The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Vegetation map development for KNRI has somewhat different protocols than for other Parks. Normally photointerpretation is preceded by extensive field work which includes plot selection and vegetation sampling using detailed descriptions which are subsequently analyzed using ordination and other statistical techniques. The data are then summarized and association descriptions are assigned to each plot or, if the association is previously unrecognized, then a new association name is assigned. Subsequently, the plots locations are compared to its photographic signature and a photointerpretive key is developed. Given the very small size of KNRI and the extensive historical impact and alteration of the vegetation a simplified technique was used. NatureServe developed a list of potential vegetation types prior to any field work. This list was referenced during the field visit and modified after comparison of site characteristics and vegetation descriptions. Aerial photographs were viewed prior to the field visit and areas of like signature were differentiated. All vegetation and land-use information was then transferred to a GIS database using the latest grayscale USGS digital orthophoto quarter-quads as the base map and using a combination of on-screen digitizing and scanning techniques. Overall thematic map accuracy for the Park is considered 100% as all interpreted polygons received a filed visit for verification.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. James W. Sewall Company developed a complete GIS coverage for the park and revised the preliminary vegetation map classes to better match the results from the cluster analysis and NMS ordination. Polygons representing vegetation stands were digitized on-screen in ArcGIS 8.3, and later in ArcMap 9.1 and 9.2, using lines drawn on the acetate overlays, base layers of 1:8,000 CIR aerial photography, orthorectified photo composite image, and plot location and data. The minimum map unit used was 0.5 ha (1.24 ac). Stereo pairs were used to double check stand signatures during the digitizing process. Photo interpretation and polygon digitization extended outside the NPS boundary, especially where vegetation units were arbitrarily truncated by the boundary. Each polygon was attributed with the name of a vegetation map class or an Anderson Level II land use category based on plot data, field observations, aerial photography signatures, and topographic maps. Data fields identifying the USNVC association inclusions within the vegetation map class were attributed to the vegetation polygons in the shapefile. The GIS coverages and shapefiles were projected to Universal Transverse Mercator (UTM) Zone 19 North American Datum 1983 (NAD83). FGDC compliant metadata (FGDC 1998a) were created with the NPS-MP ESRI extension and included with the vegetation map shapefile. A photointerpretation key to the map classes for the 2006 draft vegetation map is included as Appendix A. The composite vegetation coverage was clipped to the NPS 2002 MIMA boundary shapefile for accuracy assessment (AA). After the 2006 vegetation map was completed, the thematic accuracy of this map was assessed.

[Metadata] Hawaii Digital Soil Survey polygons for the State of Hawaii. Downloaded statewide dataset from USDA/NRCS (https://www.nrcs.usda.gov/resources/data-and-reports/gridded-soil-survey-geographic-gssurgo-database) 11/28/23. This dataset is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information.This dataset consists of georeferenced digital map data and computerized attribute data. The map data are in a state-wide extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.For more information, see metadata at https://files.hawaii.gov/dbedt/op/gis/data/soils.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

CDFW BIOS GIS Dataset, Contact: VegCAMP Vegetation Classification and Mapping Program, Description: The Manual of California Vegetation (1995) introduced a quantitatively based method for classifying and mapping vegetation. This method was used to develop a classification of vegetation for Napa Co, and to attribute the polygons of a new vegetation map. The map was produced by on-screen digitizing over USGS Digital Orthophoto Quarter Quads, and consisted of 56 landcover types (48 dominated by natural vegetation) at the alliance or aggregated alliance level, in 28,456 polygons across 2042 km2.

We constructed a time-series spatial dataset of parcel boundaries for the period 1962-2005, in roughly 4-year intervals, by digitizing historical plat maps for Dane County and combining them with the 2005 GIS digital parcel dataset. The resulting datasets enable the consistent tracking of subdivision and development for all parcels over a given time frame. The process involved 1) dissolving and merging the 2005 digital Dane County parcel dataset based on contiguity and name, 2) further merging 2005 parcels based on the hard copy 2005 Plat book, and then 3) the reverse chronological merging of parcels to reconstruct previous years, at 4-year intervals, based on historical plat books. Additional land use information such as 1) whether a structure was actually constructed (using the companion digitized aerial photo dataset), 2) cover crop, and 3) permeable surface area, can be added to these datasets at a later date.

