Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

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.

A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community-level patterns and ecological processes. In this study, we develop a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub-blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of Stipa grandis in grazed and ungrazed S. grandis communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of S. grandis by using the pair correlation function g(r) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of S. grandis were overdispersed according to the homogeneous Poisson process at 0-0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a Geographical Information System, to collect experimental data on the spatial point patterns for the populations in this grassland community.

Methods 1. Data collection using digital photographs and GIS

A flat 5 m x 5 m sampling block was chosen in a study grassland community and divided with bamboo chopsticks into 100 sub-blocks of 50 cm x 50 cm (Fig. 1). A digital camera was then mounted to a telescoping stake and positioned in the center of each sub-block to photograph vegetation within a 0.25 m2 area. Pictures were taken 1.75 m above the ground at an approximate downward angle of 90° (Fig. 2). Automatic camera settings were used for focus, lighting and shutter speed. After photographing the plot as a whole, photographs were taken of each individual plant in each sub-block. In order to identify each individual plant from the digital images, each plant was uniquely marked before the pictures were taken (Fig. 2 B).

Digital images were imported into a computer as JPEG files, and the position of each plant in the pictures was determined using GIS. This involved four steps: 1) A reference frame (Fig. 3) was established using R2V software to designate control points, or the four vertexes of each sub-block (Appendix S1), so that all plants in each sub-block were within the same reference frame. The parallax and optical distortion in the raster images was then geometrically corrected based on these selected control points; 2) Maps, or layers in GIS terminology, were set up for each species as PROJECT files (Appendix S2), and all individuals in each sub-block were digitized using R2V software (Appendix S3). For accuracy, the digitization of plant individual locations was performed manually; 3) Each plant species layer was exported from a PROJECT file to a SHAPE file in R2V software (Appendix S4); 4) Finally each species layer was opened in Arc GIS software in the SHAPE file format, and attribute data from each species layer was exported into Arc GIS to obtain the precise coordinates for each species. This last phase involved four steps of its own, from adding the data (Appendix S5), to opening the attribute table (Appendix S6), to adding new x and y coordinate fields (Appendix S7) and to obtaining the x and y coordinates and filling in the new fields (Appendix S8).

1. Data reliability assessment

To determine the accuracy of our new method, we measured the individual locations of Leymus chinensis, a perennial rhizome grass, in representative community blocks 5 m x 5 m in size in typical steppe habitat in the Inner Mongolia Autonomous Region of China in July 2010 (Fig. 4 A). As our standard for comparison, we used a ruler to measure the individual coordinates of L. chinensis. We tested for significant differences between (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler (see section 3.2 Data Analysis). If (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler, did not differ significantly, then we could conclude that our new method of measuring the coordinates of L. chinensis was reliable.

We compared the results using a t-test (Table 1). We found no significant differences in either (1) the coordinates of L. chinensis or (2) the pair correlation function g of L. chinensis. Further, we compared the pattern characteristics of L. chinensis when measured by our new method against the ruler measurements using a null model. We found that the two pattern characteristics of L. chinensis did not differ significantly based on the homogenous Poisson process or complete spatial randomness (Fig. 4 B). Thus, we concluded that the data obtained using our new method was reliable enough to perform point pattern analysis with a null model in grassland communities.

