Note: This is a large dataset. To download, go to ArcGIS Open Data Set and click the download button, and under additional resources select the shapefile or geodatabase option. The Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service. The application allows tracking and monitoring of NEPA decisions as well as the ability to create and manage KV trust fund plans at the timber sale level. This application complements its companion NRM applications, which cover the spectrum of living and non-living natural resource information. This layer represents activities of hazardous fuel treatment reduction that are polygons. All accomplishments toward the unified hazardous fuels reduction target must meet the following definition: Vegetative manipulation designed to create and maintain resilient and sustainable landscapes, including burning, mechanical treatments, and/or other methods that reduce the quantity or change the arrangement of living or dead fuel so that the intensity, severity, or effects of wildland fire are reduced within acceptable ecological parameters and consistent with land management plan objectives, or activities that maintain desired fuel conditions. These conditions should be measurable or predictable using fire behavior prediction models or fire effects models. Go to this url for full metadata description: https://data.fs.usda.gov/geodata/edw/edw_resources/meta/S_USA.Activity_HazFuelTrt_PL.xml

Public Open Space Geographic Information System data collection for Perth and Peel Metropolitan Areas

The public open space (POS) dataset contains polygon boundaries of areas defined as publicly available and open. This geographic information system (GIS) dataset was collected in 2011/2012 using ArcGIS software and aerial photography dated from 2010-2011. The data was collected across the Perth Metro and Peel Region.

POS refer to all land reserved for the provision of green space and natural environments (e.g. parks, reserves, bushland) that is freely accessible and intended for use for recreation purposes (active or passive) by the general public. Four types of “green and natural public open spaces” are distinguished: (1) Park; (2) Natural or Conservation Area; (3) School Grounds; and (4) Residual. Areas where the public are not permitted except on payment or which are available to limited and selected numbers by membership (e.g. golf courses and sports centre facilities) or setbacks and buffers required by legislation are not included.

Initially, potential POSs were identified from a combination of existing geographic information system (GIS) spatial data layers to create a generalized representation of ‘green space’ throughout the Perth metropolitan and Peel regions. Base data layers include: cadastral polygons, metropolitan and regional planning scheme polygons, school point locations, and reserve vesting polygons. The ‘green’ space layer was then visually updated and edited to represent the true boundaries of each POS using 2010-2011 aerial photography within the ArcGIS software environment. Each resulting ’green’ polygon was then classified using a decision tree into one of four possible categories: park, natural or conservation area, school grounds, or residual green space.

Following the classification process, amenity and other information about each POS was collected for polygons classified as “Park” following a protocol developed at the Centre for the Built Environment and Health (CBEH) called POSDAT (Public Open Space Desktop Auditing Tool). The parks were audited using aerial photography visualized using ArcGIS software. . The presence or absence of amenities such as sporting facilities (e.g. tennis courts, soccer fields, skate parks etc) were audited as well as information on the environmental quality (i.e. presence of water, adjacency to bushland, shade along paths, etc), recreational amenities (e.g. presence of BBQ’, café or kiosks, public access toilets) and information on selected features related to personal safety.

The data is stored in an ArcGIS File Geodatabase Feature Class (size 4MB) and has restricted access.

Data creation methodology, data definitions, and links to publications based on this data, accompany the dataset.

The following area shall be a lake trout refuge with boundaries in Lake Huron, grids 307 through 309, the north half (N½) of grid 407, and grids 408 through 410. Regulations: All commercial fishing is prohibited in the above area. Maps for general reference only: refer to text of Consent Decree 2000 for exact locations and provisions.Created a new polygon shapefile in ArcGIS 8.1. Copied selected features (as outlined in the Consent Decree 2000 documentation) of the US Department of Commerce (Bureau of the Census, Geography Division) county census (1995) layer into new shapefile. Created a new polygon shapefile in ArcGIS 8.1. The new pollygon layer was created using the snapping tool in ArcMap. Snapping to the vertices (as outlined in the Consent Decree 2000 documentation) of the MDNR (University of Michigan) Statistical Grid layer, extending the polygon boundaries beyond the preceding polygon we created, and finishing the sketch at the starting point. The new polygon feature was then commbined with the copied polygon generated from the US Department of Commerce (Bureau of the Census, Geography Division) county census (1995) layer using the union tool from the geoprocessing wizard in Arc Map. The desired features were then selected, exported as a new shapefile, and reprojected from Michigan georef to Decimal Degrees to create the final Drummond Island Lake Trout Refuge layer.The boundaries represented on consent decree maps are approximations based on the text contained in the 2000 Consent Decree. For legal descriptions of geographic extent or details pertaining to regulations for these representations refer to the original 2000 Consent Decree Document.

