Public transit routes in San Diego County managed by the San Diego County Metropolitan Transit System (MTS) and the North County Transit District (NCTD). Bus, commuter and light rail, and trolley routes managed and developed from the General Transit Feed Specification (GTFS) data available from the transitland feed registry (formerly from GTFS Data Exchange). Routes are developed from the GTFS data available through the transitland feed registry (https://transit.land/feed-registry/) or transitfeed (http://transitfeeds.com) depending on which is most current (formerly from the GTFS Data Exchange). GTFS data is provided to the exchange by the transit agencies and processed by SanGIS to create a consolidated GIS layer containing routes from both systems. SanGIS uses a publicly available ESRI ArcToolbox tool to create the GIS data layer. The toolbox can be found at http://www.arcgis.com/home/item.html?id=14189102b795412a85bc5e1e09a0bafa. This data set is created using the ROUTES.txt and SHAPES.txt GTFS data files.Routes layers for MTS and NCTD are created separately and combined into a single layer using ArcGIS tools.

Depicts the area of activities funded through BDBD and PPPP budget line item and reported through the FACTS database. The objective of the BD Program is to dispose of unwanted slash or other debris created by timber purchaser operations on timber sale contracts, stewardship contracts and permits, not disposed of by the purchaser. Activities are self-reported by Forest Service Units. The Brush Disposal Program (BD) objective of the BD Program was established in 1916. It requires all purchasers of National Forest timber make deposits to the United States for the estimated cost of disposing of brush and other debris resulting from its cutting operations. Brush disposal activities must be consistent with direction established in forest land and resource management plans, identified in environmental documents developed in accordance with the National Environmental Policy Act of 1969 (NEPA). MetadataThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

Abstract The dataset was derived by the Bioregional Assessment Programme from the GEODATA TOPO 250K Series 3 dataset. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. A clip to the boundary of the NIC bioregion of the original Geodata Topo 250k Series 3 data. Further information can be found at http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_a05f7892-ecbd-7506-e044-00144fdd4fa6/GEODATA+TOPO+250K+Series+3+%28Packaged+-+Shape+file+format%29 ## Dataset History These data have been derived from the TOPO 250K Series 3 data by clipping the data to the external boundary of the Northern Inland Catchments bioregion using the ESRI ArcGIS Analysis Clip tool. Further information can be found at http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_a05f7892-ecbd-7506-e044-00144fdd4fa6/GEODATA+TOPO+250K+Series+3+%28Packaged+-+Shape+file+format%29 ## Dataset Citation Bioregional Assessment Programme (XXXX) Northern Inland Catchments bioregion GEODATA TOPO 250K Series 3. Bioregional Assessment Derived Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/3bd6389b-be36-4e91-bc20-131e18f209c2. ## Dataset Ancestors * Derived From Natural Resource Management (NRM) Regions 2010 * Derived From Bioregional Assessment areas v03 * Derived From Bioregional Assessment areas v01 * Derived From Bioregional Assessment areas v02 * Derived From GEODATA TOPO 250K Series 3 * Derived From NSW Catchment Management Authority Boundaries 20130917 * Derived From Geological Provinces - Full Extent * Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

Tree canopy was derived from high-resolution remotely sensed data -- 2018 NAIP and 2019 LiDAR. Object-based image analysis techniques (OBIA) were employed to extract potential tree canopy and trees using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2000 and all observable errors were corrected.

This dataset contains shapefile boundaries for CA State, counties and places from the US Census Bureau's 2023 MAF/TIGER database. Current geography in the 2023 TIGER/Line Shapefiles generally reflects the boundaries of governmental units in effect as of January 1, 2023.

