This dynamic World Elevation Terrain layer returns float values representing ground heights in meters and compiles multi-resolution data from many authoritative data providers from across the globe. Heights are orthometric (sea level = 0), and water bodies that are above sea level have approximated nominal water heights.Height units: MetersUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see World Elevation Coverage Map.What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data values. Alternatively, client applications can select from numerous additional functions, applied on the server, that return rendered data. For visualizations such as multi-directional hillshade, hillshade, elevation tinted hillshade, and slope, consider using the appropriate server-side function defined on this service.Use for Analysis: Yes. This layer provides data as floating point elevation values suitable for use in analysis. There is a limit of 5000 rows x 5000 columns.Note: This layer combine data from different sources and resamples the data dynamically to the requested projection, extent and pixel size. For analyses using ArcGIS Desktop, it is recommended to filter a dataset, specify the projection, extent and cell size using the Make Image Server Layer geoprocessing tool. The extent is factor of cell size and rows/columns limit. e.g. if cell size is 10 m, the extent for analysis would be less than 50,000 m x 50,000 m.Server Functions: This layer has server functions defined for the following elevation derivatives. In ArcGIS Pro, server function can be invoked from Layer Properties - Processing Templates.

Slope Degrees Slope Percent Aspect Ellipsoidal height Hillshade Multi-Directional Hillshade Dark Multi-Directional Hillshade Elevation Tinted Hillshade Slope Map Aspect Map Mosaic Method: This image service uses a default mosaic method of "By Attribute”, using Field 'Best' and target of 0. Each of the rasters has been attributed with ‘Best’ field value that is generally a function of the pixel size such that higher resolution datasets are displayed at higher priority. Other mosaic methods can be set, but care should be taken as the order of the rasters may change. Where required, queries can also be set to display only specific datasets such as only NED or the lock raster mosaic rule used to lock to a specific dataset.Accuracy: Accuracy will vary as a function of location and data source. Please refer to the metadata available in the layer, and follow the links to the original sources for further details. An estimate of CE90 and LE90 are included as attributes, where available.This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single request.This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

Notice: this is not the latest Heat Anomalies image service.This layer contains the relative degrees Fahrenheit difference between any given pixel and the mean heat value for the city in which it is located, for every city in the contiguous United States, Alaska, Hawaii, and Puerto Rico. The Heat Anomalies is also reclassified into a Heat Severity raster also published on this site. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2023.To explore previous versions of the data, visit the links below:Full Range Heat Anomalies - USA 2022Full Range Heat Anomalies - USA 2021Full Range Heat Anomalies - USA 2020Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter or cooler than the average temperature for that same city as a whole. This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

Soil is the foundation of life on earth. More living things by weight live in the soil than upon it. It determines what crops we can grow, what structures we can build, what forests can take root.This layer contains the physical soil variable percent clay (clay).Within the subset of soil that is smaller than 2mm in size, also known as the fine earth portion, clay is defined as particles that are smaller than 0.002mm, making them only visible in an electron microscope. Clay soils contain low amounts of air, and water drains through them very slowly.This layer is a general, medium scale global predictive soil layer suitable for global mapping and decision support. In many places samples of soils do not exist so this map represents a prediction of what is most likely in that location. The predictions are made in six depth ranges by soilgrids.org, funded by ISRIC based in Wageningen, Netherlands.Each 250m pixel contains a value predicted for that area by soilgrids.org from best available data worldwide. Data for percent clay are provided at six depth ranges from the surface to 2 meters below the surface. Each variable and depth range may be accessed in the layer's multidimensional properties.Dataset SummaryPhenomenon Mapped: Proportion of clay particles (< 0.002 mm) in the fine earth fraction in g/100g (%)Cell Size: 250 metersPixel Type: 32 bit float, converted from online data that is 16 Bit Unsigned IntegerCoordinate System: Web Mercator Auxiliary Sphere, projected via nearest neighbor from goode's homolosine land (250m)Extent: World land area except AntarcticaVisible Scale: All scales are visibleNumber of Columns and Rows: 160300, 100498Source: Soilgrids.orgPublication Date: May 2020Data from the soilgrids.org mean predictions for clay were used to create this layer. You may access the percent clay in one of six depth ranges. To select one choose the depth variable in the multidimensional selector in your map client.Mean depth (cm)Actual depth range of data-2.50-5cm depth range-105-15cm depth range-22.515-30cm depth range-4530-60cm depth range-8060-100cm depth range-150100-200cm depth rangeWhat can you do with this Layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map: In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "world soils soilgrids" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "world soils soilgrids" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.This layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.More information about soilgrids layersAnswers to many questions may be found at soilgrids.org (ISRIC) frequently asked questions (faq) page about the data.To make this layer, Esri reprojected the expected value of ISRIC soil grids from soilgrids' source projection (goode's land WKID 54052) to web mercator projection, nearest neighbor, to facilitate online mapping. The resolution in web mercator projection is the same as the original projection, 250m. But keep in mind that the original dataset has been reprojected to make this web mercator version.This multidimensional soil collection serves the mean or expected value for each soil variable as calculated by soilgrids.org. For all other distributions of the soil variable, be sure to download the data directly from soilgrids.org. The data are available in VRT format and may be converted to other image formats within ArcGIS Pro.Accessing this layer's companion uncertainty layerBecause data quality varies worldwide, the uncertainty of the predicted value varies worldwide. A companion uncertainty layer exists for this layer which you can use to qualify the values you see in this map for analysis. Choose a variable and depth in the multidimensional settings of your map client to access the companion uncertainty layer.

