This all-sky catalog, described in Monet et al. (2003), consists of positions, proper motions, magnitudes, and other measured quantities for 1,045,175,762 objects. The data were derived from digitizing scans of almost 7,500 photographic plates taken from various sky surveys during the interval from 1949 to 2002. The originating plate material includes five complete coverages of the northern sky and four of the southern sky.To be included in the catalog, an object must have been detected on two different surveys because isolated, single-survey detections are unreliable. For the earlier USNO-A catalog (which was essentially a two-color, one-epoch catalog), this meant that the object must have had detectable fluxes on both the red and blue plates, and this led to the exclusion of many faint objects with non-neutral colors. Also, the larger epoch difference in the southern survey coverage meant that objects with larger proper motions tended to be excluded. USNO-B1.0 attempts to fix both of these problems. An object detected in the same band at two epochs will be included in USNO-B1.0, as will objects that have significant proper motions, although it is still the case that objects with large motions and extreme colors may be omitted. The selection algorithm requires that spatially coincident detections must be made on any two of the surveys for an object to be classified as real and be included in the catalog.The catalog is expected to be complete down to V=21. Estimated positional accuracies are 0.2 arcsec, photographic magnitude accuracies are 0.3 mag, and the accuracy for distinguishing stars from non-stellar objects is 85%.

The false-color composite image of the Death Valley regional ground-water flow system (DVRFS), an approximately 100,000 square-kilometer region of southern Nevada and California, was derived from Landsat 5 Thematic Mapper (TM) data for 1996. The image is a composite of spectral bands 2, 4, and 7 in RGB (Red-Green-Blue) space. Individual bands were processed to display their full dynamic range. The image was further processed in hue-saturation space to emphasize specific geologic features. The image was a base reference for field reconnaissance work and for developing of the DVRFS transient ground-water flow model. The DVRFS flow model is one of the most recent in a number of regional-scale models developed by the U.S. Geological Survey (USGS) for the U.S. Department of Energy (DOE) to support investigations at the Nevada Test Site (NTS) and at Yucca Mountain, Nevada (see "Larger Work Citation", Chapter A, page 8).

Notice: this is not the latest Heat Island Severity image service.This layer contains the relative heat severity for every pixel for every city in the United States, including Alaska, Hawaii, and Puerto Rico. Heat Severity is a reclassified version of Heat Anomalies raster which is also published on this site. This data is generated from 30-meter 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:Heat Severity - USA 2022Heat Severity - USA 2021Heat Severity - USA 2020Heat Severity - USA 2019Federal 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 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): 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 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). 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.

Notice: this is not the latest Heat Island Anomalies image service. For 2023 data visit https://tpl.maps.arcgis.com/home/item.html?id=e89a556263e04cb9b0b4638253ca8d10.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. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2021, with patching from summer of 2020 where necessary.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter or cooler than the average temperature for that same city as a whole. This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): 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.

👨‍💻 Author: Azhar Saleem

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Overview

The dataset contains visa statistics compiled into several Excel sheets, each dedicated to specific types of data. There are a total of 7 tables across separate worksheets. The data include visa statistics for all States fully applying the Schengen acquis and their consulates in third countries.

Worksheets and Their Contents

1. Data for Consulates:

   • Contains comprehensive visa statistics for all Schengen States and their consulates in third countries.
   • Filters are available for refined searches by Member State, third country, or specific location.
   • Subtotals for selections and total worldwide statistics for 2023 are provided.

2. Totals - Schengen State:

   • Presents basic total values disaggregated by Schengen States in alphabetical order.
   • Includes a drop-down filter for total data by individual third countries.

3. Totals by Visa Applications:

   • Basic total data per Schengen State, ordered by the number of visa applications.

4. Totals by Visas Issued:

   • Basic total data per Schengen State, ordered by the number of visas issued.

5. Visas Issued Consulates + BCP:

   • Summarizes total numbers of C uniform visas issued by individual Schengen States at consulates and border crossing points.

6. Totals - Third Country:

   • Presents total figures for each third country, ordered by the number of visa applications.
   • Includes filters for individual countries.

7. ATV Totals:

   • Summary of data on airport transit visas (ATVs).

8. Non-Schengen States:

   • Contains data for Bulgaria, Cyprus, and Romania, which do not fully apply the Schengen acquis.

