NCED is currently involved in researching the effectiveness of anaglyph maps in the classroom and are working with educators and scientists to interpret various Earth-surface processes. Based on the findings of the research, various activities and interpretive information will be developed and available for educators to use in their classrooms. Keep checking back with this website because activities and maps are always being updated. We believe that anaglyph maps are an important tool in helping students see the world and are working to further develop materials and activities to support educators in their use of the maps.

This website has various 3-D maps and supporting materials that are available for download. Maps can be printed, viewed on computer monitors, or projected on to screens for larger audiences. Keep an eye on our website for more maps, activities and new information. Let us know how you use anaglyph maps in your classroom. Email any ideas or activities you have to ncedmaps@umn.edu

Anaglyph paper maps are a cost effective offshoot of the GeoWall Project. Geowall is a high end visualization tool developed for use in the University of Minnesota's Geology and Geophysics Department. Because of its effectiveness it has been expanded to 300 institutions across the United States. GeoWall projects 3-D images and allows students to see 3-D representations but is limited because of the technology. Paper maps are a cost effective solution that allows anaglyph technology to be used in classroom and field-based applications.

Maps are best when viewed with RED/CYAN anaglyph glasses!

A note on downloading: "viewable" maps are .jpg files; "high-quality downloads" are .tif files. While it is possible to view the latter in a web-browser in most cases, the download may be slow. As an alternative, try right-clicking on the link to the high-quality download and choosing "save" from the pop-up menu that results. Save the file to your own machine, then try opening the saved copy. This may be faster than clicking directly on the link to open it in the browser.

World Map: 3-D map that highlights oceanic bathymetry and plate boundaries.

Continental United States: 3-D grayscale map of the Lower 48.

Western United States: 3-D grayscale map of the Western United States with state boundaries.

Regional Map: 3-D greyscale map stretching from Hudson Bay to the Central Great Plains. This map includes the Western Great Lakes and the Canadian Shield.

Minnesota Map: 3-D greyscale map of Minnesota with county and state boundaries.

Twin Cities: 3-D map extending beyond Minneapolis and St. Paul.

Twin Cities Confluence Map: 3-D map highlighting the confluence of the Mississippi and Minnesota Rivers. This map includes most of Minneapolis and St. Paul.

Minneapolis, MN: 3-D topographical map of South Minneapolis.

Bassets Creek, Minneapolis: 3-D topographical map of the Bassets Creek watershed.

North Minneapolis: 3-D topographical map highlighting North Minneapolis and the Mississippi River.

St. Paul, MN: 3-D topographical map of St. Paul.

Western Suburbs, Twin Cities: 3-D topographical map of St. Louis Park, Hopkins and Minnetonka area.

Minnesota River Valley Suburbs, Twin Cities: 3-D topographical map of Bloomington, Eden Prairie and Edina area.

Southern Suburbs, Twin Cities: 3-D topographical map of Burnsville, Lakeville and Prior Lake area.

Southeast Suburbs, Twin Cities: 3-D topographical map of South St. Paul, Mendota Heights, Apple Valley and Eagan area.

Northeast Suburbs, Twin Cities: 3-D topographical map of White Bear Lake, Maplewood and Roseville area.

Northwest Suburbs, Mississippi River, Twin Cities: 3-D topographical map of North Minneapolis, Brooklyn Center and Maple Grove area.

Blaine, MN: 3-D map of Blaine and the Mississippi River.

White Bear Lake, MN: 3-D topographical map of White Bear Lake and the surrounding area.

Maple Grove, MN: 3-D topographical mmap of the NW suburbs of the Twin Cities.

In 2023, Google Maps was the most downloaded map and navigation app in the United States, despite being a standard pre-installed app on Android smartphones. Waze followed, with 9.89 million downloads in the examined period. The app, which comes with maps and the possibility to access information on traffic via users reports, was developed in 2006 by the homonymous Waze company, acquired by Google in 2013.