This dataset is a modified version of the FWS developed data depicting “Highly Important Landscapes”, as outlined in Memorandum FWS/AES/058711 and provided to the Wildlife Habitat Spatial analysis Lab on October 29th 2014. Other names and acronyms used to refer to this dataset have included: Areas of Significance (AoSs - name of GIS data set provided by FWS), Strongholds (FWS), and Sagebrush Focal Areas (SFAs - BLM). The BLM will refer to these data as Sagebrush Focal Areas (SFAs). Data were provided as a series of ArcGIS map packages which, when extracted, contained several datasets each. Based on the recommendation of the FWS Geographer/Ecologist (email communication, see data originator for contact information) the dataset called “Outiline_AreasofSignificance” was utilized as the source for subsequent analysis and refinement. Metadata was not provided by the FWS for this dataset. For detailed information regarding the dataset’s creation refer to Memorandum FWS/AES/058711 or contact the FWS directly. Several operations and modifications were made to this source data, as outlined in the “Description” and “Process Step” sections of this metadata file. Generally: The source data was named by the Wildlife Habitat Spatial Analysis Lab to identify polygons as described (but not identified in the GIS) in the FWS memorandum. The Nevada/California EIS modified portions within their decision space in concert with local FWS personnel and provided the modified data back to the Wildlife Habitat Spatial Analysis Lab. Gaps around Nevada State borders, introduced by the NVCA edits, were then closed as was a large gap between the southern Idaho & southeast Oregon present in the original dataset. Features with an area below 40 acres were then identified and, based on FWS guidance, either removed or retained. Finally, guidance from BLM WO resulted in the removal of additional areas, primarily non-habitat with BLM surface or subsurface management authority. Data were then provided to each EIS for use in FEIS development. Based on guidance from WO, SFAs were to be limited to BLM decision space (surface/sub-surface management areas) within PHMA. Each EIS was asked to provide the limited SFA dataset back to the National Operations Center to ensure consistent representation and analysis. Returned SFA data, modified by each individual EIS, was then consolidated at the BLM’s National Operations Center retaining the three standardized fields contained in this dataset.Several Modifications from the original FWS dataset have been made. Below is a summary of each modification.1. The data as received from FWS: 16,514,163 acres & 1 record.2. Edited to name SFAs by Wildlife Habitat Spatial Analysis Lab:Upon receipt of the “Outiline_AreasofSignificance” dataset from the FWS, a copy was made and the one existing & unnamed record was exploded in an edit session within ArcMap. A text field, “AoS_Name”, was added. Using the maps provided with Memorandum FWS/AES/058711, polygons were manually selected and the “AoS_Name” field was calculated to match the names as illustrated. Once all polygons in the exploded dataset were appropriately named, the dataset was dissolved, resulting in one record representing each of the seven SFAs identified in the memorandum.3. The NVCA EIS made modifications in concert with local FWS staff. Metadata and detailed change descriptions were not returned with the modified data. Contact Leisa Wesch, GIS Specialist, BLM Nevada State Office, 775-861-6421, lwesch@blm.gov, for details.4. Once the data was returned to the Wildlife Habitat Spatial Analysis Lab from the NVCA EIS, gaps surrounding the State of NV were closed. These gaps were introduced by the NVCA edits, exacerbated by them, or existed in the data as provided by the FWS. The gap closing was performed in an edit session by either extending each polygon towards each other or by creating a new polygon, which covered the gap, and merging it with the existing features. In addition to the gaps around state boundaries, a large area between the S. Idaho and S.E. Oregon SFAs was filled in. To accomplish this, ADPP habitat (current as of January 2015) and BLM GSSP SMA data were used to create a new polygon representing PHMA and BLM management that connected the two existing SFAs.5. In an effort to simplify the FWS dataset, features whose areas were less than 40 acres were identified and FWS was consulted for guidance on possible removal. To do so, features from #4 above were exploded once again in an ArcMap edit session. Features whose areas were less than forty acres were selected and exported (770 total features). This dataset was provided to the FWS and then returned with specific guidance on inclusion/exclusion via email by Lara Juliusson (lara_juliusson@fws.gov). The specific guidance was:a. Remove all features whose area is less than 10 acresb. Remove features identified as slivers (the thinness ratio was calculated and slivers identified by Lara Juliusson according to https://tereshenkov.wordpress.com/2014/04/08/fighting-sliver-polygons-in-arcgis-thinness-ratio/) and whose area was less than 20 acres.c. Remove features with areas less than 20 acres NOT identified as slivers and NOT adjacent to other features.d. Keep the remainder of features identified as less than 40 acres.To accomplish “a” and “b”, above, a simple selection was applied to the dataset representing features less than 40 acres. The select by location tool was used, set to select identical, to select these features from the dataset created in step 4 above. The records count was confirmed as matching between the two data sets and then these features were deleted. To accomplish “c” above, a field (“AdjacentSH”, added by FWS but not calculated) was calculated to identify features touching or intersecting other features. A series of selections was used: first to select records 6. Based on direction from the BLM Washington Office, the portion of the Upper Missouri River Breaks National Monument (UMRBNM) that was included in the FWS SFA dataset was removed. The BLM NOC GSSP NLCS dataset was used to erase these areas from #5 above. Resulting sliver polygons were also removed and geometry was repaired.7. In addition to removing UMRBNM, the BLM Washington Office also directed the removal of Non-ADPP habitat within the SFAs, on BLM managed lands, falling outside of Designated Wilderness’ & Wilderness Study Areas. An exception was the retention of the Donkey Hills ACEC and adjacent BLM lands. The BLM NOC GSSP NLCS datasets were used in conjunction with a dataset containing all ADPP habitat, BLM SMA and BLM sub-surface management unioned into one file to identify and delete these areas.8. The resulting dataset, after steps 2 – 8 above were completed, was dissolved to the SFA name field yielding this feature class with one record per SFA area.9. Data were provided to each EIS for use in FEIS allocation decision data development.10. Data were subset to BLM decision space (surface/sub-surface) within PHMA by each EIS and returned to the NOC.11. Due to variations in field names and values, three standardized fields were created and calculated by the NOC:a. SFA Name – The name of the SFA.b. Subsurface – Binary “Yes” or “No” to indicated federal subsurface estate.c. SMA – Represents BLM, USFS, other federal and non-federal surface management 12. The consolidated data (with standardized field names and values) were dissolved on the three fields illustrated above and geometry was repaired, resulting in this dataset.

