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. Data from the gSSURGO databasewas used to create this layer. To download ready-to-use project packages of useful soil data derived from the SSURGO dataset, please visit the USA SSURGO Downloader app. Dataset SummaryPhenomenon Mapped: Soils of the United States and associated territoriesGeographic Extent: The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaCoordinate System: Web Mercator Auxiliary SphereVisible Scale: 1:144,000 to 1:1,000Source: USDA Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 What can you do with this layer?ArcGIS OnlineFeature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro.Below are just a few of the things you can do with a feature service in Online and Pro.Add this layer to a map in the map viewer. The layer is limited to scales of approximately 1:144,000 or larger but avector tile layercreated from the same data can be used at smaller scales to produce awebmapthat displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter forFarmland Class= "All areas are prime farmland" to create a map of only prime farmland.Add labels and set their propertiesCustomize the pop-up ArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of theLiving Atlas of the Worldthat provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Data DictionaryAttributesKey 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 Symbol Map 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 Rating Legend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field.Project Scale Survey 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 Version Map 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 Average Component 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 Key Component 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 -

Feature Classes are loaded onto tablet PCs and Field crews are sent to label the crop or land cover type and irrigation method for a subset of select fields or polygons. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process.Digitizing is done as Geodatabase feature classes using ArcMap 10.X with NAIP or Google imagery as a background with other layers added for reference. Updates to existing field boundaries of individual agricultural fields, urban areas and more are precisely digitized. Changes in irrigation type and land use are noted during this process.Cropland Data Layer (CDL) rasters from the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) are downloaded for the appropriate year. https://nassgeodata.gmu.edu/CropScape/Zonal Statistics geoprocessing tools are used to attribute the polygons with updated crop types from the CDL. The data is then run through several stages of comparison to historical inventories and quality checking in order to determine and produce the final attributes.LUID - Unique ID number for each polygon in the final dataset, not consistent between yearly datasets.Landuse - A general land cover classification differentiating how the land is used.Agriculture: Land managed for crop or livestock purposes.Other: A broad classification of wildland.Riparian/Wetland: Wildland influenced by a high water table, often close to surface water.Urban: Developed areas, includes urban greenspace such as parks.Water: Surface water such as wet flats, streams, and lakes.CropGroup - Groupings of broader crop categories to allow easy access to or query of all orchard or grain types etc.Description - Attribute that describes/indicates the various crop types and land use types determined by the GIS process.IRR_Method - Crop Irrigation Method carried over from statewide field surveys ending in 2015 and updated based on imagery and yearly field checks.Drip: Water is applied through lines that slowly release water onto the surface or subsurface of the crop.Dry Crop: No irrigation method is applied to this agricultural land, the crop is irrigated via natural processes.Flood: Water is diverted from ditches or pipes upland from the crop in sufficient quantities to flood the irrigated plot.None: Associated with non-agricultural landSprinkler: Water is applied above the crop via sprinklers that generally move across the field.Sub-irrigated: This land does not have irrigation water applied, but due to a high water table receives more water, and is generally closely associated with a riparian area.Acres - Calculated acreage of the polygon.State - State where the polygons are found.Basin - The hydrologic basin where the polygons are found, closely related to HUC 6. These basin boundaries were created by DWRe to include portions of other basins that have inter-basin flows for management purposes.SubArea - The subarea where the polygons are found, closely related to the HUC 8. Subareas are subdivisions of the larger hydrologic basins created by DWRe.Label_Class - Combination of Label and Class_Name fields created during processing that indicates the specific crop, irrigation, and whether the CDL classified the land as a similar crop or an “Other” crop.LABEL - A shorthand descriptive label for each crop description and irrigation type.Class_Name - The majority pixel value from the USDA CDL Cropscape raster layer within the polygon, may differ from final crop determination (Description).OldLanduse - Similar to Landuse, but splits the agricultural land further depending on irrigation. Pre-2017 datasets defined this as Landuse.LU_Group - These codes represent some in-house groupings that are useful for symbology and other summarizing.Field_Check - Indicates the year the polygon was last field checked. *New for 2019SURV_YEAR - Indicates which year/growing season the data represents.

This dataset collection contains A0 maps of the Keppel Island region based on satellite imagery and fine-scale habitat mapping of the islands and marine environment. This collection provides the source satellite imagery used to produce these maps and the habitat mapping data.

The imagery used to produce these maps was developed by blending high-resolution imagery (1 m) from ArcGIS Online with a clear-sky composite derived from Sentinel 2 imagery (10 m). The Sentinel 2 imagery was used to achieve full coverage of the entire region, while the high-resolution was used to provide detail around island areas.

The blended imagery is a derivative product of the Sentinel 2 imagery and ArcGIS Online imagery, using Photoshop to to manually blend the best portions of each imagery into the final product. The imagery is provided for the sole purpose of reproducing the A0 maps.

Methods:

The high resolution satellite composite composite was developed by manual masking and blending of a Sentinel 2 composite image and high resolution imagery from ArcGIS Online World Imagery (2019).

The Sentinel 2 composite was produced by statistically combining the clearest 10 images from 2016 - 2019. These images were manually chosen based on their very low cloud cover, lack of sun glint and clear water conditions. These images were then combined together to remove clouds and reduce the noise in the image.

The processing of the images was performed using a script in Google Earth Engine. The script combines the manually chosen imagery to estimate the clearest imagery. The dates of the images were chosen using the EOBrowser (https://www.sentinel-hub.com/explore/eobrowser) to preview all the Sentinel 2 imagery from 2015-2019. The images that were mostly free of clouds, with little or no sun glint, were recorded. Each of these dates was then viewed in Google Earth Engine with high contrast settings to identify images that had high water surface noise due to algal blooms, waves, or re-suspension. These were excluded from the list. All the images were then combined by applying a histogram analysis of each pixel, with the final image using the 40th percentile of the time series of the brightness of each pixel. This approach helps exclude effects from clouds.

