S_USA.ProclaimedForest

U.S. Forest Service Automated Lands Program (ALP) 2019-03-31T07:49:12 074158 S_USA.ProclaimedForest_Grassland vector digital data http://data.fs.usda.gov/geodata/edw/datasets.php This layer includes both Proclaimed Forest and National Grassland boundary areas. A Proclaimed Forest boundary is the boundary encompassing National Forest System land within a national forest that is set aside and reserved from the public domain by executive order or proclamation; congressional action is required to terminate a proclaimed boundary; if, at some point in time, no National Forest System land remains within the proclaimed boundary, then there is no legal significance to the proclaimed boundary, however, its legal status remains. National Grasslands are lands designated "National Grasslands" by the Secretary of Agriculture and permanently held by the Department of Agriculture for administration under Title III of the Bankhead-Jones Farm Tenant Act. This data layer is intended for read-only use. 20151231 publication Date In work As needed -150.007671 -65.699670 61.518990 18.231236 none Proclaimed Forest National Grassland NFS Lands Land Status USDA Forest Service National Grassland Proclaimed Forest ALP Land Dataset ISO 19115 Topic Categories boundaries None The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly. USFS Chief Information Office, Enterprise Data Warehouse physical Washington DC 20250 Please send an e-mail to the address below. data@fs.fed.us Esri ArcGIS 10.3.1.4959 The Regional Land Status personnel are responsible for their local data stewardship and maintenance. The overall collection nationwide consists of 100% attribute accuracy based on currentness and completeness. Quality control measures taken: this feature class is populated by an automated translation process from the source feature class that is edited and maintained by the data stewards. Only stewards with the proper role grants can edit the data. The application used for editing has built in controls to only allow valid geometry and domains to control attribute integrity. This theme is current to date of publication. Generally the source information used to record spatial locations are from: 1) on-screen digitizing with a georeferenced topographic or image background; or 2) GPS field collected positions. This data was published using Feature Manipulation Engine Software. The spatial features were extracted from the transactional database, manipulated to meet the desired output format and published to the target feature class. 20151231 Vector Simple FALSE 746 TRUE FALSE D North American 1983 GRS 1980 6378137.0 298.257222101 Roadside_buffer_30ft A collection of geographic features with the same geometry type (such as point, line, or polygon), the same attributes, and the same spatial reference. http://support.esri.com/en/knowledgebase/GISDictionary/term/feature%20class Feature Class 746 OBJECTID Internal feature number. ESRI OID 4 0 0 Sequential unique whole numbers that are automatically generated. OBJECTID FOREST_GRASSLANDNAME The name of the Proclaimed Forest or National Grassland. U.S. Forest Service String 80 0 0 Proper name. FOREST_GRASSLANDNAME SHAPE Feature geometry. ESRI Geometry 0 0 0 Coordinates defining the features. Shape SHAPE_Length Shape_Length Double 8 0 0 Length of feature in internal units. Esri Positive real numbers that are automatically generated. SHAPE_Area Shape_Area Double 8 0 0 Area of feature in internal units squared. Esri Positive real numbers that are automatically generated. Cover_Type Cover_Type Integer 4 0 0 NonNative_Cover NonNative_Cover Integer 4 0 0 GroundSurface GroundSurface Integer 4 0 0 Acres Acres Single 4 0 0 USFS Chief Information Office, Enterprise Data Warehouse physical Washington DC 20250 Please send an e-mail to the address below. data@fs.fed.us The U.S. Forest Service makes no warranty, express or implied, nor assumes any liability or responsibility for the accuracy, reliability, completeness, or utility of these geospatial data or for the improper or incorrect use of those data. The data are dynamic and may change over time. The user is responsible for verifying the limitations of the geospatial data and for using the data accordingly. 20161208 USFS Chief Information Office, Enterprise Data Warehouse physical Washington DC 20250 Please send an e-mail to the address below. data@fs.fed.us FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998 Roadside_buffer_30ft 389561.886200 408676.625600 3787965.361100 3799679.091724 1 002 Projected GCS_North_American_1983 Linear Unit: Meter (1.000000) <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/2.9.0'><WKT>PROJCS["NAD_1983_UTM_Zone_11N",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-117.