Those portions of Lake Huron grids 304 and 305 north of a line beginning at the southerly point of land on the easterly side of Dudley Bay (Cadogan Point); then running southwesterly in a straight line to the southeasterly end of Beaver Tail Point; then running westerly in a straight line to the southeasterly end of Whitefish Point in Mackinac County. Regulations: All commercial fishing is prohibited for the period from the Friday before Memorial Day through Labor Day only. Maps for general reference only: refer to text of Consent Decree 2000 for exact locations and provisions.Created a new polygon shapefile in ArcGIS 8.1. Digitized missing target area from Chippewa county 1:24,000 DRG. The new polygon feature was then commbined with the US Department of Commerce (Bureau of the Census, Geography Division) county census (1995) layer using the union tool from the geoprocessing wizard in Arc Map. The desired features were then selected and exported as a new shapefile. Created a new polygon shapefile in ArcGIS 8.1. A point was located on the USGS Mackinac county 1:24,000 DRG as outlined in the Consent Decree 2000 documentation. The new pollygon layer was created using the snapping tool in ArcMap. Starting form the above point location and heading in a clockwise direction (as outlined in the Consent Decree 2000 documentation) extending the polygon boundaries beyond the improved US Department of Commerce (Bureau of the Census, Geography Division) county census (1995) layer created earlier. The new polygon feature was then commbined with the preceding layer using the union tool from the geoprocessing wizard in Arc Map. The desired features were then selected, exported as a new shapefile, and reprojected from Michigan georef to Decimal Degrees to create the final Les Cheneaux Island Closure C layer.The boundaries represented on consent decree maps are approximations based on the text contained in the 2000 Consent Decree. For legal descriptions of geographic extent or details pertaining to regulations for these representations refer to the original 2000 Consent Decree Document.

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted GIS data cover administrative and postal divisions with up to 6 precision levels: a zip code layer and up to 5 administrative levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.

The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Boundaries Database (GIS data, Geospatial data)

• In-depth spatial analysis

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Product Features

• Coherence and precision at every level

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• Population data: Historical and future trends

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Data export methodology

Our geospatial data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

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Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

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 -

This Administration feature is the single most valuable feature maintained by the GIS Services staff. It combines the maintenance of many individual polygon features in one main overall feature.It is part of a ArcGIS Topology class maintained with our parcel and zoning features in the Editing Feature Data Set.We use the shared editing capabilities of this topology class to leverage our maintenance procedures as simply as possible. Weekly, the individual features maintained with our Administration feature are created with ArcGIS dissolve function. These include Jurisdiction boundaries, Public Safety Response areas, Voting Precincts, Schools Attendance Zones, Inspections, Library Service Zones, and more.Generally, maintenance of this feature is controlled thru shared editing performed with our parcel/zoning edits with the use of the Topology features in ArcGIS. Changes to features maintained in the Administration feature are caused by a number of issues. Parcel edits, new Public Safety Stations, changes in Voting Precincts, Police Reporting districts and other changes occur often. Most changes can be facilitated by selecting one or more “Administrative” polygons and changing the appropriate attribute value. Use of the “Cut Polygon” task may be necessary in those cases where part of a polygon must be changed from a district to another. The appropriate attribute can be changed in the affected area as necessary.

Named Waterbody is a 1:24,000-scale, polygon and line feature-based layer that includes all named waterbodies depicted on the U.S. Geological Survey (USGS) 7.5 minute topographic quadrangle maps for the State of Connecticut. This layer only includes features located in Connecticut. Named Waterbody features include water, dams, flow connectors, aqueducts, canals, ditches, shorelines, and islands. The layer does not include the marsh areas, tidal flats, rocks, shoals, or channels typically shown on USGS 7.5 minute topographic quadrangle maps. However, the layer includes linear (flow) connector features that fill in gaps between river and stream features where water passes through marshes or underground through pipelines and tunnels. Note that connectors represent general pathways and do not represent the exact location or orientation of actual underground pipelines, tunnels, aqueducts, etc. The Named Waterbody layer is comprised of polygon and line features. Polygon features represent areas of water for rivers, streams, brooks, reservoirs, lakes, ponds, bays, coves, and harbors. Polygon features also depict related information such as dams and islands. Line features represent single-line rivers and streams, flow connectors, aqueducts, canals, and ditches. Line features also enclose all polygon features in the form of shorelines, dams, and closure lines separating adjacent water features. The Named Waterbody layer is based on information from USGS topographic quadrangle maps published between 1969 and 1984 so it does not depict conditions at any one particular point in time. Also, the layer does not reflect recent changes with the course of streams or location of shorelines impacted by natural events or changes in development since the time the USGS 7.5 minute topographic quadrangle maps were published. Attribute information is comprised of codes to identify waterbody features by type, cartographically represent (symbolize) waterbody features on a map, select waterbodies appropriate to display at different map scales, identify individual waterbodies on a map by name, and describe waterbody feature area and length. The names assigned to individual waterbodies are based on information published on the USGS 7.5 minute topographic quadrangle maps or other state and local maps. The Named Waterbody layer does not include bathymetric, stream gradient, water flow, water quality, or biological habitat information. Derived from the Hydrography layer, the Named Waterbody layer was originally published in 1999. The 2005 edition includes the same water features published in 1999, however some attribute information has been slightly modified and made easier to use. Also, the 2005 edition corrects previously undetected attribute coding errors and includes the flow connector features. Connecticut Named Waterbody Polygon includes the polygon features of a layer named Named Waterbody. Named Waterbody is a 1:24,000-scale, polygon and line feature-based layer that includes all named waterbodies depicted on the U.S. Geological Survey (USGS) 7.5 minute topographic quadrangle maps for the State of Connecticut. This layer only includes features located in Connecticut. Named Waterbody features include water, dams, flow connectors, aqueducts, canals, ditches, shorelines, and islands. The layer does not include the marsh areas, tidal flats, rocks, shoals, or channels typically shown on USGS 7.5 minute topographic quadrangle maps. However, the layer includes linear (flow) connector features that fill in gaps between river and stream features where water passes through marshes or underground through pipelines and tunnels. Note that connectors represent general pathways and do not represent the exact location or orientation of actual underground pipelines, tunnels, aqueducts, etc. The Named Waterbody layer is comprised of polygon and line features. Polygon features represent areas of water for rivers, streams, brooks, reservoirs, lakes, ponds, bays, coves, and harbors. Polygon features also depict related information such as dams and islands. Line features represent single-line rivers and streams, flow connectors, aqueducts, canals, and ditches. Line features also enclose all polygon features in the form of shorelines, dams, and closure lines separating adjacent water features. The Named Waterbody layer is based on information from USGS topographic quadrangle maps published between 1969 and 1984 so it does not depict conditions at any one particular point in time. Also, the layer does not reflect recent changes with the course of streams or location of shorelines impacted by natural events or changes in development since the time the USGS 7.5 minute topographic quadrangle maps were published. Attribute information is comprised of codes to identify waterbody features by type, cartographically represent (symbolize) waterbody features on a map, select waterbodies appropriate to display at different map scales, identify individual waterbodies on a map by name, and describe waterbody feature area and length. The names assigned to individual waterbodies are based on information published on the USGS 7.5 minute topographic quadrangle maps or other state and local maps. The Named Waterbody layer does not include bathymetric, stream gradient, water flow, water quality, or biological habitat information. Derived from the Hydrography layer, the Named Waterbody layer was originally published in 1999. The 2005 edition includes the same water features published in 1999, however some attribute information has been slightly modified and made easier to use. Also, the 2005 edition corrects previously undetected attribute coding errors and includes the flow connector features.