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the NationalCooperative Soil Survey. The information was prepared by digitizingmaps, by compiling information onto a planimetric correct baseand digitizing, or by revising digitized maps using remotelysensed and other information.This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventoryof soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and linefeatures) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.The following Layers have been merged into one data layer and the goal is to have one seamless data layer in MSTM coordinate projection when NRCS completes its digital conversion process and as with any data set if you have questions or identify errors you should always go back to the orignal source for clarification.This data set is for viewing and for convience. Last update was 02-12-2008. Counties included are as follows: Adams, Alcorn, Amite, Benton, Carroll, Calhoun, Chickasaw, Choctaw, Claiborne, Clarke, Clay, Coahoma, Copiah, Covington, Desoto,Forrest, Franklin, George, Grenada, Hancock, Harrison, Hinds, Holmes, Humphreys, Issaquena, Itawamba, Jackson, Jasper, Jefferson, Jefferson Davis, Jones, Kemper, Lafayette, Lamar, Lawrence, Lauderdale, Lee, Leflore, Lincoln, Lowndes, Madison, Marion, Marsha, Monroe, Montgomery, Neshoba, Newton, Noxubee, Oktibbe, Panola, Pearl, Pike, Pontotoc, Prentiss, Quitman, Rankin, Sharkey, Simpson, Stone, Sunflower, Tallahtachie, Tate, Tippah, Tishmingo, Tunica, Union, Warren, Washington, Wathall, Webster, Wilkinson, Yalobusha, Yazoo

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

This North Slope infrastructure GIS dataset includes roads (line), pipelines (line) and developed areas (polygon) as separate feature classes. Downloads are in shapefile and geodatabase format. Major, maintained road features on the North Slope are provided. Minor connections or roads within developed areas may not be represented or are generalized. Above surface pipeline features are provided. Multiple adjacent pipelines may be represented as one pipeline, features along routes may be simplified and pipelines within developed areas omitted. Developed area features include gravel pads, material pits, constructed water features and village areas. Road locations within villages have been updated using Alaska Department of Transportation GIS data. Road, pipeline and developed area feature attributes have been assigned oil and gas unit designations using Alaska Division of Oil and Gas GIS data. The Trans-Alaska Pipeline (TAPS) was not digitized and is available via the link below. These infrastructure data were originally compiled by Audubon in 2014 and provided to the Bureau of Land Management (BLM) Rapid Ecological Assessment (REA) project for the North Slope region. Those data were edited by the Alaska Center for Conservation Science (ACCS) for the REA and released for public distribution on the BLM/REA website. The North Slope Science Initiative (NSSI) subsequently updated the REA product using high resolution imagery as a verification base and heads up digitizing to produce an initial version of this infrastructure dataset. Annual updates to these data have been performed by ACCS and funded by BLM. These updates are based on interpretation of 2022 Sentinel imagery for the Prudhoe Bay development area and other image products as available for the greater North Slope region. All locations are approximate. Neither ACCS, BLM, NSSI or other contributors to this dataset shall be held liable for improper or incorrect use of the data described and/or contained herein. In an effort to provide the most comprehensive overview possible, these updates have incorporated many data sources, using a variety compilation methods. As a result, there are a variety of limitations to the thematic and spatial accuracy of these data. The appropriate use of these data is the responsibility of the user. A link to a web map containing this infrastructure data as well as land ownership and administrative information is provided below.