Impervious surfaces are surfaces that do not allow water to pass through. Examples of these surfaces include highways, parking lots, rooftops, and airport runways. Instead of allowing rain to pass into the soil, impervious surfaces cause water to collect at the surface, then run off. An increase in impervious surface area causes an increase of water volume which needs to be managed by stormwater systems. With the flow come pollutants, which collect on impervious surfaces then discharge with the runoff into streams and the ocean. Runoff water does not enter the water table, and that can cause other management issues, such as interruptions in baseline stream flow.The NLCD imperviousness layer represents urban impervious surfaces as a percentage of developed surface over every 30-meter pixel in the United States. The layer is organized into a time series with years 2001, 2006, 2011, and 2016, for the lower 48 conterminous US states. This information may be used in conjunction with the USA NLCD Land Cover layer. Time SeriesBy default, this service will appear in your client with a time slider which allows you to play the series as an animation. The animation will advance year by year, but the layer only changes appearance every five years, in 2001, 2006, 2011, and 2016. To select just one year in the series, first turn the time series off on the time slider, then create a definition query on the layer which selects only the desired year.Time Series DescriptorMRLC issued a set of companion rasters with this impervious surface layer showing the reason why each pixel is impervious. This companion layer, called the Developed Imperviousness Descriptor, is not currently available in this map service. The descriptor layer identifies types of roads, core urban areas, and energy production sites for each impervious pixel to allow deeper analysis of developed features. The descriptor layer may be downloaded directly from MRLC and added to ArcGIS Pro.Alaska, Hawaii, and Puerto RicoAt this time Alaska, Hawaii, and Puerto Rico are not included in the time series. No new data were created for these areas since the last time MRLC updated the NLCD imperviousness layer. The older service USA NLCD Impervious Surface 2011 includes a portion of Alaska around Anchorage, but there is as yet no time series available for this part of Alaska.Dataset SummaryPhenomenon Mapped: The proportion of the landscape that is impervious to waterUnits: PercentCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: North America Albers Equal Area ConicExtent: Contiguous United StatesSource: Multi-Resolution Land Characteristics ConsortiumPublication Date: 2019ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/The National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics Consortium (MRLC). The MRLC Consortium is a partnership of federal agencies, consisting of the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the U.S. Environmental Protection Agency, the U.S. Department of Agriculture, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management and the USDA Natural Resources Conservation Service.What can you do with this layer?This layer can be used to create maps and to visualize the underlying data. This layer can be used as an analytic input 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.

The World Terrestrial Ecosystems map classifies the world into areas of similar climate, landform, and land cover, which form the basic components of any terrestrial ecosystem structure. This map is important because it uses objectively derived and globally consistent data to characterize the ecosystems at a much finer spatial resolution (250-m) than existing ecoregionalizations, and a much finer thematic resolution (431 classes) than existing global land cover products. This item was updated on Apr 14, 2023 to distinguish between Boreal and Polar climate regions in the terrestrial ecosystems. Cell Size: 250-meter Source Type: ThematicPixel Type: 16 Bit UnsignedData Projection: GCS WGS84Extent: GlobalSource: USGS, The Nature Conservancy, EsriUpdate Cycle: NoneAnalysis: Optimized for analysis What can you do with this layer?This map allows you to query the land surface pixels and returns the values of all the input parameters (landform type, landcover/vegetation type, climate region) and the name of the terrestrial ecosystem at that location. This layer can be used in analysis at global and local regions. However, for large scale spatial analysis, we have also provided an ArcGIS Pro Package that contains the original raster data with multiple table attributes. For simple mapping applications, there is also a raster tile layer. This layer can be combined with the World Protected Areas Database to assess the types of ecosystems that are protected, and progress towards meeting conservation goals. The WDPA layer updates monthly from the United Nations Environment Programme. 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 the Living Atlas Imagery Layers Optimized for Analysis Group for a complete list of imagery layers optimized for analysis. Developing the World Terrestrial EcosystemsWorld 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 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. Each combination was assigned a color using an algorithm that blended traditional color schemes for each of the three components. Every pixel in this map is symbolized by a combination of values for each of these fields. 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 More information about World Terrestrial Ecosystems can be found in this Story Map.

Notice: this is not the latest Heat Island Severity image service. For 2023 data, visit https://tpl.maps.arcgis.com/home/item.html?id=db5bdb0f0c8c4b85b8270ec67448a0b6. This layer contains the relative heat severity for every pixel for every city in the United States. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summers of 2018 and 2019.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of Arizona Dr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAADaphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

Soil is the foundation of life on earth. More living things by weight live in the soil than upon it. It determines what crops we can grow, what structures we can build, what forests can take root.This layer contains the chemical soil variable nitrogen (nitrogen).Nitrogen is an essential nutrient for sustaining life on Earth. Nitrogen is a core component of amino acids, which are the building blocks of proteins, and of nucleic acids, which are the building blocks of genetic material (RNA and DNA).This layer is a general, medium scale global predictive soil layer suitable for global mapping and decision support. In many places samples of soils do not exist so this map represents a prediction of what is most likely in that location. The predictions are made in six depth ranges by soilgrids.org, funded by ISRIC based in Wageningen, Netherlands.Each 250m pixel contains a value predicted for that area by soilgrids.org from best available data worldwide. Data for nitrogen are provided at six depth ranges from the surface to 2 meters below the surface. Each variable and depth range may be accessed in the layer's multidimensional properties.Dataset SummaryPhenomenon Mapped: Total nitrogen (N) in g/kgCell Size: 250 metersPixel Type: 32 bit float, converted from online data that is 16 Bit Unsigned IntegerCoordinate System: Web Mercator Auxiliary Sphere, projected via nearest neighbor from goode's homolosine land (250m)Extent: World land area except AntarcticaVisible Scale: All scales are visibleNumber of Columns and Rows: 160300, 100498Source: Soilgrids.orgPublication Date: May 2020Data from the soilgrids.org mean predictions for nitrogen were used to create this layer. You may access nitrogen values in one of six depth ranges. To select one choose the depth variable in the multidimensional selector in your map client.Mean depth (cm)Actual depth range of data-2.50-5cm depth range-105-15cm depth range-22.515-30cm depth range-4530-60cm depth range-8060-100cm depth range-150100-200cm depth rangeWhat can you do with this Layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map: In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "world soils soilgrids" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "world soils soilgrids" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.This layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.More information about soilgrids layersAnswers to many questions may be found at soilgrids.org (ISRIC) frequently asked questions (faq) page about the data.To make this layer, Esri reprojected the expected value of ISRIC soil grids from soilgrids' source projection (goode's land WKID 54052) to web mercator projection, nearest neighbor, to facilitate online mapping. The resolution in web mercator projection is the same as the original projection, 250m. But keep in mind that the original dataset has been reprojected to make this web mercator version.This multidimensional soil collection serves the mean or expected value for each soil variable as calculated by soilgrids.org. For all other distributions of the soil variable, be sure to download the data directly from soilgrids.org. The data are available in VRT format and may be converted to other image formats within ArcGIS Pro.Accessing this layer's companion uncertainty layerBecause data quality varies worldwide, the uncertainty of the predicted value varies worldwide. A companion uncertainty layer exists for this layer which you can use to qualify the values you see in this map for analysis. Choose a variable and depth in the multidimensional settings of your map client to access the companion uncertainty layer.