This map features Africa Land Cover at 30m resolution from MDAUS BaseVue 2013, referencing the World Land Cover 30m BaseVue 2013 layer.Land cover data represent a descriptive thematic surface for characteristics of the land's surface such as densities or types of developed areas, agricultural lands, and natural vegetation regimes. Land cover data are the result of a model, so a good way to think of the values in each cell are as the predominating value rather than the only characteristic in that cell.Land use and land cover data are critical and fundamental for environmental monitoring, planning, and assessment.Dataset SummaryBaseVue 2013 is a commercial global, land use / land cover (LULC) product developed by MDA. BaseVue covers the Earth’s entire land area, excluding Antarctica. BaseVue is independently derived from roughly 9,200 Landsat 8 images and is the highest spatial resolution (30m), most current LULC product available. The capture dates for the Landsat 8 imagery range from April 11, 2013 to June 29, 2014. The following 16 classes of land use / land cover are listed by their cell value in this layer: Deciduous Forest: Trees > 3 meters in height, canopy closure >35% (<25% inter-mixture with evergreen species) that seasonally lose their leaves, except Larch.Evergreen Forest: Trees >3 meters in height, canopy closure >35% (<25% inter-mixture with deciduous species), of species that do not lose leaves. (will include coniferous Larch regardless of deciduous nature).Shrub/Scrub: Woody vegetation <3 meters in height, > 10% ground cover. Only collect >30% ground cover.Grassland: Herbaceous grasses, > 10% cover, including pasture lands. Only collect >30% cover.Barren or Minimal Vegetation: Land with minimal vegetation (<10%) including rock, sand, clay, beaches, quarries, strip mines, and gravel pits. Salt flats, playas, and non-tidal mud flats are also included when not inundated with water.Not Used (in other MDA products 6 represents urban areas or built up areas, which have been split here in into values 20 and 21).Agriculture, General: Cultivated crop landsAgriculture, Paddy: Crop lands characterized by inundation for a substantial portion of the growing seasonWetland: Areas where the water table is at or near the surface for a substantial portion of the growing season, including herbaceous and woody species (except mangrove species)Mangrove: Coastal (tropical wetlands) dominated by Mangrove speciesWater: All water bodies greater than 0.08 hectares (1 LS pixel) including oceans, lakes, ponds, rivers, and streamsIce / Snow: Land areas covered permanently or nearly permanent with ice or snowClouds: Areas where no land cover interpretation is possible due to obstruction from clouds, cloud shadows, smoke, haze, or satellite malfunctionWoody Wetlands: Areas where forest or shrubland vegetation accounts for greater than 20% of vegetative cover and the soil or substrate periodically is saturated with, or covered by water. Only used within the continental U.S.Mixed Forest: Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75% of total tree cover. Only used within the continental U.S.Not UsedNot UsedNot UsedNot UsedHigh Density Urban: Areas with over 70% of constructed materials that are a minimum of 60 meters wide (asphalt, concrete, buildings, etc.). Includes residential areas with a mixture of constructed materials and vegetation where constructed materials account for >60%. Commercial, industrial, and transportation i.e., Train stations, airports, etc.Medium-Low Density Urban: Areas with 30%-70% of constructed materials that are a minimum of 60 meters wide (asphalt, concrete, buildings, etc.). Includes residential areas with a mixture of constructed materials and vegetation, where constructed materials account for greater than 40%. Commercial, industrial, and transportation i.e., Train stations, airports, etc.MDA updated the underlying data in late 2016 and this service was updated in February 2017. An improved selection of cloud-free images was used to produce the update, resulting in improvement of classification quality to 80% of the tiles for this service.What can you do with this layer?This layer can be used to create maps and to visualize the underlying data across the ArcGIS platform. It can also be used as an analytic input in ArcMap and ArcGIS Pro.This layer has query, identify, and export image services available. The layer is restricted to an 16,000 x 16,000 pixel limit, which represents an area of nearly 300 miles on a side. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.

This land cover data set is derived from the original raster based Globcover regional (Africa) archive. It has been post-processed to generate a vector version at national extent with the LCCS regional legend (46 classes). This database can be analyzed in the GLCN software Advanced Database Gateway (ADG), which provides a user-friendly interface and advanced functionalities to breakdown the LCCS classes in their classifiers for further aggregations and analysis.

The data set is intended for free public access.