The contrast of the image was stretched to highlight the marine features, whilst retaining detail in the land features. This was done by choosing a black point for each channel that would provide a dark setting for deep clear water. Gamma correction was then used to lighten up the dark water features, whilst not ove- exposing the brighter shallow areas.

Both the high resolution satellite imagery and Sentinel 2 imagery was combined at 1 m pixel resolution. The resolution of the Sentinel 2 tiles was up sampled to match the resolution of the high-resolution imagery. These two sets of imagery were then layered in Photoshop. The brightness of the high-resolution satellite imagery was then adjusting to match the Sentinel 2 imagery. A mask was then used to retain and blend the imagery that showed the best detail of each area. The blended tiles were then merged with the overall area imagery by performing a GDAL merge, resulting in an upscaling of the Sentinel 2 imagery to 1 m resolution.


Habitat Mapping:

A 5 m resolution habitat mapping was developed based on the satellite imagery, aerial imagery available, and monitoring site information. This habitat mapping was developed to help with monitoring site selection and for the mapping workshop with the Woppaburra TOs on North Keppel Island in Dec 2019.

The habitat maps should be considered as draft as they don't consider all available in water observations. They are primarily based on aerial and satellite images.

The habitat mapping includes: Asphalt, Buildings, Mangrove, Cabbage-tree palm, Sheoak, Other vegetation, Grass, Salt Flat, Rock, Beach Rock, Gravel, Coral, Sparse coral, Unknown not rock (macroalgae on rubble), Marine feature (rock).

This assumed layers allowed the digitisation of these features to be sped up, so for example, if there was coral growing over a marine feature then the boundary of the marine feature would need to be digitised, then the coral feature, but not the boundary between the marine feature and the coral. We knew that the coral was going to cut out from the marine feature because the coral is on top of the marine feature, saving us time in digitising this boundary. Digitisation was performed on an iPad using Procreate software and an Apple pencil to draw the features as layers in a drawing. Due to memory limitations of the iPad the region was digitised using 6000x6000 pixel tiles. The raster images were converted back to polygons and the tiles merged together.

A python script was then used to clip the layer sandwich so that there is no overlap between feature types.

Habitat Validation:

Only limited validation was performed on the habitat map. To assist in the development of the habitat mapping, nearly every YouTube video available, at the time of development (2019), on the Keppel Islands was reviewed and, where possible, georeferenced to provide a better understanding of the local habitats at the scale of the mapping, prior to the mapping being conducted. Several validation points were observed during the workshop. The map should be considered as largely unvalidated.

data/coastline/Keppels_AIMS_Coastline_2017.shp:
The coastline dataset was produced by starting with the Queensland coastline dataset by DNRME (Downloaded from http://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid={369DF13C-1BF3-45EA-9B2B-0FA785397B34} on 31 Aug 2019). This was then edited to work at a scale of 1:5000, using the aerial imagery from Queensland Globe as a reference and a high-tide satellite image from 22 Feb 2015 from Google Earth Pro. The perimeter of each island was redrawn. This line feature was then converted to a polygon using the "Lines to Polygon" QGIS tool. The Keppel island features were then saved to a shapefile by exporting with a limited extent.

data/labels/Keppel-Is-Map-Labels.shp:
This contains 70 named places in the Keppel island region. These names were sourced from literature and existing maps. Unfortunately, no provenance of the names was recorded. These names are not official. This includes the following attributes:
- Name: Name of the location. Examples Bald, Bluff
- NameSuffix: End of the name which is often a description of the feature type: Examples: Rock, Point
- TradName: Traditional name of the location
- Scale: Map scale where the label should be displayed.

data/lat/Keppel-Is-Sentinel2-2016-19_B4-LAT_Poly3m_V3.shp:
This corresponds to a rough estimate of the LAT contours around the Keppel Islands. LAT was estimated from tidal differences in Sentinel-2 imagery and light penetration in the red channel. Note this is not very calibrated and should be used as a rough guide. Only one rough in-situ validation was performed at low tide on Ko-no-mie at the edge of the reef near the education centre. This indicated that the LAT estimate was within a depth error range of about +-0.5 m.

data/habitat/Keppels_AIMS_Habitat-mapping_2019.shp:
This shapefile contains the mapped land and marine habitats. The classification type is recorded in the Type attribute.

Format:

GeoTiff (Internal JPEG format - 538 MB)
PDF (A0 regional maps - ~30MB each)
Shapefile (Habitat map, Coastline, Labels, LAT estimate)