This is a georeferenced image that responds to various projections. Download, unzip, and add the Layer file to your ArcGIS Pro map. The vertical vignette is black by default, muting the sometimes overly-bright pixels of the poles, creating a vertical gradation that begins in arctic regions and increases towards the poles. Alternatively, adjust the "Brightness" slider in the ArcGIS Pro "Appearance" tab to convert this vignette to white, if the map is light in color.The maps on the left use the vignette. Maps at right are a non-vignetted reference. The top row shows a Mercator map with black vignette. The bottom row shows a Bonne projection with the vignette set to white.Happy Vignetting! John Nelson

Soil is the foundation of life on earth. More living things by weight live in the soil than upon it. It determines what crops we can grow, what structures we can build, what forests can take root.This layer contains the chemical soil variable organic carbon density (ocd) which measures carbon mass in proportion to volume of soil (mass divided by volume.)From Agriculture Victoria: Soil carbon provides a source of nutrients through mineralisation, helps to aggregate soil particles (structure) to provide resilience to physical degradation, increases microbial activity, increases water storage and availability to plants, and protects soil from erosion.This layer is a general, medium scale global predictive soil layer suitable for global mapping and decision support. In many places samples of soils do not exist so this map represents a prediction of what is most likely in that location. The predictions are made in six depth ranges by soilgrids.org, funded by ISRIC based in Wageningen, Netherlands.Each 250m pixel contains a value predicted for that area by soilgrids.org from best available data worldwide. Data for organic carbon density are provided at six depth ranges from the surface to 2 meters below the surface. Each variable and depth range may be accessed in the layer's multidimensional properties.Dataset SummaryPhenomenon Mapped: Organic carbon density in kg/m³Cell Size: 250 metersPixel Type: 32 bit float, converted from online data that is 16 Bit Unsigned IntegerCoordinate System: Web Mercator Auxiliary Sphere, projected via nearest neighbor from goode's homolosine land (250m)Extent: World land area except AntarcticaVisible Scale: All scales are visibleNumber of Columns and Rows: 160300, 100498Source: Soilgrids.orgPublication Date: May 2020Data from the soilgrids.org mean predictions for ocd were used to create this layer. You may access organic carbon density values in one of six depth ranges. To select one choose the depth variable in the multidimensional selector in your map client.Mean depth (cm)Actual depth range of data-2.50-5cm depth range-105-15cm depth range-22.515-30cm depth range-4530-60cm depth range-8060-100cm depth range-150100-200cm depth rangeWhat can you do with this Layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map: In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "world soils soilgrids" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "world soils soilgrids" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.This layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.More information about soilgrids layersAnswers to many questions may be found at soilgrids.org (ISRIC) frequently asked questions (faq) page about the data.To make this layer, Esri reprojected the expected value of ISRIC soil grids from soilgrids' source projection (goode's land WKID 54052) to web mercator projection, nearest neighbor, to facilitate online mapping. The resolution in web mercator projection is the same as the original projection, 250m. But keep in mind that the original dataset has been reprojected to make this web mercator version.This multidimensional soil collection serves the mean or expected value for each soil variable as calculated by soilgrids.org. For all other distributions of the soil variable, be sure to download the data directly from soilgrids.org. The data are available in VRT format and may be converted to other image formats within ArcGIS Pro.Accessing this layer's companion uncertainty layerBecause data quality varies worldwide, the uncertainty of the predicted value varies worldwide. A companion uncertainty layer exists for this layer which you can use to qualify the values you see in this map for analysis. Choose a variable and depth in the multidimensional settings of your map client to access the companion uncertainty layer.

This map is the subset of the World Terrestrial Ecosystems map, prepared specifcally for the Pacific Region. The World Terrestrial Ecosystems map classifies the world into areas of similar climate, landform, and land cover, which form the basic components of any terrestrial ecosystem structure. This map is important because it uses objectively derived and globally consistent data to characterize the ecosystems at a much finer spatial resolution (250-m) than existing ecoregionalizations, and a much finer thematic resolution (431 classes) than existing global land cover products.Cell Size: 250-meter Source Type: ThematicPixel Type: 16 Bit UnsignedData Projection: GCS WGS84Extent: GlobalSource: USGS, The Nature Conservancy, EsriUpdate Cycle: NoneWhat can you do with this layer?This map allows you to query the land surface pixels and returns the values of all the input parameters (landform type, landcover/vegetation type, climate region) and the name of the terrestrial ecosystem at that location.This layer can be used in analysis at global and local regions. However, for large scale spatial analysis, we have also provided an ArcGIS Pro Package that contains the original raster data with multiple table attributes. For simple mapping applications, there is also a raster tile layer. This layer can be combined with the World Protected Areas Database to assess the types of ecosystems that are protected, and progress towards meeting conservation goals. The WDPA layer updates monthly from the United Nations Environment Programme.Developing the World Terrestrial EcosystemsWorld 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 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.Each combination was assigned a color using an algorithm that blended traditional color schemes for each of the three components. Every pixel in this map is symbolized by a combination of values for each of these fields.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 More information about World Terrestrial Ecosystems can be found in this Story Map.

This layer contains the relative heat severity for every pixel for every city in the United States. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summers of 2019 and 2020.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Pete.Aniello@tpl.org with feedback.Terms of UseYou understand and agree, and will advise any third party to whom you give any or all of the data, that The Trust for Public Land is neither responsible nor liable for any viruses or other contamination of your system arising from use of The Trust for Public Land’s data nor for any delays, inaccuracies, errors or omissions arising out of the use of the data. The Trust for Public Land’s data is distributed and transmitted "as is" without warranties of any kind, either express or implied, including without limitation, warranties of title or implied warranties of merchantability or fitness for a particular purpose. The Trust for Public Land is not responsible for any claim of loss of profit or any special, direct, indirect, incidental, consequential, and/or punitive damages that may arise from the use of the data. If you or any person to whom you make the data available are downloading or using the data for any visual output, attribution for same will be given in the following format: "This [document, map, diagram, report, etc.] was produced using data, in whole or in part, provided by The Trust for Public Land."

