Jurisdictional Unit, 2022-05-21. For use with WFDSS, IFTDSS, IRWIN, and InFORM.This is a feature service which provides Identify and Copy Feature capabilities. If fast-drawing at coarse zoom levels is a requirement, consider using the tile (map) service layer located at https://nifc.maps.arcgis.com/home/item.html?id=3b2c5daad00742cd9f9b676c09d03d13.OverviewThe Jurisdictional Agencies dataset is developed as a national land management geospatial layer, focused on representing wildland fire jurisdictional responsibility, for interagency wildland fire applications, including WFDSS (Wildland Fire Decision Support System), IFTDSS (Interagency Fuels Treatment Decision Support System), IRWIN (Interagency Reporting of Wildland Fire Information), and InFORM (Interagency Fire Occurrence Reporting Modules). It is intended to provide federal wildland fire jurisdictional boundaries on a national scale. The agency and unit names are an indication of the primary manager name and unit name, respectively, recognizing that:There may be multiple owner names.Jurisdiction may be held jointly by agencies at different levels of government (ie State and Local), especially on private lands, Some owner names may be blocked for security reasons.Some jurisdictions may not allow the distribution of owner names. Private ownerships are shown in this layer with JurisdictionalUnitIdentifier=null,JurisdictionalUnitAgency=null, JurisdictionalUnitKind=null, and LandownerKind="Private", LandownerCategory="Private". All land inside the US country boundary is covered by a polygon.Jurisdiction for privately owned land varies widely depending on state, county, or local laws and ordinances, fire workload, and other factors, and is not available in a national dataset in most cases.For publicly held lands the agency name is the surface managing agency, such as Bureau of Land Management, United States Forest Service, etc. The unit name refers to the descriptive name of the polygon (i.e. Northern California District, Boise National Forest, etc.).These data are used to automatically populate fields on the WFDSS Incident Information page.This data layer implements the NWCG Jurisdictional Unit Polygon Geospatial Data Layer Standard.Relevant NWCG Definitions and StandardsUnit2. A generic term that represents an organizational entity that only has meaning when it is contextualized by a descriptor, e.g. jurisdictional.Definition Extension: When referring to an organizational entity, a unit refers to the smallest area or lowest level. Higher levels of an organization (region, agency, department, etc) can be derived from a unit based on organization hierarchy.Unit, JurisdictionalThe governmental entity having overall land and resource management responsibility for a specific geographical area as provided by law.Definition Extension: 1) Ultimately responsible for the fire report to account for statistical fire occurrence; 2) Responsible for setting fire management objectives; 3) Jurisdiction cannot be re-assigned by agreement; 4) The nature and extent of the incident determines jurisdiction (for example, Wildfire vs. All Hazard); 5) Responsible for signing a Delegation of Authority to the Incident Commander.See also: Unit, Protecting; LandownerUnit IdentifierThis data standard specifies the standard format and rules for Unit Identifier, a code used within the wildland fire community to uniquely identify a particular government organizational unit.Landowner Kind & CategoryThis data standard provides a two-tier classification (kind and category) of landownership. Attribute Fields JurisdictionalAgencyKind Describes the type of unit Jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, and Other. A value may not be populated for all polygons.JurisdictionalAgencyCategoryDescribes the type of unit Jurisdiction using the NWCG Landowner Category data standard. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State. A value may not be populated for all polygons.JurisdictionalUnitNameThe name of the Jurisdictional Unit. Where an NWCG Unit ID exists for a polygon, this is the name used in the Name field from the NWCG Unit ID database. Where no NWCG Unit ID exists, this is the “Unit Name” or other specific, descriptive unit name field from the source dataset. A value is populated for all polygons.JurisdictionalUnitIDWhere it could be determined, this is the NWCG Standard Unit Identifier (Unit ID). Where it is unknown, the value is ‘Null’. Null Unit IDs can occur because a unit may not have a Unit ID, or because one could not be reliably determined from the source data. Not every land ownership has an NWCG Unit ID. Unit ID assignment rules are available from the Unit ID standard, linked above.LandownerKindThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. There are three valid values: Federal, Private, or Other.LandownerCategoryThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State, Private.DataSourceThe database from which the polygon originated. Be as specific as possible, identify the geodatabase name and feature class in which the polygon originated.SecondaryDataSourceIf the Data Source is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if Data Source is "PAD-US 2.1", then for a USDA Forest Service polygon, the Secondary Data Source would be "USDA FS Automated Lands Program (ALP)". For a BLM polygon in the same dataset, Secondary Source would be "Surface Management Agency (SMA)."SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.MapMethod:Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality. MapMethod will be Mixed Method by default for this layer as the data are from mixed sources. Valid Values include: GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; DigitizedTopo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Phone/Tablet; OtherDateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using 24 hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature. GeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.JoinMethodAdditional information on how the polygon was matched information in the NWCG Unit ID database.LocalNameLocalName for the polygon provided from PADUS or other source.LegendJurisdictionalAgencyJurisdictional Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.LegendLandownerAgencyLandowner Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.DataSourceYearYear that the source data for the polygon were acquired.Data InputThis dataset is based on an aggregation of 4 spatial data sources: Protected Areas Database US (PAD-US 2.1), data from Bureau of Indian Affairs regional offices, the BLM Alaska Fire Service/State of Alaska, and Census Block-Group Geometry. NWCG Unit ID and Agency Kind/Category data are tabular and sourced from UnitIDActive.txt, in the WFMI Unit ID application (https://wfmi.nifc.gov/unit_id/Publish.html). Areas of with unknown Landowner Kind/Category and Jurisdictional Agency Kind/Category are assigned LandownerKind and LandownerCategory values of "Private" by use of the non-water polygons from the Census Block-Group geometry.PAD-US 2.1:This dataset is based in large part on the USGS Protected Areas Database of the United States - PAD-US 2.`. PAD-US is a compilation of authoritative protected areas data between agencies and organizations that ultimately results in a comprehensive and accurate inventory of protected areas for the United States to meet a variety of needs (e.g. conservation, recreation, public health, transportation, energy siting, ecological, or watershed assessments and planning). Extensive documentation on PAD-US processes and data sources is available.How these data were aggregated:Boundaries, and their descriptors, available in spatial databases (i.e. shapefiles or geodatabase feature classes) from land management agencies are the desired and primary data sources in PAD-US. If these authoritative sources are unavailable, or the agency recommends another source, data may be incorporated by other aggregators such as non-governmental organizations. Data sources are tracked for each record in the PAD-US geodatabase (see below).BIA and Tribal Data:BIA and Tribal land management data are not available in PAD-US. As such, data were aggregated from BIA regional offices. These data date from 2012 and were substantially updated in 2022. Indian Trust Land affiliated with Tribes, Reservations, or BIA Agencies: These data are not considered the system of record and are not intended to be used as such. The Bureau of Indian Affairs (BIA), Branch of Wildland Fire Management (BWFM) is not the originator of these data. The

