With recent updates to the Education Institution Agreements, the Professional Plus Use Type was added as the default option to all ArcGIS Online organisations. Professional Plus will replaces the Creator user type provision on your institution's account.In the new ArcGIS User model, Professional Plus users will have access to ArcGIS Pro through ArcGIS Online.If the following changes are not made, users will not be able to access ArcGIS Pro through their ArcGIS User Identity.

If you have ever had an error message pop up in ArcGIS Online that mentions you have exceeded the user types in your account, watch this video to see how to resolve this issue.This video takes you through the steps of how to do change students and teachers user types on the rare occasion that you are required to change user types in your schools ArcGIS Online account.ArcGIS Online Administration.Video recorded - April 2020.

Laatste update: 10 november 2025Dit kan op twee plekken; via mijn.esri.nl of via de Esri Store van Esri Nederland. Beide methodes zullen in dit artikel worden besproken.

Maintaining accurate data is a concern of all GIS users. The geodatabase offers you the ability to create geographic features that represent the real world. As the real world changes, you must update these features and their attributes. When creating or updating data, you can add behavior to your features and other objects to minimize the potential for errors.After completing this course, you will be able to:Define the two types of attribute domains and discuss how they differ.Create attribute domains and use them when editing data.Create subtypes and use them when editing data.Explain the difference between an attribute domain and a subtype.

More Metadata This feature class contains the basic expected land uses, or place types, for specific areas for all of Loudoun County. The place type approach concentrates on the context of any area instead of typical land use categories and corresponding specific uses. Each place type category defines the basis expected land use for an area, but also the preferred development patterns, streetscapes, and design features to make each area visually distinctive and functional. Place types also provide greater flexibility in development than the previous planned land use approach.This purpose of this feature class is to reference the planned place types, or expected land uses in certain areas, as reflected by the Loudoun County 2019 Comprehensive Plan, which was adopted on June 20, 2019. The data is used extensively by both the Department of Planning, Building and Development but is administered by the Department of Planning. This place type layer is to be used in place of the previous planned land use layer that was retired with the adoption of the 2019 Comprehensive Plan.

The Digital Geologic-GIS Map of Mount Rainier National Park, Washington is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (mora_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (mora_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) this file (mora_geology.gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (mora_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (mora_geology_metadata_faq.pdf). Please read the mora_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: http://www.google.com/earth/index.html. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (mora_geology_metadata.txt or mora_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:62,500 and United States National Map Accuracy Standards features are within (horizontally) 31.8 meters or 104.2 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm). The GIS data projection is NAD83, UTM Zone 10N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth.

The Land Types dataset is a public domain dataset managed by the County of Los Angeles which includes various types of land use that are critical for mapmaking and geographic analysis. The data has been created to match parcel boundaries where possible and digitized from 4-inch aerial photography where more detail was needed. The sub-layers L10-L13, L14-L17, and L17-20 correspond to scale-dependent rendering, where higher level values represent increased zoom levels, displaying finer spatial detail and additional data granularity. For inquiries or further assistance regarding this layer, please contact eGIS@isd.lacounty.gov.General land types that have been captured include:BeachesGolf CoursesMuseums & AquariumsParks & Recreation CentersSports & Event VenuesColleges & UniversitiesSchools (Private, Charter, Public)Hospitals (limited set)CemeteriesTV & Movie StudiosJails & PrisonsAirport BoundariesShopping CentersMobile Home Parks (09/13)

The Digital Bedrock Geologic-GIS Map of Lincoln Boyhood National Memorial and Vicinity, Indiana is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (libo_bedrock_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (libo_bedrock_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (libo_bedrock_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (libo_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (libo_bedrock_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (libo_bedrock_geology_metadata_faq.pdf). Please read the libo_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Indiana Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (libo_bedrock_geology_metadata.txt or libo_bedrock_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

[Metadata] Description: Land Use Land Cover of main Hawaiian Islands as of 1976Source: 1:100,000 1976 Digital GIRAS (Geographic Information Retrieval and Analysis) files. Land Use and Land Cover (LULC) data consists of historical land use and land cover classification data that was based primarily on the manual interpretation of 1970's and 1980's aerial photography. Secondary sources included land use maps and surveys. There are 21 possible categories of cover type. The spatial resolution for all LULC files will depend on the format and feature type. Files in GIRAS format will have a minimum polygon area of 10 acres (4 hectares) with a minimum width of 660 feet (200 meters) for manmade features. Non-urban or natural features have a minimum polygon area of 40 acres (16 hectares) with a minimum width of 1320 feet (400 meters). Files in CTG format will have a resolution of 30 meters. May 2024: Hawaii Statewide GIS Program staff removed extraneous fields that had been added as part of the 2016 GIS database conversion and were no longer needed.For additional information, please refer to https://files.hawaii.gov/dbedt/op/gis/data/lulc.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, HI 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

The feature class indicates the specific types of motorized vehicles allowed on the designated routes and their seasons of use. The feature class is designed to be consistent with the MVUM (Motor Vehicle Use Map). It is compiled from the GIS Data Dictionary data and Infra tabular data that the administrative units have prepared for the creation of their MVUMs. Only trails with the symbol value of 5-12, 16, 17 are Forest Service System trails and contain data concerning their availability for motorized use. This data is published and refreshed on a unit by unit basis as needed. Individual unit's data must be verified and proved consistent with the published MVUMs prior to publication in the EDW. Click this link for full metadata description: Metadata _

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 layersLU18<-arc.open("Path to data")LU18<-arc.select(LU18)#####Amend dataLU18$Landuse[LU18$Class_Name=='Fallow/Idle Cropland' & LU18$Description== 'Dry Land/Other']<-"Agricultural"LU18$CropGroup[LU18$Class_Name=='Fallow/Idle Cropland' & LU18$Description== 'Dry Land/Other']<-"Fallow/Idle"LU18$IRR_Method[LU18$Class_Name=='Fallow/Idle Cropland' & LU18$Description== 'Dry Land/Other']<-"Dry Crop"arc.write("Path to data", LU18)***************************************************************************************************************************************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.