Albedo measures the reflectivity of an object. Surfaces that are black reflect little light and have low albedo values while white surfaces reflect most of the light striking them and have high albedo values. Albedo is measured on a scale of 0 (no light reflected) to 1 (100% of light reflected). Albedo is measured using a scale of 0 (no light reflected) to 1 (100% of the light is reflected). Divide each integer"s raw pixel value by one hundred to find its representative albedo value. Thus, a pixel with the value of 24 represents an albedo value of 0.24 while a pixel with the value of 38 represents the albedo value 0.38. Dataset SummaryPhenomenon Mapped: Soil albedoGeographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands, Republic of Palau, Republic of the Marshall Islands, Federated States of Micronesia, and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: WKID 5070 USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WKID 3338 WGS 1984 Albers (Alaska), WKID 4326 WGS 1984 Decimal Degrees (Guam, Republic of the Marshall Islands, Northern Mariana Islands, Republic of Palau, Federated States of Micronesia, American Samoa, and Hawaii).Units: NoneCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerSource: Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 Typical albedo values:Fresh asphalt 0.04Worn asphalt 0.12Confier forest 0.08 – 0.15Deciduous trees 0.15 – 0.18Bare soil 0.17Green grass 0.25Desert sand 0.4New concrete 0.55Ocean ice 0.5 – 0.7Fresh snow 0.8-0.9 Albedo is used in climate and water cycle models. Estimates of evapotranspiration rate and prediction of soil water balances require albedo values. Soil hydrology models that are part of water quality and resource assessment programs also require albedo. Data from the gNATSGO database was used to create the layer. This layer is derived from the 30m rasters produced by the Natural Resources Conservation Service (NRCS). The value for soil albedo is derived from the gSSURGO component table field Albedo Dry - Representative Value (albedodry_r). The value in this layer is the average value for all components of each map unit weighted by component percent (comppct_r). What can you do with this layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map: In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "albedo" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "albedo" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro. Online you can filter the layer to show subsets of the data using the filter button and the layer"s built-in raster functions. The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

On November 7, 2021, NV5 collected Quality Level 1 (QL1) lidar data across the preliminary CAL FIRE defined fire perimeter for the CZU lightning complex fire in San Mateo and Santa Cruz counties. The technical report for the lidar data collection is available here: https://fuelsmapping.com/czu_postfire_lidar_report From the QL1 postfire lidar, NV5 and Tukman Geospatial developed a set of derivatives. These derivatives are a Digital Terrain Model (DTM), a Digital Surface Model (DSM), a Hillshade derived from the DTM, a lidar intensity image, a Normalized Digital Surface Model (nDSM), a Canopy Cover raster, and a lidar intensity image. The derivatives will be used to study the effects of the CZU wildfire on the natural landscape, forests, and shrublands of Santa Cruz and San Mateo Counties. The lidar derivatives are provided as GeoTiffs available for download from ArcGIS Online and as dynamic image services. Table 1 provides more information (including download information) for the derivatives produced. The GeoTiffs can be used in desktop GIS software packages such as ArcGIS Pro and ERDAS Imagine; the image services can be used in web maps and web mapping applications by both GIS users and non-GIS users. Table 1. lidar derivatives for the CZU lightning fire footprint in San Mateo and Santa Cruz Counties

  Dataset


  Description


  Link to GeoTiff


  Link to Image Service






  Digital Terrain Model (DTM)


  Hydroflattened digital terrain model. Pixel values represent elevation above sea level of the ground.


  https://vegmap.press/czu_postfire_dtm_tif


  https://vegmap.press/czu_postfire_dtm




  Digital Surface Model (DSM)


  Pixel values in the DSM represent elevations above sea level of the ‘highest hit’ surface. The DSM provides elevation above sea level of the top of the tree canopy, the top of buildings, and the top of other features.


  https://vegmap.press/czu_postfire_dsm_tif


  https://vegmap.press/czu_postfire_dsm




  Hillshade


  The hillshade is derived from the DTM and provides a ‘shaded relief’ visualization of the earth’s surface.


  https://vegmap.press/czu_postfire_hillshade_tif


  https://vegmap.press/czu_postfire_hillshade




  Lidar Intensity


  Lidar intensity, scaled to 8-bit resolution.


  https://vegmap.press/czu_postfire_intensity_tif


  https://vegmap.press/czu_postfire_lidar_intensity




  Normalized Digital Surface Model (nDSM)


  In the nDSM, pixel values represent the maximum normalized height in feet of features such as vegetation and structures. For areas with aboveground features, pixel values represent the aboveground height of the tallest part of the feature in the 3x3 foot pixel. For areas with no aboveground features, the nDSM has pixel values of 0. 


  https://vegmap.press/czu_postfire_nDSM_tif


  https://vegmap.press/czu_postfire_nDSM




  Canopy Height Model


  The canopy height model is the normalized digital surface model, with building footprints and a small buffer surrounding them set to 0 normalized height. Building footprint data came from the prefire CHM. The datasheet for the prefire CHM is available here: https://vegmap.press/sc_chm As such, this raster mostly represents the aboveground height of the vegetation canopy.  Note that it also includes some noise (e.g., powerlines and other objects that are not vegetation), as well as some structures that weren't captured as building footprints.


  https://vegmap.press/czu_postfire_chm_tif


  https://vegmap.press/czu_postfire_chm




  Canopy Cover


  This is the Canopy Height Model, thresholded to show pixel values greater than or equal to 15 feet aboveground as 1, and all other areas as 0. As such, it is a proxy for tree canopy cover. 


  https://vegmap.press/czu_postfire_cc_tif


  https://vegmap.press/czu_postfire_cc

Related Datasets: The QL1 point cloud, from which these deliverables were acquired, is available as laz files. The laz files are downloadable by tile. See this datasheet for more information: CZU postfire QL1 point cloudCZU postfire 4-band imagery