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\Keppels_AIMS_Regional-maps

Bedrock is the solid rock at or below the land surface. Over much of Ireland, the bedrock is covered by materials such as soil and gravel. The Bedrock map shows what the land surface of Ireland would be made up of if these materials were removed. As the bedrock is commonly covered, bedrock maps are an interpretation of the available data. Geologists map and record information on the composition and structure of rock outcrops (rock which can be seen on the land surface) and boreholes (a deep narrow round hole drilled in the ground). Areas are drawn on a map to show the distribution of rocks. The Geological Lines layer shows the details of the structural geology; faults, folds and unconformities. Faults and folds are the result of great pressure being applied to rock across a whole continent or more. These rocks will either break under the pressure, forming faults, or they will bend to form folds. Faults are recorded in the Geological Lines layer as lines where the break in the rock meets the surface. Folds are shown only using the lines of their axes, synclinal (where the rock folds downwards) and anticlinal (where the rock folds upwards). Unconformities are where there is a gap in the rock record, typically where rock has been eroded away in the past and a new rock deposited on top.Geologists map and record information on the structural geology. Lines are drawn on a map to show the location and extent of these structures. The structural symbols layer is used to describe the geology of an area through dip and strike information. Dip and strike describe the behaviour of the rock bedding plane. To describe a geometric plane two values are required; the angle from horizontal that it is dipping and the direction that it is dipping. Geologists describe the dip direction by the strike value; this is the azimuth perpendicular to the steepest dip of the plane.The measurements that this layer contains give information about the geometry of the rock units under the ground. These measurements are the only way to see if the rocks are folded and faulted and how. With this information we can also start to see why the rocks have the shapes that they do.To produce this dataset, the twenty one 1:100,000 paper maps covering Ireland were digitised and borders and overlaps between map sheets were removed. We collect new data to update our map and also use data made available from other sources. This map is to the scale 1:100,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 1km.It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas).The bedrock data is shown as polygons. Each polygon holds information on the rock unit name, its description, stratigraphy code (rock layers with age profile), lithology code (rock type) and map sheet number. Each polygon is linked to the bedrock lexicon table which has more detailed information such as a definition of the rock unit, rock types, age, thickness and other comments.The geological line data is shown as lines. Each line holds information on: description of the line, bedrock 100k map sheet number, style and label information. Other information if relevant such as name, stratigraphy code (rock layers with age profile) & lithology code (rock type). Each line is linked to the bedrock linework lexicon table which has more detailed information such as a definition of the rock unit, rock types, age, thickness and other comments. The structural symbols data is shown as points. Each point holds information on: the dip angle and direction, the strike angle and a description.The outcrop data is shown as polygons.

This service provides vector polygon dataset defining the official boundaries of the 100 counties within North Carolina as well as the boundaries between North Carolina and the states which border North Carolina.The North Carolina county polygon boundary service provides location information for North Carolina State and County Boundary lines derived from the best available survey and/or Geographic Information System (GIS) data. Sources for information are the North Carolina Geodetic Survey (NCGS), NC Department of Transportation (NCDOT), United States Geological Survey (USGS), and field surveys conducted by licensed surveyors in North Carolina and neighboring states that have been approved and recorded in their respective counties. Some boundaries cannot be surveyed in cases where boundaries are coincident with river centers. North Carolina Geodetic Survey assists counties on a cooperative basis (NC General Statute 153A-18) in defining and monumenting the location of uncertain or disputed boundaries as established by law. Some counties have completed boundary surveys for at least a portion of their county boundary. However, the majority of county boundaries have not been surveyed and are represented by the best currently available data from GIS sources, including NCDOT county maps (which originally came from the USGS) and updated county parcel maps.This data is updated annually, first quarter (usually in February).MetadataThe metadata for the contained layer of the NCDOT County Boundaries Service is available through the following link:County Boundaries PolygonPoint of Contact North Carolina Department of Information Technology -Transportation, GIS UnitGIS Data and Services ConsultantContact information:gishelp@ncdot.govCentury Center – Building B1020 Birch Ridge DriveRaleigh, NC 27610Hours of service: 9:00am - 5:00pm Monday – FridayContact instructions: Please send an email with any issues, questions, or comments regarding the County Boundaries data. If it is an immediate need, please indicate as such in the subject line in an email.NCDOT GIS Unit GO! NC Product Team

OPEN Data View service. The Wildland Fire Risk Assessment project was developed by the National Park Service's Fire and Aviation Management program as a response to the devastating 2011 wildfire season. This project developed a consistent assessment method that has been applied to NPS units nationwide regardless of variations in climate, fuels, and topography.The assessment, based on Firewise® assessment forms, evaluates access, surrounding environment, construction design and materials, and resources available to protect facilities from wildland fire. The data collected during the assessment process can be used for:Identifying, planning, prioritizing and tracking fuels treatments at unit, regional and national levels, and Developing incident response plans for facilities and communities within NPS units.The original spatial data for the assessments comes from a variety of sources including the NPS Buildings Enterprise Dataset, WFDSS, NPMap Edits, manually digitized points using Esri basemaps as a reference at various scales, and GPS collection using a multitude of consumer and professional grade GPS devices. The facilities that have been assessed and assigned a facility risk rating have been ground-truthed and field verified. (In some rare occasions, facilities have been verified during remote assessments. Those that have been remotely assessed are marked as such). The resulting data is stored in a centralized geodatabase, and this publicly available feature layer allows the user to view that data.The NPS Facilities feature layer includes the following layers and related tables:Facility - A facility is defined by the NPS as an asset that the NPS desires to track and manage as a distinct identifiable entity. In the case of wildland fire risk assessments, a facility is most often a structure but in special instances, a park unit may wish to identify and assess other at-risk features such as a historic wooden bridge or an interpretive display. The facilities are assessed based on access, the surrounding environment, construction design, and protection resources and limitations, resulting in a numerical score and risk adjective rating for each facility. These ratings designate the likelihood of ignition during a wildland fire. The facilities are symbolized by their respective risk rating.Community - A community is a group of five or more facilities, a majority of which are within 600 feet of each other, that share common access and protection attributes. The community concept was developed to facilitate data collection and entry in areas with multiple facilities and where it made sense to apply treatments and tactics at a scale larger than individual facilities. Most of the community polygons are created using models in ArcMap, but some may have been created or edited in the field using a Trimble GPS unit. *The NPS Facilities layer is updated continually as new wildfire risk assessments are conducted and the Wildland Fire Risk Assessment project progresses. The assessment data contained here is the most current data available.*More information about the NPS Wildland Fire Risk Assessment Project, and the NPS Facilities data itself, can be found at the New Wildland Fire Risk Assessments website. This site provides information on the data collection process, additional ways to access the data, and how to conduct assessments yourself (for both NPS and non-NPS facilities).FACILITY ATTRIBUTES
Unit_ID NWCG Unit ID, Two letter state code and three letter unit abbreviation, for example UTZIP for Zion National Park in Utah.