The seamless, county-wide parcel layer was digitized from official Assessor Parcel (AP) Maps which were originally maintained on mylar sheets and/or maintained as individual Computer Aided Design (CAD) drawing files (e.g., DWG). The CRA office continues to maintain the official AP Maps in CAD drawings and Information Systems Department/Geographic Information Systems (ISD/GIS) staff apply updates from these maps to the seamless parcel base in the County’s Enterprise GIS. This layer is a partial view of the Information Sales System (ISS) extract, a report of property characteristics taken from the County’s Megabyte Property Tax System (MPTS). This layer may be missing some attributes (e.g., Owner Name) which may not be published to the Internet due to privacy conditions under the California Public Records Act (CPRA). Please contact the Clerk-Recorder-Assessor (CRA) office at (707) 565-1888 for information on availability, associated fees, and access to other versions of Sonoma County parcels containing additional property characteristics.The seamless parcel layer is updated and published to the Internet on a monthly basis.The seamless parcel layer was developed from the source data using the general methodology outlined below. The mylar sheets were scanned and saved to standard image file format (e.g., TIFF). The individual scanned maps or CAD drawing files were imported into GIS software and geo-referenced to their corresponding real-world locations using high resolution orthophotography as control. The standard approach was to rescale and rotate the scanned drawing (or CAD file) to match the general location on the orthophotograph. Then, appropriate control points were selected to register and rectify features on the scanned map (or CAD drawing file) to the orthophotography. In the process, features in the scanned map (or CAD drawing file) were transformed to real-world coordinates, and line features were created using “heads-up digitizing” and stored in new GIS feature classes. Recommended industry best practices were followed to minimize root mean square (RMS) error in the transformation of the data, and to ensure the integrity of the overall pattern of each AP map relative to neighboring pages. Where available Coordinate Geometry (COGO) & survey data, tied to global positioning systems (GPS) coordinates, were also referenced and input to improve the fit and absolute location of each page. The vector lines were then assembled into a polygon features, with each polygon being assigned a unique identifier, the Assessor Parcel Number (APN). The APN field in the parcel table was joined to the corresponding APN field in the assessor property characteristics table extracted from the MPTS database to create the final parcel layer. The result is a seamless parcel land base, each parcel polygon coded with a unique APN, assembled from approximately 6,000 individual map page of varying scale and accuracy, but ensuring the correct topology of each feature within the whole (i.e., no gaps or overlaps). The accuracy and quality of the parcels varies depending on the source. See the fields RANK and DESCRIPTION fields below for information on the fit assessment for each source page. These data should be used only for general reference and planning purposes. It is important to note that while these data were generated from authoritative public records, and checked for quality assurance, they do not provide survey-quality spatial accuracy and should NOT be used to interpret the true location of individual property boundary lines. Please contact the Sonoma County CRA and/or a licensed land surveyor before making a business decision that involves official boundary descriptions.

Every five years, since 1990, the Delaware Valley Regional Planning Commission has produced a GIS Land Use layer for its 9-county region. In 2000, digital orthophotography was flown by DVRPC. Utilizing this orthophotography, all Land Use annotation and digitizing was performed on-screen, or "heads-up," a first at DVRPC. Digitizing was done using ESRI ArcGIS 8 software at a 1:2400 (1 inch = 200 feet) scale.

The database represents point locations and associated stand assessment data collected within aspen stands in the Cannell Meadows Ranger District, Sequoia National Forest, Tulare County, California. The aspen inventory from Sequoia National Forest is the only dataset in the state aspen database that was derived from a publication (Pillsbury 1994). The CDFG digitized this dataset by scanning paper maps within the publication, and digitizing and geo-referencing polygons from the electronic maps. Two assessment tables were created from the tables within the publication-one was a summery of the plot information and the other was a summery of the stand information which was an average of the plots within each stand. The report assessed information such as: topographic features, stand vigor, downed logs per acre, percent conifer in stand, total number of trees and aspen trees per acre in stand, and percentage of hiding cover for fawns and does. The report also examined the number of trees per acre that were affected and/or growth was stunted by deer, cattle, insects, fungus, and various combinations of these. It also inspected what percentage of the stand could be classified as grasses, forbs, brush, and bare. Within, the geodatabase, there are 60 delineated stands and there are 201 plot assessments taken. Pillsbury 1994, N.H., Ritter, T., Drew, M., and C. Linquist. 1994. Wildlife Habitat Inventory of Aspen Stands on the Cannell Meadow Ranger District, Sequoia National Forest. Natural Resources Management Department, Cal Poly State Univ., San Luis Obispo, California. 551 p. Associated with this point layer is a polygon layer (SEQUOUIA_NF_POLY) containing aspen stands delineated in conjunction with the aspen assessment information. Data Compilation: The Aspen Delineation Project (ADP) is a collaborative effort of the U.S. Forest Service's Pacific Southwest Region, the California Department of Fish and Games Resource Assessment Program, and the California Office of Bureau of Land Management. Principal Investigator for ADP is David Burton; visit: www.aspensite.org for more information regarding the ADP. The Department of Fish and Games, Resource Assessment Program compiled this information from the collaborating agencies and other researchers, and formatted the data into a common database for the purpose of facilitating access to data related to the conservation of Quaking Aspen in California. This information portal falls within the ADP goals to help agencies and land managers identify, map, treat, and monitor aspen habitats. This dataset is a portion of a master database compiled during a year long effort in 2005 to pull together current GIS layers and maps depicting Aspen communities in California.