Dataset description: This repository contains data pertaining to the manuscript "Mawrth Vallis, Mars, classified using the NOAH-H deep-learning terrain classification system." submitted to Journal of Maps. NOAH-H Mosaics: Mawrth_Vallis_NOAHH_Mosaic_DC_IG_25cm4bit_20230121_reclass.zip This folder contain mosaics of terrain classifications for Mawrth Vallis, Mars, made by the Novelty or Anomaly Hunter - HiRISE (NOAH-H) deep learning convolutional neural network developed for the European Space Agency (ESA) by SCISYS Ltd. In coordination with the Open University Planetary Environments Group. These folders contain the NOAH-H mosaics, as well as ancillary files needed to display the NOAH-H products in geographic information software (GIS). Included are two large raster datasets, containing the NOAH-H classification for the entire study area. One uses the 14 descriptive classes of the terrain, and the other with the five interpretative groups (Barrett et al., 2022). · Mawrth_Vallis_NOAHH_Mosaic_DC_25cm4bit_20230121_reclass.tif Contains the full 14 class “Descriptive Classes” (DC) dataset, reclassified so that pixel values reflect the original NOAH-H ontology, and not the priority rankings described in Wright et al., (2022) and Barrett et al., (2022b). It is accompanied by all auxiliary files required to view the data in GIS. · Mawrth_Vallis_NOAHH_Mosaic_IG_25cm4bit_20230121_reclass.tif Contains the 5 class “Interpretive Groups” (IG) dataset, reclassified so that pixel values reflect the original NOAH-H ontology, and not the priority rankings described in Wright et al., (2022) and Barrett et al., (2022b). It is accompanied by all auxiliary files required to view the data in GIS. Symbology layer files: NOAH-H_Symbology.zip This folder contains GIS layer file and colour map files for both the Descriptive Classes (DC) and interpretive Groups (IG) versions of the classification. These can be applied to the data using the symbology options in GIS. Georeferencing Control points: Mawrth_Vallis_Final_Control_Points.zip This file contains the control points used to georeferenced the 26 individual HiRISE images which make up the mosaic. These allow publicly available HiRISE images to be aligned to the terrain in Mawrth Vallis, and thus the NOAH-H Mosaic. Twenty-six 25 cm/pixel HiRISE images of Mawrth Vallis were used as input for NOAH-H. These are:

PSP_002140_2025_RED

PSP_002074_2025_RED

ESP_057351_2020_RED

ESP_053909_2025_RED

ESP_053698_2025_RED

ESP_052274_2025_RED

ESP_051931_2025_RED

ESP_051351_2025_RED

ESP_051219_2030_RED

ESP_050217_2025_RED

ESP_046960_2025_RED

ESP_046670_2025_RED

ESP_046525_2025_RED

ESP_046459_2025_RED

ESP_046314_2025_RED

ESP_045536_2025_RED

ESP_045114_2025_RED

ESP_044903_2025_RED

ESP_043782_2025_RED

ESP_043637_2025_RED

ESP_038758_2025_RED

ESP_037795_2025_RED

ESP_037294_2025_RED

ESP_036872_2025_RED

ESP_036582_2025_RED

ESP_035804_2025_RED NOAH-H produced corresponding 25 cm/pixel rasters where each pixel is assigned a terrain class based on the corresponding pixels in the input HiRISE image. To mosaic the NOAH-H rasters together, first the input HiRISE images were georeferenced to the HRSC basemap (HMC_11E10_co5) tile, using CTX images as an intermediate step. High order (spline, in ArcGIS Pro 3.0) transformations were used to make the HiRISE images georeference closely onto the target layers. Once the HiRISE images were georeferenced, the same control points and transformations were applied to the corresponding NOAH-H rasters. To mosaic the georeferenced NOAH-H rasters the pixel values for the classes needed to be changed so that more confidently identified, and more dangerous, classes made it into the mosaic (see dataset manuscript for details. To produce a HiRISE layer which fits the NOAH-H classification, download one of the listed HiRISE images from https://www.uahirise.org/, Select the corresponding control point file from this archive and apply a spline transformation through the GIS georeferencing toolbar. Manually Mapped Aeolian Bedforms: Mawrth_Manual_TARs.zip The manually mapped data was produced by Fawdon, independently of the NOAH-H project, as an assessment of “Aeolian Hazard” at Mawrth Vallis. This was done to inform the ExoMars landing site selection process. This file contains two GIS shape files, containing the manually mapped bedforms for both the entire mapping area, and the HiRISE image ESP_046459_2025_RED where the two datasets were compared on a pixel scale. The full manual map is offset slightly from the NOAH-H, since it was digitised from bespoke HiRISE orthomosaics, rather than from the publicly available HiRISE Red band images. It is suitable for comparison to the NOAH-H data with 100m-1km aggregation as in figure 8 of the associated paper. It is not suitable for pixel scale comparison. The map of ESP_046459_2025_RED was manually georeferenced to the NOAH-H mosaic, allowing for direct pixel to pixel comparisons, as presented in figure 6 of the associated paper. Two GIS shape files are included: · Mawrth_Manual_TARs_ESP_046459_2025.shp · Mawrth_Manual_TARs_all.shp Containing the high fidelity data for ESP_046459_2025, and the medium fidelity data for the entire area respectively. The are accompanied by ancillary files needed to view them in GIS. Gridded Density Statistics This dataset contains gridded density maps of Transverse Aeolian Ridges and Boulders, as classified by the Novelty or Anomaly Hunter – HiRISE (NOAH-H). The area covered is the runner up candidate ExoMars landing site in Mawrth Vallis, Mars. These are the data shown in figures; 7, 8, and S1. Files are presented for every classified ripple and boulder class, as well as for thematic groups. These are presented as .shp GIS shapefiles, along with all auxiliary files required to view them in GIS. Gridded Density stats are available in two zip folders, one for NOAH-H predicted density, and one for manually mapped density. NOAH-H Predicted Density: Mawrth_NOAHH_1km_Grid_TAR_Boulder_Density.zip Individual classes are found in the files: · Mawrth_NOAHH_1km_Grid_8TARs.shp · Mawrth_NOAHH_1km_Grid_9TARs.shp · Mawrth_NOAHH_1km_Grid_11TARs.shp · Mawrth_NOAHH_1km_Grid_12TARs.shp · Mawrth_NOAHH_1km_Grid_13TARs.shp · Mawrth_NOAHH_1km_Grid_Boulders.shp Where the text following Grid denotes the NOAH-H classes represented, and the landform classified. E.g. 8TARs = NOAH-H TAR class 8. The following thematic groups are also included: · Mawrth_NOAHH_1km_Grid_8_11continuousTARs.shp · Mawrth_NOAHH_1km_Grid_12_13discontinuousTARs · Mawrth_NOAHH_1km_Grid_8_10largeTARs.shp · Mawrth_NOAHH_1km_Grid_11_13smallTARs.shp · Mawrth_NOAHH_1km_Grid_8_13AllTARs.shp When the numbers denote the range of NOAH-H classes which were aggregated to produce the map, followed by a description of the thematic group: “continuous”, “discontinuous”, “large”, “small”, “all”. Manually Mapped Density Plots: Mawrth_Manual_1km_Grid.zip These GIS shapefiles have the same format as the NOAH-H classified ones. Three datasets are presented for all TARs (“_allTARs”), Continuous TARs (“_con”) and Discontinuous TARs (“_dis”) · Mawrth_Manual_1km_Grid_AllTARs.shp · Mawrth_Manual_1km_Grid_Con.shp · Mawrth_Manual_1km_Grid_Dis.shp Related public datasets: The HiRISE images discussed in this work are publicly available from https://www.uahirise.org/. and are credited to NASA/JPL/University of Arizona. HRSC images are credited to the European Space Agency; Mars Express mission team, German Aerospace Center (DLR), and the Freie Universität Berlin (FUB). They are available at the ESA Planetary Science Archive (PSA) https://www.cosmos.esa.int/web/psa/mars-express and are used under the Creative Commons CC BY-SA 3.0 IGO licence. SPATIAL DATA COORDINATE SYSTEM INFORMATION All NOAH-H files and derivative density plots have the same projected coordinate system: “Equirectangular Mars” - Projection: Plate Carree - Sphere radius: 3393833.2607584 m SOFTWARE INFORMATION All GIS workflows (georeferencing, mosaicking) were conducted in ArcGIS Pro 3.0. NOAH-H is a deep learning semantic segmentation software developed by SciSys Ltd for the European Space Agency to aid preparation for the ExoMars rover mission.

