This dataset contains 50-ft contours for the Hot Springs shallowest unit of the Ouachita Mountains aquifer system potentiometric-surface map. The potentiometric-surface shows altitude at which the water level would have risen in tightly-cased wells and represents synoptic conditions during the summer of 2017. Contours were constructed from 59 water-level measurements measured in selected wells (locations in the well point dataset). Major streams and creeks were selected in the study area from the USGS National Hydrography Dataset (U.S. Geological Survey, 2017), and the spring point dataset with 18 spring altitudes calculated from 10-meter digital elevation model (DEM) data (U.S. Geological Survey, 2015; U.S. Geological Survey, 2016). After collecting, processing, and plotting the data, a potentiometric surface was generated using the interpolation method Topo to Raster in ArcMap 10.5 (Esri, 2017a). This tool is specifically designed for the creation of digital elevation models and imposes constraints that ensure a connected drainage structure and a correct representation of the surface from the provided contour data (Esri, 2017a). Once the raster surface was created, 50-ft contour interval were generated using Contour (Spatial Analyst), a spatial analyst tool (available through ArcGIS 3D Analyst toolbox) that creates a line-feature class of contours (isolines) from the raster surface (Esri, 2017b). The Topo to Raster and contouring done by ArcMap 10.5 is a rapid way to interpolate data, but computer programs do not account for hydrologic connections between groundwater and surface water. For this reason, some contours were manually adjusted based on topographical influence, a comparison with the potentiometric surface of Kresse and Hays (2009), and data-point water-level altitudes to more accurately represent the potentiometric surface. Select References: Esri, 2017a, How Topo to Raster works—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/how-topo-to-raster-works.htm. Esri, 2017b, Contour—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro Raster Surface toolset at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/contour.htm. Kresse, T.M., and Hays, P.D., 2009, Geochemistry, Comparative Analysis, and Physical and Chemical Characteristics of the Thermal Waters East of Hot Springs National Park, Arkansas, 2006-09: U.S. Geological Survey 2009–5263, 48 p., accessed November 28, 2017, at https://pubs.usgs.gov/sir/2009/5263/. U.S. Geological Survey, 2015, USGS NED 1 arc-second n35w094 1 x 1 degree ArcGrid 2015, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html. U.S. Geological Survey, 2016, USGS NED 1 arc-second n35w093 1 x 1 degree ArcGrid 2016, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html.

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

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2023 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2023.Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryWhat can you do with this layer?Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. This layer can also be used in analyses that require land use/land cover input. For example, the Zonal toolset allows a user to understand the composition of a specified area by reporting the total estimates for each of the classes. NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Class definitionsValueNameDescription1WaterAreas 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.2TreesAny significant clustering of tall (~15 feet 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).4Flooded 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.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built 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.8Bare 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.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen 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.Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five 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 L2A 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 for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.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.

tracts_17_14_percent_change

This is an ArcGIS Server Image Service of the 4-band 2021 National Agricultural Imagery Program (NAIP) orthorectified digital aerial photos of Montana. Imagery defaults to natural color. To view the imagery as false-color infrared (CIR), select band 4 as the red image, band 1 as the green, and band 2 as the blue. This data set contains imagery from the National Agriculture Imagery Program (NAIP). These data are digital aerial photos, at 60 centimeter resolution, of most of the state of Montana, taken in 2017. Due to cloud cover, wildfire smoke, and snow cover the imagery acquisition was not completed in 2017 and some areas were acquired in 2018. The data are available from the State Library in two different formats. The most accessible format is a downloadable collection of compressed county mosaic (CCM) natural color MrSID images. These data are in UTM coordinates. The FTP folder containing these images is https://ftpgeoinfo.msl.mt.gov/Data/Spatial/MSDI/Imagery/2017_NAIP/UTM_County_Mosaics. The data are available from the State Library as a collection 11,384 4-band (near infrared, red, green and blue) TIF images in UTM coordinates. Each image is about 400 megabytes. The tiling format of the TIFF imagery is based on 3.75 x 3.75 minute quarter-quadrangles with a 300 pixel buffer on all four sides. An ESRI shapefile index showing the extent and acquisition dates of the TIF images is available at https://ftpgeoinfo.msl.mt.gov/Data/Spatial/MSDI/Imagery/2017_NAIP/NAIP_2017_Index_Montana.zip. To order TIFF images from the State Library, select the quadrangles you want from the tiff index shapefile and send them to the Library, along with a storage device of sufficient size to hold them and return postage for the device.

This resource links to the Hurricane Harvey 2017 Story Map (Esri ArcGIS Online web app) [1] that provides a graphical overview and set of interactive maps to download flood depth grids, flood extent polygons, high water marks, stream gage observations, National Water Model streamflow forecasts, and several other datasets compiled before, during and after Hurricane Harvey.

November 2023 updates: Esri has deprecated the previous story map template, so a new story map has been generated. Most of the content is the same as before, with these exceptions: - The Vulnerabilities and the Harvey Stories pages have been removed, due to nonfunctioning web links to other Harvey resources out of our control. - Story map links to HydroShare resource pages have been updated to the most current HydroShare resource versions.

References [1] Hurricane Harvey Story Map [https://arcg.is/1rWLzL0]

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

Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes:

Percent of neighborhood over 8% of slope

Slope Classes

0 - 20%

400

21% -50%

300

51% - 80%

200

81%

100

Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:

Change in elevation

Relief Class ID

0 – 30 meters

10

31 meter – 90 meters

20

91 meter – 150 meters

30

151 meter – 300 meters

40

301 meter – 900 meters

50

900 meters

60

The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:

Percent of neighborhood over 8% slope in upland or lowland areas

Profile Class

Less than 50% gentle slope is in upland or lowland

0

More than 75% of gentle slope is in lowland

1

50%-75% of gentle slope is in lowland

2

50-75% of gentle slope is in upland

3

More than 75% of gentle slope is in upland

4

Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Link to landing page referenced by identifier. Service Protocol: Link to landing page referenced by identifier. Link Function: information-- dc:identifier.

