Important Note: 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 ViewerTo show just one pair of years in ArcGIS Online Map viewer, create a filter. 1. Click the filter button. 2. Next, click add expression. 3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this:4. Click the styles button. 5. Under unique values click style options. 6. Click the symbol next to No Change at the bottom of the legend. 7. Click the slider next to "enable fill" to turn the symbol off.Showing just one pair of years in ArcGIS ProTo show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro. 1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties. 2. In the dialogue that comes up, choose the tab that says processing templates. 3. On the right where it says processing template, choose the pair of years you would like to display. The processing template will stay applied for any analysis you may want to perform as well.How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer: Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe. Variable mapped: Change in land cover between 2018, 2019, or 2020 and 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 2022What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes. Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map. Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation,
clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. 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 ArcGIS Online US Geological Survey (USGS) topographic map collection now contains over 177,000 historical quadrangle maps dating from 1882 to 2006. The USGS Historical Topographic Map Explorer app brings these maps to life through an interface that guides users through the steps for exploring the map collection:

Finding the maps of interest is simple. Users can see a footprint of the map in the map view before they decide to add it to the display, and thumbnails of the maps are shown in pop-ups on the timeline. The timeline also helps users find maps because they can zoom and pan, and maps at select scales can be turned on or off by using the legend boxes to the left of the timeline. Once maps have been added to the display, users can reorder them by dragging them. Users can also download maps as zipped GeoTIFF images. Users can also share the current state of the app through a hyperlink or social media. This ArcWatch article guides you through each of these steps: https://www.esri.com/esri-news/arcwatch/1014/envisioning-the-past.

The California Energy Commission (CEC) Electric Transmission Line geospatial data layer has been created to illustrate electric transmission in California. When used in association with the other energy related geospatial data layers, viewers can analyze the geographic relationships with the electric transmission across the state. The transmission line data is used to:1. Support the CEC Transmission Planning; 2. Support the CEC electric system analysis in California;3. Enhance electric transmission communication among California electric stakeholders ;4. Support CEC's illustrations of electric infrastructureData Dictionary:Object ID: a unique, not null integer field used to uniquely identify rows in tables in a geodatabase.Name: abbreviated transmission line owner and transmission line capacity in kilovolts (kV).kV: transmission line capacity in kilovolts (kV), data structure is a text string.kV (Sort): transmission line capacity in kilovolts (kV), data structure is a numeric double.Owner: abbreviated transmission line owner name.Status - last reported operational, proposed, closed, or unknown status of the transmission line.Circuit - notes if the transmission line segment is a Single, double, or triple circuit. Null values are unknown. Type - OH is overhead transmission lines, UG is underground, UW is underwater, null values are unknown.Legend - a summarized categories of transmission line owner and transmission capacity value in kilowatts (kV) for map legend purposes.Length (Mile) - the length of the transmission line segment in miles.Length (Feet) - the length of the transmission line segment in feet.TLine Name - the name of the transmission line segment reported to the California Energy CommissionSource - the data source used by California Energy Commission.CommentsCreatorCreator DateLast EditorLast Editor DateGlobalIDShape_LengthShape

This dataset contains soil type and soil classification, by area.

If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (http://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (http://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below.

Category: Environment

Organization: Allegheny County

Department: Geographic Information Systems Group; Department of Administrative Services

Temporal Coverage: 2000

Data Notes:

Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot

Development Notes: This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties. The soil map and data used in the SSURGO product were prepared by soil scientists as part of the National Cooperative Soil Survey

Other: none

Related Document(s): Data Dictionary for SOIL_CODE

Related Document(s): https://www.nrcs.usda.gov/Internet/FSE_MANUSCRIPTS/pennsylvania/PA003/0/legends.pdf - the last page includes the soil legend for this dataset.

Frequency - Data Change: As needed

Frequency - Publishing: As needed

Data Steward Name: Eli Thomas

Data Steward Email: gishelp@alleghenycounty.us

With this mapping application, users can click anywhere within the Commonwealth of Massachusetts to find the elevation at that location in both meters and feet. The elevation data digital elevation model (DEM), in integer units, are derived from statewide Lidar (2013-2021) Terrain Data. The Vertical Datum of the lidar data used to create the DEM is NAVD88 – Geoid18 (m).