Download .zipThis file contains the data used by the Division of Wildlife for the construction of lake maps. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. Data were collected by the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths typically occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived by creating a raster file from the point bathymetry and boundary lake data. ArcGIS Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2045 Morse Rd, Bldg G-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Summary This feature class documents the fire history on CMR from 1964 - present. This is 1 of 2 feature classes, a polygon and a point. This data has a variety of different origins which leads to differing quality of data. Within the polygon feature class, this contains perimeters that were mapped using a GPS, hand digitized, on-screen digitized, and buffered circles to the estimated acreage. These 2 files should be kept together. Within the point feature class, fires with only a location of latitude/longitude, UTM coordinate, TRS and no estimated acreage were mapped using a point location. GPS started being used in 1992 when the technology became available. Records from FMIS (Fire Management Information System) were reviewed and compared to refuge records. Polygon data in FMIS only occurs from 2012 to current and many acreage estimates did not match. This dataset includes ALL fires no matter the size. This feature class documents the fire history on CMR from 1964 - present. This is 1 of 2 feature classes, a polygon and a point. This data has a variety of different origins which leads to differing quality of data. Within the polygon feature class, this contains perimeters that were mapped using a GPS, hand digitized, on-screen digitized, and buffered circles to the estimated acreage. These 2 files should be kept together. Within the point feature class, fires with only a location of latitude/longitude, UTM coordinate, TRS and no estimated acreage were mapped using a point location. GPS started being used in 1992 when the technology became available. Data origins include: Data origins include: 1) GPS Polygon-data (Best), 2) GPS Lat/Long or UTM, 3)TRS QS, 4)TRS Point, 6)Hand digitized from topo map, 7) Circle buffer, 8)Screen digitized, 9) FMIS Lat/Long. Started compiling fire history of CMR in 2007. This has been a 10 year process.FMIS doesn't include fires polygons that are less than 10 acres. This dataset has been sent to FMIS for FMIS records to be updated with correct information. The spreadsheet contains 10-15 records without spatial information and weren't included in either feature class. Fire information from 1964 - 1980 came from records Larry Eichhorn, BLM, provided to CMR staff. Mike Granger, CMR Fire Management Officer, tracked fires on an 11x17 legal pad and all this information was brought into Excel and ArcGIS. Frequently, other information about the fires were missing which made it difficult to back track and fill in missing data. Time was spent verifiying locations that were occasionally recorded incorrectly (DMS vs DD) and converting TRS into Lat/Long and/or UTM. CMR is divided into 2 different UTM zones, zone 12 and zone 13. This occasionally caused errors in projecting. Naming conventions caused confusion. Fires are frequently names by location and there are several "Soda Creek", "Rock Creek", etc fires. Fire numbers were occasionally missing or incorrect. Fires on BLM were included if they were "Assists". Also, fires on satellite refuges and the district were also included. Acreages from GIS were compared to FMIS acres. Please see documentation in ServCat (URL) to see how these were handled.

This wetland mapping project was funded by the King County Water and Land Services, Ecological Restoration and Engineering Services Unit, as part of a Best Available Science update. Wetlands within the King County boundary were mapped and classified, and reviewed by King County team members and National Wetland Inventory Staff. Wetlands were mapped and classified using: the National Wetlands Inventory (NWI) classification system (Cowardin et al., 1979) and the Landscape Position, Landform, Water Flow Path, and Water Body Type (LLWW) classification developed for the Western U.S. (Lemly et al. 2018).

The main objective for this project was to improve the knowledge of wetland extent and value within King County. In all, more than approximately 6,600 square miles of land comprise the county. King County contracted with Geospatial Services (GSS) at Saint Mary's University of Minnesota to create of high-quality National Wetlands Inventory Plus (NWIPlus) level mapping for the county. Program staff will conduct some ground truthing of data. NWIPlus is an enhanced NWI product with hydrogeomorphic-type descriptors that can facilitate predicting wetland functions. The enhanced attributes describe wetland landform, water flow path and water body type. The updated mapping will be utilized by developers and landowners to avoid wetland impacts, and may be incorporated into other GIS models which would identify potential wetland restoration projects and conservation priorities. Finalized mapping was made available through the county’s online map applications and submitted to the US Fish and Wildlife Service for addition to the National Wetlands Inventory.