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. The vegetation map was developed through on-screen digitizing of available black and white digital ortho-photographs from 1984 and 1999. The photos were compiled into a GIS with a standard set of ancillary layers provided by the park service (boundaries, roads, facilities, etc.). Using the vegetation classification as the foundation for the map legend, map units were defined with respect to interpretable patterns in the photography, and with an eye to those patterns that would be most important in natural and cultural resources management within the park. The map included 19 map classes and covered a total of 278.13 ha.

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here.

The vegetation map was developed through aerial photo interpretation of available black and white digital ortho-photographs from 1984 and 1999. The photos were compiled into a GIS with a standard set of ancillary layers provided by the park service (boundaries, roads, facilities, etc.). Using the vegetation classification as the foundation for the map legend, map units were defined with respect to interpretable patterns in the photography, and with an eye to those patterns that would be most important in natural and cultural resources management within the park.

Find Route Using Address:Use "Find address or place" in the upper left hand corner of the map to search an address. Select populated address. The map will zoom to the address. Select the location on the map where the address is located, a text box will pop up with the day/route information.Upper Icon Tools (Left to Right):Legend: Define features Layers: Toggle to Change which layers to viewBookmarks: Zoom to a specified routeTable: Data within a table viewPrint: Choose features and print mapShare: Quickly share this page with othersAllied Customer Service:CDFW@republicServices.com817.332.7301 OR 800.333.7301www.disposal.com OR www.republicservices.com Grapevine Links:Trash & GarbageRecyclingHow do I dispose of Brush & Tree Limbs?

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The polygons in this dataset are a digital representation of the distribution or extent of geological units within the area. Polygons have a range of attributes including unit name, age, lithological description and an abbreviated symbol for use in labelling the polygons. These have been extracted from the Rock Units Table held …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The polygons in this dataset are a digital representation of the distribution or extent of geological units within the area. Polygons have a range of attributes including unit name, age, lithological description and an abbreviated symbol for use in labelling the polygons. These have been extracted from the Rock Units Table held in the Department of Natural Resources, Mines and Energy Merlin Database. Purpose To display the geology polygons which define the extent of rock units. Dataset History Supplemental_Information: Data captured at 1:40 000 scale. The data set is sourced from the Department's Geoscience and Resources Database (GRDB), a component of the Mineral and Energy Resources Location and Information Network (MERLIN) corporate database.(GRDB), a component of the Mineral and Energy Resources Location and Information Network (MERLIN) corporate database. NOTE: GEOLDATA was in most cases compiled based on Datum AGD66. The map tile coverages so compiled have now been projected to geographics based on Datum GDA94. Consequently the boundaries for these map tiles will not conform to the Latitude and Longitude graticule based on Datum GDA94. Entity_and_Attribute_Information: Detailed_Description: Entity_Type: Entity_Type_Label: 9341_r Entity_Type_Definition: Polygons have a range of attributes including unit name, age, lithological description and an abbreviated symbol for use in labelling the polygons. Entity_Type_Definition_Source: The Rock Units Table held in the Department of Natural Resources, Mines and Energy Merlin Database. Attribute: Attribute_Label: FID Attribute_Definition: Internal feature number. Attribute_Definition_Source: ESRI Attribute_Domain_Values: Unrepresentable_Domain: Sequential unique whole numbers that are automatically generated. Beginning_Date_of_Attribute_Values: March 2004 Attribute: Attribute_Label: Shape Attribute_Definition: Feature geometry. Attribute_Definition_Source: ESRI Attribute_Domain_Values: Unrepresentable_Domain: Coordinates defining the features. Attribute: Attribute_Label: KEY Attribute_Definition: Unique polygon identifier and relate item for poygon attributes Attribute: Attribute_Label: ROCK_U_NAM Attribute_Definition: The Map Unit Name of the polygon. In the case of named units it comprises of the standard binomial name. Unnamed subdivisions of named units include the binomial name with a letter symbol as a suffix. Unnamed units are represented by a letter symbol, usually in combination with a map sheet number. Attribute: Attribute_Label: AGE Attribute_Definition: Geological age of unit Attribute: Attribute_Label: LITH_SUMMA Attribute_Definition: Provides a brief description of the map units as they have been described in the course of the project work, or as has appeared on relevant hard copy map legends. Attribute: Attribute_Label: ROCK_U_TYP Attribute_Definition: Provides a means of separating map units, eg for constructing a map reference. This item will contain one of the following: STRAT- Stratigraphic unit, including sedimentary, volcanic and metamorphic rock units. INTRU- Intrusive rock units; COMPST- Compound unit where the polygon includes two or more rock units, either stratigraphic, intrusive or both; COMPST- Compound unit, as above where the dominant or topmost unit is of the STRAT type; COMPIN- Compound unit, as above, where the dominant unit is of the INTRU type; WATER- Water bodies- Large dams, lakes, waterholes. Attribute: Attribute_Label: SEQUENCE_N Attribute_Definition: A numeric field to allow sorting of the rock units in approximate stratigraphic order as they would appear on a map legend. Attribute: Attribute_Label: DOMINANT_R Attribute_Definition: A simplified lithological description to allow generation of thematic maps based on broad rock types. Attribute: Attribute_Label: MAP_SYMBOL Attribute_Definition: Provides an abbreviated label for polygons. Mostly based on the letter symbols as they appear on published maps or the original hard copy compilation sheets. These are not unique across the State, but should be unique within a single map tile, and usually adjacent tiles. Attribute: Attribute_Label: NAME_100K Attribute_Definition: Name of 1:100 000 map sheet coincident with the data extent. Overview_Description: Entity_and_Attribute_Overview: Polygon Attribute information includes Polygon Key, Rock Unit Name, Age, Lithology, Rock Unit Type, Map Symbol and 1:100 000 sheet name. Dataset Citation "Queensland Department of Natural Resources, Mines and Energy" (2014) Qld 100k mapsheets - Warwick. Bioregional Assessment Source Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/3e2fa307-1f06-4873-96d3-5c3e5638894a.