Connecticut Hydrography Set:

Connecticut Hydrography Line includes the line features of a layer named Hydrography. Hydrography is a 1:24,000-scale, polygon and line feature-based layer that includes all hydrography features depicted on the U.S. Geological Survey (USGS) 7.5 minute topographic quadrangle maps for the State of Connecticut. This layer only includes features located in Connecticut. These hydrography features include waterbodies, inundation areas, marshes, dams, aqueducts, canals, ditches, shorelines, tidal flats, shoals, rocks, channels, and islands. Hydrography is comprised of polygon and line features. Polygon features represent areas of water for rivers, streams, brooks, reservoirs, lakes, ponds, bays, coves, and harbors. Polygon features also depict inundation areas, marshes, dams, aqueducts, canals, tidal flats, shoals, rocks, channels, and islands shown on the USGS 7.5 minute topographic quadrangle maps. Line features represent single-line rivers and streams, aqueducts, canals, and ditches. Line features also enclose all polygon features in the form of natural shorelines, manmade shorelines, dams, closure lines separating adjacent waterbodies, and the apparent limits for tidal flats, rocks, and areas of marsh. The layer is based on information from USGS topographic quadrangle maps published between 1969 and 1984 so it does not depict conditions at any one particular point in time. Also, the layer does not reflect recent changes with the course of streams or location of shorelines impacted by natural events or changes in development since the time the USGS 7.5 minute topographic quadrangle maps were published. Attribute information is comprised of codes to identify hydrography features by type, cartographically represent (symbolize) hydrography features on a map, select waterbodies appropriate to display at different map scales, identify individual waterbodies on a map by name, and describe feature area and length. The names assigned to individual waterbodies are based on information published on the USGS 7.5 minute topographic quadrangle maps or other state and local maps. The layer does not include bathymetric, stream gradient, water flow, water quality, or biological habitat information. This layer was originally published in 1994. The 2005 edition includes the same water features published in 1994, however some attribute information has been slightly modified and made easier to use. Also, the 2005 edition corrects previously undetected attribute coding errors.

Connecticut Hydrography Polygon includes the polygon features of a layer named Hydrography. Hydrography is a 1:24,000-scale, polygon and line feature-based layer that includes all hydrography features depicted on the U.S. Geological Survey (USGS) 7.5 minute topographic quadrangle maps for the State of Connecticut. This layer only includes features located in Connecticut. These hydrography features include waterbodies, inundation areas, marshes, dams, aqueducts, canals, ditches, shorelines, tidal flats, shoals, rocks, channels, and islands. Hydrography is comprised of polygon and line features. Polygon features represent areas of water for rivers, streams, brooks, reservoirs, lakes, ponds, bays, coves, and harbors. Polygon features also depict inundation areas, marshes, dams, aqueducts, canals, tidal flats, shoals, rocks, channels, and islands shown on the USGS 7.5 minute topographic quadrangle maps. Line features represent single-line rivers and streams, aqueducts, canals, and ditches. Line features also enclose all polygon features in the form of natural shorelines, manmade shorelines, dams, closure lines separating adjacent waterbodies, and the apparent limits for tidal flats, rocks, and areas of marsh. The layer is based on information from USGS topographic quadrangle maps published between 1969 and 1984 so it does not depict conditions at any one particular point in time. Also, the layer does not reflect recent changes with the course of streams or location of shorelines impacted by natural events or changes in development since the time the USGS 7.5 minute topographic quadrangle maps were published. Attribute information is comprised of codes to identify hydrography features by type, cartographically represent (symbolize) hydrography features on a map, select waterbodies appropriate to display at different map scales, identify individual waterbodies on a map by name, and describe feature area and length. The names assigned to individual waterbodies are based on information published on the USGS 7.5 minute topographic quadrangle maps or other state and local maps. The layer does not include bathymetric, stream gradient, water flow, water quality, or biological habitat information. This layer was originally published in 1994. The 2005 edition includes the same water features published in 1994, however some attribute information has been slightly modified and made easier to use. Also, the 2005 edition corrects previously undetected attribute coding errors.