The shape file's attributes contain the following fields: -Area (sqm) -ID -Gridcode (Globcover cell value) -LCCCode (unique LCCS code)

You can download a zip archive containing: -the shape file (.shp) -the ArcGis layer file with global legend (.lyr) -the ArcView 3 legend file (.avl) -the LCCS legend tables (.xls)

Supplemental Information:

This land cover product is a vector version (ESRI shape) of the Globcover archive that was published in 2008 as result of an initiative launched in 2004 by the European Space Agency (ESA). Globcover is currently the most recent (2005) and resoluted (300 m) datasets on land cover globally. Given the need of this valuable information for environmental studies, natural resources management and policy formulation, through activities of the Global Land Cover Network (GLCN) programme, the Globcover has been reprocessed to generate databases at national extent that can be analyzed through the Advanced Database Gateway software (ADG) by GLCN. ADG is a cross-cutting interrogation software that allows the easy and fast recombination of land cover polygons according to the individual end-user requirements. Aggregated land cover classes can be generated not only by name, but also using the set of existing classifiers. ADG uses land cover data with a Land Cover Classification System (LCCS) legend. The ADG software is available for download on the GLCN web site at http://www.glcn.org/sof_7_en.jsp

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Antonio Martucci

Data lineage:

This land cover database is provided as ESRI shape file (vector format) and derives from reprocessing the raster based Globcover database (regional version). Globcover has undergone the following process: a) vectoralization at the national extent using ESRI ArcGis (arcinfo) 9.3; b) topological reconstruction (custom AML scripts launched inside ArcGis-arcinfo 9.3); c) simplification of areas according to a minimum mapping unit of 0.1 skim (10 ha) (custom AML scripts launched inside ArcGis-arcinfo 9.3); application of the FAO/UNEP Land Cover Classification System (LCCS) legend (46 classes); final processing to assure full compatibility with the GLCN software Advanced Database Gateway (ADG).

Online resources:

Download - Land cover of Zambia 46 classes - Shape file format

GLOBCOVER on the ESA Web site

As part of THEIA (the French Data and Services center for continental surfaces) CIRAD's TETIS research unit is developing an automated mapping method based on the Moringa chain that minimizes interactions with users by automating most image analysis and processing. The methodology uses jointly a Very High Spatial Resolution image (Spot6/7 or Pleiades) and one or more time series of High Spatial Resolution optical images such as Sentinel-2, Landsat-8 and Sentinel-1 for a classification combining segmentation and object classification (use of the Random Forest algorithm) driven by a learning database constituted from in situ collection and photo-interpretation. The land use maps, in ESRI shapefile format, are produced as part of the GABIR project (Gestion Agricole des Biomasses à l'échelle de l'Ile de la Réunion) and are all distributed on CIRAD's spatial data catalogue in Réunion: http://aware.cirad.fr/ This Dataverse entry concerns the maps produced, for the year 2019, using a mosaic of Spot6/7 images to calculate segmentation (extraction of homogeneous objects from the image). We use a field database with a nested nomenclature with 3 levels of accuracy allowing us to produce a classification by level. The most detailed level distinguishing crop types has an overall accuracy of 88% and a Kappa index of 0.86. Level 2, distinguishing crop groups, has an overall accuracy of 92% and a Kappa index of 0.93. Level 1, distinguishing major land use groups, has an overall accuracy of 97% and a Kappa index of 0.95. A detailed sheet presenting the validation method and results is available for download. Dans le cadre du Centre d’Expertise Scientifique Occupation des Sols de THEIA, l’UMR TETIS du CIRAD développe une méthode de cartographie automatisée fondée sur la chaine Moringa qui minimise les interactions avec les utilisateurs par l’automatisation de la plupart des processus d’analyse et de traitement des images. La méthodologie utilise conjointement une image à Très Haute Résolution Spatiale (Spot6/7 ou Pléiades) et une ou plusieurs séries temporelles d’images optiques à Haute Résolution Spatiale type Sentinel-2, Landsat-8 et Sentinel-1 pour une classification combinant segmentation et classification objet (utilisation de l’algorithme Random Forest) entrainée par une base de données d’apprentissage constituée à partir de collecte in situ et de photo-interprétation. Les cartes d'occupation du sol, diffusées au format vecteur Esri Shape, sont réalisées dans le cadre du projet GABIR (Gestion Agricole des Biomasses à l’échelle de l'Ile de la Réunion) et sont toutes diffusées sur le catalogue de données spatiales du Cirad à la Réunion : http://aware.cirad.fr/ Cette fiche du Dataverse concerne les cartes produites, pour l'année 2019, en utilisant une mosaïque d'images Spot6/7 pour calculer la segmentation (extraction d'objets homogènes à partir de l'image). Nous utilisons une base de données terrain ayant une nomenclature emboitée avec 3 niveaux de précision nous permettant de produire une classification par niveau. Le niveau le plus détaillé distinguant les types de cultures présente une précision globale de 88% et un indice de Kappa est de 0,86. Le niveau 2, distinguant les groupes de cultures présente une précision globale de 92% et un indice de Kappa est de 0,93. Le niveau 1, distinguant les grands groupes d'occupation du sol présente une précision globale de 97% et un indice de Kappa est de 0,95. Une fiche détaillée présentant la méthode et les résultats de validation est téléchargeable