The map extent is the Arctic, defined as the Arctic Bioclimate Zone, the area of the Earth with tundra vegetation and an Arctic climate and Arctic flora. It excludes tundra regions that lack an Arctic flora, such as the boreal oceanic areas of Iceland, the Aleutian Island, and the alpine tundra regions south of latitudinal tree line. Tundra is a physiognomic descriptor of low-growing vegetation beyond the cold limit of tree growth, both at high elevation (alpine tundra) and at high latitude (arctic tundra). Tundra vegetation types are composed of various combinations of herbaceous plants, shrubs, mosses and lichens. Tree line defines the southern limit of the Arctic Bioclimate Zone. In some regions of the Arctic, especially Canada and Chukotka, the forest tundra transition is gradual and interpretation of treeline directly from the AVHRR imagery was not possible. Back to Circumpolar Arctic Vegetation Map Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes: AVHRR Biomass 2010, AVHRR Biomass Trend 1982-2010, AVHRR False Color Infrared 1993-1995, AVHRR NDVI 1993-1995, AVHRR NDVI Trend 1982-2010, AVHRR Summer Warmth Index 1982-2003, Bioclimate Subzone, Coastline and Treeline, Elevation, Floristic Provinces, Lake Cover, Landscape Physiography, Landscape Age, Substrate Chemistry, Vegetation References Elvebakk, A. 1999. Bioclimate delimitation and subdivisions of the Arctic. Pages 81-112 in I. Nordal and V. Y. Razzhivin, editors. The Species Concept in the High North - A Panarctic Flora Initiative. The Norwegian Academy of Science and Letters, Oslo. Yurtsev, B. A. 1994. Floristic divisions of the Arctic. Journal of Vegetation Science 5:765-776.

This layer displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020.By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map ViewerTo show just one pair of years in ArcGIS Online Map viewer, create a filter.1. Click the filter button.2. Next, click add expression.3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this:4. Click the styles button.5. Under unique values click style options.6. Click the symbol next to No Change at the bottom of the legend.7. Click the slider next to "enable fill" to turn the symbol off.Showing just one pair of years in ArcGIS ProTo show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro.1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties.2. In the dialogue that comes up, choose the tab that says processing templates.3. On the right where it says processing template, choose the pair of years you would like to display.The processing template will stay applied for any analysis you may want to perform as well.How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer:Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe.Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes.Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map.Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

Yukon Bioclimate Zones and Subzones Version 1.0 is derived from a 30 m Digital Elevation Model (DEM) and a set of "rule-polygons". Each rule-polygon contains attributes that define upper and lower elevation limits of the bioclimate zone/subzone(s) that occur within the rule-polygon. Rule-polygon attributes and extent is defined by field data, expert observation and/or available imagery. Where available, rule-polygons were derived from plot data representative of climate (i.e. reference site). Yukon Bioclimate Zones and Subzones Version 1.0 may be used at scales larger than 1:250,000 with caution. This mapping is deliberately extended across the ocean, lakes, glaciers, etc to facilitate intersection with a terrestrial landcover layer of the user's choice. A map legend and map for this version is published in Southern Lakes Boreal Low Subzone (BOLsl): A Field Guide to Ecosite (Environment Yukon 2017). The Yukon Bioclimate Classification and Mapping project is ongoing, and subject to periodic updates or revisions. Because of this, the onus is on the end-user to ensure that they are using the most current version of the data. Although every effort has been made to ensure the correctness of the report and spatial products, there still may be errors. Please report errors in the data to the Custodian. Distributed from GeoYukon by the Government of Yukon . Discover more digital map data and interactive maps from Yukon's digital map data collection. For more information: geomatics.help@yukon.ca

