Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations.Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals.Dataset SummaryPhenomenon Mapped: Soils of the United States and associated territoriesCoordinate System: Web Mercator Auxiliary SphereExtent: The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaVisible Scale: 1:144,000 to 1:1,000Number of Features: 36,569,286Source: USDA Natural Resources Conservation ServicePublication Date: December 2021Data from the gSSURGO database was used to create this layer.AttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them.Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units.Area SymbolSpatial VersionMap Unit SymbolMap Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field.Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability RatingLegend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field.Project ScaleSurvey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields.Survey Area VersionTabular VersionMap Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field.Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - PresenceRating for Manure and Food Processing Waste - Weighted AverageComponent Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected.Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent KeyComponent Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r).Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence - High ValueTotal Subsidence - Low ValueTotal Subsidence - Representative ValueTotal Subsidence - High ValueCrop Productivity IndexEsri SymbologyThis field was created to provide symbology based on the Taxonomic Order field (taxorder). Because some mapunits have a null value for soil order, a custom script was used to populate this field using the Component Name (compname) and Mapunit Name (muname) fields. This field was created using the dominant soil order of each mapunit.Esri SymbologyHorizon TableEach map unit polygon has one or more components and each component has one or more layers known as horizons. To incorporate this field from the Horizon table into the attributes for this layer, a custom script was used to first calculate the mean value weighted by thickness of the horizon for each component and then a mean value of components weighted by the Component Percentage Representative Value field for each map unit. K-Factor Rock FreeEsri Soil OrderThese fields were calculated from the Component table using a model that included the Pivot Table Tool, the Summarize Tool and a custom script. The first 11 fields provide the sum of Component Percentage Representative Value for each soil order for each map unit. The Soil Order Dominant Condition field was calculated by selecting the highest value in the preceding 11 soil order fields. In the case of tied values the component with the lowest average slope value (slope_r) was selected. If both soil order and slope were tied the first value in the table was selected.Percent AlfisolsPercent AndisolsPercent AridisolsPercent EntisolsPercent GelisolsPercent HistosolsPercent InceptisolsPercent MollisolsPercent SpodosolsPercent UltisolsPercent VertisolsSoil Order - Dominant ConditionEsri Popup StringThis field contains a text string calculated by Esri that is used to create a basic pop-up using some of the more popular SSURGO attributes.Map Unit KeyThe Mapunit key field is found

Download .zipA soil mapping unit designates a specific type of soil which has unique characteristics including texture, slope, and erosion class.

These soils were digitized from the paper final soil survey field sheets. The final soil survey was published in March of 1992. 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

These data are collected for the NASA-funded BioSCape Project: “Impacts of Invasive Alien Species on Biodiversity and Ecosystem Functioning”. This particular dataset was curated to guide the sampling of the BioSCape Causal Inference Training Dataset and was prepared following these pre-established criteria:Uniform areas are grouped together in terms of four pre-selected environmental variablesEnvironmental variables were selected and categorized to minimize the total number of mapping units (+-150)

The SSURGO database contains information about soil as collected by the National Cooperative Soil Survey over the course of a century. The information can be displayed in tables or as maps and is available for most areas in the United States and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS (Natural Resources Conservation Service). The information was gathered by walking over the land and observing the soil. Many soil samples were analyzed in laboratories. The maps outline areas called map units. The map units describe soils and other components that have unique properties, interpretations, and productivity. The information was collected at scales ranging from 1:12,000 to 1:63,360. More details were gathered at a scale of 1:12,000 than at a scale of 1:63,360. The mapping is intended for natural resource planning and management by landowners, townships, and counties. Some knowledge of soils data and map scale is necessary to avoid misunderstandings. The maps are linked in the database to information about the component soils and their properties for each map unit. Each map unit may contain one to three major components and some minor components. The map units are typically named for the major components. Examples of information available from the database include available water capacity, soil reaction, electrical conductivity, and frequency of flooding; yields for cropland, woodland, rangeland, and pastureland; and limitations affecting recreational development, building site development, and other engineering uses. SSURGO datasets consist of map data, tabular data, and information about how the maps and tables were created. The extent of a SSURGO dataset is a soil survey area, which may consist of a single county, multiple counties, or parts of multiple counties. SSURGO map data can be viewed in the Web Soil Survey or downloaded in ESRI® Shapefile format. The coordinate systems are Geographic. Attribute data can be downloaded in text format that can be imported into a Microsoft® Access® database. A complete SSURGO dataset consists of:

GIS data (as ESRI® Shapefiles) attribute data (dbf files - a multitude of separate tables) database template (MS Access format - this helps with understanding the structure and linkages of the various tables) metadata

Resources in this dataset:Resource Title: SSURGO Metadata - Tables and Columns Report. File Name: SSURGO_Metadata_-_Tables_and_Columns.pdfResource Description: This report contains a complete listing of all columns in each database table. Please see SSURGO Metadata - Table Column Descriptions Report for more detailed descriptions of each column.

Find the Soil Survey Geographic (SSURGO) web site at https://www.nrcs.usda.gov/wps/portal/nrcs/detail/vt/soils/?cid=nrcs142p2_010596#Datamart Title: SSURGO Metadata - Table Column Descriptions Report. File Name: SSURGO_Metadata_-_Table_Column_Descriptions.pdfResource Description: This report contains the descriptions of all columns in each database table. Please see SSURGO Metadata - Tables and Columns Report for a complete listing of all columns in each database table.

Find the Soil Survey Geographic (SSURGO) web site at https://www.nrcs.usda.gov/wps/portal/nrcs/detail/vt/soils/?cid=nrcs142p2_010596#Datamart Title: SSURGO Data Dictionary. File Name: SSURGO 2.3.2 Data Dictionary.csvResource Description: CSV version of the data dictionary

This data release comprises four datasets that represent Phosphorus concentration in the A and C horizon of soils within the conterminous United States. The source dataset is a slight modification of the data published in the report "Geochemical and mineralogical data for soils of the conterminous United States", http://pubs.usgs.gov/ds/801/. Data were interpolated for use in models associated with the National Water Quality Assessment program of the USGS. Two datasets are of Phosphorus in soil A horizon and C horizon, interpolated using Inverse Distance Weight (IDW) algorithm. Two datasets are of Phosphorus in soil A horizon and C horizon, interpolated using IDW, and then aggregated to Geologic Mapping Units (GMUs).

The North Carolina Department of Environmental Quality (DEQ), Division of Energy, Minerals, and Land Resources (DEMLR), North Carolina Geological Survey (NCGS), in cooperation with the North Carolina Center for Geographic Information and Analysis (CGIA), developed the GIS data set version of the Geologic Map of North Carolina 1985. The data represents the digital equivalent of the official State Geology map (1:500,000- scale), but was digitized from (1:250,000-scale) base maps. There is one additional data set which accompany this layer, dikes.This geology is considered deprecated by the NCGS. There is currently no replacement for it that covers the whole state. We are working on a replacement that will be made available to the public in the near future (2025). This new data set will be in the United States Geological Survey (USGS) Geologic Map Schema (GeMS). When first released this data set will only cover a portion of the State at a scale of 1:100,000. This dataset will be dynamic and expand to cover the whole state by as early as 2030 at which point this layer will be removed from service.

Download .zipA soil mapping unit designates a specific type of soil which has unique characteristics including texture, slope, and erosion class.

This coverage was converted to digital format from a recompilation of the original soil survey onto a mylar USGS quadrangle base at a scale of 1:24000. The original soil survey was published in July of 1976 by the USDA, Natural Resources Conservation Service. Recompilation was accomplished by soil scientists from the ODNR, Division of Soil and Water Conservation. The individual quadrangle maps were scanned by a commercial data conversion firm at a density of 500 dots per inch and then converted to ARC Grid format. The GRID files for each quadrangle were then converted to ARC/INFO coverages and edited through various software routines and manual heads-up digitizing. Quadrangle coverages were converted to state plane coordinates, edgematched and then joined into a final county coverage. Because the quadrangle base is in NAD27 all work was done in NAD 27. After the completion of this work this coverage was created in NAD 83 using the ARC/INFO project command.