King County completed this work as part of a Landscape Level 1 wetlands assessment. This work fits into the counties Wetland Program Plan (“The Plan”) and its goal of providing greater projection of wetlands and aquatic resources statewide. This work is overseen and is supported by the King County Wetland Program, within the Water and Land Services Department. The project, entitled “King County Wetland Inventory Update, King County, WA ” used geospatial techniques and image interpretation processes to remotely map and classify wetlands (includes deepwater habitats) and riparian areas in King County, WA. Wetlands for the project area were mapped and classified using on-screen digitizing methods in a Geographical Information System (GIS). This process was supported by development of a selective image interpretation key that resulted from field verification of image signatures and wetland classifications. Wetland image interpretation employed a variety of input image and collateral data sources, as well as field verification techniques. All mapping was completed at an on-screen scale of 1:5,000 or larger in compliance with national wetland mapping standards. The primary source imagery for mapping consisted of Eagleview, 2021, one-quarter foot, true-color pictometry. 8-bit, tiled orthophotography in TIFF format published by King County and mosaiced by GSS. Collateral data used in the mapping process included Light Detection and Ranging (LiDAR) Digital Elevation Model (DEM) 1.5 ft resolution and LiDAR derived products such as hillshade, contours, depth grids, and synthetic flow networks; King County Digital Surface Model Vegetation Height; King County Coho intrinsic potential stream layer; Beaver Intrinsic Potential (BIP); Historic National Wetland Inventory (NWI); National Hydrography Dataset (NHD) springs and watershed boundaries; ESRI basemap imagery; and Google Earth Time Slider True Color Imagery (GE); King County wetland layers; King County Stormwater features; King County wetland mitigation sites; King County Habitat Restoration sites; and Wetland Intrinsic Potential (WIP). All feature creation and attribution were completed with on-screen digitization procedures using ESRI, ArcGIS Pro 3.2.0 with advanced editing tools. For wetland mapping and classification projects at the landscape level, a desktop computer heads-up digitizing process is performed referencing the Federal Geographic Data Committee (FGDC) Wetlands Mapping Standard (FGDC-STD-015-2009, FGDC 2009) and the FGDC Classification of Wetlands and Deepwater Habitats of the United States Standard (FGDC-STD-004-2013, FGDC 2013). Field reviews are used to address questions regarding image interpretation, land use practices, classification of wetland type and verification of preliminary mapping. The King County inventory of wetlands used source imagery and collateral data to identify and classify features within the FGDC Standards (FGDC-STD-015-2009, FGDC 2009; FGDC-STD-004-2013, FGDC 2013). The projects Target Mapping Unit was 0.25 acres; however, features mapped beyond this TMU by request of King County and at the interpreters discretion. Following this process, the King County inventory went through a standardized Quality Assurance and Quality Control (QA/QC) process with the United States Fish and Wildlife Service (USFWS) NWI program, King County, and GSS’s internal QAQC review.

Download .zipThese files contain the lake boundary data used by the Ohio Division of Wildlife for the construction of lake maps. Lake boundary data was derived by digitizing Ohio Statewide Imagery Program (OSIP-1) data. Additional details on the digitizing process are available on request.

Lake boundary: http://ogrip.oit.ohio.gov/ProjectsInitiatives/StatewideImagery.aspxContact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2103 Morse Rd, Bldg G-2Columbus, OH, 43287Telephone: 614-265-6520Email: gis.support@dnr.ohio.gov

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.

This dataset (lineaments_ln_ll.shp) comprises structural features compiled into GIS format from existing literature, published up to 2003. The data represent fault/lineament locations known or inferred in the Alberta Plains. We have chosen to digitize and publish all lineaments from source maps even where they extended beyond the Alberta boundary. Each compiled feature is characterized by a set of attributes including: affected formations (oldest affected and oldest non-affected stratigraphic unit), fault type, fault sense of displacement, evidence used to infer the fault/lineament, original reference information and publication scale, and an estimate of the georeferencing error. The completeness of the captured attribute set varies for each feature as a function of the level of detail in the source article. The data set should be used cautiously. First, the original authors' interpretation of subsurface faults, particularly of 'basement faults', from air photo or satellite imagery lineaments is tenuous. Second, the vast majority of faults inferred in the foreland basin (Alberta Plains) east of the deformation front are normal-slip faults. although only the dip slip component has been inferred, some of these faults may also have a strike-slip component, generally not accounted for. Third, the location of lineaments includes cumulative errors inherent in the process of transferring into GIS lineaments traced by hand in the pre-computer era on small scale (regional) paper-copy maps. Such errors include spatial imprecisions in original lineament identification and drawing and errors in georefencing of the source map, as well as minor errors introduced during lineament digitization. Although each of them is minor at the scale of the original map, the cumulative effect of these errors may be significant and even misleading for large-scale (township or larger) projects.