Usage of navigation apps in the U.S. As of 2021, less than two in 10 U.S. adults were using a voice assistant in their cars, in order to place voice calls or follow voice directions to a destination. Navigation apps generally offer the possibility for users to download maps to access when offline. Native iOS app Apple Maps, which does not offer this possibility, was by far the navigation app with the highest data consumption, while Google-owned Waze used only 0.23 MB per 20 minutes.

Usage of navigation apps worldwide In July 2022, Google Maps was the second most popular Google-owned mobile app, with 13.35 million downloads from global users during the examined month. In China, the Gaode Map app, which is operated along with other navigation services by the Alibaba owned AutoNavi, had approximately 730 million monthly active users as of September 2022.

This application is intended for informational purposes only and is not an operational product. The tool provides the capability to access, view and interact with satellite imagery, and shows the latest view of Earth as it appears from space.For additional imagery from NOAA's GOES East and GOES West satellites, please visit our Imagery and Data page or our cooperative institute partners at CIRA and CIMSS.This website should not be used to support operational observation, forecasting, emergency, or disaster mitigation operations, either public or private. In addition, we do not provide weather forecasts on this site — that is the mission of the National Weather Service. Please contact them for any forecast questions or issues. Using the Maps​What does the Layering Options icon mean?The Layering Options widget provides a list of operational layers and their symbols, and allows you to turn individual layers on and off. The order in which layers appear in this widget corresponds to the layer order in the map. The top layer ‘checked’ will indicate what you are viewing in the map, and you may be unable to view the layers below.Layers with expansion arrows indicate that they contain sublayers or subtypes.What does the Time Slider icon do?The Time Slider widget enables you to view temporal layers in a map, and play the animation to see how the data changes over time. Using this widget, you can control the animation of the data with buttons to play and pause, go to the previous time period, and go to the next time period.Do these maps work on mobile devices and different browsers?Yes!Why are there black stripes / missing data on the map?NOAA Satellite Maps is for informational purposes only and is not an operational product; there are times when data is not available.Why does the imagery load slowly?This map viewer does not load pre-generated web-ready graphics and animations like many satellite imagery apps you may be used to seeing. Instead, it downloads geospatial data from our data servers through a Map Service, and the app in your browser renders the imagery in real-time. Each pixel needs to be rendered and geolocated on the web map for it to load.How can I get the raw data and download the GIS World File for the images I choose?The geospatial data Map Service for the NOAA Satellite Maps GOES satellite imagery is located on our Satellite Maps ArcGIS REST Web Service ( available here ).We support open information sharing and integration through this RESTful Service, which can be used by a multitude of GIS software packages and web map applications (both open and licensed).Data is for display purposes only, and should not be used operationally.Are there any restrictions on using this imagery?NOAA supports an open data policy and we encourage publication of imagery from NOAA Satellite Maps; when doing so, please cite it as "NOAA" and also consider including a permalink (such as this one) to allow others to explore the imagery.For acknowledgment in scientific journals, please use:We acknowledge the use of imagery from the NOAA Satellite Maps application: LINKThis imagery is not copyrighted. You may use this material for educational or informational purposes, including photo collections, textbooks, public exhibits, computer graphical simulations and internet web pages. This general permission extends to personal web pages. About this satellite imageryWhat am I looking at in these maps?In this map you are seeing the past 24 hours (updated approximately every 10 minutes) of the Western Hemisphere and Pacific Ocean, as seen by the NOAA GOES East (GOES-16) and GOES West (GOES-18) satellites. In this map you can also view four different ‘layers’. The views show ‘GeoColor’, ‘infrared’, and ‘water vapor’.This maps shows the coverage area of the GOES East and GOES West satellites. GOES East, which orbits the Earth from 75.2 degrees west longitude, provides a continuous view of the Western Hemisphere, from the West Coast of Africa to North and South America. GOES West, which orbits the Earth at 137.2 degrees west longitude, sees western North and South America and the central and eastern Pacific Ocean all the way to New Zealand.What does the GOES GeoColor imagery show?The 'Merged