This dataset is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information.

This dataset consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

Note: This metadata record was created by MnGeo to serve as a generic record for all SSURGO data sets within Minnesota. See the individual county metadata records created by NRCS for county-specific information; these records are included in the data set download files.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. An ArcInfo(tm) (ESRI) GIS database was designed for WICA using the National Park GIS Database Design, Layout, and Procedures created by the BOR. This was created through Arc Macro Language (AML) scripts that helped automate the transfer process and ensure that all spatial and attribute data was consistent and stored properly. Actual transfer of information from the interpreted aerial photographs to a digital, geo-referenced format involved two techniques, scanning (for the vegetation classes) and on-screen digitizing (for the land-use classes). Both techniques required the use of 14 digital black-and-white orthophoto quarter quadrangles (DOQQ's) covering the study area. Transferred information was used to create vegetation polygon coverages and ancillary linear coverages in ArcInfo(tm) for each WICA DOQQ. Attribute information including vegetation map unit, location, and aerial photo number was subsequently entered for all polygons.

This dataset depicts the boundaries of the Suisun Marsh.

This layer contains the fire perimeters from the previous calendar year, and those dating back to 1878, for California. Perimeters are sourced from the Fire and Resource Assessment Program (FRAP) and are updated shortly after the end of each calendar year. Information below is from the FRAP web site. There is also a tile cache version of this layer.About the Perimeters in this LayerInitially CAL FIRE and the USDA Forest Service jointly developed a fire perimeter GIS layer for public and private lands throughout California. The data covered the period 1950 to 2001 and included USFS wildland fires 10 acres and greater, and CAL FIRE fires 300 acres and greater. BLM and NPS joined the effort in 2002, collecting fires 10 acres and greater. Also in 2002, CAL FIRE’s criteria expanded to include timber fires 10 acres and greater in size, brush fires 50 acres and greater in size, grass fires 300 acres and greater in size, wildland fires destroying three or more structures, and wildland fires causing $300,000 or more in damage. As of 2014, the monetary requirement was dropped and the damage requirement is 3 or more habitable structures or commercial structures.In 1989, CAL FIRE units were requested to fill in gaps in their fire perimeter data as part of the California Fire Plan. FRAP provided each unit with a preliminary map of 1950-89 fire perimeters. Unit personnel also verified the pre-1989 perimeter maps to determine if any fires were missing or should be re-mapped. Each CAL FIRE Unit then generated a list of 300+ acre fires that started since 1989 using the CAL FIRE Emergency Activity Reporting System (EARS). The CAL FIRE personnel used this list to gather post-1989 perimeter maps for digitizing. The final product is a statewide GIS layer spanning the period 1950-1999.CAL FIRE has completed inventory for the majority of its historical perimeters back to 1950. BLM fire perimeters are complete from 2002 to the present. The USFS has submitted records as far back as 1878. The NPS records date to 1921.About the ProgramFRAP compiles fire perimeters and has established an on-going fire perimeter data capture process. CAL FIRE, the United States Forest Service Region 5, the Bureau of Land Management, and the National Park Service jointly develop the fire perimeter GIS layer for public and private lands throughout California at the end of the calendar year. Upon release, the data is current as of the last calendar year.The fire perimeter database represents the most complete digital record of fire perimeters in California. However it is still incomplete in many respects. Fire perimeter database users must exercise caution to avoid inaccurate or erroneous conclusions. For more information on potential errors and their source please review the methodology section of these pages.The fire perimeters database is an Esri ArcGIS file geodatabase with three data layers (feature classes):A layer depicting wildfire perimeters from contributing agencies current as of the previous fire year;A layer depicting prescribed fires supplied from contributing agencies current as of the previous fire year;A layer representing non-prescribed fire fuel reduction projects that were initially included in the database. Fuels reduction projects that are non prescribed fire are no longer included.All three are available in this layer. Additionally, you can find related web maps, view layers set up for individual years or decades, and tile layers here.Recommended Uses There are many uses for fire perimeter data. For example, it is used on incidents to locate recently burned areas that may affect fire behavior (see map left).Other uses include:Improving fire prevention, suppression, and initial attack success.Reduce and track hazards and risks in urban interface areas.Provide information for fire ecology studies for example studying fire effects on vegetation over time. Download the Fire Perimeter GIS data hereDownload a statewide map of Fire Perimeters hereSource: Fire and Resource Assessment Program (FRAP)