This coverage replaces the old Lorain County Soil coverage created with the OCAP raster system. Anyone who has a need for this old coverage should email or phone the contact person.

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

Soil Landscape Map Units have recognizable topographic features formed on a particular geological material and containing a limited and defined range of soils. They therefore have similar land and soil characteristics and land suitability. Each Soil Landscape Map Unit incorporates a Land System, and a Soil Landscape Unit specific to that Land System. Each Soil Landscape Unit has an assigned Land Type.

description: This dataset consists of general soil association units. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) dataset published in 1994. It consists of a broad based inventory of soils and non-soil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped of 1:250,000 in the continental U.S., Hawaii, Puerto, and the Virgin Islands and 1:1,000,000 in Alaska. The dataset was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined. Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the entire map unit. This dataset consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1- by 2-degree topographic quadrangle units and merged into a seamless national dataset. 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. These data provide information about soil features on or near the surface of the Earth. Data were collected as part of the National Cooperative Soil Survey. These data are intended for geographic display and analysis at the state, regional, and national level. The data should be displayed and analyzed at scales appropriate for 1:250,000-scale data.; abstract: This dataset consists of general soil association units. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) dataset published in 1994. It consists of a broad based inventory of soils and non-soil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped of 1:250,000 in the continental U.S., Hawaii, Puerto, and the Virgin Islands and 1:1,000,000 in Alaska. The dataset was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined. Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the entire map unit. This dataset consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1- by 2-degree topographic quadrangle units and merged into a seamless national dataset. 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. These data provide information about soil features on or near the surface of the Earth. Data were collected as part of the National Cooperative Soil Survey. These data are intended for geographic display and analysis at the state, regional, and national level. The data should be displayed and analyzed at scales appropriate for 1:250,000-scale data.

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. Just over 698 acres, including 214 acres in the authorized boundary of HOME and an additional 484 acres in the environs, were mapped using ten map classes (Figure 5). This included four land cover classes and six vegetation classes. Of all the map units, the most frequent was Fraxinus pennsylvanica / Ulmus spp. / Celtis occidentalis Forest with 21 polygons. Fraxinus pennsylvanica / Ulmus spp. / Celtis occidentalis Forest was also the most abundant map unit in terms of area other than cropfields in the environs, covering 219 acres (89 hectares) or about 13% of the project area. All of the frequencies for each map unit (i.e., number of polygons) along with acreage per map unit are listed in Table 3. Normally the standard minimum mapping unit for NPS vegetation mapping projects is defined as 0.5 hectare. However this is a nominal unit and due to the small size of HOME and the resolution of the imagery it was reduced to allow for more detail in the mapping. Therefore, 13 of the total 60 polygons were under 0.5 hectare. The average area of polygons for this project was 28.8 acres (11.6 hectares).

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. SSURGO depicts information about the kinds and distribution of soils on the landscape. The soil map and data used in the SSURGO product were prepared by soil scientists as part of the National Cooperative Soil Survey.