Notice: this is not the latest Heat Island Anomalies image service.This layer contains the relative degrees Fahrenheit difference between any given pixel and the mean heat value for the city in which it is located, for every city in the contiguous United States, Alaska, Hawaii, and Puerto Rico. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2022, with patching from summer of 2021 where necessary.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter or cooler than the average temperature for that same city as a whole. This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

Abstract This dataset and its metadata statement were developed for the Bioregional Assessment Programme and are presented here as originally supplied. The dataset was created by the Bioregional Assessment Programme for use in cartographic outputs in Gippsland Basin bioregion product 1.2. The processes undertaken to produce this dataset are described in the History field in this metadata statement. This dataset has been superseded by Cartographic masks for map products GIP 120 v03. ## Purpose Cartographic masks for map products GIP_120, used for clear annotation and masking unwanted features from report maps. ## Dataset History Rectangular polygon shapefile masks were created around selected feature labels from the following datasets: GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) - GUID: 96ebf889-f726-4967-9964-714fb57d679b Victoria Mining Licences - 13 May 2015 - GUID: c9c1dff4-01c7-4669-a033-d8a9f674cd5a A shapefile was created for the use of masking data to highlight text. Method: * A new polygon shapefile was created with no content * The shapefile was then populated in an ArcMap editing session by digitizing polygons which surround text. * ArcMAP's Advanced Drawing Option was then used to mask data behind text. ## Dataset Citation Bioregional Assessment Programme (XXXX) Cartographic masks for map products GIP 120 v02. Bioregional Assessment Derived Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/39945fcc-d1a7-49c4-a011-ca595c42ec51. ## Dataset Ancestors * Derived From GEODATA TOPO 250K Series 3 * Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) * Derived From Victoria Mining Licences - 13 May 2015

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This resource contains raster datasets created using ArcGIS to analyse groundwater levels in the Namoi subregion.

Purpose

These data layers were created in ArcGIS as part of the analysis to investigate surface water - groundwater connectivity in the Namoi subregion. The data layers provide several of the figures presented in the Namoi 2.1.5\tSurface water - groundwater interactions report.

Dataset History

Extracted points inside Namoi subregion boundary. Converted bore and pipe values to Hydrocode format, changed heading of 'Value' column to 'Waterlevel' and removed unnecessary columns then joined to Updated_NSW_GroundWaterLevel_data_analysis_v01\NGIS_NSW_Bore_Join_Hydmeas_unique_bores.shp clipped to only include those bores within the Namoi subregion.

Selected only those bores with sample dates between >=26/4/2012 and <31/7/2012. Then removed 4 gauges due to anomalous ref_pt_height values or WaterElev values higher than Land_Elev values.

Then added new columns of calculations:

WaterElev = TsRefElev - Water_Leve

DepthWater = WaterElev - Ref_pt_height

Ref_pt_height = TsRefElev - LandElev

Alternatively - Selected only those bores with sample dates between >=1/5/2006 and <1/7/2006

2012_Wat_Elev - This raster was created by interpolating Water_Elev field points from HydmeasJune2012_only.shp, using Spatial Analyst - Topo to Raster tool. And using the alluvium boundary (NAM_113_Aquifer1_NamoiAlluviums.shp) as a boundary input source.

12_dw_olp_enf - Select out only those bores that are in both source files.

Then using depthwater in Topo to Raster, with alluvium as the boundary, ENFORCE field chosen, and using only those bores present in 2012 and 2006 dataset.

2012dw1km_alu - Clipped the 'watercourselines' layer to the Namoi Subregion, then selected 'Major' water courses only. Then used the Geoprocessing 'Buffer' tool to create a polygon delineating an area 1km around all the major streams in the Namoi subregion.

selected points from HydmeasJune2012_only.shp that were within 1km of features the WatercourseLines then used the selected points and the 1km buffer around the major water courses and the Topo to Raster tool in Spatial analyst to create the raster.

Then used the alluvium boundary to truncate the raster, to limit to the area of interest.

12_minus_06 - Select out bores from the 2006 dataset that are also in the 2012 dataset. Then create a raster using depth_water in topo to raster, with ENFORCE field chosen to remove sinks, and alluvium as boundary. Then, using Map Algebra - Raster Calculator, subtract the raster just created from 12_dw_olp_enf

Dataset Citation

Bioregional Assessment Programme (2017) Namoi bore analysis rasters. Bioregional Assessment Derived Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/7604087e-859c-4a92-8548-0aa274e8a226.

Dataset Ancestors

• Derived From Bioregional Assessment areas v02

• Derived From Gippsland Project boundary

• Derived From Bioregional Assessment areas v04

• Derived From Upper Namoi groundwater management zones

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From Victoria - Seamless Geology 2014

• Derived From GIS analysis of HYDMEAS - Hydstra Groundwater Measurement Update: NSW Office of Water - Nov2013

• Derived From Bioregional Assessment areas v01

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013

This Existing Vegetation (Eveg) polygon feature class is a CALVEG (Classification and Assessment with LANDSAT of Visible Ecological Groupings) map product at a scale of 1:24,000 for CALVEG Zone 7, the South Coast . Source imagery for this layer ranges from the year 2002 to 2010.The CALVEG classification system was used for vegetation typing and crosswalked to other classification systems in this database. USGS Land Use / Land Cover Anderson 1 classification system is included in the database to meet national standard requirements. Mapping standards meet requirements of the USDA Forest Service as defined by the FS GIS data dictionary, FGDC Vegetation standards and the FS Existing Vegetation Classification and Mapping Technical Guide. Regional add-ons are retained for crosswalking to the California Wildlife Habitat Relationship System (CWHR). For a description of CALVEG and a data dictionary for codes in this database, go to the Existing Vegetation Layer Description at http://www.fs.usda.gov/detail/r5/landmanagement/resourcemanagement/?cid=stelprdb5365219. For an index of CALVEG zones, go to http://www.fs.usda.gov/detail/r5/landmanagement/resourcemanagement/?cid=stelprdb5347192 and select the link called Existing Vegetation Tiles Index. For a CALVEG mapping status by scale and year, go to http://www.fs.usda.gov/detail/r5/landmanagement/resourcemanagement/?cid=stelprdb5347192 and select the "Existing Vegetation Mapping Status by Year, Scale and Project" link. *******Note: This layer is comprised of "multi-part" features, spatially separate polygons sharing the same attributes and stored as a single feature. A group of islands could be represented as a multi-part polygon feature. This allows for reduction in the size of the database and portability across a network. For analysis purposes however, it is wise to select a smaller area of interest and break apart features using the "Multipart To Singlepart" tool in ArcGIS. In its entirety, a "single-part" format of this feature class can potentially be more than one million polygons.