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0],AUTHORITY["EPSG",26911]]</WKT><XOrigin>-5120900</XOrigin><YOrigin>-9998100</YOrigin><XYScale>10000</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><WKID>26911</WKID><LatestWKID>26911</LatestWKID></ProjectedCoordinateSystem> NAD_1983_UTM_Zone_11N 20230911 08401200 20231227 13471200 1.0 50000 5000 ItemDescription 20231016 09511400 TRUE USFS Chief Information Office, Enterprise Data Warehouse Please send an e-mail to the address below. Washington DC 20250 data@fs.fed.us 20231227 ArcGIS Metadata 1.0 USFS Chief Information Office, Enterprise Data Warehouse Please send an e-mail to the address below. Washington DC 20250 data@fs.fed.us http://data.fs.usda.gov/geodata/edw/datasets.php File Geodatabase Feature Class Roadside_buffer_30ft 2019-03-31T07:49:12 U.S. Forest Service Automated Lands Program (ALP) vector digital data <DIV STYLE="text-align:Left;"><DIV><DIV>The following tasks were performed to create the final spatial layer:Task 1 – Data Collection and Field CollectionThis task included collecting and organizing remotely sensed data to support the digitizing effort. Aerial imagery was collected from the U.S. Department of Agriculture Farm Service Agency's (FSA) National Agriculture Imagery Program (NAIP). This imagery is from 2022 and is at a 0.6-meter resolution. Additionally, 0.5-meter resolution Esri aerial imagery from 2022 will be used. County specific imagery servers were also utilized and many of these have less than 1-meter resolution. For example, Ventura County has an imagery server that has 7.5-centimeter resolution. These imagery sources cover a temporal range including the late growing season and autumn; Google Earth's historical imagery provided additional seasonal portrayals. Elevation data and derivatives were collected from the U.S. Geologic Survey's (USGS) 3D Elevation Program (3DEP). The following aerial imagery was used for this project:NAIP 2020 4-band 60cm resolution aerial imagery for California - <A href="https://map.dfg.ca.gov/arcgis/services/Base_Remote_Sensing/NAIP_2020_4Band/ImageServer" STYLE="text-decoration:underline;">https://map.dfg.ca.gov/arcgis/services/Base_Remote_Sensing/NAIP_2020_4Band/ImageServer</A>NAIP 2022 CIR 60cm resolution aerial imagery for California - <A href="https://map.dfg.ca.gov/arcgis/services/Base_Remote_Sensing/NAIP_2022_CIR/ImageServer" STYLE="text-decoration:underline;">https://map.dfg.ca.gov/arcgis/services/Base_Remote_Sensing/NAIP_2022_CIR/ImageServer</A>LA County Aerial Imagery 2017 – 0.7 inch resolution - <A href="https://utility.arcgis.com/usrsvcs/servers/2a21a26708cf4fdaa3b2ee6d859afcc4/services/LARIAC5/LARIAC5_WebMercator/ImageServer" STYLE="text-decoration:underline;">https://utility.arcgis.com/usrsvcs/servers/2a21a26708cf4fdaa3b2ee6d859afcc4/services/LARIAC5/LARIAC5_WebMercator/ImageServer</A>2016 Aerial Imagery Monterey – 4-inch resolution - <A href="https://maps.fodis.net/server/services/BaseMaps/2016_Aerial_Imagery_Monterey/ImageServer" STYLE="text-decoration:underline;">https://maps.fodis.net/server/services/BaseMaps/2016_Aerial_Imagery_Monterey/ImageServer</A>Riverside_County_2020_State - 4-inch resolution - <A href="https://gis.countyofriverside.us/arcgis_public/services/Aerials_StatePlane/Riverside_County_2020_State/ImageServer" STYLE="text-decoration:underline;">https://gis.countyofriverside.us/arcgis_public/services/Aerials_StatePlane/Riverside_County_2020_State/ImageServer</A>2022 San Bernardino County Imagery- 4-inch resolution - <A href="https://maps.sbcounty.gov/arcgis/rest/services/Y2022_pua_cache/MapServer" STYLE="text-decoration:underline;">https://maps.sbcounty.gov/arcgis/rest/services/Y2022_pua_cache/MapServer</A>San Diego 2020 – 9-inch resolution - <A href="https://gis.sandag.org/sdgis/services/Imagery/SD2020/ImageServer" STYLE="text-decoration:underline;">https://gis.sandag.org/sdgis/services/Imagery/SD2020/ImageServer</A>San Diego County Imagery_2019 – 9-inch resolution - <A href="https://gis.sangis.org/maps/services/Public/Imagery_2019/ImageServer" STYLE="text-decoration:underline;">https://gis.sangis.org/maps/services/Public/Imagery_2019/ImageServer</A>Vexcel True Ortho Imagery - 7.5cm - Ventura County – 3-inch resolution - <A href="https://utility.arcgis.com/usrsvcs/servers/1af5ae9cc25e4afc875c3d1b7c23794b/services/vexcel_bluesky_urban_ov_rgb_ventura/ImageServer" STYLE="text-decoration:underline;">https://utility.arcgis.com/usrsvcs/servers/1af5ae9cc25e4afc875c3d1b7c23794b/services/vexcel_bluesky_urban_ov_rgb_ventura/ImageServer</A>ESRI Imagery – 50cm resolution - <A href="https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer" STYLE="text-decoration:underline;">https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer</A>Task 2 – Roadway Edge Digitizing and BufferingHeads-up digitizing using aerial imagery and a hillshade and slope created from 1-meter digital elevation models (DEM) were used to digitize the edge of roadway. The roadway edge is defined as the edge of cleared shoulder of road, not edge of paved surface. A variety of recent high resolution aerial imagery was used to help digitize the roadway edge. In addition to aerial imagery, elevation data was used for this task. Using a combination of hillshade and slope rasters, the edge of road, the road associated slope breaklines, and the extent of cut and fill slopes were identified. Task 3 – Digitizing and Attributing Roadside Vegetation PolygonsThe roadside edge polygon was buffered by 30 feet, excluding the input polygon from buffer. Fields were added for Ground Surface, Cover Type, and Non-Native Cover. Coded domains were created for each of these fields as follows:Ground Surface1 Native2 