LandCover_RBLOBJECTIDInternal feature number.EsriSequential unique whole numbers that are automatically generated.ShapeFeature geometry.EsriCoordinates defining the features.classColloquial name for the landcover class.Shape_AreaArea of feature in internal units squared.EsriPositive real numbers that are automatically generated.Shape_LengthLength of feature in internal units.EsriPositive real numbers that are automatically generated.AcresNVCS_Group_MacrogroupNational Vegetation Classification System macrogroup name. More information can be found at nvcs.org.NVCS_Alliance_AssociationNational Vegetation Classification System alliance or association name. More information can be found at nvcs.org. The alliance or association name may not be present for all landcover types, depending on the specificity that was available. In some cases, multiple alliances or associations may be present as well, because grouping was necessary in order to reach the acceptable level of accuracy.U.S. Fish and Wildlife Service, Pacific Southwest RegionLandcover and Vegetation, Ruby Lake NWRU.S. Fish and Wildlife ServiceSacramento, CA20150929None.This data layer was compiled for use with natural resource mapping and GIS applications. It is inappropriate to use these data for legal purposes.Ground condition20150929This dataset represents vegetation and landcover for Ruby Lake NWR. It was produced by the U.S. Fish and Wildlife Service, with field data collection provided by the University of Nevada, Reno. The process was iterative and took place over two calendar years and two field seasons. Additional data points were acquired in order to validate the map product and to develop a product that met a minimal accuracy level of 80%. The final classification is based on 2013 National Agricultural Imagery Program (NAIP) orthophotography, produced by the U.S. Department of Agriculture but additional datasets were also utilized, including a digital elevation model. The classification methodology uses a hybrid approach of pixel-based supervised classification and object-based analysis. Additional information on the methodology can be found in the Supplemental Info section.A full, detailed report is also available from the U.S. Fish and Wildlife Service, Pacific Southwest Region office.This dataset was created to assist with planning and management of Ruby Lake NWR. This vegetation dataset was produced through interpretation of multiple datasets, assisted by field data and site visits. The primary dataset utilized is NAIP orthoimagery from August of 2013. The NAIP imagery was combined with several addition derived products. These included a Normalized Difference Vegetation Index and a texture layer, derived from the NAIP imagery, as well as a digital elevation model and a canopy height model, both created from a LiDAR dataset acquired over the refuge in 2009. All of these inputs were stacked in one layer with a consistent pixel size and coordinate system and then analyzed using the RandomForest tool in R software. This tool uses a data mining approach with multiple decision trees to find patterns in the data. The tool is trained with known landcover types provided by the GIS analyst. In addition to using the RandomForest algorithm, the NAIP imagery was also divided into segments using eCognition Developer software. The pixel-based output from the RandomForest analysis was then converted to polygons by using a Majority zonal statistics in ArcMap. Note that the RandomForest analysis was run several times before an acceptable level of accuracy was reached. Following conversion to polygons, additional manual edits were made to problem areas and in specific areas, again in an effort to improve the overall accuracy of the product. An independent collection of over 400 ground control points was used in the validation and the overall accuracy was found to be 86%. IrregularU.S. Fish and Wildlife Service, Division of Refuge PlanningJustin EptingGIS Specialistmailling and physical2800 Cottage WaySacramentoCA95825(916)414-6511justin_epting@fws.govNo warranty expressed or implied is made regarding the accuracy or utility of the data and information on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This disclaimer applies both to individual use of the data, and information, and aggregate use with other data and information. It is also strongly recommended that careful attention be paid to the contents of the metadata file associated with these data and information. The USFWS shall not be held liable for improper or incorrect use of the data and information described and/or contained herein.