This dataset corresponds to land area polygons of Australian coastline and surrounding islands. It was generated from 10 m Sentinel 2 imagery from 2022 - 2024 using the Normalized Difference Water Index (NDWI) to distinguish land from water. It was estimated from composite imagery made up from images where the tide is above the mean sea level. The coastline approximately corresponds to the mean high water level. This dataset was created as part of the NESP MaC 3.17 northern Australian Reef mapping project. It was developed to allow the inshore edge of digitised fringing reef features to be neatly clipped to the land areas without requiring manual digitisation of the neighbouring coastline. This required a coastline polygon with an edge positional error of below 50 m so as to not distort the shape of small fringing reefs. We found that existing coastline datasets such as the Geodata Coast 100K 2004 and the Australian Hydrographic Office (AHO) Australian land and coastline dataset did not meet our needs. The scale of the Geodata Coast 100K 2004 was too coarse to represent small islands and the the positional error of the Australian Hydrographic Office (AHO) Australian land and coastline dataset was too high (typically 80 m) for our application as the errors would have introduced significant errors in the shape of small fringing reefs. The Digital Earth Australia Coastline (GA) dataset was sufficiently accurate and detailed however the format of the data was unsuitable for our application as the coast was expressed as disconnected line features between rivers, rather than a closed polygon of the land areas. We did however base our approach on the process developed for the DEA coastline described in Bishop-Taylor et al., 2021 (https://doi.org/10.1016/j.rse.2021.112734). Adapting it to our existing Sentinel 2 Google Earth processing pipeline. The difference between the approach used for the DEA coastline and this dataset was the DEA coastline performed the tidal calculations and filtering at the pixel level, where as in this dataset we only estimated a single tidal level for each whole Sentinel image scene. This was done for computational simplicity and to align with our existing Google Earth Engine image processing code. The images in the stack were sorted by this tidal estimate and those with a tidal high greater than the mean seal level were combined into the composite. The Sentinel 2 satellite follows a sun synchronous orbit and so does not observe the full range of tidal levels. This observed tidal range varies spatially due to the relative timing of peak tides with satellite image timing. We made no accommodation for variation in the tidal levels of the images used to calculate the coastline, other than selecting images that were above the mean tide level. This means tidal height that the dataset coastline corresponds to will vary spatially. While this approach is less precise than that used in the DEA Coastline the resulting errors were sufficiently low to meet the project goals.
This simplified approach was chosen because it integrated well with our existing Sentinel 2 processing pipeline for generating composite imagery. To verify the accuracy of this dataset we manually checked the generated coastline with high resolution imagery (ArcGIS World Imagery). We found that 90% of the coastline polygons in this dataset have a horizontal position error of less than 20 m when compared to high-resolution imagery, except for isolated failure cases. During our manual checks we identified some areas where our algorithm can lead to falsely identifying land or not identifying land. We identified specific scenarios, or 'failure modes,' where our algorithm struggled to distinguish between land and water. These are shown in the image "Potential failure modes": a) The coastline is pushed out due to breaking waves (example: western coast, S2 tile ID 49KPG). b) False land polygons are created because of very turbid water due to suspended sediment. In clear water areas the near infrared channel is almost black, starkly different to the bright land areas. In very highly turbid waters the suspended sediment appears in the near infrared channel, raising its brightness to a level where it starts to overlap with the brightness of the dimmest land features. (example: Joseph Bonaparte Gulf, S2 tile ID 52LEJ). This results in turbid rivers not being correctly mapped. In version 1-1 of the dataset the rivers across northern Australia were manually corrected for these failures. c) Very shallow, gentle sloping areas are not recognised as water and the coastline is pushed out (example: Mornington Island, S2 tile ID 54KUG). Update: A second review of this area indicated that the mapped coastline is likely to be very close to the try coastline. d) The coastline is lower than the mean high water level (example: Great Keppel (Wop-pa) Island, S2 tile ID 55KHQ). Some of these potential failure modes could probably be addressed in the future by using a higher resolution tide calculation and using adjusted NDWI thresholds per region to accommodate for regional differences. Some of these failure modes are likely due to the near infrared channel (B8) being able to penetrate the water approximately 0.5 m leading to errors in very shallow areas. Some additional failures include: - Interpreting jetties as land - Interpreting oil rigs as land - Bridges being interpreted as land, cutting off rivers Methods: The coastline polygons were created in four separate steps: 1. Create above mean sea level (AMSL) composite images. 2. Calculate the Normalized Difference Water Index (NDWI) and visualise as a grey scale image. 3. Generate vector polygons from the grey scale image using a NDWI threshold. 4. Clean up and merge polygons. To create the AMSL composite images, multiple Sentinel 2 images were combined using the Google Earth Engine. The core algorithm was: 1. For each Sentinel 2 tile filter the "COPERNICUS/S2_HARMONIZED" image collection by - tile ID - maximum cloud cover 20% - date between '2022-01-01' and '2024-06-30' - asset_size > 100000000 (remove small fragments of tiles) 2. Remove high sun-glint images (see "High sun-glint image detection" for more information). 3. Split images by "SENSING_ORBIT_NUMBER" (see "Using SENSING_ORBIT_NUMBER for a more balanced composite" for more information). 4. Iterate over all images in the split collections to predict the tide elevation for each image from the image timestamp (see "Tide prediction" for more information). 5. Remove images where tide elevation is below mean sea level. 6. Select maximum of 200 images with AMSL tide elevation. 7. Combine SENSING_ORBIT_NUMBER collections into one image collection. 8. Remove sun-glint and apply atmospheric correction on each image (see "Sun-glint removal and atmospheric correction" for more information). 9. Duplicate image collection to first create a composite image without cloud masking and using the 15th percentile of the images in the collection (i.e. for each pixel the 15th percentile value of all images is used). 10. Apply cloud masking to all images in the original image collection (see "Cloud Masking" for more information) and create a composite by using the 15th percentile of the images in the collection (i.e. for each pixel the 15th percentile value of all images is used). 11. Combine the two composite images (no cloud mask composite and cloud mask composite). This solves the problem of some coral cays and islands being misinterpreted as clouds and therefore creating holes in the composite image. These holes are "plugged" with the underlying composite without cloud masking. (Lawrey et al. 2022) Next, for each image the NDWI was calculated: 1. Calculate the normalised difference using the B3 (green) and B8 (near infrared). 2. Shift the value range from between -1 and +1 to values between 1 and 255 (0 reserved as no-data value). 3. Export image as 8 bit unsigned Integer grey scale image. During the next step, we generated vector polygons from the grey scale image using a NDWI threshold: 1. Upscale image to 5 m resolution using bilinear interpolation. This was to help smooth the coastline and reduce the error introduced by the jagged pixel edges. 2. Apply a threshold to create a binary image (see "NDWI Threshold" for more information) with the value 1 for land and 2 for water (0: no data). 3. Create polygons for land values (1) in the binary image. 4. Export as shapefile. Finally, we created a single layer from the vectorised images: 1. Merge and dissolve all vector layers in QGIS. 2. Perform smoothing (QGIS toolbox, Iterations 1, Offset 0.25, Maximum node angle to smooth 180). 3. Perform simplification (QGIS toolbox, tolerance 0.00003). 4. Remove polygon vertices on the inner circle to fill out the continental Australia. 5. Perform manual QA/QC. In this step we removed false polygons created due to sun glint and breaking waves. We also removed very small features (1 – 1.5 pixel sized features, e.g. single mangrove trees) by calculating the area of each feature (in m2) and removing features smaller than 200 m2. 15th percentile composite: The composite image was created using the 15th percentile of the pixels values in the image stack. The 15th percentile was chosen, in preference to the median, to select darker pixels in the stack as these tend to correspond to images with clearer water conditions and higher tides. High sun-glint image detection: Images with high sun-glint can lead to lower quality composite images. To determine high sun-glint images, a land mask was first applied to the image to only retain water pixels. This land mask was estimated using NDWI. The proportion of the water pixels in the near-infrared and short-wave infrared bands above a sun-glint threshold was calculated. Images with a high proportion were then filtered out of the image collection.
Sun-glint removal and atmospheric correction: The Top of Atmosphere L1