Gridded SSURGO (gSSURGO) is similar to the standard product from the United States Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) Soil Survey Geographic (SSURGO) Database, but is in the Environmental Systems Research Institute, Inc. (ESRI®) file geodatabase format. A file geodatabase has the capacity to store significantly more data and thus greater spatial extents than the traditional SSURGO product. This allows for statewide or even Conterminous United States (CONUS) tiling of data. gSSURGO contains all of the original soil attribute tables in SSURGO. All spatial data are stored within the geodatabase instead of externally as separate shape files. Both SSURGO and gSSURGO are considered products of the National Cooperative Soil Survey (NCSS). An important addition to the new format is a 10-meter raster (MapunitRaster_10m) of the map unit soil polygons feature class, which provides statewide coverage in a single layer. The CONUS database includes a 30-meter raster because of size constraints. This new addition provides greater performance and important analysis capabilities to users of soils data. Statewide tiles consist of soil survey areas needed to provide full coverage for a given State. In order to create a true statewide soils layer, some clipping of excess soil survey area gSSURGO data may be required. The new format also includes a national Value Added Look Up (valu) Table that has several new “ready to map” attributes.Other Documents to Reference:gSSURGO FactsheetgSSURGO User Guide ArcMap version 2.4Soil Data Development Toolbox User Guide v5 for ArcMapgSSURGO Mapping Detailed GuidegSSURGO Valu1 table column descriptions

This feature layer, Botetourt Co Karst, identifies areas having karst or the potential for the development of karst and pseudokarst in Botetourt County, VA. The data associated with this layer was obtained from the United States Geological Survey. Source and date:A map of karst in the United States was downloaded as a shapefile from the USGS Karst Report. The report provides context and information on karst and offers a downloadable digital map. Accessed in August of 2020.Purpose:As written in the source website:These data were compiled to delineate the distribution of karst and potential karst and pseudokarst areas of the United States. The data in this report are preliminary and there is an expectation of upgrade in content, quality, and resolution in future versions. The data are released as an Open-File Report to expedite transfer of this information to various users across the United States. These data were compiled from multiple sources at various spatial resolutions. They are intended for use as guidance in determining the distribution of areas of potential karst at national, State, and regional scales. Because of differences in projection and scale of the various geologic datasets, spatial errors and location inconsistencies are particularly noticeable along some State boundaries, particularly coastlines and riparian borders. These data should not be used to define boundaries for site-specific applications or for legal purposes.Processing:ABRA created this feature layer by importing the downloaded shapefile into ArcGIS and clipping it to Botetourt County, VA.Symbolization:The following symbolization is how it appears in the Rocky Forge Wind online map provided by ABRA.Karst: light yellow polygon