Types of Visas

1. Airport Transit Visas (ATVs):

   • Allows transit through the international transit area of airports in Member States without entering the territory.
   • Can be issued for single or multiple airport transits.

2. Short Stay Visas:

   • Uniform Short Stay Visas: Allows a stay in all Member States for up to 90 days within a 180-day period. Issued for single or multiple entries.
   • Short Stay Visas with Limited Territorial Validity (LTV): Valid only in the territory of the issuing Member State(s).

Member States Fully Applying the Schengen Acquis

Includes Austria, Belgium, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, and Switzerland. Liechtenstein does not issue its own Schengen visas.

Visualization OverviewThis visualization represents a "false color" band combination (Red = 3, Green = 6, Blue = 7) of data collected by the MODIS instrument on the NASA Terra satellite. The imagery is most useful for distinguishing water in its various states (e.g. liquid, ice, and snow). For example, clouds over snow, ice cloud versus water cloud; or floods from dense vegetation. At its highest resolution, this visualization represents the underlying data scaled to a resolution of 250m per pixel at the equator.The MODIS Corrected Reflectance product retains visible aerosols for a natural-looking visualization, though gross atmospheric effects (e.g. Rayleigh scattering) have been removed. The following guidelines will aid in understanding this visualization. See here for additional information on how this "false color" band combination highlights these physical characteristics of the Earth.Thick ice and snow appear a vivid red (or dark pink), while ice crystals in clouds will appear pinkish.Vegetation will appear green.Naturally bare soil, like a desert, will appear bright cyan.Liquid water on the ground will appear very dark, while water droplets in clouds will appear white.Sediments in water will appear dark red.Multi-Spectral BandsThe following table lists the MODIS bands that are utilized to create this visualization. See here for a full description of all MODIS bands.BandDescriptionWavelength (µm)Resolution (m)3Visible (Blue)0.459 - 0.4795006Shortwave IR1.628 - 1.6525007Shortwave IR2.105 - 2.155500Temporal CoverageBy default, this layer will display the imagery currently available for today’s date. This imagery is a "daily composite" that is assembled from hundreds of individual data files. When viewing imagery for “today,” you may notice that only a portion of the map has imagery. This is because the visualization is continually updated as the satellite collects more data. To view imagery over time, you can update the layer properties to enable time animation and configure time settings. Currently, this layer is available from present back to the start of the mission (February 24th, 2000).NASA Global Imagery Browse Services (GIBS), NASA Worldview, & NASA LANCEThis visualization is provided through the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery for hundreds of NASA Earth science datasets and science parameters. Through its services, and the NASA Worldview client, GIBS enables interactive exploration of NASA's Earth imagery for a broad range of users. The data and imagery are generated within 3 hours of acquisition through the NASA LANCE capability.Esri and NASA Collaborative ServicesThis visualization is made available through an ArcGIS image service hosted on Esri servers and facilitates access to a NASA GIBS service endpoint. For each image service request, the Esri server issues multiple requests to the GIBS service, processes and assembles the responses, and returns a proper mosaic image to the user. Processing occurs on-the-fly for each and every request to ensure that any update to the GIBS imagery is immediately available to the user. As such, availability of this visualization is dependent on both the Esri and the NASA GIBS services.

In fig1.txt, we provide the data points used for Fig. 1. The first column lists the overlap O, as defined in Eq. (9). The next two columns present the experimental data for Gamma_st and their associated error bars. The fourth column shows the principal quantum number n, and at the end of each line, the corresponding element for which the calculation was performed is indicated.

The experimental data for Molybdenum were taken from:
https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.56.2368
while the remaining data points come from the PS209 experiment:
https://arxiv.org/pdf/nucl-ex/0103008

The columns in fig1_inset.txt represent: the diffuseness sigma, the uncertainty of the linear fit DeltaGamma_0, and the fitted value of Gamma_0 in the last column.

In fig2_points.txt, we list the mass numbers A, the principal quantum numbers n, and the element names. The sources of the articles from which the data were obtained are also included.

In fig2_nucleus.txt, we provide the data points used to plot Eq. (10) from the paper:the corresponding principal quantum number n and mass number A.

In fig2_stbo.txt, the first column lists the principal quantum number n. The second and third columns show the mass numbers A at which the ratio Gamma_st / Gamma_Bohr is equal to 1/10 and 1, respectively.