Downloadable DataU.S. Fish and Wildlife Service, Division of Refuge PlanningJustin EptingGIS Specialistmailling and physical2800 Cottage WaySacramentoCA95825(916)414-6511justin_epting@fws.govFGDC Content Standard for Digital Geospatial MetadataFGDC-STD-001-199820150929U.S. Fish and Wildlife Service, Division of Refuge PlanningJustin EptingGIS Specialistmailling and physical2800 Cottage WaySacramentoCA95825(916)414-6511justin_epting@fws.govTests for integrity have not been performed.Features represented have not been tested for completenesscoordinate pair0.000010.00001metersNorth American Datum of 1983Geodetic Reference System 19806378137.000000298.257222Justin EptingU.S. Fish and Wildlife Service, Division of Refuge PlanningGIS Specialist(916)414-65112800 Cottage WaySacramentoCA95825justin_epting@fws.gov20151229Justin EptingU.S. Fish and Wildlife Service, Division of Refuge PlanningGIS Specialist(916)414-65112800 Cottage WaySacramentoCA95825justin_epting@fws.govFile Geodatabase Feature ClassLandcover and Vegetation, Ruby Lake NWR2015-09-29U.S. Fish and Wildlife Service, Pacific Southwest RegionU.S. Fish and Wildlife ServiceSacramento, CA<DIV STYLE="text-align:Left;"><DIV><P><SPAN>This dataset represents vegetation and landcover for Ruby Lake NWR. It was produced by the U.S. Fish and Wildlife Service, with field data collection provided by the University of Nevada, Reno. The process was iterative and took place over two calendar years and two field seasons. Additional data points were acquired in order to validate the map product and to develop a product that met a minimal accuracy level of 80%. The final classification is based on 2013 National Agricultural Imagery Program (NAIP) orthophotography, produced by the U.S. Department of Agriculture but additional datasets were also utilized, including a digital elevation model. The classification methodology uses a hybrid approach of pixel-based supervised classification and object-based analysis. Additional information on the methodology can be found in the Supplemental Info section.A full, detailed report is also available from the U.S. Fish and Wildlife Service, Pacific Southwest Region office.</SPAN></P></DIV></DIV>This dataset was created to assist with planning and management of Ruby Lake NWR.Justin EptingU.S. Fish and Wildlife Service, Division of Refuge PlanningGIS Specialist(916)414-65112800 Cottage WaySacramentoCA95825justin_epting@fws.govNo warranty expressed or implied is made regarding the accuracy or utility of the data and information on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This disclaimer applies both to individual use of the data, and information, and aggregate use with other data and information. It is also strongly recommended that careful attention be paid to the contents of the metadata file associated with these data and information. The USFWS shall not be held liable for improper or incorrect use of the data and information described and/or contained herein.<DIV STYLE="text-align:Left;"><DIV><P><SPAN>This data layer was compiled for use with natural resource mapping and GIS applications. It is inappropriate to use these data for legal purposes.</SPAN></P></DIV></DIV>Ground condition2015-09-29This vegetation dataset was produced through interpretation of multiple datasets, assisted by field data and site visits. The primary dataset utilized is NAIP orthoimagery from August of 2013. The NAIP imagery was combined with several addition derived products. These included a Normalized Difference Vegetation Index and a texture layer, derived from the NAIP imagery, as well as a digital elevation model and a canopy height model, both created from a LiDAR dataset acquired over the refuge in 2009. All of these inputs were stacked in one layer with a consistent pixel size and coordinate system and then analyzed using the RandomForest tool in R software. This tool uses a data mining approach with multiple decision trees to find patterns in the data. The tool is trained with known landcover types provided by the GIS analyst. In addition to using the RandomForest algorithm, the NAIP imagery was also divided into segments using eCognition Developer software. The pixel-based output from the RandomForest analysis was then converted to polygons by using a Majority zonal statistics in ArcMap. Note that the RandomForest analysis was run several times before an acceptable level of accuracy was reached. Following conversion to polygons, additional manual edits were made to problem areas and in specific areas, again in an effort to improve the overall accuracy of the product. An independent collection of over 400 ground control points was used in the validation and the overall accuracy was found to be 86%.Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri ArcGIS 10.3.1.49591-115.540416-115.39455740.30195940.038637U.S. Fish and Wildlife Service, Pacific Southwest RegionRuby Lake NWRUSFWSnational wildlife refugelandcoverhabitatvegetationNevadasagebrushshrublandwetlandjuniperTests for integrity have not been performed.Features represented have not been tested for completenessdatasetEPSG8.6.211