GeoColor’ map shows the coverage area of the GOES East and GOES West satellites and includes the entire Western Hemisphere and most of the Pacific Ocean. This imagery uses a combination of visible and infrared channels and is updated approximately every 15 minutes in real time. GeoColor imagery approximates how the human eye would see Earth from space during daylight hours, and is created by combining several of the spectral channels from the Advanced Baseline Imager (ABI) – the primary instrument on the GOES satellites. The wavelengths of reflected sunlight from the red and blue portions of the spectrum are merged with a simulated green wavelength component, creating RGB (red-green-blue) imagery. At night, infrared imagery shows high clouds as white and low clouds and fog as light blue. The static city lights background basemap is derived from a single composite image from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day Night Band. For example, temporary power outages will not be visible. Learn more.What does the GOES infrared map show?The 'GOES infrared' map displays heat radiating off of clouds and the surface of the Earth and is updated every 15 minutes in near real time. Higher clouds colorized in orange often correspond to more active weather systems. This infrared band is one of 12 channels on the Advanced Baseline Imager, the primary instrument on both the GOES East and West satellites. on the GOES the multiple GOES East ABI sensor’s infrared bands, and is updated every 15 minutes in real time. Infrared satellite imagery can be "colorized" or "color-enhanced" to bring out details in cloud patterns. These color enhancements are useful to meteorologists because they signify “brightness temperatures,” which are approximately the temperature of the radiating body, whether it be a cloud or the Earth’s surface. In this imagery, yellow and orange areas signify taller/colder clouds, which often correlate with more active weather systems. Blue areas are usually “clear sky,” while pale white areas typically indicate low-level clouds. During a hurricane, cloud top temperatures will be higher (and colder), and therefore appear dark red. This imagery is derived from band #13 on the GOES East and GOES West Advanced Baseline Imager.How does infrared satellite imagery work?The infrared (IR) band detects radiation that is emitted by the Earth’s surface, atmosphere and clouds, in the “infrared window” portion of the spectrum. The radiation has a wavelength near 10.3 micrometers, and the term “window” means that it passes through the atmosphere with relatively little absorption by gases such as water vapor. It is useful for estimating the emitting temperature of the Earth’s surface and cloud tops. A major advantage of the IR band is that it can sense energy at night, so this imagery is available 24 hours a day.What do the colors on the infrared map represent?In this imagery, yellow and orange areas signify taller/colder clouds, which often correlate with more active weather systems. Blue areas are clear sky, while pale white areas indicate low-level clouds, or potentially frozen surfaces. Learn more about this weather imagery.What does the GOES water vapor map layer show?The GOES ‘water vapor’ map displays the concentration and location of clouds and water vapor in the atmosphere and shows data from both the GOES East and GOES West satellites. Imagery is updated approximately every 15 minutes in real time. Water vapor imagery, which is useful for determining locations of moisture and atmospheric circulations, is created using a wavelength of energy sensitive to the content of water vapor in the atmosphere. In this imagery, green-blue and white areas indicate the presence of high water vapor or moisture content, whereas dark orange and brown areas indicate little or no moisture present. This imagery is derived from band #10 on the GOES East and GOES West Advanced Baseline Imager.What do the colors on the water vapor map represent?In this imagery, green-blue and white areas indicate the presence of high water vapor or moisture content, whereas dark orange and brown areas indicate less moisture present. Learn more about this water vapor imagery.About the satellitesWhat are the GOES satellites?NOAA’s most sophisticated Geostationary Operational Environmental Satellites (GOES), known as the GOES-R Series, provide advanced imagery and atmospheric measurements of Earth’s Western Hemisphere, real-time mapping of lightning activity, and improved monitoring of solar activity and space weather.The first satellite in the series, GOES-R, now known as GOES-16, was launched in 2016 and is currently operational as NOAA’s GOES East satellite. In 2018, NOAA launched another satellite in the series, GOES-T, which joined GOES-16 in orbit as GOES-18. GOES-17 became operational as GOES West in January 2023.Together, GOES East and GOES West provide coverage of the Western Hemisphere and most of the Pacific Ocean, from the west coast of Africa all the way to New Zealand. Each satellite orbits the Earth from about 22,200 miles away.