DEFINITION:Tax Law POINT is a generalized point representation of lands enrolled in the Managed Forest and Forest Crop Law Programs, collectively referred to as Tax Law Layers. Points are located at the center point of each 40-acre quarter-quarter section in which land is enrolled. Points do not identify specific enrollment location. Acreage enrolled from fractional or government lots are located either to the most approximate QQ, Q or S as possible. (Enrolled parcels are represented by the PLSS shape they lie within; however, the actual size of the enrolled property may be as small as 0.1 acres). The GIS layer was last updated March 5, 2025 to reflect conditions as of January 1, 2025. Corrections are made to the data throughout the year that may not be reflected in this snapshot.FEATURE TYPE(S):PointGEOGRAPHIC EXTENT:StatewideSOURCE SCALE:VariedPROJECTION:Wisconsin Transverse Mercator NAD 1983/1991 (WTM83/91)WKID: 3071PURPOSE/BACKGROUND:Wisconsin’s forest tax laws encourage sustainable forest management on private lands by providing a property tax incentive to landowners. Both the Managed Forest Law (MFL) and Forest Crop Law (FCL) encourage proper management of woodlands not only in their purposes and policies, but through a written management plan for a landowner’s property. The management plan incorporates landowner objectives, timber management, wildlife management, water quality and the environment as a whole to create healthy and productive forest. In exchange for following a written management plan and program rules, landowners pay forest tax law program rates in lieu of regular property taxes.FCL lands are open to the public for the following activities: hunting and fishing.MFL lands enrolled as open are open to the public for the following activities: hunting, fishing, hiking, sight-seeing, and cross-country skiing.Additional rules regarding public access may be reviewed here: https://dnr.wisconsin.gov/topic/forestlandowners/mflThe GIS feature class was created to be used in the Open Private Forest Lands web mapping application (Private Forest Lands Open to Public Recreation).Open Private Forest Lands (OPFL) Project Background:Provide a simple GIS web mapping application to display the approximate representations of over 1.3 million acres of Forest Tax Law lands (Managed Forest and Forest Crop Law) open to the public for hunting, hiking, fishing, cross-country skiing, and sightseeing. Display information to allow the public to access the lands without spending a lot of time cross checking plat books or contacting local county offices or the county Land Information Offices.Update Frequency:Semi-Annual (January, September). Edits to Tax Law entries can occur throughout the year, but most changes are not effective until January 1 except for landowner information. Landowner information edits are updated in the spatial views on a weekly basis. In addition, Forestry will re-generate taxlaw shapes as significant improvements to the data are completed. In January of each year, the feature class is re-generated to reflect new entries, changes to access, etc. effective January 1. January update: Update to reflect enrollments as of January.September update: Pre-hunting season update.The GIS layer was last updated March 5, 2025 to reflect conditions as of January 1, 2025. Corrections are made to the data throughout the year that may not be reflected in this snapshot.ATTRIBUTES:Field Descriptions:ORDER_NO: (c, 12) The Forestry property code of the feature. (Use as join field for if linking to landowner table information.)Format: 2-digit cnty – 3 digit seq no – 4 digit year of entryEx. 11-234-2013DNR_CTY_NO: (n, 2) The 2-digit DNR county code representing the predominant county in which the DTRSQQ falls.Format: Numbers, No commasEx: 37 (Marathon County)CNTY_NAME: (T, 11) County name of the predominant county in which the DTRSQQ falls.Ex: MarathonENTRY_YEAR: (t, 4) The year in which the order number was entered into the taxlaw program.Format: YYYYEx: 1999TAX_TYPE: (t, 3) Indicates whether the polygon is enrolled in MFL or FCL.Format: ALL CAPSPossible Values:MFL: Managed Forest LawFCL: Forest Crop LawAC_OP_PLS: (double) Acres, associated to the identified order number, that are enrolled in the identified PLSS as open. Additional polygons (DTRSQQ records) may contain additional acreage for the associated order number (see AC_OP_ORD for total open acreage associated with this order number). NOTE: This is not the total number of acres open with this DTRSQQ record. Any other order numbers with acreage in this DTRSQQ are identified in separate records by order number.AC_CL_PLS: (double) Acres, associated to the identified order number, that are enrolled in the identified PLSS as closed. Additional polygons (DTRSQQ records) may contain additional acreage for the associated order number (see AC_CL_ORD for total closed acreage associated with this order number). NOTE: This is not the total number of acres closed with this DTRSQQ record. Any other order numbers with acreage in this DTRSQQ are identified in separate records by order number.AC_TOT_PLS: (double) Total acres, associated to the identified order number, that are enrolled in the identified PLSS. Additional polygons (DTRSQQ records) may contain additional acreage for the associated order number (see AC_TOT_ORD for total acreage associated with this order number). NOTE: This is not the total number of acres enrolled within this DTRSQQ record. Any other order numbers with acreage in this DTRSQQ are identified in separate records by order number.ORDER_YRS: (t, 2) Total number of years the order will be enrolled in the program (under the associated order number). Format: Plan or order lengths are either 25 or 50 yearsEx: 50ORDER_EXP: (t, 20) Date that order number expires. All orders end on December 31. Format: December 31, YYYYEx: December 31, 2015OWNER_TEXT: (t, 30) Type of ownership. Ownership could be: Individual, Joint, Corporation, LLC, Partnership, LLP, Trust, etc.ACCNT_TYPE: (t, 1) Type of account.Possible Values:S: Small Account – landowners generally have less than 1,000 acres of forest land and the accounts are managed by DNR field foresters.L: Large Account – landowners generally have 1,000 acres or more of forest land and the accounts are managed by DNR Forest TaxAC_OP_ORD: (double) Total open acreage associated with the order number. AC_CL_ORD: (double) Total closed acreage associated with the order number.AC_TOT_ORD: (double) Total acreage associated with the order number.DTRSQQ_CO: (long) A concatenation of direction, township, range, section, quarter section, and quarter-quarter section used to approximate the location of the order number (or part of the order number). Each order number has separate records for each DTRSQQ where the order number resides. (Data source: 24K Landnet Spatial Database Technical Documentation)Format:1st Digit = Direction2nd & 3rd Digits = Township4th & 5th Digits = Range6th & 7th Digits = Section8th Digit = Quarter9th Digit = Quarter-QuarterEx: 441012812LEGAL_D_CO: (t, 5) Code describing legal description identified by order number.Format: 1st character:Blank = Entire (Govt Lot)D = Entire (PLSS)P = Part ofE = Entire Excluding ROW2nd character:L = Govt LotBlank = PLSSCharacters 3-5:If PLSS, 001-016 are StandardIf PLSS, 017-060 are FractionalIf Govt Lot, this is the Govt Lot #Ex: PL003LEGAL_DESC: (t, 100) Translated legal description code. Ex: GOV LOT 3, PART OFDTRSLD_TXT: (t, 2380) Field generated to convert DTRSQQ and legal description codes to a text description of the PLSS where the enrollment is located. Includes a note indicating if a record includes a fractional correction.Ex: T02-R01W-S05, Part of the NE of the NW (fractional correction)PARCEL_NO: (t, 255) County created parcel number. (Parcel level information not yet available for all records.)Format: Varies by countyEx: 07-04-59MCD_NAME: (t, 50) Municipal Civil Division (MCD) name.Ex: Solon SpringsMCD_TYPE_C: (t, 1) Type of Municipal Civil Division (MCD). Format: ALL CAPSPossible Values:T: TownV: VillageC: CityPLSS_LEVEL: (t, 2) PLSS level to which the record is located. Format: ALL CAPSPossible Values:QQ: Quarter-quarterQ: QuarterS: SectionCHNG_BY: (c, 30) The user who last updated the record.Ex: klauscCHNG_DATE: (date) Date the record was last changed.Format: MM/DD/YYYY Ex: 10/23/2012ACCESS: (t, 1) Indicates whether the quarter-quarter contains areas which are open to the public, closed to the public, or both.Format: ALL CAPSPossible Values:O: QQ contains areas that are Open to the publicC: QQ contains areas that are Closed to the publicB: QQ contains Both open and closed areas.ADDITIONAL INFORMATION:Tax law programs: https://dnr.wisconsin.gov/topic/forestlandowners/mflWeb mapping application: https://dnr.wisconsin.gov/topic/forestlandowners/opentopublicappCONTACT PERSON(S):GIS contact: Laura Waddle - GIS Specialist, (608) 320-4648, Laura.Waddle@wisconsin.govResource contact: <>R.J. Wickham - Tax Law Section Chief, (920) 369-6248, Richard.Wickham@wisconsin.govCOPYRIGHT:The material is for the noncommercial use of the general public. The fair use guidelines of the U.S. copyright statutes apply to all material on the Department of Natural Resources Webpages and linked agency Webpages. The Department of Natural Resources shall remain the sole and exclusive owner of all rights, title and interest in and to all specifically copyrighted information created and posted for inclusion in this system. Photographs and graphics on the Department website are either the property of the state department or the state agency that holds a license to use and display the material. For copy or use of information on the Department website that is outside of the fair use provisions of copyright law, please seek permission from the individual listed as responsible for the page. If you have any questions on using material on the Department web pages please e-mail the specific