This web map is a subset of World Terrestrial Ecosystems to focus on Central Asia and Caucasus Region.The World Terrestrial Ecosystems map classifies the world into areas of similar climate, landform, and land cover, which form the basic components of any terrestrial ecosystem structure. This map is important because it uses objectively derived and globally consistent data to characterize the ecosystems at a much finer spatial resolution (250-m) than existing ecoregionalizations, and a much finer thematic resolution (431 classes) than existing global land cover products. This item was updated on Apr 14, 2023 to distinguish between Boreal and Polar climate regions in the terrestrial ecosystems. Cell Size: 250-meter Source Type: ThematicPixel Type: 16 Bit UnsignedData Projection: GCS WGS84Extent: GlobalSource: USGS, The Nature Conservancy, EsriUpdate Cycle: NoneWhat can you do with this layer?This map allows you to query the land surface pixels and returns the values of all the input parameters (landform type, landcover/vegetation type, climate region) and the name of the terrestrial ecosystem at that location.This layer can be used in analysis at global and local regions. However, for large scale spatial analysis, we have also provided an ArcGIS Pro Package that contains the original raster data with multiple table attributes. For simple mapping applications, there is also a raster tile layer. This layer can be combined with the World Protected Areas Database to assess the types of ecosystems that are protected, and progress towards meeting conservation goals. The WDPA layer updates monthly from the United Nations Environment Programme.Developing the World Terrestrial EcosystemsWorld 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 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.Each combination was assigned a color using an algorithm that blended traditional color schemes for each of the three components. Every pixel in this map is symbolized by a combination of values for each of these fields.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 More information about World Terrestrial Ecosystems can be found in this Story Map.