This National Geographic Style Map (World Edition) web map provides a reference map for the world that includes administrative boundaries, cities, protected areas, highways, roads, railways, water features, buildings, and landmarks, overlaid on shaded relief and a colorized physical ecosystems base for added context to conservation and biodiversity topics. Alignment of boundaries is a presentation of the feature provided by our data vendors and does not imply endorsement by Esri, National Geographic or any governing authority.This basemap, included in the ArcGIS Living Atlas of the World, uses the National Geographic Style vector tile layer and the National Geographic Style Base and World Hillshade raster tile layers.The vector tile layer in this web map is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.

This map charts out EPA Level 3 ecoregions that are considered deserts in North America. Furthermore, decades of climate data from NOAA have been clipped and measured for each desert, the data was then used to generate Walter-Leith Climate Summary Diagrams which are a double-y axis chart that normalizes precipitation and temperature to identify months of general humidity or aridity.Artwork/Illustrations of Desert Flora & Fauna by Marissa WallData:CMAP Precipitation & Temperature data provided by the NOAA PSL, Boulder, Colorado, USA from their website at https://psl.noaa.gov

GapMaps Live is a simple to use location intelligence platform available in over 25 countries globally which allows you to visualise your own data integrated with the best Map Data available so your team can make faster, smarter and more confident retail location decisions.

This is a raster-based suitability map of landfill sites produced after the February 6, 2023, Türkiye earthquakes centred on Kahramanmaraş - Pazarcık and Kahramanmaraş - Elbistan. In this study, a site selection model was developed using open-source Geographic Information Systems (GIS) software and the Best-Worst Method (BWM), one of the Multi-Criteria Decision-Making Methods, to determine the most suitable landfill areas immediately after the earthquake.The suitability map of the landfill sites can be accessed through the Serverless Cloud-GIS based Disaster Management Portal at https://web.itu.edu.tr/metemu/nominal/deprem.htmlThe pairwise comparison matrix, weight calculation, and sensitivity analysis are also provided in the MS Excel file.

To create this app:

This 3D basemap presents OpenStreetMap (OSM) data and other data sources and is hosted by Esri using the OpenStreetMap style.Esri created the Places and Labels, Trees, and OpenStreetMap layers from the Daylight map distribution of OSM data, which is supported by Facebook and supplemented with additional data from Microsoft. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project and is excited to make this new scene available to the OSM, GIS, and Developer communities.The Buildings layer (beta) presents open buildings data that has been processed and hosted by Esri. Esri created this buildings scene layer using data from the Overture Maps Foundation (OMF) which is supported by Meta, Microsoft, Amazon, TomTom, Esri and other members. Overture includes data from many sources, including OpenStreetMap (OSM). The 3D buildings layer will be updated each month with the latest version of Overture data, which includes the latest updates from OSM, Esri Community Maps, and other sources.Overture Maps is a collaborative project to create reliable, easy-to-use, and interoperable open map data. Member companies work to bring together the best available open datasets, and the resulting data can be downloaded from Microsoft Azure or Amazon S3. Esri is a member of the OMF project and is excited to make this 3D web scene available to the ArcGIS user community.