With the dawning of renewed interest in one of South Australia’s historic gold provinces, driven by the newly acquired data from the Department’s highly successful Gawler Craton Airborne Survey (GCAS), the Geological Survey of South Australia has... With the dawning of renewed interest in one of South Australia’s historic gold provinces, driven by the newly acquired data from the Department’s highly successful Gawler Craton Airborne Survey (GCAS), the Geological Survey of South Australia has teamed up with researchers from CSIRO to generate new ideas for tackling the challenge of exploring through the cover of the greater Tarcoola area. Part of this project has included undertaking the compilation of a regolith material and landform map. A variety of regolith mapping data is already available for the central Gawler Craton, but the records vary in scale, detail and consistency. Existing regolith map data for the greater Tarcoola area include GSSA’s state-wide regolith layer, plus the four regolith maps of Bon Bon-Eba, Half Moon Lake, Edoldeh Tank and Tunkillia compiled as part of the Cooperative Research Centre for Landscape Environments and Mineral Exploration (CRC LEME) work done in the early 2000s. The subject newly compiled regolith map for the greater Tarcoola area covers the Malbooma, Tarcoola and Kingoonya 1:100,000 scale mapsheet areas. Mapping was based on the 1:100,000 State geology dataset and used the RTMAP (regolith landform mapping) scheme devised by Pain et al. (2007). The precision of mapping was improved by the use of remote sensing data (including new Landsat 8 imagery) as well as high resolution digital elevation and radiometric data from the GCAS Regions 9A and 9B coverage. In addition, one field trip made to the study area provided vital on-ground observations. This resulted in the definition of 22 regolith landform units, with the map legend giving information about regolith materials and landform defining each of these units. Because mineralisation in regolith is often related to intense induration, e.g. uranium and gold in calcretes, induration of regolith materials was also mapped. Calcrete, silcrete and ferricrete have been included as three separate induration/duricrust units. The polygon line work for the regolith map was compiled using ArcGIS 10.6. For each regolith polygon, twelve attributes were captured during the mapping process, including regolith materials and landform name, description, RTMAP code and map symbol, as well as the TI (transported vs. in-situ) and RED (residual-erosional-depositional) schemas, which are based on previous regolith mapping undertaken by GSSA. It should be noted that the subject map represents merely the surface distribution and physical expression of regolith units; it does not include any information about their thickness, and gives only limited, relative conclusions about their stratigraphy and age. Outcropping bedrock has been assigned to map units on the basis of its geological province, lithology, stratigraphy and/or age. Scant information about bedrock weathering intensity was available from existing datasets.

This app offers an interactive legend allowing users a more holistic experience with the 2016 Nigeria Population Density Map. In this app, unlike the web map, users can interact with the legend. By clicking on categories defined in the legend, they can focus on particular categories/ranges that are more relevant to them.

The Soil Geographical Data Base of France at Scale 1:1,000,000 is part of the European Soil Geographical Data Base of Europe. It is the resulting product of a collaborative project involving all the European Union and neighbouring countries. It is a simplified representation of the diversity and spatial variability of the soil coverage for France. The methodology used to differentiate and name the main soil types is based on the terminology of the F.A.O. legend for the Soil Map of the World at Scale 1:5,000,000. This terminology has been refined and adapted to take account of the specificities of the landscapes in Europe. It is itself founded on the distinction of the main pedogenetic processes leading to soil differentiation. The database contains a list of Soil Typological Units (STU). Besides the soil names they represent, these units are described by variables (attributes) specifying the nature and properties of the soils: for example the texture, the water regime, etc. The geographical representation was chosen at a scale corresponding to the 1:1,000,000. At this scale, it is not feasible to delineate the STUs. Therefore they are grouped into Soil Mapping Units (SMU) to form soil associations and to illustrate the functioning of pedological systems within the landscapes. Harmonisation of the soil data from the member countries is based on a dictionary giving the definition for each occurrence of the variables. Considering the scale, the precision of the variables is weak. Furthermore these variables were estimated over large areas by expert judgement rather than measured on local soil samples. This expertise results from synthesis and generalisation tasks of national or regional maps published at more detailed scales, for example 1:50,000 or 1:25,000 scales. Delineation of the Soil Mapping Units is also the result of expertise and experience. The spatial variability of soils is very important and is difficult to express at global levels of precision. Quality indices of the information (purity and confidence level) are included with the data in order to guide usage.

OverviewThis data set, a collaboration between the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland, Google, USGS, and NASA, measures areas of tree cover loss across all global land (except Antarctica and other Arctic islands) at approximately 30 × 30 meter resolution. The data were generated using multispectral satellite imagery from the Landsat 5 thematic mapper (TM), the Landsat 7 thematic mapper plus (ETM+), and the Landsat 8 Operational Land Imager (OLI) sensors. Over 1 million satellite images were processed and analyzed, including over 600,000 Landsat 7 images for the 2000-2012 interval, and more than 400,000 Landsat 5, 7, and 8 images for updates for the 2011-2020 interval. The clear land surface observations in the satellite images were assembled and a supervised learning algorithm was applied to identify per pixel tree cover loss. In this data set, “tree cover” is defined as all vegetation greater than 5 meters in height, and may take the form of natural forests or plantations across a range of canopy densities. Tree cover loss is defined as “stand replacement disturbance,” or the complete removal of tree cover canopy at the Landsat pixel scale. Tree cover loss may be the result of human activities, including forestry practices such as timber harvesting or deforestation (the conversion of natural forest to other land uses), as well as natural causes such as disease or storm damage. Fire is another widespread cause of tree cover loss, and can be either natural or human-induced. This data set has been updated five times since its creation, and now includes loss up to 2020 (Version 1.8). The analysis method has been modified in numerous ways, including new data for the target year, re-processed data for previous years (2011 and 2012 for the Version 1.1 update, 2012 and 2013 for the Version 1.2 update, and 2014 for the Version 1.3 update), and improved modelling and calibration. These modifications improve change detection for 2011-2020, including better detection of boreal loss due to fire, smallholder rotation agriculture in tropical forests, selective logging, and short cycle plantations. Eventually, a future “Version 2.0” will include reprocessing for 2000-2010 data, but in the meantime integrated use of the original data and Version 1.8 should be performed with caution. Read more about the Version 1.8 update here. When zoomed out (< zoom level 13), pixels of loss are shaded according to the density of loss at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover loss, whereas pixels with lighter shading indicate a lower concentration of tree cover loss. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13). The tree cover canopy density of the displayed data varies according to the selection - use the legend on the map to change the minimum tree cover canopy density threshold.Frequency of updates: AnnualDate of content: 2001-2020Resolution: 30x30m