An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protection's CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990+. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system. This service depicts the WHR13 Type from the fveg dataset (with Wildlife Habitat Relationship classes grouped into 13 major land cover types). The full dataset can be downloaded in raster format here: GIS Mapping and Data Analytics | CAL FIRE The service represents the latest release of the data, and is updated when a new version is released. Currently it represents fveg22_1.

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.

General Plan Land Use is a Polygon FeatureClass representing It is primarily used to indicate the General Plan Land Use designations within the City Limits. Updates to the layer are requested to the GIS Division by Community Development as directed by the City Council. The latest amendment is from September 2018 per resolution 18-055. General Plan Land Use has the following fields:

OBJECTID: Unique identifier automatically generated by Esri type: OID, length: 4, domain: none

LAND_USE: The land use code associated with the feature type: String, length: 16, domain: none

LABEL: The label associated with the feature type: String, length: 80, domain: none

ResolutionNumber: The resolution number type: String, length: 50, domain: none

GlobalID: Unique identifier automatically generated for features in enterprise database type: GlobalID, length: 38, domain: none

Shape: Field that stores geographic coordinates associated with feature type: Geometry, length: 4, domain: noneShape.STArea()The area of the geometric featuretype: Double, length: noneShape.STLength() The length of the geometric featuretype: Double, length: none

DescriptionThe features in this layer have been created from information extracted from SAP. When an SAP user is mapping a project from the CJ20N transaction, these GIS representations are created.Used by SAP GIS Locator web app to read/write projects GIS data from SAP PRD environment. From 9/19/2016 onward.Last UpdateContinuouslyUpdate FrequencyContinuouslyData OwnerDivision of Transportation DevelopmentData ContactGIS Support UnitCollection MethodProjectionNAD83 / UTM zone 13NCoverage AreaStatewideTemporalDisclaimer/LimitationsThere are no restrictions and legal prerequisites for using the data set. The State of Colorado assumes no liability relating to the completeness, correctness, or fitness for use of this data.

Author: A Lisson, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 8Resource type: lessonSubject topic(s): gis, geographic thinkingRegion: united statesStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.Objectives: Students will be able to:

1. Explain the difference between two types of geospatial technologies - GPS and GIS.
2. Develop basic skills to effectively manipulate and use GPS receivers and ArcGIS software.
3. Explain uses of GPS and GIS.Summary: Students use GPS coordinates to discover geocaches at a local park, and they use ArcGIS to layer maps about the park. Frontenac State park is the example, but any park or area (including school grounds) could be used. Students also investigate careers that use GIS.

Retirement Notice: This item is in mature support as of February 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.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 Viewer To 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:ProductNameis2020-2021 By 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 Pro To 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 2021 Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022 What 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. Rangeland Open 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

The Source Layer was combined with the Southern California Council of Governments (SCAG) 2008 Land Use layer to produce the resistance layer for species movement in Omniscape. This regional land use map aggregates local municipal land use maps from the numerous local planning agencies within the study area. The specificity of each of the 100 land use types in the resulting dataset, and the precise polygon delineations, provides a detailed estimate of land use intensity and boundaries suitable for converting to the relatively high 10’ pixel resolution of this study. Many of the 100 land use types provided were relatively duplicative because they represented different local naming and classification conventions for the same general land use. I reviewed the descriptions of each type and reclassified them into 15 general types based on assumed land use intensity and potential resistance to species movement (see Table 3-1). Refining resistance values considering road traffic volumes, more detailed land use for the City of Los Angeles, building footprints, and other mapped barriers is a planned next step for application. The approach presented here is somewhat of a hybrid between purely landcover and land use based approaches. First, I consider remotely sensed surface cover for differentiating vegetated vs. non-vegetated pixels using the 2016 LA County Urban Forest Canopy Assessment dataset and CALVEG. I assigned vegetated pixels, regardless of the underlying SCAG land use type they fall within, with relatively low resistance scores based on their source values, but reversed (i.e. source scores of 10 receive a resistance score of 1, 9 = 2, 8 = 3, etc.). Adjusting vegetated pixel resistance based on underlying land use context may be appropriate in this more urbanized area and should be explored as a next step in application. Next, I assigned resistance values to non-vegetated pixels based on the underlying SCAG land use type. Values of 23 to 300 were given for non-vegetated pixels, which reflects similar values to McRae et al (2016). Land uses, such as commercial and high rise residential, were assumed to produce higher “edge effects”, such as light, noise, traffic, or litter, or other land use impacts that may reduce native species movement and were given the highest resistance values. Non-vegetated pixels within land uses such as low density residential and open space were given the lowest resistance values since they were assumed to produce less edge effects. Campus, office, and industrial land uses were given moderate resistance values. The ocean was given a “no data” value because this study is intended to assess terrestrial permeability only (including small water bodies).Data source: 2006 10-foot Digital Elevation Model (DEM) – LARIAC – Public Domain