In support of the proposed Susitna-Watana Hydroelectric Project, the Alaska Division of Geological & Geophysical Surveys (DGGS) developed a Geographic Information System (GIS)-based geologic compilation of published and unpublished maps for twelve, inch-to-mile (1:63,360-scale) quadrangles encompassing the proposed hydroelectric project footprint, including the anticipated reservoir and surrounding area. DGGS geologists reviewed and analyzed existing geologic mapping for quality and completeness, and the maps were converted for use in GIS. The conversion process included scanning and georeferencing the original hard-copy map documents, creating a geodatabase, digitizing the geologic data, assigning attributes, and producing a digital data product for public release. The best available geologic mapping was synthesized into a single compilation data layer, and is packaged along with georeferenced scans and digitized vector files of the original geologic source maps. Bedrock geology was reviewed and revised by an independent contractor to ensure consistency with current geologic interpretations of the area. This geodatabase product will be a valuable reference resource for developers, planners, and scientists working on the hydroelectric project, as well as for any other projects in the area.

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) source code for performing this analysis is provided in the function NN_depth_ensembling.m and the figure included on this landing page provides a flow chart illustrating the four different neural network-based depth retrieval methods. As examples of the resulting models, MATLAB *.mat data files containing the best-performing neural network model for each site are provided below, along with a file that lists the PlanetScope image identifiers for the images that were used for each site. To develop and test this new NNDR approach, the method was applied to satellite images from three rivers across the U.S.: the American, Colorado, and Potomac. For each site, field measurements of water depth available through other data releases were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: X_mean-spec.tif, X_mean-depth.tif, X_NN-depth.tif, and X-single-image.tif, where X denotes the site name. The spatial resolution of the depth maps is 3 meters and the pixel values within each map are water depth estimates in units of meters.

The goal of the Iowa BMP (Best Management Practices) Mapping Project is to provide a complete baseline set of BMPs dating from the 2007-2010 timeframe for use in watershed modeling, historic occurrence, and future practice tracking. The BMPs mapped are: Terraces, Water and Sediment Control Basins (WASCOB), Grassed Waterways, Pond Dams, Contour Strip Cropping and Contour Buffer Strips. We can't guarantee that mapped practices meet NRCS standards or that they are actually the indicated practice since no ground reference analysis is being performed. Data being utilized to digitize the BMPs include LiDAR derived products such as DEM, Hillshade and Slope grids; CIR aerial photography from the 2007-2010 timeframe, NAIP aerial photography and historic aerial photography.

These Soil Mapping Data Packages include 1. a Soil Map dataset which includes the equivalents to Soil Project Boundaries, Soil Survey Spatial View mapping polygons with attributes from the Soil Name and Layer Files, plus + A Soil Site dataset which includes soil pit site information and detailed soil pit descriptions and any associated lab analyses, and + The Soil Data Dictionary which documents the fields and allowable codes within the data. The Soil Map geodatabase contains the 'best available' data ranging from 1:20,000 scale to 1:250,000 scale with overlapping data removed. The choice of the datasets that remain is based on connectivity to the soil attributes (soil name and layer files), map scale and survey date. (Note: the BC Soil Landscapes of Canada (BCSLC) 1:1,000,000 data has not been included in the Soil_Map or SIFT, but is available from: CANSIS. (A complete soils data package with overlapping soil survey mapping and BCSLC is available on request. Note that the soil survey data with attributes can also be viewed interactively in the [Soil Information Finder Tool](The Soil Map dataset is also available for interactive map viewing or as KMZs from the Soil Information Finder Tool website.

The Story Map Basic application is a simple map viewer with a minimalist user interface. Apart from the title bar, an optional legend, and a configurable search box the map fills the screen. Use this app to let your map speak for itself. Your users can click features on the map to get more information in pop-ups. The Story Map Basic application puts all the emphasis on your map, so it works best when your map has great cartography and tells a clear story.You can create a Basic story map by sharing a web map as an application from the map viewer. You can also click the 'Create a Web App' button on this page to create a story map with this application. Optionally, the application source code can be downloaded for further customization and hosted on your own web server.For more information about the Story Map Basic application, a step-by-step tutorial, and a gallery of examples, please see this page on the Esri Story Maps website.