Contained within 3rd Edition (1957) of the Atlas of Canada is a map that shows the division of Canada into climatic regions according to the classification of the climates of the world developed by W. Koppen. Koppen first divided the world into five major divisions to which he assigned the letters A, B, C, D, and E. The letters represent the range of divisions from tropical climate (A) to polar climate (E). There are no A climates in Canada. The descriptions of the four remaining major divisions are given in the map legend. Koppen then divided the large divisions into a number of climatic types in accordance with temperature differences and variations in the amounts and distribution of precipitation, on the basis of which he added certain letters to the initial letter denoting the major division. The definitions of the additional letters which apply in Canada are also given when they first appear in the map legend. Thus b is defined under Csb and the definition is, therefore, not repeated under Cfb, Dfb or Dsb. For this map, the temperature and precipitation criteria established by Koppen have been applied to Canadian data for a standard thirty year period (1921 to 1950 inclusive).

Hydric soils are defined as soils that formed under conditions of saturation, flooding, or ponding long enough during the growing season to develop anaerobic conditions in the upper part. Under natural conditions, these soils are either saturated or inundated long enough during the growing season to support the growth and reproduction of hydrophytic vegetation. For additional documentation including a description of the map legend for Hydric Soils, refer to the CT ECO Complete Resource Guide for Hydric Soils.

All soil information included in the CT ECO maps and map viewers is from the Natural Resources Conservation Service (NRCS) Web Soil Survey (WSS), which is based on information originally published on the set of Soil Survey quarter quadrangle maps that cover Connecticut.

Hydric soil have a number of agricultural and nonagricultural applications. These include assistance in land-use planning, conservation planning, and assessment of potential wildlife habitat. A combination of the hydric soil, hydrophytic vegetation, and hydrology properties define wetlands as described in the National Food Security Act Manual (Soil Conservation Service, 1994) and the Corps of Engineers (COE) Wetlands Delineation Manual (Environmental Laboratory, 1987) and COE Regional Supplements for the Clean Water Act Section 404 permit program. Section 404 requires a permit from the COE for the discharge of dredged or fill material into the waters of the United States, including wetlands. Therefore, an area that meets the hydric soil definition must also meet the hydrophytic vegetation and wetland hydrology definitions in order for it to be correctly classified as a jurisdictional wetland.

The land cover database provides information on the land cover distribution by administrative divisions (States). The dataset was created using the FAO/GLCN methodology and tools. The land cover mapping was carried out with the interpretation of an integrated coverage of GLS Landsat satellite images (2000 and circa 2005-2007, band composite 432) 30m res., acquired for the whole extent of Sudan, and improved with updated higher resolution SPOT (2009-2010) 2.5-5m, 10-20m res., IRS (2007) 15-22m res. and Aster images (2005-2010) 15m res. covering the agricultural areas. This approach was adopted to emphasize the land cover features in the agricultural production areas which were derived from the existing Africover Sudan data base dated circa 2002. The legend was prepared using the Land Cover Classification System (LCCS*). The database is divided in two parts, West and East, named: merge_F_West.shp (293,781 polygons) merge_F_East.shp (197,895 polygons) The two parts cover the entire Sudan. The “merge” suffix means that the database is merged according to the States, i.e. each Land Cover polygon has a label (in the dbf table) of the State in which the polygon is located. The merge_F_East.shp covers the following States: Blue Nile, El Gadarif, El Gezira, Kassala, Khartoum, Red Sea, River Nile, Sinnar, White Nile. The merge_F_West.shp covers the following States: Northern, Northern Darfur, Northern Kordufan, Southern Darfur, Southern Kordufan, Western Darfur. The legend is the key of the database. All the information included in the database is implicit in the legend. The legend is defined according to the LCCS (Land Cover Classification System), which is the classification system developed by GLCN to systematically describe the land cover.

This data set, a collaboration between the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland, Google, USGS, and NASA, measures areas of tree cover loss across all global land (except Antarctica and other Arctic islands) at approximately 30 × 30 meter resolution. The data were generated using multispectral satellite imagery from the Landsat 5 thematic mapper (TM), the Landsat 7 thematic mapper plus (ETM+), and the Landsat 8 Operational Land Imager (OLI) sensors. Over 1 million satellite images were processed and analyzed, including over 600,000 Landsat 7 images for the 2000-2012 interval, and more than 400,000 Landsat 5, 7, and 8 images for updates for the 2011-2022 interval. The clear land surface observations in the satellite images were assembled and a supervised learning algorithm was applied to identify per pixel tree cover loss.In this data set, “tree cover” is defined as all vegetation greater than 5 meters in height, and may take the form of natural forests or plantations across a range of canopy densities. Tree cover loss is defined as “stand replacement disturbance,” or the complete removal of tree cover canopy at the Landsat pixel scale. Tree cover loss may be the result of human activities, including forestry practices such as timber harvesting or deforestation (the conversion of natural forest to other land uses), as well as natural causes such as disease or storm damage. Fire is another widespread cause of tree cover loss, and can be either natural or human-induced.This data set has been updated five times since its creation, and now includes loss up to 2022 (Version 1.10). The analysis method has been modified in numerous ways, including new data for the target year, re-processed data for previous years (2011 and 2012 for the Version 1.1 update, 2012 and 2013 for the Version 1.2 update, and 2014 for the Version 1.3 update), and improved modelling and calibration. These modifications improve change detection for 2011-2022, including better detection of boreal loss due to fire, smallholder rotation agriculture in tropical forests, selective losing, and short cycle plantations. Eventually, a future “Version 2.0” will include reprocessing for 2000-2010 data, but in the meantime integrated use of the original data and Version 1.7 should be performed with caution. Read more about the Version 1.7 update here.When zoomed out (< zoom level 13), pixels of loss are shaded according to the density of loss at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover loss, whereas pixels with lighter shading indicate a lower concentration of tree cover loss. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13).The tree cover canopy density of the displayed data varies according to the selection - use the legend on the map to change the minimum tree cover canopy density threshold.