In fig2_stem.txt, the first column lists the principal quantum number n. The next five columns show the mass numbers A at which the ratio of strong to electromagnetic interaction Gamma_st / Gamma_EM is equal to 1/1000 (second column), 1/100 (third), 1/10 (fourth), 1 (fifth), and 10 (sixth).

The file Model_comparison.pdf shows a comparison between the predictions of the linear model given by Eq. (10) and a model that incorporates saturation effects, as described by Eq. (A.1). It illustrates the difference of the mass number A predicted by both models for which Gamma_ST = Gamma_EM at a given principal number n (solid blue line in Fig. 2). The corresponding data is provided in diff_stem.txt, with the following columns: the first column lists the principal quantum number n; the second column gives the mass number A from the extended model; the third column gives the mass number A from the linear model. It also illustrates the difference of the mass number A predicted by both models for which Gamma_st = Gamma_Bohr at a given principal number n (solid red line in Fig. 2). The corresponding data is provided in diff_stbo.txt, with the following columns: the first column lists the principal quantum number n; the second column gives the mass number A from the extended model; the third column gives the mass number A from the linear model.

This digital spatial data set consists of the aquifer base elevation contours (50-foot contour interval) for part of the High Plains aquifer in the central United States. This subset of the High Plains aquifer covers the Republican River Basin in Nebraska, Kansas, and Colorado upstream from the streamflow station on the Republican River near Hardy, Nebraska, near the Kansas/Nebraska border. In Nebraska, the digitized contours extend to the South Platte, Platte, and Little Blue Rivers. In Colorado and Kansas, the digital contours extend to the edge of the High Plains aquifer. These boundaries were chosen to simplify boundary conditions for a computer simulation model being used for a hydrologic study of the Republican River Basin.

In May 2021, the Grand Canyon Monitoring and Research Center (GCMRC) of the U.S. Geological Survey’s (USGS), Southwest Biological Science Center (SBSC) acquired airborne multispectral high resolution data for the Colorado River in Grand Canyon in Arizona, USA. The imagery data consist of four bands (Band 1 – red, Band 2 – green, Band 3 – blue, and Band 4 – near infrared) with a ground resolution of 20 centimeters (cm). These image data are available to the public as 16-bit GeoTIFF files, which can be read and used by most geographic information system (GIS) and image-processing software. The spatial reference of the image data are in the State Plane (SP) map projection using the central Arizona zone (FIPS 0202) and the North American Datum of 1983 (NAD83) National Adjustment of 2011 (NA2011). The airborne data acquisition was conducted under contract by Fugro Earthdata Inc (Fugro) using two fixed wing aircraft from May 29th to June 4th, 2021 at flight altitudes from approximately 2,440 to 3,350 meters above mean sea level. Fugro produced a corridor-wide mosaic using the best possible flight line images with the least amount of smear, the smallest shadow extent, and clearest, most glint-free water possible. The mosaic delivered by Fugro was then further corrected by GCMRC for smear, shadow extent and water clarity as described in the process steps of this metadata and for previous image acquisitions in Durning et al. (2016) and Davis (2012). 47 ground controls points (GCPs) were used to conduct an independent spatial accuracy assessment by GCMRC. The accuracy calculated from the GCPs is reported at 95% confidence as 0.514 m and a Root Mean Square Error (RMSE) of 0.297 m.

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."

The EnviroAtlas St. Louis, Missouri Meter-Scale Urban Land Cover (MULC) dataset comprises 4188 km2 around the city of St. Louis and surrounding land in parts of eleven counties within Illinois and Missouri. These MULC data and maps were derived from several sources from multiple years: LiDAR (2008-2012); 1-m pixel, four-band (red, green, blue, and near-infrared) leaf-on aerial photography acquired from the United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP, 2012, 2014-2016); leaf-off 6-inch pixel four-band imagery (2015) as well as ancillary vector data (e.g., roads, building footprints.). Eight land cover classes were mapped: Water, Impervious Surfaces, Soil/Barren, Tree/Forested, Grass/Herbaceous Non Woody Vegetation, Agriculture, and Wetlands (Woody and Emergent). Wetlands were delineated using the best available existing wetlands data, which was a National Wetlands Inventory (NWI) layer. An analysis of 745 completely random and 226 stratified random photo-interpreted land cover reference points yielded a simple overall user's accuracy (MAX) of 81% and an overall fuzzy user's accuracy (RIGHT) of 90% (see confusion matrices below). This dataset was produced in three phases by the University of Missouri and the East-West Gateway Council of Governments for the Missouri Resource Assessment Partnership (MoRAP) and the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets ).