World Imagery provides one meter or better satellite and aerial imagery for most of the world’s landmass and lower resolution satellite imagery worldwide. The map is currently comprised of the following sources: Worldwide 15-m resolution TerraColor imagery at small and medium map scales.Maxar imagery basemap products around the world: Vivid Premium at 15-cm HD resolution for select metropolitan areas, Vivid Advanced 30-cm HD for more than 1,000 metropolitan areas, and Vivid Standard from 1.2-m to 0.6-cm resolution for the most of the world, with 30-cm HD across the United States and parts of Western Europe. More information on the Maxar products is included below. High-resolution aerial photography contributed by the GIS User Community. This imagery ranges from 30-cm to 3-cm resolution. You can contribute your imagery to this map and have it served by Esri via the Community Maps Program. Maxar Basemap ProductsVivid PremiumProvides committed image currency in a high-resolution, high-quality image layer over defined metropolitan and high-interest areas across the globe. The product provides 15-cm HD resolution imagery.Vivid AdvancedProvides committed image currency in a high-resolution, high-quality image layer over defined metropolitan and high-interest areas across the globe. The product includes a mix of native 30-cm and 30-cm HD resolution imagery.Vivid StandardProvides a visually consistent and continuous image layer over large areas through advanced image mosaicking techniques, including tonal balancing and seamline blending across thousands of image strips. Available from 1.2-m down to 30-cm HD. More on Maxar HD. Imagery UpdatesYou can use the Updates Mode in the World Imagery Wayback app to learn more about recent and pending updates. Accessing this information requires a user login with an ArcGIS organizational account. CitationsThis layer includes imagery provider, collection date, resolution, accuracy, and source of the imagery. With the Identify tool in ArcGIS Desktop or the ArcGIS Online Map Viewer you can see imagery citations. Citations returned apply only to the available imagery at that location and scale. You may need to zoom in to view the best available imagery. Citations can also be accessed in the World Imagery with Metadata web map.UseYou can add this layer to the ArcGIS Online Map Viewer, ArcGIS Desktop, or ArcGIS Pro. To view this layer with a useful reference overlay, open the Imagery Hybrid web map.FeedbackHave you ever seen a problem in the Esri World Imagery Map that you wanted to report? You can use the Imagery Map Feedback web map to provide comments on issues. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.

This dataset is a series of digital map-posters accompanying the AdaptNRM Guide: Helping Biodiversity Adapt: supporting climate adaptation planning using a community-level modelling approach.

These represent supporting materials and information about the community-level biodiversity models applied to climate change. Map posters are organised by four biological groups (vascular plants, mammals, reptiles and amphibians), two climate change scenario (1990-2050 MIROC5 and CanESM2 for RCP8.5), and five measures of change in biodiversity.

The map-posters present the nationally consistent data at locally relevant resolutions in eight parts – representing broad groupings of NRM regions based on the cluster boundaries used for climate adaptation planning (http://www.environment.gov.au/climate-change/adaptation) and also Nationally.

Map-posters are provided in PNG image format at moderate resolution (300dpi) to suit A0 printing. The posters were designed to meet A0 print size and digital viewing resolution of map detail. An additional set in PDF image format has been created for ease of download for initial exploration and printing on A3 paper. Some text elements and map features may be fuzzy at this resolution.

Each map-poster contains four dataset images coloured using standard legends encompassing the potential range of the measure, even if that range is not represented in the dataset itself or across the map extent.

Most map series are provided in two parts: part 1 shows the two climate scenarios for vascular plants and mammals and part 2 shows reptiles and amphibians. Eight cluster maps for each series have a different colour theme and map extent. A national series is also provided. Annotation briefly outlines the topics presented in the Guide so that each poster stands alone for quick reference.

An additional 77 National maps presenting the probability distributions of each of 77 vegetation types – NVIS 4.1 major vegetation subgroups (NVIS subgroups) - are currently in preparation.