This digital soil survey information is used by soil scientists, hydrologists, ecologists, planners and other land managers to locate, compare, and select suitable areas for major kinds of land uses; to identify areas that need more intensive investigations; and to evaluate various management alternatives and predict the effects of the particular alternative on the land. Other intended uses of the soil survey include, but are not limited to, providing federal, state, and private organizations with resource information as it relates to activities such as power transmission right-of-way, coastal zone management, forest land management plans, mineral and energy exploration and development, and site suitability for buildings and dwellings. Tongass National Forest soil scientists began mapping soils in the early 1960s. By 1992 mapping was largely completed for approximately 10 million acres of the forest. During mapping, polylines were created using tones and textures on aerial photographs and field-verification. Soil map units were digitized from polygons drawn on 1:31,680 scale Mylar maps. Polygons on aerial photos were traced onto Mylar overlay sheets using a rapidiograph pen, which is accurate to .035 inches of the source data. Polygons were digitized to .001 inches of their location of the digitizing source (Mylar overlay). Accounting for the possibility of cumulative errors during transfer and digitizing, positional accuracy may vary by ± 250 feet. More recent inventories like Yakutat and South Kruzof were pre-mapped using on-screen digitizing with orthophotos and contours as base maps. Historically, the forest was divided into three soil survey areas-Stikine, Chatham, and Ketchikan. These areas are indicated in the FOREST field of the attribute table as follows: 2 = Stikine, 3 = Chatham, 5 = Ketchikan. By the end of the 1990s the digital soil inventory for the three survey areas on the forest were aggregated into one feature class. Beginning in the late 2000s an effort was made to move the soil inventory to Web Soil Survey (WSS). Each survey area was correlated separately. Updates to line work have occurred since 2010 to include areas not previously mapped. In 2020 a fourth area, the Yakutat Forelands was incorporated in WSS and the forest-wide feature class updated with that information. Line work for the southern half of Kruzof Island is included in this feature class but is currently in the correlation process and is not yet available on WSS. The update in 2020 also used all available line work from WSS to make the forest-wide dataset consistent with the data on WSS. Stikine Area (FOREST = 2): All lands within the Stikine Administrative Area have been mapped. This includes all federal, state, and private lands, including wilderness. The soil is mapped at different intensities across the area based on their Land Use Designations (LUDs) in the Tongass Land Management Plan, USDA-FS, 1979. Generally, areas designated for intensive land use (LUD III) are mapped at larger scales (Order 3 level, 1:15,840), while other areas designated for low intensity land use (LUD I&II) are mapped at smaller scales (Order 4 level, 1:31,680). Some areas that are currently LUD I&II were mapped to Order 3 prior to designation. All of the Stikine Area is mapped to an Order 3 level with the exception of the following, which were mapped to Order 4: the Stikine-LeConte Wilderness Area (Farm and Dry Islands are mapped to Order 3), Anan Creek area, and mainland areas designated for semi-remote recreation use. For exact locations, see Preliminary Soil Resource Inventory Report, Stikine Area. Order 3 surveys were mapped on 1:15,840 scale aerial photos. This resulted in map delineations no smaller than approximately 3 acres, ranging up to several hundred acres. The map units in the Order 3 survey area are composed of soil associations, some consociations and some complexes. The Order 4 surveys were mapped on 1:31,680 scale high-altitude infrared aerial photographs. This resulted in map units no smaller than approximately 10 acres and ranged as high as 500 acres in size. The map units in the Order 4 survey area are composed of phases of soil families, or subgroups. Design of initial mapping units in the Stikine area was strongly influenced by soil-vegetation relationships. This is referred to as the "Soil Ecosystem" type of mapping units, which are defined based on natural vegetation types, corresponding soil properties and associated landform types. Map units were also broken out by slope class.Chatham Area (FOREST = 3): The Chatham Area soil survey covers approximately 4.5 million acres of the Tongass National Forest. The inventory occurred in two stages and was done at two levels of detail. An Order 3 survey was conducted from 1981 to 1984, and an Order 4 survey was conducted from 1987 to 1989. Wilderness areas, national monuments, ANILCA additions, state, private and native lands were not mapped. The Order 3 survey is composed