The map displays a tile service that shows the DEM using a custom color ramp along with Lidar-derived shaded relief image. The symbology was created by MassGIS staff in ArcGIS Pro using the 'multiply' layer blending option. At medium and large scales the MassGIS Map Features for Imagery tile layer displays atop the imagery.Click the "i" button in the lower left to view a legend.This application is hosted by MassGIS at ArcGIS Online.

This dataset contains data derived from the GEOSAT satellite radar altimeter wave measuring program. Maps have been produced from processed data, showing attributes including mean significant wave height and the 100 year mean significant wave.

Format: shapefile.

Quality - Scope: Dataset. Absolute External Positional Accuracy: +/- one degree. Non Quantitative accuracy: Attributes are assumed to be correct.

Dataset measures wave height in metres, at 0.25m intervals:

Cover_Name, Item_Name, Description: exwave, GRID-CODE, Numercial code to index the polygons exwave, WAVE_HEIGHT_(M), Deep water wave height ranging 0-4.25 m signifying the 100 year mean significant wave.

Conceptual consistency: Coverages are topologically consistent. No particular tests conducted by ERIN. Completeness omission: Complete for the Australian continent. Lineage: ERIN: Data was projected to Geographics using the WGS84 spheroid and datum to be compatible for viewing through the Australian Coastal Atlas. The data was attributed with the range of wave height in metres, at an interval of 0.25metres.

CSIRO: All CAMRIS data were stored in VAX files, MS-DOS R-base files and as a microcomputer dataset accessible under the LUPIS (Land Use Planning Information System) land allocation package. CAMRIS was established using SPANS Geographic Information System (GIS) software running under a UNIX operating system on an IBM RS 6000 platform. A summary follows of processing completed by the CSIRO: 1. r-BASE: Information imported into r-BASE from a number of different sources (ie Digitised, scanned, CD-ROM, NOAA World Ocean Atlas, Atlas of Australian Soils, NOAA GEODAS archive and The Complete Book of Australian Weather). 2. From the information held in r-BASE a BASE Table was generated incorporating specific fields. 3. SPANS environment: Works on creating a UNIVERSE with a geographic projection - Equidistant Conic (Simple Conic) and Lambert Conformal Conic, Spheroid: International Astronomical Union 1965 (Australia/Sth America); the Lower left corner and the longitude and latitude of the centre point. 4. BASE Table imported into SPANS and a BASE Map generated. 5. Categorise Maps - created from the BASE map and table by selecting out specified fields, a desired window size (ie continental or continent and oceans) and resolution level (ie the quad tree level). 6. Rasterise maps specifying key parameters such as: number of bits, resolution (quad tree level 8 lowest - 16 highest) and the window size (usually 00 or cn). 7. Gifs produced using categorised maps with a title, legend, scale and long/lat grid. 8. Supplied to ERIN with .bil; .hdr; .gif; Arc export files .e00; and text files .asc and .txt formats. 9. The reference coastline for CAMRIS was the mean high water mark (AUSLIG 1:100 000 topographic map series).

3D Viewer showcases your scene with default 3D navigation tools, including zoom controls, pan, rotate, and compass. Include a Locator map and Bookmarks to provide context to your scene and guide viewers to points of interest. Line of sight, measure, and slice tools allow viewers to interpret 3D data. Set the option to disable scroll in the app to seamlessly embed this app in another app or site. Examples:Present detailed 3D view of a mountainous region at a large scale while the 2D locator map provides an overview of where you are in the worldDisplay a detailed 3D plan for new urban development that app viewers can explore with slice and measurement toolsAllow users to visualize the impact of shadows on your scene using daylight animationData RequirementsThis app has no data requirements.Key App Capabilities3D navigation and compass tool - Allow app viewers to pan or rotate the scene and orient their view to NorthLocator map - Show a map with the app's map area in the context of a broader areaLine of sight - Visualize if one or multiple targets are visible from an observation pointMeasurement tools - Provide tools to measure distance and areaSlice - Exclude specific layers to change the view of a sceneBookmarks - Zooms and pans the map to a collection of preset extents that are saved in the sceneDisable scroll - Prevents the map from zooming when app users scrollHome, Zoom Controls, Legend, Layer List, SearchSupportabilityThis web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

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