Example citations:

Williams KJ, Raisbeck-Brown N, Prober S, Harwood T (2015) Generalised projected distribution of vegetation types – NVIS 4.1 major vegetation subgroups (1990 and 2050), A0 map-poster 8.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

Williams KJ, Raisbeck-Brown N, Harwood T, Prober S (2015) Revegetation benefit (cleared natural areas) for vascular plants and mammals (1990-2050), A0 map-poster 9.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

This dataset has been delivered incrementally. Please check that you are accessing the latest version of the dataset. Lineage: The map posters show case the scientific data. The data layers have been developed at approximately 250m resolution (9 second) across the Australian continent to incorporate the interaction between climate and topography, and are best viewed using a geographic information system (GIS). Each data layers is 1Gb, and inaccessible to non-GIS users. The map posters provide easy access to the scientific data, enabling the outputs to be viewed at high resolution with geographical context information provided.

Maps were generated using layout and drawing tools in ArcGIS 10.2.2

A check list of map posters and datasets is provided with the collection.

Map Series: 7.(1-77) National probability distribution of vegetation type – NVIS 4.1 major vegetation subgroup pre-1750 #0x

8.1 Generalised projected distribution of vegetation types (NVIS subgroups) (1990 and 2050)

9.1 Revegetation benefit (cleared natural areas) for plants and mammals (1990-2050)

9.2 Revegetation benefit (cleared natural areas) for reptiles and amphibians (1990-2050)

10.1 Need for assisted dispersal for vascular plants and mammals (1990-2050)

10.2 Need for assisted dispersal for reptiles and amphibians (1990-2050)

11.1 Refugial potential for vascular plants and mammals (1990-2050)

11.1 Refugial potential for reptiles and amphibians (1990-2050)

12.1 Climate-driven future revegetation benefit for vascular plants and mammals (1990-2050)

12.2 Climate-driven future revegetation benefit for vascular reptiles and amphibians (1990-2050)

This dataset comprises soil property mapping across the whole State of Victoria at 6 prescribed depths. The set depths are 0 to 5 cm, 5 to 15 cm, 15 to 30 cm, 30 to 60 cm, 60 to 100 cm and 100 to …Show full descriptionThis dataset comprises soil property mapping across the whole State of Victoria at 6 prescribed depths. The set depths are 0 to 5 cm, 5 to 15 cm, 15 to 30 cm, 30 to 60 cm, 60 to 100 cm and 100 to 200 cm. The mapped soil properties are pH (1:5 water), EC (dS/m), % clay and soil organic carbon (SOC %). The dataset has been created by the Understanding Soil and Farming Systems project (CMI 102922)and is referred to as Version 1.0 of the Victorian Digital Soil Map (VIC DSM 1.0). Soil point data stored in the Victorian Soil Information System (VSIS) from over 6,000 sites has been standardised to the set depths (using equal area splines or a value weighting derived from the proportional contruibution of each sample to the depth class). This processed data was used to attribute soil land units from a collection of surveys (mapped at 1:100k or better) collated to provide the best map unit coverage across the State. Only data from sites that match the soil type of the dominant soil within the land unit being attributed were used. Sites and land units were assigned an Australian Soil Classification (to the Suborder level) to aid this process. The raw profile data stored in the VSIS (as of March 2013) used to produce these maps were: pH data were either laboratory based (1:5 soil/water suspension) or field pH (Raupach and Tucker 1959). Clay % was laboratory derived particle size data (PSA all methods), or converted field observations of texture class (McKenzie et al. 2000). Organic Carbon measurements methods was either Walkley and Black or Heanes wet oxidation. Electical Conductivity was 1:5 soil/water extract (dS/m). The data is available in polygonal format (i.e. the land units) with soil property median value, standard deviation and assignment qualifier attributes. ESRI grids in ascii format at 100 m cell resolution have been generated from the attributed land unit polygon dataset for each soil property at each depth interval. The assignment qualifiers have been created in order to provide a level of quality evaluation for the soil property assignment to each polygon. Reliability maps generated from these qualifiers have been produced together with each soil property map. The strength of these products is our ability to leverage on the significant investment in soil site and survey mapping data procurement and the capture of tacit knowledge of former soil surveyors. A revised version of these digital soil maps is due to be released at the end of 2014.