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.

Random sample points were generated in ArcGIS. Points were buffered 40 meters from the park boundary and 80 meters from another point. The minimum mapping unit used in delineating vegetation polygons was 0.5 hectare. All random points were selected within the park boundary to avoid any private land issues. Randomly selected site locations were loaded onto a Garmin GPS unit for field navigation. All accuracy assessment field work was completed on June 26, 2012. Field staff was provided with a GPS unit, dichotomous key for mapping vegetation map classes and vegetation class definitions. Plot shape and size varied according to the extent of the vegetation class patch containing the sample point. Circular 0.25 hectare (28 m radius) plots were used for larger patches while circular 0.1 hectare (18 m radius) plots were used for small patches approaching the minimum mapping unit. A circular plot size of 0.5 hectare (40 m radius) was used to capture information for a single large homogenous patch. In all cases, plot size exceeded the minimum patch size for PIPE.

Minimum Mapping Unit = 0.5 hectare Minimum Patch Size=.007 hectares Total Size = 55 Polygons Average Polygon Size = 5.39 acres (2.18 hectares) Overall Thematic Accuracy = 97.9% Project Completion Date: 12/2013

Description: Lake Huron grid 303 within the area encompassed by a line from Coats Point on Marquette Island along the western shore of said island to Cube Point; then due west to the shore of Brulee Point (Mismer Bay Point); then southeasterly along said shore to the southern tip of Brulee Point; then southeasterly to Coats Point on Marquette Island. Regulations: Tribal Commercial Fishing open to trap net fishing targeting whitefish shall be permitted in the area described above. Maps for general reference only: refer to text of Consent Decree 2000 for exact locations and provisions.Created a new polygon shapefile in ArcGIS 8.1. A point was located on the USGS Mackinac county 1:24,000 DRG as outlined in the Consent Decree 2000 documentation. The new pollygon layer was created using the snapping tool in ArcMap. Starting form the above point location and heading in a counter clockwise direction (as outlined in the Consent Decree 2000 documentation) extending the polygon boundaries beyond the US Department of Commerce (Bureau of the Census, Geography Division) county census (1995) layer. The new polygon feature was then commbined with the US Department of Commerce (Bureau of the Census, Geography Division) county census (1995) layer using the union tool from the geoprocessing wizard in Arc Map. The desired features were then selected, exported as a new shapefile, and reprojected from Michigan georef to Decimal Degrees to create the final Les Cheneaux Island Trap Net layer.The boundaries represented on consent decree maps are approximations based on the text contained in the 2000 Consent Decree. For legal descriptions of geographic extent or details pertaining to regulations for these representations refer to the original 2000 Consent Decree Document.

