2014062310394600FALSE20241208120843002025101516133900CLC_V3_1_STATE_clip.tif00252652.496521799592.49652145555.784485781585.7844851file://C:\Users\alexist\Desktop\raster_zips\raster_prep\CLC_V3_1_STATE_clip.tifLocal Area NetworkProjectedGCS_North_American_1983_HARNLinear Unit: Meter (1.000000)FDEP_Albers_HARN<ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.4.0'><WKT>PROJCS["FDEP_Albers_HARN",GEOGCS["GCS_North_American_1983_HARN",DATUM["D_North_American_1983_HARN",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Albers"],PARAMETER["False_Easting",400000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-84.0],PARAMETER["Standard_Parallel_1",24.0],PARAMETER["Standard_Parallel_2",31.5],PARAMETER["Latitude_Of_Origin",24.0],UNIT["Meter",1.0]]</WKT><XOrigin>-19550100</XOrigin><YOrigin>-7526400</YOrigin><XYScale>225743210.67916927</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision></ProjectedCoordinateSystem>32FALSELZW1TIFFTRUEdiscreteunsigned integer2.1474836e+09FGDC CSDGM Metadata1500000005000FGDC1.0Microsoft Windows 10 Version 10.0 (Build 19045) ; Esri ArcGIS 13.4.0.55405Cooperative Land Cover v3.1 Raster - State Classes51872.4003798394.275781608.241156746.24721-87.647558-79.82614231.04287124.45487211-87.639382-79.81360631.04266824.353591The purpose of the Cooperative Land Cover Map is to fill a priority data gap of Florida's State Wildlife Action Plan (SWAP) for improved habitat mapping. In addition it provides significantly improved data for scrub and sandhill, priority habitats of the SWAP. The map will inform a variety of conservation and management activities in Florida.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.</SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN>Changes from Version 3.0 to Version 3.1:</SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN>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. </SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN>Domains have been removed from the attribute table, and a class name field has been added for SITE and STATE level classes.</SPAN></P><P STYLE="margin:0 0 11 0;"><SPAN>The Dry Tortugas have been reincorporated.</SPAN></P><P STYLE="margin:0 0 0 0;"><SPAN><SPAN>The geographic extent has been revised for the Coastal Upland and Dry Prairie classes. Rural Open and the Extractive classes underwent a more thorough review. </SPAN></SPAN></P><P STYLE="margin:0 0 7 0;"><SPAN /></P><P /><P /><P><SPAN /></P></DIV></DIV></DIV>Data compiled from multiple sources by Florida Fish and Wildlife Conservation Comission (FWC) and Florida Natural Areas Inventory (FNAI). Land CoverHabitatNatural CommunityVegetationFlorida Land Cover Classification SystemFlorida Land Cover and Forms Classification SystemData are intended to be used for general informational and planning purposes and not appropriate for legal, regulatory and/or cadastral purposesdataset002file://\\Keaton\Projects\LandCover_Map\Coop_LC_Map\CLC_polys_MOD_2011DEC.gdbLocal Area NetworkFile Geodatabase Feature ClassRasterCustom130793621052652.496521 45555.78448552652.496521 781585.784485799592.496521 781585.784485799592.496521 45555.784485426122.496521 413570.7844857469410.0000007360310.00000020251015This data set represents the updated land use and land cover for the state of Florida.FWC, FNAITable78VAT_CLC_v3_1_STATEOIDOIDOID400Internal feature number.EsriSequential unique whole numbers that are automatically generated.ValueCorresponds to the Florida Land Cover Classification System class codeFWC/FNAIIntegerValueInteger10100CountCell ValuesESRIAutomatically generated.CountDouble1900Area_AcresSpatial calculationsESRICalculated valueArea_AcresDouble1900STATEFlorida Land Cover Classification System class codeFWC/FNAIIntegerSTATEInteger10100NAME_STATEFlorida Land Cover Classification System class nameFWC/FNAINAME_STATEString25400LegendLegendInteger10100Band_11833121.0000001110.00000032/9j/4AAQSkZJRgABAQEAAAAAAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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