Yukon Bioclimate Zones and Subzones Version 1.0 is derived from a 30 m Digital Elevation Model (DEM) and a set of "rule-polygons". Each rule-polygon contains attributes that define upper and lower elevation limits of the bioclimate zone/subzone(s) that occur within the rule-polygon. Rule-polygon attributes and extent is defined by field data, expert observation and/or available imagery. Where available, rule-polygons were derived from plot data representative of climate (i.e. reference site). Yukon Bioclimate Zones and Subzones Version 1.0 may be used at scales larger than 1:250,000 with caution. This mapping is deliberately extended across the ocean, lakes, glaciers, etc to facilitate intersection with a terrestrial landcover layer of the user's choice. A map legend and map for this version is published in Southern Lakes Boreal Low Subzone (BOLsl): A Field Guide to Ecosite (Environment Yukon 2017). The Yukon Bioclimate Classification and Mapping project is ongoing, and subject to periodic updates or revisions. Because of this, the onus is on the end-user to ensure that they are using the most current version of the data. Although every effort has been made to ensure the correctness of the report and spatial products, there still may be errors. Please report errors in the data to the Custodian. Distributed from GeoYukon by the Government of Yukon . Discover more digital map data and interactive maps from Yukon's digital map data collection. For more information: geomatics.help@yukon.ca

--------------------------------------------------------------------------------------------------------------------------This layer has been deprecated as of 1/15/2025. The newest available dataset can be found here: https://nifc.maps.arcgis.com/home/item.html?id=4107b5d1debf4305ba00e929b7e5971This dataset will remain available until 6/6/2025 to make the transition to the new data source as seamless as possible for the wildland fire community.--------------------------------------------------------------------------------------------------------------------------Jurisdictional Unit, 2023-07-19. For use with WFDSS, IFTDSS, IRWIN, and InFORM.This is a feature service which provides Identify and Copy Feature capabilities. If fast-drawing at coarse zoom levels is a requirement, consider using the tile (map) service layer located at https://nifc.maps.arcgis.com/home/item.html?id=3b2c5daad00742cd9f9b676c09d03d13.OverviewThe Jurisdictional Agencies dataset is developed as a national land management geospatial layer, focused on representing wildland fire jurisdictional responsibility, for interagency wildland fire applications, including WFDSS (Wildland Fire Decision Support System), IFTDSS (Interagency Fuels Treatment Decision Support System), IRWIN (Interagency Reporting of Wildland Fire Information), and InFORM (Interagency Fire Occurrence Reporting Modules). It is intended to provide federal wildland fire jurisdictional boundaries on a national scale. The agency and unit names are an indication of the primary manager name and unit name, respectively, recognizing that:There may be multiple owner names.Jurisdiction may be held jointly by agencies at different levels of government (ie State and Local), especially on private lands, Some owner names may be blocked for security reasons.Some jurisdictions may not allow the distribution of owner names. Private ownerships are shown in this layer with JurisdictionalUnitIdentifier=null,JurisdictionalUnitAgency=null, JurisdictionalUnitKind=null, and LandownerKind="Private", LandownerCategory="Private". All land inside the US country boundary is covered by a polygon.Jurisdiction for privately owned land varies widely depending on state, county, or local laws and ordinances, fire workload, and other factors, and is not available in a national dataset in most cases.For publicly held lands the agency name is the surface managing agency, such as Bureau of Land Management, United States Forest Service, etc. The unit name refers to the descriptive name of the polygon (i.e. Northern California District, Boise National Forest, etc.).These data are used to automatically populate fields on the WFDSS Incident Information page, in IRWIN, INFORM Wildfire, and INFORM Fuels.Relevant NWCG Definitions and StandardsUnit2. A generic term that represents an organizational entity that only has meaning when it is contextualized by a descriptor, e.g. jurisdictional.Definition Extension: When referring to an organizational entity, a unit refers to the smallest area or lowest level. Higher levels of an organization (region, agency, department, etc) can be derived from a unit based on organization hierarchy.Unit, JurisdictionalThe governmental entity having overall land and resource management responsibility for a specific geographical area as provided by law.Definition Extension: 1) Ultimately responsible for the fire report to account for statistical fire occurrence; 2) Responsible for setting fire management objectives; 3) Jurisdiction cannot be re-assigned by agreement; 4) The nature and extent of the incident determines jurisdiction (for example, Wildfire vs. All Hazard); 5) Responsible for signing a Delegation of Authority to the Incident Commander.See also: Unit, Protecting; LandownerUnit IdentifierThis data standard specifies the standard format and rules for Unit Identifier, a code used within the wildland fire community to uniquely identify a particular government organizational unit.Landowner Kind & CategoryThis data standard provides a two-tier classification (kind and category) of landownership. Attribute Fields JurisdictionalAgencyKind Describes the type of unit Jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, and Other. A value may not be populated for all polygons.JurisdictionalAgencyCategoryDescribes the type of unit Jurisdiction using the NWCG Landowner Category data standard. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State. A value may not be populated for all polygons.JurisdictionalUnitNameThe name of the Jurisdictional Unit. Where an NWCG Unit ID exists for a polygon, this is the name used in the Name field from the NWCG Unit ID database. Where no NWCG Unit ID exists, this is the “Unit Name” or other specific, descriptive unit name field from the source dataset. A value is populated for all polygons.JurisdictionalUnitIDWhere it could be determined, this is the NWCG Standard Unit Identifier (Unit ID). Where it is unknown, the value is ‘Null’. Null Unit IDs can occur because a unit may not have a Unit ID, or because one could not be reliably determined from the source data. Not every land ownership has an NWCG Unit ID. Unit ID assignment rules are available from the Unit ID standard, linked above.LandownerKindThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons.LandownerCategoryThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons.DataSourceThe database from which the polygon originated. Be as specific as possible, identify the geodatabase name and feature class in which the polygon originated.SecondaryDataSourceIf the Data Source is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if Data Source is "PAD-US 2.1", then for a USDA Forest Service polygon, the Secondary Data Source would be "USDA FS Automated Lands Program (ALP)". For a BLM polygon in the same dataset, Secondary Source would be "Surface Management Agency (SMA)."SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.MapMethod:Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality. MapMethod will be Mixed Method by default for this layer as the data are from mixed sources. Valid Values include: GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; DigitizedTopo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Phone/Tablet; OtherDateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using 24 hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature.GeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.JoinMethodAdditional information on how the polygon was matched information in the NWCG Unit ID database.LocalNameLocalName for the polygon provided from PADUS or other source.LegendJurisdictionalAgencyJurisdictional Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.LegendLandownerAgencyLandowner Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.DataSourceYearYear that the source data for the polygon were acquired.Data InputThis dataset is based on an aggregation of 4 spatial data sources: Protected Areas Database US (PAD-US 2.1), data from Bureau of Indian Affairs regional offices, the BLM Alaska Fire Service/State of Alaska, and Census Block-Group Geometry. NWCG Unit ID and Agency Kind/Category data are tabular and sourced from UnitIDActive.txt, in the WFMI Unit ID application (https://wfmi.nifc.gov/unit_id/Publish.html). Areas of with unknown Landowner Kind/Category and Jurisdictional Agency Kind/Category are assigned LandownerKind and LandownerCategory values of "Private" by use of the non-water polygons from the Census Block-Group geometry.PAD-US 2.1:This dataset is based in large part on the USGS Protected Areas Database of the United States - PAD-US 2.`. PAD-US is a compilation of authoritative protected areas data between agencies and organizations that ultimately results in a comprehensive and accurate inventory of protected areas for the United States to meet a variety of needs (e.g. conservation, recreation, public health, transportation, energy siting, ecological, or watershed assessments and planning). Extensive documentation on PAD-US processes and data sources is available.How these data were aggregated:Boundaries, and their descriptors, available in spatial databases (i.e. shapefiles or geodatabase feature classes) from land management agencies are the desired and primary data sources in PAD-US. If these authoritative sources are unavailable, or the agency recommends another source, data may be incorporated by other aggregators such as non-governmental organizations. Data sources are tracked for each record in the PAD-US geodatabase (see below).FWS Data:The FWS Interest layer was crosswalked to the NWCG Jurisdictional Unit/Agency data standard and FWS boundaries were updated from this dataset.NPS Data:Specific NPS