This parcel lines feature class represents the current city parcel lines within the City of Los Angeles. It shares topology with the Landbase Parcel_polygons feature class. The Mapping and Land Records Division of the Bureau of Engineering, Department of Public Works provides the most current geographic information of the public right of way, ownership and land record information. The legal boundaries are determined on the ground by license surveyors in the State of California, and by recorded documents from the Los Angeles County Recorder's office and the City Clerk's office of the City of Los Angeles. Parcel and ownership information are available on NavigateLA, a website hosted by the Bureau of Engineering, Department of Public Works.Associated information about the landbase parcel lines is entered into attributes. Principal attributes include:CV_LAYER: is the principal field that describes the various types of lines like street and freeway right-of-ways, tract, lots, government property and easements lines, private street lines, utility right-of-ways, and ownership lines. For a complete list of attribute values, please refer to Landbase_parcel_lines_data_dictionary.Landbase parcels lines layer was created in geographical information systems (GIS) software to display the location of parcel lots. The parcels lines layer is a feature class in the LACityLandbaseData.gdb Geodatabase dataset. The layer consists of spatial data as a line feature class and attribute data for the features. The lines are derived from the polygon feature class in the landbase parcels layer, and information about the lines is entered into attributes. The CV_LAYER field values describe the various types of lines. The right-of-way, row, line features consist of CV_LAYER = 6, CV_LAYER = 106, and portions of CV_LAYER = 1 where that line is both the city boundary and the parcel line. In some cases, a parcel line will share two different type descriptions. Refer to CV_LAYER field metadata for further explantion. Parcel information should only be added to the Landbase Parcels layer if documentation exists, such as a Deed or a Plan approved by the City Council. When seeking the definitive description of real property, consult the recorded Deed or Plan.List of Fields:ASSETIDCV_LAYER: This value is a number representing a different type of user-assigned layer. Each of the line segments in the landbase parcels lines are assigned one of the CV_LAYER numbers, representing a different type of line work, described below. In some cases, a parcel line will share two different type descriptions. Such as, a parcel line may have CV_LAYER = 1 City Boundary line, and it is a CV_LAYER = 8 Tract line. The Tract line description is used first, and the City Boundary line description is used second. When selecting City Boundary line using CV_LAYER = 1, then (special way to select data...). The right-of-way, row, line features consist of CV_LAYER = 6, CV_LAYER = 106, and portions of CV_LAYER = 1 where that line is both the city boundary and the parcel line. Values: • 50 - Lot cut linework. • 38 - Freeway ease as easement lines. • 108 - Tract lines that are private street lines. • 8 - Tract lines, Rancho lines, Freeway (Fwy), and Right of way lines. • 30 - Former city boundary lines; other city or county boundary line. • 34 - Overlap lines. • 6 - Right of way (R/W) sidelines. • 19 - LA City easement lines. • 21 - All governmental lines (Fee). • 37 - APN (BPP) lines shown on tax assessors map (PCL maps); but no new PIN is created for the parcels polygon feature. • 48 - Subdivision title anno shown for ownership purpose (lot cut). • 10 - Lot lines. • 68 - SBBM (San Bernardino Base Meridian) section lines. • 1 - Boundary lines (existing). • 110 - Lot lines that are private street lines. • 18 - All governmental easement lines (except LA City and State freeway ease right of way lines). • 106 - Fwy traveled roadway lines; Dash right of way lines; Railroad and transmission lines. • 0 - Cadastral format.SHAPE: Feature geometry.OBJECTID: Internal feature number.ID: A unique numeric identifier of the polygon. The ID value is the last part of the PIN field value.MAPSHEET: The alpha-numeric mapsheet number, which refers to a valid B-map or A-map number on the Cadastral tract index map. Values: • B, A, -5A - Any of these alpha-numeric combinations are used, whereas the underlined spaces are the numbers.

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.

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.2018 marked the first year a comparison could be made using the CDL methodology. The comparison between 2017 and 2018 showed a large change in agricultural land use to other land use. It was determined this shift was due to crop land being allowed to sit fallow for a season and did not represent a shift away from agricultural land. The following code amended the data:*************************************************************************************************************************************####On 02/07/2020 this dataset was amended with the following R script to better reflect agricultural land changes:require(arcgisbinding)arc.check_product()####Bring in layersLU17<-arc.open("Path to data")LU17<-arc.select(LU17)#####Amend dataLU17$Landuse[LU17$Class_Name=='Fallow/Idle Cropland' & LU17$Description== 'Dry Land/Other']<-"Agricultural"LU17$CropGroup[LU17$Class_Name=='Fallow/Idle Cropland' & LU17$Description== 'Dry Land/Other']<-"Fallow/Idle"LU17$IRR_Method[LU17$Class_Name=='Fallow/Idle Cropland' & LU17$Description== 'Dry Land/Other']<-"Dry Crop"arc.write("Path to data", LU17)*************************************************************************************************************************************LUID -Unique ID number for each polygon in the final dataset, matches object.Landuse - Land use type, similar to land cover and represents our own categories of how the land is used.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 Methods.Acres - Calculated acreage of the polygon.State - Spatial intersection identifying the State where the polygons are found.County - Spatial intersection identifying the County where the polygons are found.Basin - Spatial intersection identifying the Basin where the polygons are found. Basins, or Utah Hydrologic Basins are large watersheds created by DWRe.SubArea - Spatial intersection identifying the Subarea where the polygons are found. 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 specific cover and use types.LABEL - Old shorthand descriptive label for each crop and irrigation type or land use type.Class_Name - Zonal Statistics majority value derived from the USDA CDL Cropscape raster layer, may differ from final crop determination.OldLanduse - This is the old short code found under landuse in past datasets and is kept to maintain connectivity with historical data.LU_Group - These codes represent some in-house groupings that are useful for symbology and other summarizing.SURV_YEAR - Indicates which year/growing season the data represents. Is useful when comparing to past layers.