Supplementary materials used in the following studies:Kwong, I. H. Y., Lai, D. Y. F., Wong, F. K. K., & Fung, T. (Manuscript submitted for publication). Integrating five decades of Landsat imagery for territory-wide habitat mapping and change detection in a subtropical metropolitan city.Kwong, I. H. Y. (2025). Spatio-Temporal Changes in Habitat Type and Quality in Hong Kong Using a 50-Year Archive of Remote Sensing Imagery [Doctoral thesis, Department of Geography and Resource Management, The Chinese University of Hong Kong].Kwong, I. H. Y., Lai, D. Y. F., Wong, F. K. K., & Fung, T. (2025). Spatial variations in forest succession rates revealed from multi-temporal habitat maps using Landsat imagery in subtropical Hong Kong. European Geosciences Union (EGU) General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025. https://doi.org/10.5194/egusphere-egu25-2667. [Poster Presentation: https://presentations.copernicus.org/EGU25/EGU25-2667_presentation-h291057.pdf]Disclaimer: All datasets described here are for reference only. No express or implied warranty or representation is given to the accuracy or completeness of the data or its appropriateness for use in any particular circumstances.GIS mapping results:All raster layers (GeoTiff format) have a pixel size of 30 m covering the 1117-km2 terrestrial area in Hong Kong in this study (Hong Kong 1980 Grid coordinate system). The time period of 1973–2022 was divided into 10 five-year periods in the mapping process.HabitatMapHK_6class_yyyy-yyyy.tif: Raster data showing the 6 habitat classes mapped in this study. Pixel values range from 1 to 6 representing woodland, shrubland, grassland, barren land, built-up area, and water respectively.HabitatMapHK_EstimatedArea.csv: Area coverage (km2) of different habitat classes, as well as their confidence intervals, as mapped in this study.HabitatMapHK_6class_ArcGISsymbology.lyrx: Used to apply the suggested symbology in ArcGIS Pro.ClassificationProbability_yyyy-yyyy.tif: The probability values belonging to each class for every pixel. They were the intermediate products generated from the classification workflow and used to determine the final class with the highest probability and compute the forest index in this study. The sum of probabilities for all six classes is equal to 1. A scale factor of 10000 was applied to the GeoTiff files for storage convenience.HabitatMapHK_8class_yyyy-yyyy.tif: Based on the 6-class outputs, two more classes are added in this product, including wetland (pixel value 7) and plantation (pixel value 8), to serve as inputs for the habitat quality model.HabitatQualityHK_yyyy-yyyy.tif: Habitat quality maps produced in this study. The pixel value is a continuous variable ranging from 0 to 1, with 1 meaning the highest habitat quality.GIS supplementary data:All datasets were collected and compiled from January to June 2024 and represent the conditions at that time.Environmental Raster:DistanceFromCoast.tif: Geometric distance (m) from the coastline.Elevation.tif: Terrain height (m) from a LiDAR-based digital terrain model.Hillfire_10periods.tif: Hill fires occurred in each five-year period, based on burn-area products by Chan et al. (2023) and manual digitisation for early years.Insolation.tif: Annual amount of incoming solar radiation (kWh/m2) computed using SAGA GIS.Landslide_10periods.tif: Landslides occurred in each five-year period, based on the Enhanced Natural Terrain Landslide Inventory (Dias et al., 2009).Northness.tif: Terrain aspect from 1 (due north) to -1 (due south) computed from the DTM.Precipitation.tif: Annual precipitation (mm) (average between 1991-2020) from Hong Kong Observatory.Slope.tif: Steepness (°) of the ground surface computed from the DTM.SoilCEC.tif: Cation exchange capacity (CEC) (mmol/kg) of topsoil from Luo et al. (2007).SoilOrganicMatter.tif: Organic matter content (%) of topsoil from Luo et al. (2007).Temperature.tif: Annual mean temperature (°C) from Morgan and Guénard (2019).TopographicWetnessIndex.tif: Amount of water accumulation due to topographic effects computed using SAGA GIS.Typhoon_10periods.tif: Wind speed (km/h) estimated from WindNinja based on maximum hourly mean wind records associated with typhoon events in each five-year period.WindSpeed.tif: Mean wind speed (km/h) estimated from WindNinja based on monthly prevailing wind records.Human Activities:BuiltupAreas_10periods_shp.zip: Shapefile (polygons) of built-up areas, with attributes on the years of construction (estimated from topographic maps) and density (high and low). It was used as a threat factor in habitat quality mapping and variables in habitat changes.CountryParksProtectedAreas_shp.zip: Shapefile (polygons) of protected areas (Country Parks, Special Areas, etc.), with attributes on the years of designation and revision. It was used as a protection factor in habitat quality mapping and variables in habitat changes.PollutionSource_shp.zip: Shapefile (polygons) of pollution sources (landfills, power stations, and incineration plants), with attributes on the years of construction and closure. It was used as a threat factor in habitat quality mapping.Roads_10periods_shp.zip: Shapefile (polylines) of roads, with attributes on the years of construction (estimated from topographic maps) and type (main and secondary). It was used as a threat factor in habitat quality mapping.Mapping Reference:ForestIndex_FieldCollectedReferenceData.csv: Field survey records of habitat types which were used to evaluate the forest index variable in this study.HabitatMapHK_FieldCollectedReferenceData.csv: Field survey records of habitat types which were used to assess the habitat mapping results in this study.HabitatMapHK_OfficeInterpretedReferenceData.csv: Reference points where the habitat class in each period was determined through visual interpretation of the aerial photographs and other historical records. The points were used for both training and validation of the habitat maps in this study.HabitatQualityHK_FieldSurveyedEcologicalValue2008.csv: Field survey records of ecological values in 2008 which were used to evaluate the habitat quality maps in this study.LandsatHK_CrossSensorCalibrationPoints.csv: Selected points that were assumed to remain unchanged over time and used to cross-calibrate different Landsat sensors in this study.LandsatHK_ImageMetadata.csv: Metadata of the Landsat imagery (1,100 downloaded scenes and 607 valid scenes after pre-processing) acquired and processed in this study.Plantation_1975_1990_2008_2019.tif: Pixels that were identified as plantations on four existing maps in different years (1975, 1990, 2008, 2019), as represented by the four layers contained in this raster file respectively. These pixels were used to help extract plantation class on the habitat map (when producing habitat quality) and denote areas with plantation activities (when modelling habitat changes) in this study.SpeciesObsHK_SpeciesChecklist.csv: A species checklist of 7 taxa in Hong Kong (Plants, Butterflies, Birds, Reptiles, Dragonflies, Amphibians, Mammals) compiled from AFCD, Hong Kong Biodiversity Information Hub, and other secondary sources. Species of conservation concern are identified based on local assessments (Corlett et al., 2000; Fellowes et al., 2002), environmental protection laws, and national and global assessments. The checklist was used to match with the iNaturalist observation data to compute biodiversity metrics at grid levels and evaluate habitat quality maps in this study.SpeciesObsHK_SynonymList.csv: A list of species name synonyms for matching names used in iNaturalist and other secondary sources with the species checklist. It was used to pre-process the iNaturalist observation data and unify the species names from different records in this study.Analysis scripts:Part 1: Mapping Vegetation Habitats from a Satellite Image Time-SeriesP1_01_SearchAndDownloadFromGEE.ipynb: Query and download all available Landsat 1-9 imagery covering the study area using Google Earth Engine. Atmospheric correction is performed if necessary.P1_02_Preprocess_part1.py: Some basic pre-processing steps after downloading the images from cloud platform to local computer, such as mosaicking adjacent scenes and reprojecting to local coordinate system.P1_03_TopographicCorrection.R: SCS+C topographic correction based on terrain slope, aspect, sun azimuth and sun elevation angles.P1_04_CrossSensorCal.R: Cross-calibration of different Landsat sensors based on pseudo-invariant features, followed by computing variables for image classification.P1_05_ImageComposite.R: Create image composites (median and standard deviation statistics) by combining all imagery acquired in the same period.P1_06_ExtractPixelValue.R: Extract pixel values at the locations of reference points.P1_07_TrainingDataStat.R: Summarise the characteristics of pixel values (e.g., spectral reflectance) of each habitat class and Landsat sensor.P1_08_TrainRFModel.R: Train the Random Forest model, fuse probability outputs from each image, evaluate the model accuracies with cross-validation, and create the final model for classifying the entire dataset.P1_09_TestProcedures.R: Modify the classification procedures and re-run the Random Forest models to evaluate their impacts on the classification accuracies.P1_10_ApplyModel.R: Apply the Random Forest model and fusion steps to all images to create the habitat map for each period.P1_11_AreaCoverage.R: Obtain the area coverage of each class on the habitat map as well as the confidence interval of the area estimates.P1_12_CompareFieldData.R: Assess the accuracies of the habitat maps by overlaying with field-collected points and LiDAR height information at different times.P1_13_SurvivalAnalysis.R: Analyse the number of years required for transitioning between vegetation classes as well as the correlations between transition times and environmental variables.Part 2: Computing Habitat Quality Maps with Reference to