Fire_Bldg_ID User maintained unique ID for Facility layer.

Building ID Unique Id from the NPS Enterprise Buildings dataset.

FMSS ID Unique ID for the facility in the NPS FMSS database.

Community ID Unique ID linking facility to a community

Assess Scale Indicates if the facility is part of a community/ will be included in a community assessment. Communities are pre-defined by regional GIS staff and visible in this map as a blue perimeter.
Answer "Yes" if you are adding a facility point within a predefined community.

Common Name Name of the structure. In most cases, the name comes from the NPS FMSS database.

Map Label Numerical label used for mapping purposes.

Owner Indicates who owns the structure being assessed.

Facilty Type Indicates the facility type OR if the facility has been REMOVED, DESTROYED, has NO WILDLAND RISK, is PRIVATE - NO SURVEY REQUIRED or DOES NOT REQUIRE A SURVEY (because it is planned for removal).

Facility Use What is the primary use of the facility?

Building Occupied Is the building occupied?

Community Name Name of the community the facility is located within, if any.

Field Crew Field crew completing the assessment.

Last Site Visit Date Date which the facility was visited and assessment data reviewed/updated.

Location General location within the unit – may use FMUs, watersheds, or other identifier. One location may contain multiple communities and individual facilities. Locations are used to filter data for reports and map products.

PrimaryAccess Primary method of accessing the facility.

IngressEgress Number of routes into and away from the facility.

AccessWidth Width of the road or driveway used to access the facility.

AccessCond Grade and surface material of the road or driveway used to access the facility.

BridgeCond Condition, based on load limits and construction.

Turnaround Describes how close can a fire apparatus drive to the facility and once there, whether it can turnaround.

BldgNum Is the facility clearly signed or numbered?

FuelLoad Fuel loading within 300 ft of the facility (see appendix D of the Wildfire Risk Assessment User Guide)

FuelType Predominant fuel type within 300 ft of the facility.

DefensibleSpace Amount of defensible space around the facility, see criteria for evaluating defensible space in the Wildfire Risk Assessment User Guide.

Topography Predominant slope within 300 ft of facility.

RoofMat Roofing material used on the facility.

SidingMat Siding material used on the facility.

Foundation Describes the facility’s foundation.

Fencing Indicates presence of any wooden attachments, fencing, decking, pergola, etc. and fuels clearance around those attachments.

Firewood Firewood distance from facility.

Propane Inidicates if a propane tank exists within 200 feet of a structure and if there is any fuels clearance around the propane tank(s).

Hazmat List of hazmat existing on the site.

WaterSupply Water supply available to the facility.

OverheadHaz Identifies the presence of overhead hazards that will limit aerial firefighting efforts.

SafetyZone Identifies the presence of any potential safety zones.

SZRadius Radius of any potential safety zones.

Obstacles Additional obstacles, not already included in assessment, that will limit firefighting efforts- to include items such as UXO, hazmat,etc. If there are additional obstacles, be sure to comment in Assessment Comments or Tactic descriptions where appropriate.

TriageCategory Refer to IRPG for descriptions of each category. This information will be displayed in the NIFS Structure Triage layer for incident response.

Score Sum of attribute values for all assessment elements including access, environment, structure and protection portions of the assessment.

Rating Wildland fire risk rating based on score. Ratings are No Wildland Risk, Low, Moderate and High. Rating indicates likelihood if facility igniting if a wildland fire occurs.

ProtectionLevel Inidcates structures which are priority for protection during a wildfire. For Alaska Region data, indicates identified protection level for structure. For lower 48, enter ‘Unknown’ unless specified by local unit.

ProtLevelApprovalName Name of person who designated Protection Level

ProtLevelApprovalDate Date Protection Level Designated

ResourcesOfConcern Indicates if it is necessary to contact park staff before engaging in suppression activities because special resources (natural, cultural, historic) of concern are present?

AssessComments Explain any aspects of the assessment that require extra detail.

RegionCode NPS Region Code - AKR, IMR, NER, NCR, MWR, PWR or SER

UnitCode

NPS Unit Code

ReasonIncluded Why is the point in the dataset – NPS owned, Treatment Planning, Protection Responsibility, Planning (other than treatments). Intent of the dataset is to document wildfire risk for NPS owned structures. Other structures or facilities may be included at the discretion of the unit's fire management staff.

Restriction How can the data be shared – Unrestricted, Restricted - No Third Party Release, Restricted – Originating Agency Concurrence, Restricted – Affected Cultural Group Concurrence, Restricted - No Release, Unknown. Only unrestricted data is included in this dataset.

Local_ID Field which can be used to store unique ids linking back to any local datasets.

RevisitInterval How many years will it take for the fuels to change significantly enough to change the score and rating for this facility?

IsVisited Use this field to keep track of what you have done during a field session. Filter on this field to see what has been assessed and what still needs visited during a field data collection session.

DeleteThis Users enter yes if this is this a duplicate or was no facility found.
If you know the facility was REMOVED or DESTROYED, go back to Facility Type and enter that information there.