primarily of areas referred to as "Land Use Designations (LUDs) III and IV in the Tongass Land Management Plan, USDA-FS, 1979. LUD III were managed for a combination of uses, including recreation and some timber harvest. LUD IV were allocated to intensive resource use and development opportunities, primarily timber harvest and mining. Both LUD III and IV areas required the greater detail of an Order 3 survey. The Order 4 survey is composed primarily of LUD II. LUD II areas were allocated to roadless area management. The lower intensity management of LUD II justified a less detailed Order 4 survey. For exact locations, see Chatham Area Ecological Unit Inventory User Guide, figure 1. The inventory area was pre-mapped on either color aerial photographs at a scale of 1:15,840 (Order 3) or high altitude, color infrared aerial photographs at a scale of 1:63,360 (Order 4). South Kruzof soil survey covers about 60,795 acres of the Tongass National Forest. It represents the soils on the young Mount Edgecumbe volcanic field. The area was initially mapped during the 1981 to 1984 Order 3 Chatham soil survey. A second effort to gather more data began in 1994 but was not completed at that time. The effort to map South Kruzof restarted during 2009 and was completed in 2011. It was mapped digitally at a scale of 1:31,680 on 1998 2-meter black and white Digital Ortho Quads. The Yakutat soil survey covers about 487,758 acres of the Tongass, primarily on the Yakutat Forelands. This survey was also started during the 1981 to 1984 Order 3 soil survey. Additional data was collected in 1987, 1989, 1991, 1992, and 1993. The Yakutat survey was picked up again in 2009 and completed in 2013, although the mountainous areas are still unmapped. Yakutat was mapped digitally at a scale of 1:31,680 on 2008 Color 1 meter Digital Ortho Quarter Quads. The NRCS completed correlation on the Yakutat mapping area in 2020 but has not completed correlation of South Kruzof. The Chatham inventory was strongly influenced by soil-landform relationships. Additionally, vegetation, geology, and soils information was used to stratify the landscape into natural integral units that reflect ecological processes. Map units were also broken out by slope classes. The mapping criteria are based on features that may be either directly observed or inferred from natural landscape and vegetative features viewed on an aerial photograph. The intent of the mapping is to delineate integral ecological units that provide information required to achieve National Forest System management objectives. The Yakutat SMUs are nested in the landtype associations (LTAs) that were mapped in Landtype Associations of the Yakutat Foreland by Michael Shephard and Terry Brock (Technical Publication No. R10-TP-109, 2002). These LTAs were generalized for the soil survey.Ketchikan Area (FOREST = 5): The Ketchikan soil survey area covers approximately 3 million acres. It includes all of the area previously known as the Ketchikan Administrative Area except the following: Misty Fjords National Monument Wilderness and non-wilderness areas, the South Prince of Wales area and large tracts of federal (Bureau of Land Management), state, private borough and municipal lands. These unmapped lands are found on Cleveland Peninsula, Revillagigedo Island, Sukkwan Island, Long Island, Dall Island and Prince of Wales Island. Areas within the Ketchikan Area Soil Survey are mapped at different levels of intensity. Those designated as moderate and intensive development under the 1997 Tongass Land Management Plan (1997 TLMP) Revision, are mapped at an Order 3 level. Most wilderness areas were not included in the soil survey, although some areas now designated as wilderness and National Monument or 'Mostly Natural Setting' were mapped prior to those designations. These areas include: outside islands (Noyes, Lulu and Baker), Mt Calder/Mt. Holbrook Area, Salmon Bay, Coronation Island, Maurelle Islands, Warren Island, and the Karta River. Some other lands identified in the 1997 TLMP Revision under Wilderness and National Monument and 'Mostly Natural' settings were mapped at an Order 4 level. These areas include: Duke, Hotspur and Cat Islands, Cleveland Peninsula (North of Yes Bay), Bell Island, area east of Naha Bay, and area north of Cholmondeley Sound. For exact locations, see Ketchikan Area Soil Survey User Guide, Tongass N.F., p. 13. The Order 3 surveys were mapped on 1:15,840 or 1:40,000 aerial photos. This resulted in map delineations no smaller than approximately 3 acres ranging up to several hundred acres. The Order 3 survey areas are composed approximately of one-third each of map units of soil consociations, associations and complexes. The Order 4 surveys were mapped on 1:15,840 colored aerial photographs. This resulted in map delineations no smaller than approximately 10 acres and ranging as high as 500 acres in size. The map units are composed of phases of series, soil families, or subgroups.The criteria