The EnviroAtlas New Haven, CT EnviroAtlas Meter-scale Urban Land Cover (MULC) Data were generated from United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1 m spatial scale acquired on September 25, 2014. Seven land cover classes were mapped: water, impervious surfaces, soil and barren land, trees, and grass-herbaceous non-woody vegetation,as well as woody wetlands and emergent herbaceous wetlands. An accuracy assessment of 500 completely random and 50 stratified random photo-interpreted reference points yielded an overall MAX accuracy of 89 percent and an overall RIGHT accuracy of 92 percent. The area mapped is the US Census Bureau's 2010 Urban Statistical Area for New Haven, CT plus a 1 km buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The Minneapolis-St. Paul, MN EnviroAtlas Meter-scale Urban Land Cover (MULC) data were generated from four-band (red, green, blue, and near infrared) aerial photography provided by the United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP). The NAIP imagery for the state of Minnesota was collected during the summer and fall of 2010. Lidar data and relevant ancillary datasets contributed to the classification. Eight land cover types were classified: water, impervious surface, soil and barren land, trees and forest, grass and herbaceous, agriculture, woody wetland, and emergent wetland. An accuracy assessment of 644 completely random and 62 stratified random photointerpreted reference points yielded an overall User's Accuracy of 83 percent. The boundary of this data layer is delineated by the US Census Bureau's 2010 Urban Statistical Area for Minneapolis-St. Paul, MN plus a 1-km buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The Philadelphia, PA Meter-Scale Urban Land Cover (MULC) dataset comprises 7184 km2 around the city of Philadelphia and surrounding land in parts of fourteen counties within four states (PA, DE, NJ, MD): New Castle County in Delaware and Cecil County Maryland; Bucks, Chester, Lancaster, Montgomery, Philadelphia, and Delaware Counties in Pennsylvania; and Burlington, Mercer, Camden, Gloucester, Salmen and Atlantic Counties in New Jersey. These MULC data and maps were derived from several sources from multiple years: leaf-off LiDAR; 1-m pixel, four-band (red, green, blue, and near-infrared) leaf-on aerial photography acquired from the United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP); 1-ft pixel orthoimagery; additional leaf-on and leaf-off imagery as well as ancillary vector data (e.g., roads, building footprints.). Ten land cover classes were mapped: Water, Impervious Surfaces, Soil/Barren, Tree/Forested, Shrub, Grass/Herbaceous NonWoody Vegetation, Agriculture, Orchard, and Wetlands (Woody and Emergent). Wetlands were delineated using the best available existing wetlands data, which was a National Wetlands Inventory (NWI) layer. An analysis of 600 completely random and 251 stratified random photo-interpreted land cover reference points yielded a simple overall user's accuracy (MAX) of 78% and an overall fuzzy user's accuracy (RIGHT) of 86% (see confusion matrices below). This dataset was produced by the University of Vermont Spatial Analysis Laboratory, the United States Forest Service Urban Tree Canopy (UTC) assessment program, and the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The EnviroAtlas Portland, OR Meter-Scale Urban Land Cover (MULC) dataset includes data for the Portland metropolitan area plus the city of Vancouver, Washington and various smaller towns and rural areas in Oregon and Washington. The total area classified was approximately 2160 square kilometers. The land cover data were generated from 1-m, four-band (red, green, blue, and near-infrared) aerial photography acquired from the United States Department of Agriculture's National Agriculture Imagery Program. Imagery for Oregon was collected in 2012, and imagery for Washington was collected in 2011. In addition, ancillary datasets were derived for the classification from two LiDAR datasets collected in 2007 and one LiDAR dataset collected in 2010. Eight land cover classes were mapped: water, impervious surfaces, soil and barren land, trees and forest, grass and herbaceous non-woody vegetation, agriculture, and wetlands (both woody and emergent). An accuracy assessment using 600 completely random and 54 stratified random land cover reference points yielded an overall accuracy of 78.6%. Using a liberal interpretation with similar classes (e.g. soil / grass, soil / agriculture) the overall fuzzy accuracy is 91.4%. For more information on fuzzy accuracy assessment see the overview section. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This figure shows how the length of ragweed pollen season changed at 11 locations in the central United States and Canada between 1995 and 2015. Data were provided by Dr. Lewis Ziska of the U.S. Department of Agriculture, Agricultural Research Service. Red circles represent a longer pollen season; the blue circle represents a shorter season. Larger circles indicate larger changes. For more information: www.epa.gov/climatechange/science/indicators

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.