Data_Source

FirewiseZone1 List of treatments needed to

Brief Methods: In version 2 of the Sierra Nevada Multi-source Meadow Polygons Compilation, polygon boundaries from the original layer (SNMMPC_v1 - https://meadows.ucdavis.edu/data/4) were updated using ‘heads-up’ digitization from high-resolution (1m) NAIP imagery. In version 1, only polygons larger than one acre were retained in the published layer. In version 2, existing polygon boundaries were split, reduced in size, or merged, and additional polygons not captured in the original layer were digitized. If split, original IDs from version 1 were retained for one half and a new ID was created for the other half. In instances where adjacent meadows were merged together, only one ID was retained and the unused ID was “decommissioned”. If digitized, a new sequential ID was assigned. AcknowledgementsTim Lindemann, Dave Weixelman, Carol Clark, Stacey Mikulovsky, Qiqi Jiang, Joel Grapentine, Kirk Evans - USDA Forest Service, Pacific Southwest Region Wes Kitlasten - U.S. Geological Survey Sarah Yarnell, Ryan Peek, Nick Santos - UC Davis, Center for Watershed Sciences Anna Fryjoff-Hung - UC Merced Meadow Polygon Attributes FieldDescriptionAREA_ACREMeadow area in acresSTATEState in which the meadow is located (CA or NV)ID*Unique meadow identifier UCDSNMxxxxxx*Note: IDs are non-sequential* HUC12Unique identifier for the Hydrologic Unit Code (HUC), level 12, in which the meadow is locatedOWNERSHIPLand ownership status (multiple sources)EDGE_COMPLEXITYGives an indication of the meadow's exposure to external conditions EDGE COMPLEXITY = (MEADOWperimeter/EAC perimeter) [EAC = Equal Area Circle]DOM_ROCKTYPEDominant rock type on which the meadow is located based on the USGS layerVEG_MAJORITYVegetation majority based on the LANDFIRE layer (GROUPVEG attribute)SOIL_SURVEYSoil survey from which SOIL_COKEY, MAPUNIT_Kf, MAPUNIT_ClayTot_r, SOIL_MUKEY, and SOIL_COMP_NAME were assigned to each meadow (SSURGO or STATSGO depending on layer coverage)SOIL_MUKEYMapunit Key: Unique identifier for the Mapunit in which the meadow is locatedSOIL_COKEYComponent Key: Unique identifier for the major component of the mapunit in which the meadow is located SOIL_COMP_NAMEComponent Name: Name of the soil component with the highest representative value in the mapunit in which the meadow is located MAPUNIT_KfK factor: A soil erodibility factor that quantifies the susceptibility of soil particles to detachment by water. Low: 0.05-0.2 Moderate: 0.25-0.4, High: >0.4MAPUNIT_ClayTot_rRepresentative value (%)of total clayCATCHMENT_AREAThe approximate area of the upstream catchment exiting through the meadow(sq. m)ELEV_MEANMean elevation (m)ELEV_RANGEElevation range (m) across each meadowED_MIN_FStopo_ROADSMinimum Euclidean Distance (m) to Forest Service Topographic Map Data Transportation Roads ED_MIN_FStopo_TRAILSMinimum Euclidean Distance (m) to Forest Service Topographic Map Data Transportation Trails ED_MIN_LAKEMinimum Euclidean Distance (m) to lake edges ED_MIN_FLOWMinimum Euclidean Distance (m) to NHD Streams/Rivers ED_MIN_SEEPMinimum Euclidean Distance (m) to NHD Seeps/Springs MDW_DEM_SLOPEMedian DEM based slope (in degrees)STRM_SLOPE_GRADELength-weighted average slope of all NHD flowline segments in each meadow. Given for meadows with flowlines. Meadows without flowlines are null for this attribute.POUR_POINT_LATLatitude of the lowest point along a flowline at which water flows out of the meadow in decimal degrees(meadow with no flowline has null value) POUR_POINT_LONLongitude of the lowest point along a flowline at which water flows out of the meadow in decimal degrees(meadow with no flowline has null value) HGM_TypeDominant meadow hydrogeomorphic (HGM) type LAT_DDLatitude of polygon centroid in decimal degreesLONG_DDLongitude of polygon centroid in decimal degreesShape_LengthMeadow perimeter in metersShape_AreaMeadow area in sq. metersDetailed Attribute Descriptions:GeologyField: DOM_ROCKTYPEData Source: USGS - https://pubs.usgs.gov/of/2005/1305/Dominant rock type was attributed to the meadow polygons based on available state geology layers. Using Zonal Statisitics in ArcGIS, the most abundant lithology in the map unit (ROCKTYPE1) was identified for each meadow. VegetationField: VEG_MAJORITYData Source: LANDFIRE - https://www.landfire.gov/version_comparison.php?mosaic=YUsing Zonal Statisitics in ArcGIS, the 2014 LANDFIRE dataset was used to attribute generalized vegetation (GROUPVEG) to the meadow polygons. SoilsFields: SOIL_SURVEY, SOIL_MUKEY, SOIL_COKEY, SOIL_COMP_NAME, MAPUNIT_Kf, MAPUNIT_ClayTot_rData Source: USDA, Natural Resources Conservation ServiceSSURGO: https://gdg.sc.egov.usda.gov/STATSGO: https://websoilsurvey.sc.egov.usda.gov/App/HomePage.htmSSURGO (1:24,000 scale) datasets were compiled for the entirety of the study area. Gaps were filled with compiled STATSGO data (1:250,000 scale). Components were assigned based on the soil component with the