The Equalization use code BI, Business Improved includes uses that are reclassified as Commercial/Office. Uses may also fall into Public/Institutional, however, all BI parcels are not manually checked for reclassification.The Equalization use code MM, Miscellaneous Business includes uses that are reclassified as Recreation and Conservation, Multiple Family, Commercial/Office, or Mobile Home Park.The Equalization use code ME, Miscellaneous Exempt includes uses that are reclassified as Recreation & Conservation, Public/Institutional, Transportation, Utility & Communication, Industrial (municipal landfills), or any Single Family classification.The Equalization use code KI, Condominium Improved includes uses that are reclassified as Multiple Family, Single Family, or Commercial/Office. Any parcel with an Equalization use code of FI, Farm Improved or FV, Farm Vacant is considered to be an active agricultural use.Any parcel with an Equalization use code of II, Industrial Improved is considered to be an industrial use.Any parcel with an Equalization use code of FC, Farm Conservation is considered to be a recreational/conservation use.Any parcel with an Equalization use code of AI, Apartment Improved is considered to be a multiple family residential use.Any parcel with an Equalization use code of UI, Utility Improved is considered to be a transportation, utility, or communication related use.Parcels with an Equalization use code of DI, Developmental Improved are reclassified as Single Family or Vacant.Any parcel with an Equalization use code of DV for Developmental Vacant may be reclassified as Vacant, Recreation & Conservation, (golf courses) or Industrial (mining or extractive).Polygons in the ROW region of the parcel coverage will be classified as Recreation and Conservation, and Commercial/Office, Vacant, Road ROW, and Railroad ROW.Because of inconsistencies in Use Code data, unique uses, and the goal of creating an accurate coverage that is not limited by its metadata, there may be exceptions from these assumptions.Exceptions:There are nine isolated cases where the land use would be tremendously overstated if the whole parcel was shown in a single use. In these cases, the polygon was split to show the use of the rest of the parcel vacant. These parcels are listed below: 01-29-451-001 01-35-300-014 04-08-200-002 04-24-100-004 07-13-301-006 14-04-376-002 18-19-476-015 21-10-200-001 21-10-200-002

The African surficial lithology dataset is a map of parent materials - a mix of bedrock geology and unconsolidated surficial materials classes. The goal was to produce a map that reflected the key geological parent materials which act as primary determinants in the distribution of African vegetation /ecosystems. It is a compilation and reclassification of twelve digital geology, soil and lithology databases. Nineteen surficial lithology classes were delineated in Africa based on geology, soil and landform. Whenever available, multiple sources of ancillary digital data, hard copy maps and literature were reviewed to assist in the reclassification of the source data to the African surficial lithology classification. Of particular note, due to the varying spatial and classification resolutions of the geologic source data, the African surficial lithology map varies in spatial complexity and classification detail across Africa. Purpose: The African surficial lithology data was developed as a primary input dataset for an African Ecological Footprint mapping project undertaken by the U.S. Geological Survey and The Nature Conservancy. The project used a biophysical stratification approach which was based on mapping the major structural components of ecosystems (land surface forms, lithology, isobioclimates and biogeographic regions). These unique physical components, which are considered as the fundamental building blocks of ecosystems, were reviewed by regional vegetation and landscape ecology experts and used in a classification and regression tree (CART) inductive model to map intermediate scale African ecosystems.

GEBCO’s gridded bathymetric data sets are global terrain models for ocean and land. This map shows the areas where changes in elevation/depth has occurred between GEBCO Gridded products. Change values are represented in meters. This particular layer shows the absolute value of the difference (in meters) between GEBCO 2020 and GEBCO 2021 gridded data products. The equation used to compute change is abs(GEBCO 2021 - GEBCO 2020). Then a reclass is applied to reclassify all pixels that are equal to zero as "No Data", helping further emphasize the locations where change has occurred. GEBCO aims to provide the most authoritative, publicly available bathymetry data sets for the world’s oceans.More Information about GEBCO: https://www.gebco.net/Dataset attribution for products used to create this layer:GEBCO Compilation Group (2020) GEBCO 2020 Grid (doi:10.5285/a29c5465-b138-234d-e053-6c86abc040b9)GEBCO Compilation Group (2021) GEBCO 2021 Grid (doi:10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f)For more GEBCO related layers and maps please visit the GEBCO ArcGIS Online Group.

This layer represents an estimate of the footprint of human settlement in 2020. It is intended as a fast-drawing cartographic layer to augment base maps and to focus a map reader's attention on the location of human population. This layer is not intended for analysis.This layer was derived from the 2020 slice of the WorldPop Population Density 2000-2020 100m and 1km layers. WorldPop modeled this population footprint based on imagery datasets and population data from national statistical organizations and the United Nations. Zooming in to very large scales will often show discrepancies between reality and this or any model. Like all data sources imagery and population counts are subject to many types of error, thus this gridded footprint contains errors of omission and commission. The imagery base maps available in ArcGIS Online were not used in WorldPop's model. Imagery only informs the model of characteristics that indicate a potential for settlement, and cannot intrinsically indicate whether any or how many people live in a building. Also see the Urban Density Footprint layer, which like this layer, is intended to provide a fast-drawing cartographic context for urban populations.The following processing steps were used to produce this layer in ArcGIS Pro:1. Int tool (Spatial Analyst) to truncate double precision values; all values less than 0.99 become 0.2. Reclassify tool (Spatial Analyst) to set values 0 through 14 to NoData (Null) and all other values become 1. The figure of 14 was empirically derived as a good balance between reducing errors of commission, i.e., false-positive cells with lower values, while not introducing errors of omission by eliminating obviously populated cells.3. Copy Raster tool with Output Coordinate System environment set to Web Mercator, bit depth to 1 bit, and NoData Value to 0.Source:WorldPop Population Density 2000-2020 100m, which is created from WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation. The DOI for the original WorldPop.org total population population data is 10.5258/SOTON/WP00645.

The Bolstering Ecosystems Against Coastal Harm Act (BEACH Act, Pub. L.118-117) was enacted on November 25, 2024. This law amended the Coastal Barrier Resources Act (CBRA) and adopted 195 new or revised maps for 454 units of the CBRS in 13 states. The revised maps were produced by the U.S. Fish and Wildlife Service (FWS) through the Hurricane Sandy Remapping Project and other efforts and are available through the CBRS Mapper.

The revised maps remove about 1,400 acres from the CBRS, correcting mapping errors affecting about 955 structures. The revised maps also expand the CBRS by about 294,000 acres and add 275 structures to the CBRS. The revised maps reclassify certain areas from System Units to Otherwise Protected Areas and vice versa. Learn more about the differences between these two types of units. This web map is a component of the FWS BEACH Act viewer, which allows users to see where these changes were made. The change polygons in this layer are a visualization tool only and may include some errors, such as:

Offshore areas depicted as either “additions” or “removals” where we altered the length of the boundaries where they extend into bodies of water. These are not actual changes, as the offshore extent of the units is not defined by the polygons, but by either the 30- or 20-foot bathymetric contour (depending on the area).

Minor topological differences that occur in the nationwide dataset that did not exist in the locally projected data (these errors are imperceptible except through geoprocessing).For more information, including summaries of the changes made to each unit, visit our webpage on the BEACH Act.

Users seeking documentation regarding whether a particular property is within or outside of the CBRS should use the CBRS Validation Tool rather than this web map. Questions? Contact cbra@fws.gov.