Download .zipThis coverage shows the geographic distribution of sanitary sewer service in 1979, as provided by the Eastgate Development and Transportation Agency.

This coverage was digitized from boundaries drafted onto USGS quadrangle maps utilizing a run length encoding technique sampling along horizontal lines which represented the midline of cells with a height of 250 ft. The measurement increment along these horizontal lines was one decafoot (10 feet). The quadrangle files were then merged into a county file which was subsequently converted to ARC/INFO format.

The user should bear in mind that this coverage is only an approximation of the boundaries as originally drafted .

Additional details on the digitizing process are available on request.

Original coverage data was converted from the .e00 file to a more standard ESRI shapefile(s) in November 2014.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesReal Estate & Land ManagementReal Estate and Lands Management2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Download .zipThese files contain the lake boundary data used by the Ohio Division of Wildlife for the construction of lake maps. Lake boundary data was derived by digitizing Ohio Statewide Imagery Program (OSIP-1) data. Additional details on the digitizing process are available on request.

Lake boundary: http://ogrip.oit.ohio.gov/ProjectsInitiatives/StatewideImagery.aspxContact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2045 Morse Rd, Bldg G-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Download .zipThis theme shows existing and planned sewer facility service areas for each municipality in the county in 1982. The areas were delineated on USGS Quadrangle maps by the Lima-Allen County Regional Planning Commission.

This coverage was digitized from a USGS quadrangle map base utilizing a run length encoding technique sampling along horizontal lines which represented the midline of cells with a height of 250 ft. The measurement increment along these horizontal lines was one decafoot (10 feet). The quadrangle files were then merged into a county file which was subsequently converted to ARC/INFO format.

The user should bear in mind that this coverage is only an approximation of the boundaries as originally drafted . Additional details on the digitizing process are available on request.

Original coverage data was converted from the .e00 file to a more standard ESRI shapefile(s) in November 2014.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesReal Estate & Land ManagementReal Estate and Lands Management2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Download .zipThe analysis combines the Natural Resources Conservation Service (NRCS) limitations for septic tank absorption fields with land use information from the ODNR GIS Services of the ODNR. The NRCS has rated the soils in Marion County for the following criteria: total subsidence, evidence of past flooding, depth to bedrock (depth to rock, thin layer, seepage), depth to high water table (ponding, wetness), permeability (percs slowly, poor filter), and slope.

The soils used in this analysis were digitized from the paper final soil survey field sheets. These sheets were taped together to form an area covering each of the USGS 7.5 minute quadrangle maps in the county. The areas for each quadrangle were then digitized using run-length encoding technique sampling along horizontal lines which represented the midline of cells with a height of 250 feet. The measurement increment along these lines was one decafoot (10 feet). The quadrangle files were then merged into a county file which has subsequently been converted to Arc/Info format.

The user should bear in mind that this coverage is only an approximation of the soil survey and should not be used for site specific analysis.

Additional details of the digitizing process are available upon request.

Original coverage data was converted from the .e00 file to a more standard ESRI shapefile(s) in November 2014.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesReal Estate & Land ManagementReal Estate and Lands Management2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Download .zipThese files contain the lake boundary data used by the Ohio Division of Wildlife for the construction of lake maps. Lake boundary data was derived by digitizing Ohio Statewide Imagery Program (OSIP-1) data. Additional details on the digitizing process are available on request.