Cut Slope3 Fill Slope4 UrbanCover Type1 Conifer Forest2 Oak Woodland3 Mixed Conifer/Hardwood4 Riparian5 Shrubland6 Grassland7 Urban8 Rock/BarrenNon-Native Cover1 <10%2 10-50%3 50-75%4 75-100%5 UrbanHeads up digitizing was then employed to “chop up” (split) the 30-foot roadside edge buffer. Google Earth’s Street View was also used extensively. Having a layer in GIS of tenth of mile road markers, as well as a KMZ of these points in Google Earth, allowed GIS technicians to go back and forth between GIS and Google Earth seamlessly. First, each road segment was divided up according to ground surface type. USGS’ 3DEP hillshade layer was heavily relied on for this process. Roadfill, roadcut, and native surface were heads-up digitized from DEM, hillshade, and slope rasters. Native slopes are typically easily distinguished from cuts and fills by the smooth and uniform nature of the cuts and fills. The border between cuts and fills and native terrain is usually easily identified by a combination of slope and aspect. Publicly available data available 1-meter resolution DEM data proved to be sufficient, and 0.6-meter or better NAIP imagery complemented this process very well.Second, the roadside polygons were further divided and attributed for cover type. Google Earth's Street View was used as a separate reference tool for accurate vegetative attributing when aerial imagery was insufficient or was obscured by canopies.Lastly, the roadside polygons were additionally divided and attributed for percent cover of non-native grasses. All roadside grasses were assumed to be non-native per Amendment 1 (Q&A) of this contract, where the USFS stated “Along Southern California roadways, if an area is grassy – it will be the non-native plant communities we are targeting.” A combination of aerial and Google Street View imagery allowed DJ&A’s GIS and environmental staff to divide roadside polygons according to the percentage groups (less than 10, 10 to 50, 50 to 75, and greater than 75). Given that Google’s Street View images are taken throughout the calendar year, technicians had to consider plant phenology at the date of the imagery and deduce what the non-native grass cover would be at the height of the growing season.Task 4 – Ground Truthing 5%DJ&A's botanist is currently in the process of field verifing 5% of the area digitized. The ground truthing was divided across the four Southern California National Forests’ geographic areas as well as across native vegetation types and sought COR approval on final project miles for ground truthing. Methods for ground truthing involve navigating to each identified polygon(s); establishing a global positioning system (GPS) point (or points, depending on location and accessibility); taking a minimum of one photo to document the vegetation community, as viewed from the road edge; compiling a species list of the main non-native vegetation present; recording cover class for non-native vegetation; and confirming the native vegetation type. Photos of other vegetative features of interest may be taken when deemed appropriate by the botanist. For ground truthing activities associated with bridges or high-speed interstates, crew safety is of primary importance. If unable to successfully identify the above-listed features, the location was replaced with a different, pre-approved location to ensure that the total 5% of project miles are field verified.</DIV></DIV></DIV> The purpose of this contract is to map vegetation, with a particular focus on non-native annual grasses and forbs, along primary roadways through wildlands in the four Southern California National Forests (Los Padres, Angeles, San Bernardino and Cleveland National Forests). The mapping area is 3,380 acres along 495 road miles. These non-native annual plant communities are usually dominated by a suite of non-native annual grass species (Bromus spp, Avena spp and others). Non-native plant communities will also have an abundance of forb species, especially various mustards, filaree and weedy composites like Yellow Starthistle or Tocalote. These non-native species form a fine fuel bed that dries early in the season and is highly prone to ignitions from common roadside ignition sources such as sparks from passing vehicles, hot catalytic converters, and thrown cigarettes. Ignitions data show that these roadside areas are the most frequent locations for new wildland fire ignitions in the Southern California National Forests. This contract mapped these non-native plant communities and produced a geospatial polygon layer that will be used to prioritize project areas for restoration or other methods for reducing ignition probability along roadways. This contract did not map specific non-native species, but rather locations where non-native annual plant communities dominate or are invading roadside vegetation communities. USFS Chief Information Office, Enterprise Data Warehouse Please send an e-mail to the address below. Washington DC 20250 data@fs.fed.us ISO 19115 Topic Categories boundaries Land Status NFS Lands ALP Land Dataset Proclaimed Forest National Grassland Proclaimed Forest National Grassland USDA Forest Service Southern California National Forests roadsides non-native annual grasses The U.S. Forest Service makes no warranty, express or implied, nor assumes any liability or responsibility for the accuracy, reliability, completeness, or utility of these geospatial data or for the improper or incorrect use of those data. The data are dynamic and may change over time. The user is responsible for verifying the limitations of the geospatial data and for using the data accordingly. <DIV STYLE="text-align:Left;"><DIV><DIV>The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly.</DIV></DIV></DIV> Esri ArcGIS 12.9.4.32739 -150.007671 -65.69967 18.231236 61.51899 publication Date 2015-12-31 1 -121.00000 -116.000000 37.000000 32.000000 The Forest Service has multiple types of boundaries represented by different feature classes (layers): Administrative, Ownership and Proclaimed. 1) ADMINISTRATIVE boundaries (e.g. AdministrativeForest and RangerDistrict feature classes) encompass National Forest System lands managed by an administrative unit. These are dynamic layers that should not be considered "legal" boundaries as they are simply intended to identify the specific organizational units that administer areas. As lands are acquired and disposed, the administrative boundaries are adjusted to expand or shrink accordingly. Please note that ranger districts are sub units of National Forests. An administrative forest boundary can contain one or more Proclaimed National Forests, National Grasslands, Purchase Units, Research and Experimental Areas, Land Utilization Projects and various "Other" Areas. If needed, OWNERSHIP boundaries (e.g. BasicOwnership and SurfaceOwnership feature classes) should be reviewed along with these datasets to determine parcels that are federally managed within the administrative boundaries. 2) OWNERSHIP boundaries (e.g. BasicOwnership and SurfaceOwnership feature classes) represent parcels that are tied to legal transactions of ownership. These are parcels of Federal land managed by the USDA Forest Service. Please note that the BasicOwnership layer is simply a dissolved version of the SurfaceOwnership layer. 3) PROCLAIMED boundaries (e.g. ProclaimedForest and ProclaimedForest_Grassland) encompass areas of National Forest System land that is set aside and reserved from public domain by executive order or proclamation. Please note that the ProclaimedForest layer contains only proclaimed forests while ProclaimedForest_Grassland layer contains both proclaimed forests and proclaimed grasslands. For boundaries that reflect current National Forest System lands managed by an administrative unit, see the ADMINISTRATIVE boundaries (AdministrativeForest and RangerDistrict feature classes). For a visual comparison of the different kinds of USFS boundary datasets maintained by the USFS, see the Forest Service Boundary Comparison map at https://usfs.maps.arcgis.com/apps/CompareAnalysis/index.html?appid=fe7b9f56217949a291356f08cfccb119. USFS boundaries are often referenced in national datasets maintained by other federal agencies. Please note that variations may be found between USFS data and other boundary datasets due to differing update frequencies. PAD-US (Protected Areas Database of the United States), maintained by the U.S. Geological Survey, is a "best available" inventory of protected areas including data provided by managing agencies and organizations including the Forest Service. For more information see https://gapanalysis.usgs.gov/padus/data/metadata/. SMA (Surface Management Agency), maintained by the Bureau of Land Management, depicts Federal land for the United States and classifies this land by its active Federal surface managing agency. It uses data provided by the Forest Service and other agencies, combined with National Regional Offices collection efforts. For more information see https://landscape.blm.gov/geoportal/catalog/search/resource/details.page?uuid=%7B2A8B8906-7711-4AF7-9510-C6C7FD991177%7D. USFS, DJ&A Quality control measures taken: this feature class is populated by an automated translation process from the source feature class that is edited and maintained by the data stewards. Only stewards with the proper role grants can edit the data. The application used for editing has built in controls to only allow valid geometry and domains to control attribute integrity. Quality control measures taken: this feature class is populated by an automated translation process from the source feature class that is edited and maintained by the data stewards. Only stewards with the proper role grants can edit the data. The application used for editing has built in controls to only allow valid geometry and domains to control attribute integrity. This theme is current to date of publication. The Regional Land Status personnel are responsible for their local data stewardship and maintenance. The overall collection nationwide consists of 100% attribute accuracy based on currentness and completeness. Generally the source information used to record spatial locations are from: 1) on-screen digitizing with a georeferenced topographic or image background; or 2) GPS field collected positions. This data was published using Feature Manipulation Engine Software. 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