DescriptionThis is a vector tile layer built from the same data as the Jurisdictional Units Public feature service located here: https://nifc.maps.arcgis.com/home/item.html?id=4107b5d1debf4305ba00e929b7e5971a. This service can be used alone as a fast-drawing background layer, or used in combination with the feature service when Identify and Copy Feature capabilities are needed. At fine zoom levels, the feature service will be needed.OverviewThe Jurisdictional Units dataset outlines wildland fire jurisdictional boundaries for federal, state, and local government entities on a national scale and is used within multiple wildland fire systems including the Wildland Fire Decision Support System (WFDSS), the Interior Fuels and Post-Fire Reporting System (IFPRS), the Interagency Fuels Treatment Decision Support System (IFTDSS), the Interagency Fire Occurrence Reporting Modules (InFORM), the Interagency Reporting of Wildland Fire Information System (IRWIN), and the Wildland Computer-Aided Dispatch Enterprise System (WildCAD-E).In this dataset, agency and unit names are an indication of the primary manager’s name and unit name, respectively, recognizing that:There may be multiple owner names.Jurisdiction may be held jointly by agencies at different levels of government (ie State and Local), especially on private lands, Some owner names may be blocked for security reasons.Some jurisdictions may not allow the distribution of owner names. Private ownerships are shown in this layer with JurisdictionalUnitIID=null, JurisdictionalKind=null, and LandownerKind="Private", LandownerCategory="Private". All land inside the US country boundary is covered by a polygon.Jurisdiction for privately owned land varies widely depending on state, county, or local laws and ordinances, fire workload, and other factors, and is not available in a national dataset in most cases.For publicly held lands the agency name is the surface managing agency, such as Bureau of Land Management, United States Forest Service, etc. The unit name refers to the descriptive name of the polygon (i.e. Northern California District, Boise National Forest, etc.).AttributesField NameDefinitionGeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. Not populated for Census Block Groups.JurisdictionalUnitIDWhere it could be determined, this is the NWCG Unit Identifier (Unit ID). Where it is unknown, the value is ‘Null’. Null Unit IDs can occur because a unit may not have a Unit ID, or because one could not be reliably determined from the source data. Not every land ownership has an NWCG Unit ID. Unit ID assignment rules are available in the Unit ID standard.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.JurisdictionalUnitNameThe name of the Jurisdictional Unit. Where an NWCG Unit ID exists for a polygon, this is the name used in the Name field from the NWCG Unit ID database. Where no NWCG Unit ID exists, this is the “Unit Name” or other specific, descriptive unit name field from the source dataset. A value is populated for all polygons except for Census Blocks Group and for PAD-US polygons that did not have an associated name.LocalNameLocal name for the polygon provided from agency authoritative data, PAD-US, or other source.JurisdictionalKindDescribes the type of unit jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, Other, and Private. A value is not populated for Census Block Groups.JurisdictionalCategoryDescribes the type of unit jurisdiction using the NWCG Landowner Category data standard. Valid values include: BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, State, OtherLoc (other local, not in the standard), Private, and ANCSA. A value is not populated for Census Block Groups.LandownerKindThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. Legal values align with the NWCG Landowner Kind data standard. A value is populated for all polygons.LandownerCategoryThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. Legal values align with the NWCG Landowner Category data standard. A value is populated for all polygons.LandownerDepartmentFederal department information that aligns with a unit’s landownerCategory information. Legal values include: Department of Agriculture, Department of Interior, Department of Defense, and Department of Energy. A value is not populated for all polygons.DataSourceThe database from which the polygon originated. An effort is made to be as specific as possible (i.e. identify the geodatabase name and feature class in which the polygon originated).SecondaryDataSourceIf the DataSource field is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if DataSource is "PAD-US 4.0", then for a TNC polygon, the SecondaryDataSource would be " TNC_PADUS2_0_SA2015_Public_gdb ".SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.DataSourceYearYear that the source data for the polygon were acquired.MapMethodControlled vocabulary to define how the geospatial feature was derived. MapMethod will be Mixed Methods by default for this layer as the data are from mixed sources. Valid Values include: GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; DigitizedTopo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Phone/Tablet; Other.DateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using the 24-hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature.JoinMethodAdditional information on how the polygon was matched to information in the NWCG Unit ID database.LegendJurisdictionalCategoryJurisdictionalCategory values grouped for more intuitive use in a map legend or summary table. Census Block Groups are classified as “No Unit”.LegendLandownerCategoryLandownerCategory values grouped for more intuitive use in a map legend or summary table.Other Relevant NWCG Definition StandardsUnitA generic term that represents an organizational entity that only has meaning when it is contextualized by a descriptor, e.g. jurisdictional.Definition Extension: When referring to an organizational entity, a unit refers to the smallest area or lowest level. Higher levels of an organization (region, agency, department, etc.) can be derived from a unit based on organization hierarchy.Unit, JurisdictionalThe governmental entity having overall land and resource management responsibility for a specific geographical area as provided by law.Definition Extension: 1) Ultimately responsible for the fire report to account for statistical fire occurrence; 2) Responsible for setting fire management objectives; 3) Jurisdiction cannot be re-assigned by agreement; 4) The nature and extent of the incident determines jurisdiction (for example, Wildfire vs. All Hazard); 5) Responsible for signing a Delegation of Authority to the Incident Commander.See also: Protecting Unit; LandownerData SourcesThis dataset is an aggregation of multiple spatial data sources: • Authoritative land ownership records from BIA, BLM, NPS, USFS, USFWS, and the Alaska Fire Service/State of Alaska• The Protected Areas Database US (PAD-US 4.0)• Census Block-Group Geometry BIA and Tribal Data:BIA and Tribal land management data were aggregated from BIA regional offices. These data date from 2012 and were reviewed/updated in 2024. Indian Trust Land affiliated with Tribes, Reservations, or BIA Agencies: These data are not considered the system of record and are not intended to be used as such. The Bureau of Indian Affairs (BIA), Branch of Wildland Fire Management (BWFM) is not the originator of these data. The spatial data coverage is a consolidation of the best available records/data received from each of the 12 BIA Regional Offices. The data are no better than the original sources from which they were derived. Care was taken when consolidating these files. However, BWFM cannot accept any responsibility for errors, omissions, or positional accuracy in the original digital data. The information contained in these data is dynamic and is continually changing. Updates to these data will be made whenever such data are received from a Regional Office. The BWFM gives no guarantee, expressed, written, or implied, regarding the accuracy, reliability, or completeness of these data.Alaska:The state of Alaska and Alaska Fire Service (BLM) co-manage a process to aggregate authoritative land ownership, management, and jurisdictional boundary data, based on Master Title Plats. Data ProcessingTo compile this dataset, the authoritative land ownership records and the PAD-US data mentioned above were crosswalked into the Jurisdictional Unit Polygon schema and aggregated through a series of python scripts and FME models. Once aggregated, steps were taken to reduce overlaps within the data. All overlap areas larger than 300 acres were manually examined and removed with the assistance of fire management SMEs. Once overlaps were removed, Census Block Group geometry were crosswalked to the Jurisdictional Unit Polygon schema and appended in areas in which no jurisdictional boundaries were recorded within the authoritative land ownership records and the PAD-US data. Census Block Group geometries represent areas of unknown Landowner Kind/Category and