This dataset consists of the vector version of the Land Cover Map 2015 (LCM2015) for Great Britain. The vector data set is the core LCM data set from which the full range of other LCM2015 products is derived. It provides a number of attributes including land cover at the target class level (given as an integer value and also as text), the number of pixels within the polygon classified as each land cover type and a probability value provided by the classification algorithm (for full details see the LCM2015 Dataset Documentation). The 21 target classes are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. LCM2015 is a land cover map of the UK which was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. LCM2015 consists of a range of raster and vector products and users should familiarise themselves with the full range (see related records, the CEH web site and the LCM2015 Dataset documentation) to select the product most suited to their needs. LCM2015 was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. It is one of a series of land cover maps, produced by UKCEH since 1990. They include versions in 1990, 2000, 2007, 2015, 2017, 2018 and 2019.

These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive layer (resdept) and surface rock size and cover. Accuracy and error estimated using a 10-fold cross validation indicated a range of model performances with coefficient of variation (R2) for models ranging from 0.20 to 0.76 with mean of 0.52 and a standard deviation of 0.12. Models of pH, om and ec had the best accuracy (R2 > 0.6). Most texture fractions, CaCO3, and SAR models had R2 values from 0.5-0.6. Models of kwfact, dbovendry, resdept, rock models, gypsum and awc had R2 values from 0.4-0.5 excepting near surface models which tended to perform better. Very fine sands and 200 cm estimates for other models generally performed poorly (R2 from 0.2-0.4), and sample size for the 200 cm models was too low for reliable model building. More than 90% of the soils data used was sampled since 2000, but some older samples are included. Uncertainty estimates were also developed by creating relative prediction intervals, which allow end users to evaluate uncertainty easily.

This dataset comprises soil property mapping across the whole State of Victoria at 6 prescribed depths. The set depths are 0 to 5 cm, 5 to 15 cm, 15 to 30 cm, 30 to 60 cm, 60 to 100 cm and 100 to 200 cm. The mapped soil properties are pH (1:5 water), EC (dS/m), % clay and soil organic carbon (SOC %). The dataset has been created by the Understanding Soil and Farming Systems project (CMI 102922)and is referred to as Version 1.0 of the Victorian Digital Soil Map (VIC DSM 1.0). Soil point data stored in the Victorian Soil Information System (VSIS) from over 6,000 sites has been standardised to the set depths (using equal area splines or a value weighting derived from the proportional contruibution of each sample to the depth class). This processed data was used to attribute soil land units from a collection of surveys (mapped at 1:100k or better) collated to provide the best map unit coverage across the State. Only data from sites that match the soil type of the dominant soil within the land unit being attributed were used. Sites and land units were assigned an Australian Soil Classification (to the Suborder level) to aid this process. The raw profile data stored in the VSIS (as of March 2013) used to produce these maps were: pH data were either laboratory based (1:5 soil/water suspension) or field pH (Raupach and Tucker 1959). Clay % was laboratory derived particle size data (PSA all methods), or converted field observations of texture class (McKenzie et al. 2000). Organic Carbon measurements methods was either Walkley and Black or Heanes wet oxidation. Electical Conductivity was 1:5 soil/water extract (dS/m). The data is available in polygonal format (i.e. the land units) with soil property median value, standard deviation and assignment qualifier attributes. ESRI grids in ascii format at 100 m cell resolution have been generated from the attributed land unit polygon dataset for each soil property at each depth interval. The assignment qualifiers have been created in order to provide a level of quality evaluation for the soil property assignment to each polygon. Reliability maps generated from these qualifiers have been produced together with each soil property map. The strength of these products is our ability to leverage on the significant investment in soil site and survey mapping data procurement and the capture of tacit knowledge of former soil surveyors. A revised version of these digital soil maps is due to be released at the end of 2014.