For large areas, like Washington State, download as a file geodatabase. Large data sets like this one, for the State of Washington, may exceed the limits for downloading as shape files, excel files, or KML files. For areas less than a county, you may use the map to zoom to your area and download as shape file, excel or KML, if that format is desired.Information for SOILS data layer was derived from the Private Forest Land Grading system (PFLG) and subsequent soil surveys. PFLG was a five-year mapping program completed in 1980 for the purpose of forestland taxation. It was funded by the Washington State Department of Revenue. The Department of Natural Resources, Soil Conservation Service (now known as the Natural Resources Conservation Service or NRCS), USDA Forest Service and Washington State University conducted soil mapping cooperatively following national soil survey standards. Private lands having the potential of supporting commercial forests were surveyed along with interspersed small areas of State lands, Indian tribal lands, and federal lands. Because this was a cooperative soil survey project, agricultural and non-commercial forestlands were included within some survey areas. After the Department of Natural Resources originally developed its geographic information system, digitized soil map unit delineations and a few soil attributes were transferred to the system. Remaining PFLG soil attributes were later added and are now available through associated lookup tables. SCS (NRCS) soils data on agricultural lands also have been subsequently added to this data layer. The SOILS data layer includes approximately 1,100 townships with wholly or partially digitized soils data. State and private lands which have the potential of supporting commercial forest stands were surveyed. Some Indian tribal and federal lands were surveyed. Because this was a cooperative soils survey project, agricultural and non-commercial forestlands were also included within some survey areas. After the Department of Natural Resources originally developed its geographic information system, digitized soils delineations and a few soil attributes were transferred to the system. Remaining PFLG soil attributes were added at a later time and are now available through associated lookup tables. SCS soils data on agricultural lands also have subsequently been added to this data layer. This layer includes approximately 1, 100 townships with wholly or partially digitized soils data (2,101 townships would provide complete coverage of the state of Washington).-