The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes farm and ranch sales plus the number and value of machines and trucks owned by operators from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: Farm and Ranch Sales, Machinery and Truck inventory and ValueCoordinate System: Web Mercator Auxiliary SphereExtent: United States including Hawaii and AlaskaVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Number of Operations - AnimalsSales in US Dollars - AnimalsNumber of Operations - CropsSales in US Dollars - CropsTotal Value in US Dollars - MachineryTractors - InventoryTrucks Including Pickups - InventoryAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

This dataset, produced by Impact Observatory, Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene collections from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%. These maps have been improved from Impact Observatory’s previous release and provide a relative reduction in the amount of anomalous change between classes, particularly between “Bare” and any of the vegetative classes “Trees,” “Crops,” “Flooded Vegetation,” and “Rangeland”. This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery. Data can be accessed directly from the Registry of Open Data on AWS, from the STAC 1.0.0 endpoint, or from the IO Store for a specific Area of Interest (AOI).

This layer is subset of World Ecological Facets Landform Classes Image Layer. Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

This web map shows the Voter Registration Statistics 2017 within the 18 districts of Hong Kong. It is a subset of data made available by the Registration and Electoral Office under the Government of Hong Kong Special Administrative Region (the “Government”) at https://DATA.GOV.HK/ (“DATA.GOV.HK”). The source data is in CSV & XLSX format and has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of DATA.GOV.HK at https://data.gov.hk.

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2024 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2024. Key Properties Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryAnalysis: Optimized for analysisClass Definitions: ValueNameDescription1WaterAreas 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.2TreesAny significant clustering of tall (~15 feet 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).4Flooded 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.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built 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.8Bare 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.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen 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.NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and class isolation for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer displays all classes.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent year is displayed. To discover and isolate specific years for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer.Zonal Statistics is a common tool used for understanding the composition of a specified area by reporting the total estimates for each of the classes. GeneralIf you are new to Sentinel-2 LULC, the Sentinel-2 Land Cover Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide.Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five 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 L2A 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 for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. 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.

ESRI Population Projections by Local Authority. Published by Department of Housing, Local Government, and Heritage. Available under the license Creative Commons Attribution Share-Alike 4.0 (CC-BY-SA-4.0).Population projection by scenario, year of age and local authority, for the 4 scenarios described in the project methodology for years 2017-2040. https://www.esri.ie/publications/regional-demographics-and-structural-housing-demand-at-a-county-level

The 4 scenarios are:

Baseline/Business as usual – based on medium term projections for the economy with an underlying assumption that net inwards migration would converge to 15,000 p.a. by 2024 and remain at that level throughout the projection horizon.

50:50 City – based on a similar outlook in terms of net inwards migration but whereby population growth is distributed in line with the objectives of the National Planning Framework (See National Policy Objectives 1a and 2a of https://npf.ie/wp-content/uploads/Project-Ireland-2040-NPF.pdf)

High Migration – assumes that net inwards migration stays at an elevated level throughout the projection horizon (net inwards migration of 30,000 p.a)

Low Migration - assumes that net inwards migration falls to net inwards migration of 5,000 by 2022 before converging back to the business as usual levels (i.e. net inwards migration of 15,000 p.a.) by 2027 and remaining at that level thereafter....

The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes corn production from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: 2017 Corn ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United States and HawaiiVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Operations with SalesSales in US DollarsGrain - Area Harvested in AcresGrain - Operations with Area HarvestedGrain - Production in BushelsGrain - Irrigated Area Harvested in AcresGrain - Operations with Irrigated Area HarvestedSilage - Area Harvested in AcresSilage - Operations with Area HarvestedSilage - Production in TonsSilage - Irrigated Area Harvested in AcresSilage - Operations with Area HarvestedTraditional or Indian - Area Harvested in AcresTraditional or Indian - Operations with Area HarvestedTraditional or Indian - Production in PoundsTraditional or Indian - Irrigated Area Harvested in AcresTraditional or Indian - Operations with Area HarvestedAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes cotton production from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: 2017 Cotton ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United StatesVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Area Harvested in AcresOperations with Area HarvestedOperations with SalesProduction in BalesSales in US DollarsIrrigated Area Harvested in AcresOperations with Irrigated Area HarvestedAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

This layer shows poverty status by age group. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer is symbolized to show the percentage of the population whose income falls below the Federal poverty line. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the map filter settings.A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2017-2021ACS Table(s): B17020 (Not all lines of these ACS tables are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Data Preparation: Data curated from Esri Living Atlas clipped to Census Tract boundaries that are within or adjacent to the City of Tempe boundaryDate of Census update: December 8, 2022National Figures: data.census.govAdditional Census data notes and data processing notes are available at the Esri Living Atlas Layer:https://tempegov.maps.arcgis.com/home/item.html?id=0e468b75bca545ee8dc4b039cbb5aff6 (Esri's Living Atlas always shows latest data)

The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes cattle production from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: 2017 Cattle ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United States, Alaska, and HawaiiVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Cattle - Operations with SalesCattle - Sales in US DollarsCattle - Sales in HeadDairy - Operations with SalesDairy - Sales in US DollarsAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

US_Fire_Perimeters_2017