Soil is the foundation of life on earth. More living things by weight live in the soil than upon it. It determines what crops we can grow, what structures we can build, what forests can take root.This layer contains the physical soil variable percent silt (silt).Within the subset of soil that is smaller than 2mm in size, also known as the fine earth portion, silt is defined as particles that are equal to or are between 0.002mm and 0.05mm in size. Silty soils are usually more fertile than other types of soil, with a good balance of air circulation and water retention.This layer is a general, medium scale global predictive soil layer suitable for global mapping and decision support. In many places samples of soils do not exist so this map represents a prediction of what is most likely in that location. The predictions are made in six depth ranges by soilgrids.org, funded by ISRIC based in Wageningen, Netherlands.Each 250m pixel contains a value predicted for that area by soilgrids.org from best available data worldwide. Data for silt are provided at six depth ranges from the surface to 2 meters below the surface. Each variable and depth range may be accessed in the layer's multidimensional properties.Dataset SummaryPhenomenon Mapped: Proportion of silt particles (≥ 0.002 mm and ≤ 0.05 mm) in the fine earth fraction in g/100g (%)Cell Size: 250 metersPixel Type: 32 bit float, converted from online data that is 16 Bit Unsigned IntegerCoordinate System: Web Mercator Auxiliary Sphere, projected via nearest neighbor from goode's homolosine land (250m)Extent: World land area except AntarcticaVisible Scale: All scales are visibleNumber of Columns and Rows: 160300, 100498Source: Soilgrids.orgPublication Date: May 2020Data from the soilgrids.org mean predictions for silt were used to create this layer. You may access the percent silt in one of six depth ranges. To select one choose the depth variable in the multidimensional selector in your map client.Mean depth (cm)Actual depth range of data-2.50-5cm depth range-105-15cm depth range-22.515-30cm depth range-4530-60cm depth range-8060-100cm depth range-150100-200cm depth rangeWhat can you do with this Layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map: In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "world soils soilgrids" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "world soils soilgrids" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.This layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.More information about soilgrids layersAnswers to many questions may be found at soilgrids.org (ISRIC) frequently asked questions (faq) page about the data.To make this layer, Esri reprojected the expected value of ISRIC soil grids from soilgrids' source projection (goode's land WKID 54052) to web mercator projection, nearest neighbor, to facilitate online mapping. The resolution in web mercator projection is the same as the original projection, 250m. But keep in mind that the original dataset has been reprojected to make this web mercator version.This multidimensional soil collection serves the mean or expected value for each soil variable as calculated by soilgrids.org. For all other distributions of the soil variable, be sure to download the data directly from soilgrids.org. The data are available in VRT format and may be converted to other image formats within ArcGIS Pro.Accessing this layer's companion uncertainty layerBecause data quality varies worldwide, the uncertainty of the predicted value varies worldwide. A companion uncertainty layer exists for this layer which you can use to qualify the values you see in this map for analysis. Choose a variable and depth in the multidimensional settings of your map client to access the companion uncertainty layer.

The Wind Integration National Dataset (WIND) Toolkit, developed by the National Renewable Energy Laboratory (NREL), provides modeled wind speeds at multiple elevations. Instantaneous wind measurements were analyzed from more than 126,000 sites in the continental United States for the years 2007–2013. The model results were mapped on a 2-km grid. A subset of the contiguous United States data for 2012 is shown here. Offshore data is shown to 50 nautical miles.Time Extent: Annual 2012Units: m/sCell Size: 2 kmSource Type: StretchedPixel Type: 32 Bit FloatData Projection: GCS WGS84Mosaic Projection:  WGS 1984 Web MercatorExtent: Contiguous United StatesSource: NREL Wind Integration National Dataset v1.1WIND is an update and expansion of the Eastern Wind Integration Data Set and Western Wind Integration Data Set. It supports the next generation of wind integration studies.Accessing Elevation InformationEach of the 9 elevation slices can be accessed, visualized, and analyzed. In ArcGIS Pro, go to the Multidimensional Ribbon and use the Elevation pull-down menu. In ArcGIS Online, it is best to use Web Map Viewer Classic where the elevation slider will automatically appear on the righthand side. The elevation slider will be available in the new Map Viewer in an upcoming release. What can you do with this layer?This layer may be added to maps to visualize and quickly interrogate each pixel value. The pop-up provides the pixel’s wind speed value.This analytical imagery tile layer can be used in analysis. For example, the layer may be added to ArcGIS Pro and proposed wind turbine locations can be used to Sample the layer at multiple elevation to determine the optimal hub height. Source data can be accessed on Amazon Web ServicesUsers of the WIND Toolkit should use the following citations:Draxl, C., B.M. Hodge, A. Clifton, and J. McCaa. 2015. Overview and Meteorological Validation of the Wind Integration National Dataset Toolkit (Technical Report, NREL/TP-5000-61740). Golden, CO: National Renewable Energy Laboratory.Draxl, C., B.M. Hodge, A. Clifton, and J. McCaa. 2015. "The Wind Integration National Dataset (WIND) Toolkit." Applied Energy 151: 355366.King, J., A. Clifton, and B.M. Hodge. 2014. Validation of Power Output for the WIND Toolkit (Technical Report, NREL/TP-5D00-61714). Golden, CO: National Renewable Energy Laboratory.

WorldClim 2.1 provides downscaled estimates of climate variables as monthly means over the period of 1970-2000 based on interpolated station measurements. Here we provide analytical image services of precipitation for each month along with an annual mean. Each time step is accessible from a processing template.Time Extent: Monthly/Annual 1970-2000Units: mm/monthCell Size: 2.5 minutes (~5 km)Source Type: StretchedPixel Type: 16 Bit IntegerData Projection: GCS WGS84Mosaic Projection: GCS WGS84Extent: GlobalSource: WorldClim v2.1Using Processing Templates to Access TimeThere are 13 processing templates applied to this service, each providing access to the 12 monthly and 1 annual mean precipitation layers. To apply these in ArcGIS Online, select the Image Display options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left-hand menu. From the Processing Template pull down menu, select the version to display.What can you do with this layer?This layer may be added to maps to visualize and quickly interrogate each pixel value. The pop-up provides a graph of the time series along with the calculated annual mean value.This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro and an area count of precipitation may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from month to month to show seasonal patterns.To calculate precipitation by land area, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Source Data: The datasets behind this layer were extracted from GeoTIF files produced by WorldClim at 2.5 minutes resolution. The mean of the 12 GeoTIFs was calculated (annual), and the 13 rasters were converted to Cloud Optimized GeoTIFF format and added to a mosaic dataset.Citation: Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315.

This layer contains the relative heat severity for every pixel for every city in the United States. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summers of 2018 and 2019.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of Arizona Dr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAADaphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Pete.Aniello@tpl.org with feedback.