World Terrestrial Ecosystems are areas of climate, landform and land cover that form the basic components of terrestrial ecosystem structure. This map is the first-of-its-kind effort to characterize and map global terrestrial ecosystems at a much finer spatial resolution (250 m) than existing ecoregionalizations, and a much finer thematic resolution than existing global land cover products.This map is important because the ecologically relevant distinctions are authoritatively defined and modeled using globally consistent objectively derived data.World Terrestrial Ecosystems map was produced by adopting and modifying the Intergovernmental Panel on Climate Change (IPCC) approach on the definition of Terrestrial Ecosystems and development of standardized (default) global climate regions using the values of environmental moisture regime and temperature regime. We then combined the values of Global Climate Regions, Landforms and matrix-forming vegetation assemblage or land use, using the ArcGIS Combine tool (Spatial Analyst) to produce World Ecosystems Dataset. This combination resulted of 431 World Ecosystems classes. You can see the legend below.This layer provides access to a cached map service created by Esri in partnership with U.S. Geological Survey's Climate and Land Use Change Program and The Nature Conservancy. The work from this collaboration is documented in the publication:Sayre et al. 2020. An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems - Global Ecology and Conservation. You can access and view World Terrestrial Ecosystems Image File. You can access and have an high-level understanding of this dataset from the Introduction to World Terrestrial Ecosystems Story Map. You can download this dataset as ArcGIS World Ecosystems Pro Package.