highest representative value in the map unit in which the meadow was located. For each component, the clay and Kf values from the top-most horizon were assigned to each meadow polygon using Zonal Statistics. Note: MAPUNIT_Kf may be null if the mapunit dominant condition is a miscellaneous area component such as Rock outcrop. Also, forested components with organic litter surface horizons will also return a null K-factor when the surface horizon K-factor is used.STATSGO does not have the detail for approximation of soil properties in the mountain meadows. The polygons are so big (Order 4) that they do not recognize the soils in the meadows as unique components, so there are no data for the meadows anywhere in those map units. As for the K and clay values for CA790 (Yosemite NP), because it is a new survey, O horizons were populated for those components. There may be a similar issue with the Tahoe Basin. NRCS does not populate the K factor for O horizons. And, at least at the time, NRCS is not populating any mineral material in the O horizons. Many NRCS national interpretations have been edited to look at the first mineral horizon and exclude the O. There is also a lot of Rock Outcrop and no horizon data are populated for those components.Slope Field: MDW_DEM_SLOPE Data Source: USGS 10m DEMThe median Digital elevation model (DEM) based slope (in degrees) was assigned via Zonal Statistics to each meadow.All meadows have a value for this attribute. Field: STREAM_SLOPE_GRADEData Source: USGS National Hydrograpy Dataset (NHD) - https://nhd.usgs.gov/data.htmlA length-weighted average slope of all NHD flowline segments was calculated within each meadow polygon. Meadows with no NHD flowline will have a NULL value for this attribute. Catchment AreaField: MDW_CATCHMENT_AREA (sq meters)Data Source: USGS NHDPlus V2, NHDPlusHydrodem- http://www.horizon-systems.com/NHDPlus/NHDPlusV2_home.phpScript Source: USGS, Wes Kitlasten; USFS, Kirk Evans, Carol ClarkUsing python scripting and the Watershed tool in ArcGIS, the area of the upstream catchment exiting through the meadow was obtained using a flow direction raster created from the NHDPlusHydrodem.Euclidean Distance Fields: ED_MIN_SEEP, ED_MIN_LAKE, ED_MIN_FLOW, ED_MIN_FSTopo_ROADS, ED_MIN_FSTopo_TRAILSData Source: USGS National Hydrograpy Dataset (NHD) - https://nhd.usgs.gov/data.htmlFSTopo - https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=FSTopoUsing the Euclidean Distance (Spatial Analyst) tool in ArcGIS, the minimum distance to each meadow was calculated for NHD Springs/Seeps, NHD Streams/Rivers (flow), NHD Waterbodies (lakes), and FS Topographic Transportation Trails and Roads. HGM Type During the mapping process, the dominant Hydrogeomorphic (HGM) type (Weixelman et al 2011) was estimated for each meadow larger than one acre. Visual inspection of NAIP 1-m resolution imagery was used in this process. DEM layers were used to estimate the landform position. The USGS hydrographic layer was used to determine locations of flowlines. Google Earth imagery was used to estimate greenness during the summer months. Meadows are often composed of more than one HGM type. In this effort, the dominant type was estimated. HGM types have not yet been estimated for Yosemite and Sequoia Kings Canyon National Parks. Types were mapped according to the following visual interpretation. Meadows adjacent to lakes or reservoirs and at nearly the same elevation as the Water bodyLacustrine Fringe (LF)1’. Not as above2Meadow sites located in an obvious topographic depression. 32’. Not as above4Sites with obvious standing water after mid-summer or vegetation remaining dark green after mid-summer. Depressional Perennial (DEPP)3’. Not as above. Sites with no standing water after mid-summer or apparently not remaining dark green after mid-summer.Depressional Seasonal (DEPS)Meadows with a flow line (using the USGS hydrographic layer) entering from above the meadow and exiting below the meadow, or meadows located in a swale or drainway ………………………………Riparian (RIP)4’. Not as above5Meadows fed by a spring or seep. No flowline entering from above the meadow. Typically occurring on hillslopes or toeslopes. In addition, the USGS DEM layer was used to look for the text label “Springs” and/or a symbol indicating a spring. Discharge Slope (DS)5’. Dry meadows without a visible flowline entering from above the meadow, vegetation greenness disappears by mid-summer. No apparent groundwater inputs from springs or seeps. May occur in a swale, drainageway, gentle hillslope, or crest. Dry (Dry)OwnershipField: OWNERSHIPData Sources by priority:USDA Forest Service Basic Ownership (OWNERCLASSIFICATION) - https://data.fs.usda.gov/geodata/edw/datasets.php?dsetCategory=boundariesNational Parks Service (UNIT_NAME) - https://irma.nps.gov/DataStore/California Protected Areas Database – CPAD (LAYER) - http://www.calands.org/Protected Area Database-US (CBI Edition) Version 2.1 (OWN_NAME) -