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. To produce the digital map, a combination of 1:12,000-scale ortho imagery acquired in 2003, 2004, and 2005 and all of the GPS-referenced ground data were used to interpret the complex patterns of vegetation and land-use. All imagery was acquired from the U.S. Department of Agriculture - Farm Service Agency’s Aerial Photography Field Office and the National Agriculture Imagery Program. In the end, 32 map units (13 vegetated and 19 land-use) were developed and directly cross-walked or matched to corresponding plant associations and land-use classes. All of the interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases using ArcGIS© software. Draft maps were printed, field tested, reviewed, and revised. One hundred-twenty four accuracy assessment (AA) data points were collected in 2006 and used to determine the map’s accuracy. After final revisions, the accuracy assessment revealed an overall thematic accuracy of 89%. Project Size = 6,784 acres San Antonio Missions National Historical Park = 844 acres Map Classes = 32 13 Vegetated 19 Non-vegetated Minimum Mapping Unit = ½ hectare is the program standard but this was modified at SAAN to ¼ acre. Total Size = 1,122 Polygons Average Polygon Size = 6 acres Overall Thematic Accuracy = 89%

Data from more than 75,000 community public supply wells were acquired from national and state agencies. Using the information provided by the agencies, along with surficial and bedrock geologic maps, the wells were assigned to a national Principal Aquifer (PA) as defined in USGS (2003) or a Secondary Hydrogeologic Region (SHR) as defined in Belitz et al. (2018). Collectively, both PAs and SHRs are referred to as Hydrogeologic Regions (HRs). The HR identifies the primary source of water for the well. The locations of the wells were generalized so that they plot in the center of a 2 kilometer square grid. The county information provided identifies the county the well is located within based on it’s actual location, not the generalized location. A total of 76,354 wells were assigned to an HR, 41 wells were assigned to "basement" or "bedrock" because there was not enough information to assign to a specific HR.

description: This data set consists of general soil association units. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) data set published in 1994. It consists of a broad based inventory of soils and nonsoil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. The data set was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined. Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the whole map unit. This data set consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1-by 2-degree topographic quadrangle units and merged into a seamless national data set. It is distributed in state/territory and national extents. The soil map units are linked to attributes in the National Soil Information System data base which gives the proportionate extent of the component soils and their properties. (Note: MnGeo adapted the NRCS metadata record to create this record using the Minnesota Geographic Metadata Guidelines.); abstract: This data set consists of general soil association units. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) data set published in 1994. It consists of a broad based inventory of soils and nonsoil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. The data set was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined. Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the whole map unit. This data set consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1-by 2-degree topographic quadrangle units and merged into a seamless national data set. It is distributed in state/territory and national extents. The soil map units are linked to attributes in the National Soil Information System data base which gives the proportionate extent of the component soils and their properties. (Note: MnGeo adapted the NRCS metadata record to create this record using the Minnesota Geographic Metadata Guidelines.)

This dataset contains the Soil map of the Grand-Duchy of Luxembourg at scale of 1:100.000. It contains a classification of the complete territory in 29 soil mapping units (SMU) containing information on texture, stoniness, nature of coarse fragments, drainage and simplified pedogenetic classification. The dataset has been published in 1969. Description copied from catalog.inspire.geoportail.lu.

These are products of the Soil and Landscape Grid of Australia Facility generated through disaggregation of the Western Australian soil mapping. There are 9 soil attribute products available from the Soil Facility: Available Water Holding Capacity - Volumetric (AWC); Bulk Density - Whole Earth (BDw); Bulk Density - Fine Earth (BDf); Clay (CLY); Course Fragments (CFG); Electrical Conductivity (ECD); pH Water (pHw); Sand (SND); Silt (SLT).

Each soil attribute product is a collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm & 100-200cm, consistent with the Specifications of the GlobalSoilMap.