This dataset accompanies Open File Report 2009-02. Between 1971 and 1983, the Alberta Research Council created a series of hydrogeological maps of Alberta. The geologists examined the sediment types present and used existing water well information to assign yield values to distinct zones within the mapped areas. They also looked at the materials, generally to a depth of 305 metres (1000 feet) below ground surface, and added the yields of the sediments encountered within this interval to arrive at a yield value for the whole. Alberta Geological Survey compiled the shapefiles for the yield polygons, digitized by the Prairie Farm Rehabilitation Agency, and then digitized the remaining linework for the remaining map areas. Afterwards, we created a geodatabase of the yield polygons for the entire province and assigned yield values to the polygons based on the original maps. We also assigned the most likely formation name, age and lithology to the yield polygon.

This dataset contains soil type and soil classification, by area. If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (http://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (http://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below. Category: Environment Organization: Allegheny County Department: Geographic Information Systems Group; Department of Administrative Services Temporal Coverage: 2000 Data Notes: Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot Development Notes: This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties. The soil map and data used in the SSURGO product were prepared by soil scientists as part of the National Cooperative Soil Survey Other: none Related Document(s): Data Dictionary for SOIL_CODE Related Document(s): https://res1wwwd-o-tnrcsd-o-tusdad-o-tgov.vcapture.xyz/Internet/FSE_MANUSCRIPTS/pennsylvania/PA003/0/legends.pdf - the last page includes the soil legend for this dataset. Frequency - Data Change: As needed Frequency - Publishing: As needed Data Steward Name: Eli Thomas Data Steward Email: gishelp@alleghenycounty.us

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. The vegetation classification and mapping processes were conducted essentially in tandem. Mappers and ecologists conferred to review the list of potential associations, as well as the appropriate scale for mapping. Photos were viewed in stereo and preliminary polygon boundaries were delineated with a .30-mm rapidograph pen on polypropylene sleeves placed over the aerial photos. Preliminary polygons were classified and labeled with their appropriate USNVC association using the aerial photograph interpretation key and USNVC descriptions, and by conferring with NatureServe ecologists. The initial line work was also used to determine a sampling scheme for plot and observation data collection, and the USNVC association list resulting from the field work was used to aid polygon classification. Once delineations were groundtruthed and rectified, USNVC association-level polygon line work was transferred to GIS shapefiles via onscreen digitizing in ArcView v.3.2a (ESRI 1992–2000). USNVC association and Anderson Level II (modified) land use names and codes were added to the attribute table of the vegetation shapefile. A separate wetland map for the park was created from the vegetation map polygons belonging to the Saturated Cold-deciduous Forest and Saturated Temperate Perennial Forb Vegetation formations.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. An ArcInfo (copyright ESRI) GIS database was designed for THRO using the National Park GIS Database Design, Layout, and Procedures created by RSGIG. This was created through Arc Macro Language (AML) scripts that helped automate the transfer process and ensure that all spatial and attribute data was consistent and stored properly. Actual transfer of information from the interpreted aerial photographs to a digital, geo-referenced format involved two techniques, scanning (for the vegetation classes) and on-screen digitizing (for the land-use classes). Transferred information used to create vegetation polygon coverages and linear coverages in ArcInfo were based on quarter-quad borders. Attribute information including vegetation map unit, _location, and aerial photo number was subsequently entered for all polygons. In addition, the spatial database has an FGDC-compliant metadata file.

This dataset represents the depth to groundwater in Utah, defined by areas. Original manuscript maps were provided by the Utah Geological Survey (UGS) and digitized by AGRC. The final data products were verified and approved by the UGS. Procedures_Used: Data was digitized by AGRC with ARC/INFO software from the reproduction film positive at 1:750,000 scale. After modifications. the final data products were verified and approved by the UGS. Reviews_Applied_to_Data: Possible distortion of the original manuscription (conceivably introduced through the photo-reduction process) was detected during digitizing. The digitized linework was visually compared to the source document. Differences in the base map data as depicted on this dataset are revealed when compared to other sources (eg. the shoreline of the Great Salt Lake). This disparity does not reflect the quality of the data.