Brief Methods: In version 2 of the Sierra Nevada Multi-source Meadow Polygons Compilation, polygon boundaries from the original layer (SNMMPC_v1 - https://meadows.ucdavis.edu/data/4) were updated using ‘heads-up’ digitization from high-resolution (1m) NAIP imagery. In version 1, only polygons larger than one acre were retained in the published layer. In version 2, existing polygon boundaries were split, reduced in size, or merged, and additional polygons not captured in the original layer were digitized. If split, original IDs from version 1 were retained for one half and a new ID was created for the other half. In instances where adjacent meadows were merged together, only one ID was retained and the unused ID was “decommissioned”. If digitized, a new sequential ID was assigned. AcknowledgementsTim Lindemann, Dave Weixelman, Carol Clark, Stacey Mikulovsky, Qiqi Jiang, Joel Grapentine, Kirk Evans - USDA Forest Service, Pacific Southwest Region Wes Kitlasten - U.S. Geological Survey Sarah Yarnell, Ryan Peek, Nick Santos - UC Davis, Center for Watershed Sciences Anna Fryjoff-Hung - UC Merced Meadow Polygon Attributes Field DescriptionAREA_ACRE Meadow area in acresSTATE State in which the meadow is located (CA or NV)ID* Unique meadow identifier UCDSNMxxxxxx*Note: IDs are non-sequential* HUC12 Unique identifier for the Hydrologic Unit Code (HUC), level 12, in which the meadow is locatedOWNERSHIP Land ownership status (multiple sources)EDGE_COMPLEXITY Gives an indication of the meadow's exposure to external conditions EDGE COMPLEXITY = (MEADOWperimeter/EAC perimeter) [EAC = Equal Area Circle]DOM_ROCKTYPE Dominant rock type on which the meadow is located based on the USGS layerVEG_MAJORITY Vegetation majority based on the LANDFIRE layer (GROUPVEG attribute)SOIL_SURVEY Soil survey from which SOIL_COKEY, MAPUNIT_Kf, MAPUNIT_ClayTot_r, SOIL_MUKEY, and SOIL_COMP_NAME were assigned to each meadow (SSURGO or STATSGO depending on layer coverage)SOIL_MUKEY Mapunit Key: Unique identifier for the Mapunit in which the meadow is locatedSOIL_COKEY Component Key: Unique identifier for the major component of the mapunit in which the meadow is located SOIL_COMP_NAME Component Name: Name of the soil component with the highest representative value in the mapunit in which the meadow is located MAPUNIT_Kf K factor: A soil erodibility factor that quantifies the susceptibility of soil particles to detachment by water. Low: 0.05-0.2 Moderate: 0.25-0.4, High: >0.4MAPUNIT_ClayTot_r Representative value (%)of total clayCATCHMENT_AREA The approximate area of the upstream catchment exiting through the meadow(sq. m)ELEV_MEAN Mean elevation (m)ELEV_RANGE Elevation range (m) across each meadowED_MIN_FStopo_ROADS Minimum Euclidean Distance (m) to Forest Service Topographic Map Data Transportation Roads ED_MIN_FStopo_TRAILS Minimum Euclidean Distance (m) to Forest Service Topographic Map Data Transportation Trails ED_MIN_LAKE Minimum Euclidean Distance (m) to lake edges ED_MIN_FLOW Minimum Euclidean Distance (m) to NHD Streams/Rivers ED_MIN_SEEP Minimum Euclidean Distance (m) to NHD Seeps/Springs MDW_DEM_SLOPE Median DEM based slope (in degrees)STRM_SLOPE_GRADE Length-weighted average slope of all NHD flowline segments in each meadow. Given for meadows with flowlines. Meadows without flowlines are null for this attribute.POUR_POINT_LAT Latitude of the lowest point along a flowline at which water flows out of the meadow in decimal degrees(meadow with no flowline has null value) POUR_POINT_LON Longitude of the lowest point along a flowline at which water flows out of the meadow in decimal degrees(meadow with no flowline has null value) HGM_Type Dominant meadow hydrogeomorphic (HGM) type LAT_DD Latitude of polygon centroid in decimal degreesLONG_DD Longitude of polygon centroid in decimal degreesShape_Length Meadow perimeter in metersShape_Area Meadow area in sq. meters Detailed Attribute Descriptions:GeologyField: DOM_ROCKTYPEData Source: USGS - https://pubs.usgs.gov/of/2005/1305/Dominant rock type was attributed to the meadow polygons based on available state geology layers. Using Zonal Statisitics in ArcGIS, the most abundant lithology in the map unit (ROCKTYPE1) was identified for each meadow. VegetationField: VEG_MAJORITYData Source: LANDFIRE - https://www.landfire.gov/version_comparison.php?mosaic=YUsing Zonal Statisitics in ArcGIS, the 2014 LANDFIRE dataset was used to attribute generalized vegetation (GROUPVEG) to the meadow polygons. SoilsFields: SOIL_SURVEY, SOIL_MUKEY, SOIL_COKEY, SOIL_COMP_NAME, MAPUNIT_Kf, MAPUNIT_ClayTot_rData Source: USDA, Natural Resources Conservation ServiceSSURGO: https://gdg.sc.egov.usda.gov/STATSGO: https://websoilsurvey.sc.egov.usda.gov/App/HomePage.htmSSURGO (1:24,000 scale) datasets were compiled for the entirety of the study area. Gaps were filled with compiled STATSGO data (1:250,000 scale). Components were assigned based on the soil component with the highest representative value in the map unit in which the meadow was located. For each component, the clay and Kf values from the top-most horizon were assigned to each meadow polygon using Zonal Statistics. Note: MAPUNIT_Kf may be null if the mapunit dominant condition is a miscellaneous area component such as Rock outcrop. Also, forested components with organic litter surface horizons will also return a null K-factor when the surface horizon K-factor is used.STATSGO does not have the detail for approximation of soil properties in the mountain meadows. The polygons are so big (Order 4) that they do not recognize the soils in the meadows as unique components, so there are no data for the meadows anywhere in those map units. As for the K and clay values for CA790 (Yosemite NP), because it is a new survey, O horizons were populated for those components. There may be a similar issue with the Tahoe Basin. NRCS does not populate the K factor for O horizons. And, at least at the time, NRCS is not populating any mineral material in the O horizons. Many NRCS national interpretations have been edited to look at the first mineral horizon and exclude the O. There is also a lot of Rock Outcrop and no horizon data are populated for those components.Slope Field: MDW_DEM_SLOPE Data Source: USGS 10m DEMThe median Digital elevation model (DEM) based slope (in degrees) was assigned via Zonal Statistics to each meadow.All meadows have a value for this attribute. Field: STREAM_SLOPE_GRADEData Source: USGS National Hydrograpy Dataset (NHD) - https://nhd.usgs.gov/data.htmlA length-weighted average slope of all NHD flowline segments was calculated within each meadow polygon. Meadows with no NHD flowline will have a NULL value for this attribute. Catchment AreaField: MDW_CATCHMENT_AREA (sq meters)Data Source: USGS NHDPlus V2, NHDPlusHydrodem- http://www.horizon-systems.com/NHDPlus/NHDPlusV2_home.phpScript Source: USGS, Wes Kitlasten; USFS, Kirk Evans, Carol ClarkUsing python scripting and the Watershed tool in ArcGIS, the area of the upstream catchment exiting through the meadow was obtained using a flow direction raster created from the NHDPlusHydrodem.Euclidean Distance Fields: ED_MIN_SEEP, ED_MIN_LAKE, ED_MIN_FLOW, ED_MIN_FSTopo_ROADS, ED_MIN_FSTopo_TRAILSData Source: USGS National Hydrograpy Dataset (NHD) - https://nhd.usgs.gov/data.htmlFSTopo - https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=FSTopoUsing the Euclidean Distance (Spatial Analyst) tool in ArcGIS, the minimum distance to each meadow was calculated for NHD Springs/Seeps, NHD Streams/Rivers (flow), NHD Waterbodies (lakes), and FS Topographic Transportation Trails and Roads. HGM Type During the mapping process, the dominant Hydrogeomorphic (HGM) type (Weixelman et al 2011) was estimated for each meadow larger than one acre. Visual inspection of NAIP 1-m resolution imagery was used in this process. DEM layers were used to estimate the landform position. The USGS hydrographic layer was used to determine locations of flowlines. Google Earth imagery was used to estimate greenness during the summer months. Meadows are often composed of more than one HGM type. In this effort, the dominant type was estimated. HGM types have not yet been estimated for Yosemite and Sequoia Kings Canyon National Parks. Types were mapped according to the following visual interpretation. 1. Meadows adjacent to lakes or reservoirs and at nearly the same elevation as the Water bodyLacustrine Fringe (LF)1’. Not as above22. Meadow sites located in an obvious topographic depression. 32’. Not as above43. Sites with obvious standing water after mid-summer or vegetation remaining dark green after mid-summer. Depressional Perennial (DEPP)3’. Not as above. Sites with no standing water after mid-summer or apparently not remaining dark green after mid-summer.Depressional Seasonal (DEPS)4. Meadows with a flow line (using the USGS hydrographic layer) entering from above the meadow and exiting below the meadow, or meadows located in a swale or drainway ………………………………Riparian (RIP)4’. Not as above55. Meadows fed by a spring or seep. No flowline entering from above the meadow. Typically occurring on hillslopes or toeslopes. In addition, the USGS DEM layer was used to look for the text label “Springs” and/or a symbol indicating a spring. Discharge Slope (DS)5’. Dry meadows without a visible flowline entering from above the meadow, vegetation greenness disappears by mid-summer. No apparent groundwater inputs from springs or seeps. May occur in a swale, drainageway, gentle hillslope, or crest. Dry (Dry)OwnershipField: OWNERSHIPData Sources by priority:1. USDA Forest Service Basic Ownership (OWNERCLASSIFICATION) - https://data.fs.usda.gov/geodata/edw/datasets.php?dsetCategory=boundaries1. National Parks Service (UNIT_NAME) - https://irma.nps.gov/DataStore/1. California Protected Areas Database – CPAD (LAYER) - http://www.calands.org/1. Protected Area Database-US (CBI Edition) Version 2.1 (OWN_NAME) -