The DSMART tool (Odgers et al. 2014) tool was used in a downscaling process to translate legacy soil landscape mapping to 3” resolution (approx. 100m cell size) raster predictions of soil classes (Holmes et al. Submitted). The soil class maps were then used to produce corresponding soil property surfaces using the PROPR tool (Odgers et al. 2015; Odgers et al. Submitted). Legacy mapping was compiled for the state of WA from surveys ranging in map scale from 1:20,000 to 1:2,000,000 (Schoknecht et al., 2004). The polygons are attributed with the soils and proportions of soils within polygons however individual soils were not explicitly spatially defined. These new disaggregated map products aim to incorporate expert soil surveyor knowledge embodied in legacy polygon soil maps, while providing re-interpreted soil spatial information at a scale that is more suited to on-ground decision making.

Note: The DSMART-derived dissagregated legacy soil mapping products provide different spatial predictions of soil properties to the national TERN Soil Grid products derived by Cubist (data mining) and kriging based on site data by Viscarra Rossel et al. (Submitted). Where they overlap, the national prediction layers and DSMART products can be considered complementary predictions. They will offer varying spatial reliability (/ uncertainty) depending on the availability of representative site data (for national predictions) and the scale and expertise of legacy mapping. The national predictions and DSMART disaggregated layers have also been merged as a means to present the best available (lowest statistical uncertainty) data from both products (Clifford et al. In Prep).

Previous versions of this collection contained Depths layers. These have been removed as the units do not comply with Global Soil Map specifications. Lineage: The soil attribute maps are generated using novel spatial modelling and digital soil mapping techniques to disaggregate legacy soil mapping.

Legacy soil mapping: Polygon-based soil mapping for Western Australia’s agricultural zone was developed via WA’s Department of Agriculture and Food (Schoknecht et al., 2004). Seventy-three soil classes (termed ‘WA soil groups’ Schoknecht and Pathan, 2013) have been defined to capture the range of variation in soil profiles across this area. While legacy soil mapping does not explicitly map the distribution of these soil classes, estimates of their percentage composition and associated soil properties are available for each soil landscape map unit (polygon).

Disaggregation of soil classes: The DSMART algorithm (version 1, described in Odgers et al. 2014) was used to produce fine-resolution raster predictions for the probability of occurrence of each soil class. This uses random virtual sampling within each map unit (with sampling weighted by the expected proportions of each soil class) to build predictions for the distribution of soil classes based on relationships with environmental covariate layers (e.g. elevation, terrain attributes, climate, remote sensing vegetation indices, radiometrics). The algorithm was run 100 times then averaged to create probabilistic estimates for soil class spatial distributions.

Soil property predictions: The PROPR algorithm (Odgers et al. 2015) was used to generate soil property maps (and their associated uncertainty) using reference soil property data and the soil class probability maps create through the above DSMART disaggregation step.

Western Australia’s expert defined typical range of soil properties by soil class was used to provide reference soil properties to PROPR. These estimates were made separately for each physiographic zone across WA, and are based on available profile data and surveyor experience. Uncertainty bounds were determined by the minimum and maximum soil properties at the ‘qualified soil group’ level, and the property value of the most common soil in the map unit was used to define the typical soil property. This methodology was previously developed to meet the specifications of McKenzie et al. (2012) and provides expert soil surveyor estimates for map unit area composition and representative profile properties. Depth averaging was applied to the regional variant profile data to obtain property values at the specified GlobalSoilMap depth intervals. Then area-weighted soil property averages were calculated for each subgroup soil class. This process is documented further in Odgers et al. (Submitted).

These products are derived from disaggregation of legacy soil mapping in the agricultural zone of South Australia using the DSMART tool (Odgers et al. 2014a); produced for the Soil and Landscape Grid of Australia Facility. There are 10 soil attribute products available from the Soil Facility: Available Water Capacity (AWC); Bulk Density - Whole Earth (BDw); Cation Exchange Capacity (CEC); Clay (CLY); Coarse Fragments (CFG); Electrical Conductivity (ECD); Organic Carbon (SOC); pH - CaCl2( pHc); Sand (SND); Silt (SLT).

Each soil attribute product is a collection of 6 depth slices (except for effective depth and total depth). Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm & 100-200cm, consistent with the specifications of the GlobalSoilMap.

The DSMART tool was used in a downscaling process to translate legacy soil landscape mapping to 3” resolution (approx. 100m cell size) raster predictions of soil classes and corresponding soil properties. Legacy mapping was performed at 1:50,000 and 1:100,000 scales to delineate associated soils within polygons however individual soils were not explicitly spatially defined. These new disaggregated map products aim to incorporate expert soil surveyor knowledge embodied in legacy polygon soil maps, while providing re-interpreted soil spatial information at a scale that is more suited to on-ground decision making.

Note: The DSMART-derived dissagregated legacy soil mapping products provide different spatial predictions of soil properties to the national TERN Soil Grid products derived by Cubist (data mining) kriging based on site data by Viscarra Rossel et al. (2014). Where they overlap, the national prediction layers and DSMART products can be considered complementary predictions. They will offer varying spatial reliability (/ uncertainty) depending on the availability of representative site data (for national predictions) and the scale and expertise of legacy mapping. The national predictions and DSMART disaggregated layers have also been merged as a means to present the best available (lowest statistical uncertainty) data from both products (Clifford et al. 2014).

Previous versions of this collection contained Depths layers. These have been removed as the units do not comply with Global Soil Map specifications. Lineage: The soil attribute maps are generated using novel spatial modelling and digital soil mapping techniques to disaggregate legacy soil mapping.

Legacy soil mapping: Polygon-based soil mapping for South Australia’s agricultural zone was developed via SA’s State Land and Soil Mapping Program (DEWNR 2014, Hall et al. 2009). Sixty one soil classes (termed ‘subgroup soils’) have been defined to capture the range of variation in soil profiles across this area. While legacy soil mapping does not explicitly map the distribution of these soil classes, estimates of their percentage composition and associated soil properties are available for each soil landscape map unit (polygon).

Disaggregation of soil classes: The DSMART algorithm (version 1, described in Odgers et al. 2014) was used to produce fine-resolution raster predictions for the probability of occurrence of each soil class. This uses random virtual sampling within each map unit (with sampling weighted by the expected proportions of each soil class) to build predictions for the distribution of soil classes based on relationships with environmental covariate layers (e.g. elevation, terrain attributes, climate, remote sensing vegetation indices, radiometrics). The algorithm was run 100 times then averaged to create probabilistic estimates for soil class spatial distributions.

Soil property predictions: The PROPR algorithm (Odgers et al. 2015b) was used to generate soil property maps (and their associated uncertainty) using reference soil property data and the soil class probability maps create through the above DSMART disaggregation step.

South Australia’s national- or ASRIS-format soil mapping was used to provide reference soil properties. This dataset was previously developed to meet the specifications of McKenzie et al. (2012) and provides expert soil surveyor estimates for map unit area composition and representative profile properties of approximately 1500 regional variants of the original sixty one ‘subgroup soil’ classes. Equal area depth smoothing splines were applied to the regional variant profile data to obtain property values at the specified GlobalSoilMap depth intervals. Then area-weighted soil property averages were calculated for each subgroup soil class. This process is documented further in Odgers et al. (2015a).

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.

In 2002 and 2003, we returned to Cowpens National Battlefield to follow-up on the first three goals and to cooperate with the University of Georgia Center for Remote Sensing and Mapping Science on their project to map all vegetation communities in the park. We supplied the University of Georgia team with all plot data already collected and a dichotomous key to the communities of the park and we walked throughout the park to help them identify unique mapping units. Since photointerpreters rely heavily on canopy species composition, understory species composition, and disturbance to classify polygons and ecologists rely just as heavily on the shrub and herb layer to classify types, the mapping units and the vegetation classification units do not always “crosswalk” (match up) perfectly. The last step of the project was to reconcile mapping units with vegetation associations to produce mapping units that match up well with the ecological units of the National Vegetation Classification. We continue to work with the University of Georgia team on the mapping; the vegetation map will be produced separately by the Center for Remote Sensing and Mapping Science and will include any crosswalk as specified in the cooperative agreement.

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.