The soils_sv resolves one to many relationships and as such is one of those special "DNR" spatial views ( ie. is implemented similar to a feature class). Column names may not match between SOILS_SV and the originating datasets. Use limitations

This Spatial View is available to Washingotn DNR users and those with access to the Washington State Uplands IMS site.

The following cautions only apply to one-to-many and many-to-many spatial views! Use these in the metadata only if the SV is one-to-many or many-to-many.

CAUTIONS: Area and Length Calculations: Use care when summarizing or totaling area or length calculations from spatial views with one-to-many or many-to-many relationships. One-to-many or many-to-many relationships between tabular and spatial data create multiple features in the same geometry. In other words, if there are two or more records in the table that correspond to the same feature (a single polygon, line or point), the spatial view will contain an identical copy of that feature's geometry for every corresponding record in the table. Area and length calculations should be performed carefully, to ensure they are not being exaggerated by including copies of the same feature's geometry.

Symbolizing Spatial Features:
Use care when symbolizing data in one-to-many or many-to-many spatial views. If there are multiple attributes tied to the same feature, symbolizing with a solid fill may mask other important features within the spatial view. This can be most commonly seen when symbolizing features based on a field with multiple table records.

Labeling Spatial Features: Spatial views with one-to-many or many-to-many relationships may present duplicate labels for those features with multiple table records. This is because there are multiple features in the same geometry, and each one receives a label.Soils Metadata