Using the latest statewide NAIP Imagery and ArcGIS 10, all boundaries of individual agricultural fields, urban areas, and significant riparian areas are precisely digitized.

Once the process of boundary digitizing is done, the polygons are loaded onto tablet PCs. Field crews are then sent to field check the crop and irrigation type for each agricultural polygon and label the shapefiles accordingly. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process. This improved process has saved the division much time and money and even greater savings will be realized as the new statewide field boundaries are completed.

Once processed and quality checked, the data is filed in the State Geographic Information Database (SGID) maintained by the State Automated Geographic Reference Center (AGRC). Once in the SGID, the data becomes available to the public. At this point, the data is also ready for use in preparing various planning studies.

In conducting water-related land use inventories, the division attempts to inventory all lands or areas that consume or evaporate water other than natural precipitation. Areas not inventoried are mainly desert, rangeland and forested areas.

Wet/open water areas and dry land agriculture areas are mapped if they are within or border irrigated lands. As a result, the numbers of acres of wet/open water areas and dry land agriculture reported by the division may not represent all such areas in a basin or county.

During land use inventories, the division uses 11 hydrologic basins as the basic collection units. County data is obtained from the basin data. The water-related land use data collected statewide covers more than 2.6 million acres of dry and irrigated agricultural land. This represents about 5 percent of the total land area in the state.

Due to changes in methodology, improvements in imagery, and upgrades in software and hardware, increasingly more refined inventories have been made in each succeeding year of the Water-Related Land Use Inventory. While this improves the data we report, it also makes comparisons to past years difficult. Making comparisons between datasets is still useful; however, increases or decreases in acres reported should not be construed to represent definite trends or total amounts of change up or down. To estimate such trends or change, more analysis is required.

Features include urban and industrial areas in addition to agricultural areas. Water-related features such as rivers, lakes, and wetlands are also included.