Notice: this is not the latest Heat Island Severity image service.This layer contains the relative heat severity for every pixel for every city in the United States, including Alaska, Hawaii, and Puerto Rico. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2022, patched with data from 2021 where necessary.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter 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): 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.

The National Agriculture Imagery Program (NAIP) is administered by the U.S. Department of Agriculture's Farm Production and Conservation Business Center (FPAC-BC) Geospatial Enterprise Operations (GEO) Branch. NAIP acquires aerial imagery for the contiguous United States (CONUS) during the agricultural growing season, or leaf-on conditions and may contain as much as 10 percent cloud cover per tile. The images are orthorectified which combines the image characteristics of an aerial photograph with the georeferenced qualities of a map. NAIP acquisition from 2002-2017 was at a ground sample distance (GSD) of 1 or 2-meter. In 2018, the ground sample distance standard changed to 0.6 meter with the option for 0.3 meter. The 2025 acquisition consists of approximately half of the states delivered at 60cm and the other half at 30cm ground sample distance. The repeat flying cycle has also changed to no more than a 3-year cycle (generally every other year for most states) from its 5-year cycle back in 2003-2009. Each individual image tile is based on a 3.75-minute longitude by 3.75-minute latitude quarter quadrangle, originally with a 300-meter buffer on all four sides. In 2024 the buffer was changed to 12-meters on all four sides. Tiles in the NAIP collection are natural color (red, green, and blue bands) or color near infrared (red, green, blue, and near infrared bands).

The Tacoma, WA Meter Urban Land Cover (MULC) dataset was generated from 1-meter image pixel resolution data sourced from 2011 USDA National Agricultural Imagery Program (NAIP) four band (red, green, blue and near infrared) aerial ortho-imagery. LiDAR point density data (9/m^2 point spacing), available for years 2010 and 2011, was used to map most areas of Pierce County, and supplemental LiDAR data, available for earlier years, was used to map area segments where 2010-2011 LiDAR data was not available. A 7-band stack consisting of 1-meter, four-band (red, green, blue, and near-infrared) aerial photography, Normalized Difference Vegetation Index (NDVI), and lidar-derived intensity and height above ground was created and primarily used to classify land cover using Genie Pro automated feature extraction software. Several ancillary datasets, such as hydrographic and transportation feature data, were edited in ArcGIS and integrated into the classification workflow. The Tacoma, WA land cover dataset includes data for the Tacoma metropolitan area of Pierce County, WA, and encompasses a total approximate area of 1,995 square kilometers. The thematic landcover data is confined to areas included within the US Census Bureau's 2010 Urban Statistical area boundaries for Pierce County, WA, with an additional 1km buffer extension that include bits of Kitsap, King and Thurston Counties. The following seven land cover classes were mapped: Water, Impervious, Soil or Barren, Trees or Forest, Grass or Herbaceous, Woody Wetlands and Emergent Wetlands. Mapped water bodies were generated using combined high-resolution LiDAR and ancillary hydrological data. Integrated wetland features were derived using ancillary National Wetlands Inventory (NWI) polygon data (version 2), downloaded from the Unites States Fish and Wildlife Service (USFWS) Wetland Mapper web mapping service (https://www.fws.gov/wetlands/data/mapper.html). Metadata for the NWI wetlands data layer can be found at http://www.fws.gov/wetlands/Data/Metadata.html. An accuracy assessment of the classified product, using 482 completely random and 46 (Soil/Barren) stratified random photo-interpreted land cover reference sample points yielded an overall user's accuracy (MAX) of 85.1 percent and a fuzzy user's accuracy (RIGHT) of 86.9 percent. For data workflow processing details see Overview Description section. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (http://enviroatlas.epa.gov/EnviroAtlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (http://enviroatlas.epa.gov/EnviroAtlas/DataFactSheets).