Lake boundary: http://ogrip.oit.ohio.gov/ProjectsInitiatives/StatewideImagery.aspxContact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2045 Morse Rd, Bldg G-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Download .zipThis file contains the data used by the Division of Wildlife for the construction of lake maps. Data was collected in the Ohio State Plane Coordinate System for both the northern and southern state planes in the Lambert Projection Zone. Except for the lakes in extreme western Ohio which is in UTM zone 16N the majority of lakes are in UTM zone 17N and datum NAD83. Data were collected by the Ohio Division of Wildlife using a Trimble GPS Pathfinder Pro XRS receiver and Recon datalogger. Geocoding of depths typically occurred during water levels that were ± 60 cm of full recreational pool while transversing the reservoir at 100m intervals driving at a vessel speed of 2.0-2.5 m/s. Depth contour lines were derived by creating a raster file from the point bathymetry and boundary lake data. ArcGIS Spatial Analyst Interpolation tool outputs point data that is then changed into polyline contours using the Spatial Analyst Surface tool. Additional details on the digitizing process are available upon request.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2071 Morse Rd, Bldg G-2Columbus, OH, 43255Telephone: 614-265-6488Email: gis.support@dnr.ohio.gov

Download .zipThese files contain the lake boundary data used by the Ohio Division of Wildlife for the construction of lake maps. Lake boundary data was derived by digitizing Ohio Statewide Imagery Program (OSIP-1) data. Additional details on the digitizing process are available on request.

Lake boundary: http://ogrip.oit.ohio.gov/ProjectsInitiatives/StatewideImagery.aspxContact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2090 Morse Rd, Bldg G-2Columbus, OH, 43274Telephone: 614-265-6507Email: gis.support@dnr.ohio.gov

Download .zipThis coverage represents individual ditch projects and their corresponding watersheds as provided by the Fulton County Engineer's Office.

This coverage was digitized from data drafted onto USGS quadrangle maps utilizing a run length encoding technique sampling along horizontal lines which represented the midline of cells with a height of 250 feet. The measurement increment along these lines was one decafoot (10 ft.) The quadrangle files were then merged into a county file which was subsequently converted to ARC/ INFO format.

The user should bear in mind that this coverage is only an approximation of the ditch boundaries as drafted and due to the nature of the raster digitizing process employed at that time the ditches themselves are represented as somewhat discontinuous polygons.

Additional details on the digitizing process are available on request.

Original coverage data was converted from the .e00 file to a more standard ESRI shapefile(s) in November 2014.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesReal Estate & Land ManagementReal Estate and Lands Management2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Download .zipThis theme shows general watersheds for Richland County, as digitized from US Geological survey quadrangle maps annotated by the Richland County Planning Commission in 1987.

This coverage was digitized from paper maps at a scale of 1:24000. Digitizing utilized run-length encoding techniques, sampling along horizontal lines which represented the midline of cells with a height of 250 feet. The measurement increment along these lines was one decafoot (10 feet) Additional details of the digitizing process are available upon request.

The coverage was subsequently converted to Arc/Info vector format.

Original coverage data was converted from the .e00 file to a more standard ESRI shapefile(s) in November 2014.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesReal Estate & Land ManagementReal Estate and Lands Management2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Download .zipThese files contain the lake boundary data used by the Ohio Division of Wildlife for the construction of lake maps. Lake boundary data was derived by digitizing Ohio Statewide Imagery Program (OSIP-1) data. Additional details on the digitizing process are available on request.

Lake boundary: http://ogrip.oit.ohio.gov/ProjectsInitiatives/StatewideImagery.aspxContact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Wildlife2059 Morse Rd, Bldg G-2Columbus, OH, 43243Telephone: 614-265-6476Email: gis.support@dnr.ohio.gov

Download .zipThis ground-water resources theme shows an estimate of sustainable yield available from the aquifers in the area. It was digitized from a paper county map with a scale of 1:62500.

Digitizing used run length encoding techniques, sampling along horizontal lines which represented the midline of cells with a height of 250 ft. The horizontal measurement increment along these lines was one decafoot (10 feet). Additional details of the digitizing process are available on request. The coverage was subsequently converted to an Arc/Info vector format.

Original coverage data was converted from the .e00 file to a more standard ESRI shapefile(s) in November 2014.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesReal Estate & Land ManagementReal Estate and Lands Management2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov