The way to access Layers Quickly.

Quick Layers is an Add-In for ArcGIS Pro 3 that allows rapid access to the DNR's Geospatial Data Resource Site (GDRS). The GDRS is a data structure that serves core geospatial dataset and applications for not only DNR, but many state agencies, and supports the Minnesota Geospatial Commons. Data added from Quick Layers is pre-symbolized, helping to standardize visualization and map production. Current version: 3.11

To use Quick Layers with the GDRS, there's no need to download QuickLayers from this location. Instead, download a full copy or a custom subset of the public GDRS (including Quick Layers for ArcGIS Pro 3) using GDRS Manager.

Quick Layers also allows users to save and share their own pre-symbolized layers, thus increasing efficiency and consistency across the enterprise.

Installation:

After using GDRS Manager to create a GDRS, including Quick Layers, add the path to the Quick Layers addin to the list of shared folders:
1. Open ArcGIS Pro
2. Project -> Add-In Manager -> Options
3. Click add folder, and enter the location of the Quick Layers Pro app. For example, if your GDRS is mapped to the V drive, the path would be V:\gdrs\apps\pub\us_mn_state_dnr\quick_layers_pro3
4. After you do this, the Quick Layers ribbon will be available. To also add Quick Layers to the Quick Access Toolbar at the top, right click Quick Layers, and select Add to Quick Access Toolbar

The link below is only for those who are using Quick Layers without a GDRS. To get the most functionality out of Quick Layers, don't install it separately, but instead download it as part of a GDRS build using GDRS Manager.

This crash dataset does include crashes from 2023 up until near the middle of July that have been reviewed and loaded into the Maine DOT Asset Warehouse. This crash dataset is static and was put together as an example showing the clustering functionality in ArcGIS Online. In addition the dataset was designed with columns that include data items at the Unit and Persons levels of a crash. The feature layer visualization by default will show the crashes aggregated by the predominant crash type along the corridor. The aggregation settings can be toggled off if desired and crashes can be viewed by the type of crash. Both the aggregation and standard Feature Layer configurations do include popup settings that have been configured.As mentioned above, the Feature Layer itself has been configured to include a standard unique value renderer based on Crash Type and the layer also includes clustering aggregation configurations that could be toggled on or off if the user were to add this layer to a new ArcGIS Online Map. Clustering and aggregation options in ArcGIS Online provide functionality that is not yet available in the latest version of ArcGIS Pro (<=3.1). This additional configuration includes how to show the popup content for the cluster of crashes. Users interested in learning more about clustering and aggregation in ArcGIS Online and some more advanced options should see the following ESRI article (https://www.esri.com/arcgis-blog/products/arcgis-online/mapping/summarize-and-explore-point-clusters-with-arcade-in-popups/).Popups have been configured for both the clusters and the individual crashes. The individual crashes themselves do include multiple tables within a single text element. The bottom table does include data items that pertain to at a maximum of three units for a crash. If a crash includes just one unit then this bottom table will include only 2 columns. For each additional unit involved in a crash an additional column will appear listing out those data items that pertain to that unit up to a maximum of 3 units. There are crashes that do include more than 3 units and information for these additional units is not currently included in the dataset at the moment. The crash data items available in this Feature Layer representation includes many of the same data items from the Crash Layer (10 Years) that are available for use in Maine DOT's Public Map Viewer Application that can be accessed from the following link(https://www.maine.gov/mdot/mapviewer/?added=Crashes%20-%2010%20Years). However this crash data includes data items that are not yet available in other GIS Crash Departments used in visualizations by the department currently. These additional data items can be aggregated using other presentation types such as a Chart, but could also be filtered in the map. Users should refer to the unit count associated to each crash and be aware when a units information may not be visible in those situations where there are four or more units involved in a crash.

Retirement Notice: This item is in mature support as of June 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.Areas protected from conversion include areas that are permanently protected and managed for biodiversity such as Wilderness Areas and National Parks. In addition to protected lands, portions of areas protected from conversion includes multiple-use lands that are subject to extractive uses such as mining, logging, and off-highway vehicle use. These areas are managed to maintain a mostly undeveloped landscape including many areas managed by the Bureau of Land Management and US Forest Service. The Protected Areas Database of the United States classifies lands into four GAP Status classes. This layer displays lands managed for biodiversity conservation (GAP Status 1 and 2) and multiple-use lands (GAP Status 3). Dataset SummaryPhenomenon Mapped: Protected and multiple-use lands (GAP Status 1, 2, and 3) Units: MetersCell Size: 30.92208102 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, Northern Mariana Islands and American Samoa.Source: USGS National Gap Analysis Program PAD-US version 3.0Publication Date: July 2022 ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/ This layer displays protected areas from the Protected Areas Database of the United States version 3.0 created by the USGS National Gap Analysis Program. This layer displays areas managed for biodiversity where natural disturbances are allowed to proceed or are mimicked by management (GAP Status 1), areas managed for biodiversity where natural disturbance is suppressed (GAP Status 2), and multiple-use lands where extract activities are allowed (GAP Status 3). The source data for this layer are available here. A feature layer published from this dataset is also available. The polygon vector layer was converted to raster layers using the Polygon to Raster Tool using the National Elevation Dataset 1 arc second product as a snap raster. The service behind this layer was published with 8 functions allowing the user to select different views of the service. Other layers created from this service using functions include:USA Protected AreasUSA Unprotected AreasUSA Protected Areas - Gap Status 1-4USA Protected Areas - Gap Status 1USA Protected Areas - Gap Status 2USA Protected Areas - Gap Status 3USA Protected Areas - Gap Status 4 What can you do with this layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "Protected from Land Cover Conversion" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "Protected from Land Cover Conversion" in the search box, browse to the layer then click OK. In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.

This ArcGIS Online hosted feature service displays perimeters from the National Incident Feature Service (NIFS) that meet ALL of the following criteria:

This web map features a vector basemap of OpenStreetMap (OSM) data created and hosted by Esri. Esri produced this vector tile basemap in ArcGIS Pro from a live replica of OSM data, hosted by Esri, and rendered using a creative cartographic style emulating a blueprint technical drawing. The vector tiles are updated every few weeks with the latest OSM data. This vector basemap is freely available for any user or developer to build into their web map or web mapping apps.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 vector basemap available available to the OSM, GIS, and Developer communities.

This crash dataset does include crashes from 2023 up until the end of May that have been reviewed and loaded into the Maine DOT Asset Warehouse. This crash dataset is static and was put together as an example showing the clustering functionality in ArcGIS Online. In addition the dataset was designed with columns that include data items at the Unit and Persons levels of a crash. The feature layer visualization by default will show the crashes aggregated by the predominant crash type along the corridor. The aggregation settings can be toggled off if desired and crashes can be viewed by the type of crash. Both the aggregation and standard Feature Layer configurations do include popup settings that have been configured.As mentioned above, the Feature Layer itself has been configured to include a standard unique value renderer based on Crash Type and the layer also includes clustering aggregation configurations that could be toggled on or off if the user were to add this layer to a new ArcGIS Online Map. Clustering and aggregation options in ArcGIS Online provide functionality that is not yet available in the latest version of ArcGIS Pro (<=3.1). This additional configuration includes how to show the popup content for the cluster of crashes. Users interested in learning more about clustering and aggregation in ArcGIS Online and some more advanced options should see the following ESRI article (https://www.esri.com/arcgis-blog/products/arcgis-online/mapping/summarize-and-explore-point-clusters-with-arcade-in-popups/).Popups have been configured for both the clusters and the individual crashes. The individual crashes themselves do include multiple tables within a single text element. The bottom table does include data items that pertain to at a maximum of three units for a crash. If a crash includes just one unit then this bottom table will include only 2 columns. For each additional unit involved in a crash an additional column will appear listing out those data items that pertain to that unit up to a maximum of 3 units. There are crashes that do include more than 3 units and information for these additional units is not currently included in the dataset at the moment. The crash data items available in this Feature Layer representation includes many of the same data items from the Crash Layer (10 Years) that are available for use in Maine DOT's Public Map Viewer Application that can be accessed from the following link(https://www.maine.gov/mdot/mapviewer/?added=Crashes%20-%2010%20Years). However this crash data includes data items that are not yet available in other GIS Crash Departments used in visualizations by the department currently. These additional data items can be aggregated using other presentation types such as a Chart, but could also be filtered in the map. Users should refer to the unit count associated to each crash and be aware when a units information may not be visible in those situations where there are four or more units involved in a crash.

This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years since 1992. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2019Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: AnnualWhat can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Different Classifications Available to MapFive processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display.Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year.In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009.This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover.Land Cover ProcessingTo provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015.Source dataThe datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.phpCitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

This dataset represents crashes on the full extent of Minot Ave between 2012 and May 2023. The Feature Layer itself has been configured to include a standard unique value renderer based on Crash Type and the layer also includes clustering aggregation configurations that could be toggled on or off if the user were to add this layer to a new ArcGIS Online Map. Clustering and aggreagation options in ArcGIS Online provide functionality that is not yet available in the latest version of ArcGIS Pro (<=3.1). This additional configuration includes how to show the popup content for the cluster of crashes. Users interested in learning more about clustering and aggregation in ArcGIS Online and some more advanced options should see the following ESRI article (https://www.esri.com/arcgis-blog/products/arcgis-online/mapping/summarize-and-explore-point-clusters-with-arcade-in-popups/).Popups have been configured for both the clusters and the individual crashes. The individual crashes themselves do include multiple tables within a single text element. The bottom table does include data items that pertain to at a maximum of three units for a crash. If a crash includes just one unit then this bottom table will include only 2 columns. For each additional unit involved in a crash an additional column will appear listing out those data items that pertain to that unit up to a maximum of 3 units. There are crashes that do include more than 3 units and information for these additional units is not currently included in the dataset at the moment. The crash data items available in this Feature Layer representation includes many of the same data items from the Crash Layer (10 Years) that are available for use in Maine DOT's Public Map Viewer Application that can be accessed from the following link(https://www.maine.gov/mdot/mapviewer/?added=Crashes%20-%2010%20Years). However this crash data includes data items that are not yet available in other GIS Crash Departments used in visualizations by the department currently. These additional data items can be aggregated using other presentation types such as a Chart, but could also be filtered in the map. Users should refer to the unit count associated to each crash and be aware when a units information may not be visible in those situations where there are four or more units involved in a crash.

The Digital Geologic-GIS Map of the Rozel Quadrangle, Utah is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (roze_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (roze_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (gosp_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (gosp_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (roze_geology_metadata_faq.pdf). Please read the gosp_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (roze_geology_metadata.txt or roze_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This webmap is a subset of Global Landcover 1992 - 2020 Image Layer. You can access the source data from here. This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years 1992-2020. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2020Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: Annual until 2020, no updates thereafterWhat can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Different Classifications Available to MapFive processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display.Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year.In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009.This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover.Land Cover ProcessingTo provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015.Source dataThe datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.phpCitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

This service is available to all ArcGIS Online users with organizational accounts. For more information on this service, including the terms of use, visit us online at https://goto.arcgisonline.com/earthobs3/ESA_CCI_Land_Cover_Time_SeriesThis layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years 1992-2020. These are available at the European Space Agency Climate Change Initiative website. Time Extent: 1992-2020Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: Annual until 2020, no updates thereafter What can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth. Different Classifications Available to Map Five processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190). Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University. Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display. Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year. In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009. This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover. Land Cover ProcessingTo provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015.Source dataThe datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.php CitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf More technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc *Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

The Digital Geologic-GIS Map of the Craters of the Moon National Monument Area, Idaho is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (cotm_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (cotm_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (crmo_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (crmo_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (cotm_geology_metadata_faq.pdf). Please read the crmo_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (cotm_geology_metadata.txt or cotm_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual _location as presented by this dataset. Users of this data should thus not assume the _location of features is exactly where they are portrayed in Google Earth, ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The combined processes of evaporation and transpiration, known as evapotranspiration (ET), plays a key role in the water cycle. Precipitation that falls on land can either run off in streams and rivers, soak into the ground, or return to the atmosphere through evapotranspiration. Water that evaporates returns directly to the atmosphere while water that is transpired is taken up by plant roots and lost to the atmosphere through the leaves.Evapotranspiration data can be used to calculate regional water and energy balance and soil water status and provides key information for water resource management. Potential evapotranspiration, the amount of ET that would occur if soil moisture were not limited, is a purely meteorological characteristic, based on air temperature, solar radiation, and wind speed. Actual evapotranspiration also depends on water availability, so it might occur at very close to the potential rate in a rainforest, but be much lower in a desert despite the higher potential there.Dataset SummaryPhenomenon Mapped: EvapotranspirationUnits: Millimeters per yearCell Size: 927.6623821756539 metersSource Type: ContinuousPixel Type: 16-bit unsigned integerData Coordinate System: Web Mercator Auxiliary SphereExtent: Global Source: University of Montana Numerical Terradynamic Simulation GroupPublication Date: March 10, 2015ArcGIS Server URL: https://landscape6.arcgis.com/arcgis/This layer provides access to a 1km cell sized raster of average annual evaporative loss from the land surface, measured in mm/year. Data are from the MOD16 Global Evapotranspiration Product, which is derived from MODIS imagery by a team of researchers at the University of Montana. This algorithm, which involves estimating land surface temperature and albedo and using them to solve the Penman-Monteith equation, is not valid over urban or barren land so these are shown as NoData, as is any open water. For all other pixels, the algorithm was used to estimate evapotranspiration for every 8-day period from 2000 to 2014 and these estimates have been averaged together to come up with the annual normal. You can also get access to the monthly totals using the MODIS Toolbox.What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "evapotranspiration" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "evapotranspiration" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

This space-time cube contains basic population and housing variables for Public Use Microdata Areas (PUMAs), annually from 2010 to 2023. The variables are from the American Community Survey (ACS) 1-year estimates.A space-time cube is a powerful data structure used to visualize and analyze spatio-temporal data in ArcGIS Pro. Some examples of what you can do with this space-time cube: Create a compelling three-dimensional visualization of homeownership rate through timeFind emerging hot spots of specific race or Hispanic origin groupsIdentify change points of vacant housing unitsForecast future population valuesTo access this space-time cube, click Download, then unzip the downloaded folder. The folder contains a space-time cube (.nc), a file geodatabase (.gdb) containing the PUMA boundaries, and a csv file (.csv) describing the ACS variables in the space-time cube.To view a short tutorial on getting started with this space-time cube, read this blog article. To learn more about how to create and work with space-time cubes in ArcGIS Pro, view the learning path.placeholderSpace Time Cube ContentsSpatial unit and extent: 2020 vintage Public Use Microdata Areas (PUMA) boundaries for the entire United States, Puerto Rico, and Guam. Downloaded from US Census TIGER geodatabases National Sub-State Geography Database, with water and coastlines erased using 2023 500k TIGER Cartographic Boundary Shapefiles. Temporal interval and extent: one year interval, between 2010 and 2023 .Data source: ACS 1-year estimates downloaded from data.census.gov for each year between 2010 and 2023 (except 2020). Table(s) B01001, B03002, B05003, B05011, B19049, B25002, B25003, B25058, B25077.Variables: includes 32 variables on the following themes: population, race and Hispanic origin, foreign-born, housing occupancy, and housing tenure. To view a full listing of the variables, consult the .csv file contained within the downloaded folder.Processing Notes and Usage Tips The space-time cube contains variables that are directly sources from ACS, plus variables that have been calculated using ACS variables. The calculated variables can be identified by the “_calc_” stub in the field name. The spreadsheet contained within the downloaded folder provides more information on each variable source and calculation. It also contains field aliases, which can optionally be used to add aliases to the space-time cube layer or any other feature classes which are derived from the space-time cube (see blog article for information on how to do this). The field aliases were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. The ACS did not publish 1 year estimates for 2020. The variable values for this year were imputed using the temporal trend method of the Create Space Time Cube from Defined Locations tool, which uses the Interpolated Univariate Spline method from the SciPy Interpolation package. This can introduce some unexpected artifacts in the values for this year, for example: count statistics may include decimal places or may become negative, and variables that should sum together to reach the total of another variable may not. Therefore it is advised to take caution when making any conclusions from analysis which are focused around this year. The PUMA boundaries change after each decennial census. For the time series of this space-time cube, there was a boundary change between 2011 and 2012 (from the 2000 census to 2010), and another between 2021 and 2022 (from the 2010 census to 2020). Therefore, apportionment was required for all years between 2010 and 2021 to be able to accurately create a time series based on the 2020 PUMA geographies. A weighted apportionment approach was used, applying either population or housing weights depending on the variable. Apportionment enables us to create longer time-series or time-series which are more current, however it also adds an additional source of error to the ACS estimates. A version of this space-time cube without apportionment, for 2012 to 2021, is provided at LINK TO OTHER CUBE. ACS update the population controls after every decennial census, which can sometimes cause slight shifts in values. For this space-time cube, these happened between from 2011 and 2012, and 2021 and 2022. Therefore it is advised to take caution when making any conclusions from analysis which are focused around these years. A version of this space-time cube without these effects, for 2012 to 2021, is provided at LINK TO OTHER CUBE. In order to have access to the latest functionality, it is recommended to use the most recent version of ArcGIS Pro to work with the space-time cube. In particular, in ArcGIS Pro 3.5, significant enhancements were made to space-time cube visualization workflows. Native space-time cube analysis and visualization is not currently supported in ArcGIS Online. However once visualization or analysis has taken place in ArcGIS Pro, the resulting space-time cube layer can be published as a Web Scene, which can be visualized in Scene Viewer.ACS InformationInformation about the United States Census Bureau's American Community Survey (ACS): About the Survey Geography & ACS Technical Documentation News & UpdatesPlease cite the Census and ACS when using this data.Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.

This crash dataset does include crashes from 2023 up until near the middle of July that have been reviewed and loaded into the Maine DOT Asset Warehouse. This crash dataset is static and was put together as an example showing the clustering functionality in ArcGIS Online. In addition the dataset was designed with columns that include data items at the Unit and Persons levels of a crash. The feature layer visualization by default will show the crashes aggregated by the predominant crash type along the corridor. The aggregation settings can be toggled off if desired and crashes can be viewed by the type of crash. Both the aggregation and standard Feature Layer configurations do include popup settings that have been configured.As mentioned above, the Feature Layer itself has been configured to include a standard unique value renderer based on Crash Type and the layer also includes clustering aggregation configurations that could be toggled on or off if the user were to add this layer to a new ArcGIS Online Map. Clustering and aggregation options in ArcGIS Online provide functionality that is not yet available in the latest version of ArcGIS Pro (<=3.1). This additional configuration includes how to show the popup content for the cluster of crashes. Users interested in learning more about clustering and aggregation in ArcGIS Online and some more advanced options should see the following ESRI article (https://www.esri.com/arcgis-blog/products/arcgis-online/mapping/summarize-and-explore-point-clusters-with-arcade-in-popups/).Popups have been configured for both the clusters and the individual crashes. The individual crashes themselves do include multiple tables within a single text element. The bottom table does include data items that pertain to at a maximum of three units for a crash. If a crash includes just one unit then this bottom table will include only 2 columns. For each additional unit involved in a crash an additional column will appear listing out those data items that pertain to that unit up to a maximum of 3 units. There are crashes that do include more than 3 units and information for these additional units is not currently included in the dataset at the moment. The crash data items available in this Feature Layer representation includes many of the same data items from the Crash Layer (10 Years) that are available for use in Maine DOT's Public Map Viewer Application that can be accessed from the following link(https://www.maine.gov/mdot/mapviewer/?added=Crashes%20-%2010%20Years). However this crash data includes data items that are not yet available in other GIS Crash Departments used in visualizations by the department currently. These additional data items can be aggregated using other presentation types such as a Chart, but could also be filtered in the map. Users should refer to the unit count associated to each crash and be aware when a units information may not be visible in those situations where there are four or more units involved in a crash.

The combined processes of evaporation and transpiration, known as evapotranspiration (ET), plays a key role in the water cycle. Precipitation that falls on land can either run off in streams and rivers, soak into the ground, or return to the atmosphere through evapotranspiration. Water that evaporates returns directly to the atmosphere while water that is transpired is taken up by plant roots and lost to the atmosphere through the leaves.Evapotranspiration data can be used to calculate regional water and energy balance and soil water status and provides key information for water resource management. Potential evapotranspiration, the amount of ET that would occur if soil moisture were not limited, is a purely meteorological characteristic, based on air temperature, solar radiation, and wind speed. Actual evapotranspiration also depends on water availability, so it might occur at very close to the potential rate in a rainforest, but be much lower in a desert despite the higher potential there.Dataset SummaryPhenomenon Mapped: EvapotranspirationUnits: Millimeters per yearCell Size: 927.6623821756539 metersSource Type: ContinuousPixel Type: 16-bit unsigned integerData Coordinate System: Web Mercator Auxiliary SphereExtent: Global Source: University of Montana Numerical Terradynamic Simulation GroupPublication Date: March 10, 2015ArcGIS Server URL: https://landscape6.arcgis.com/arcgis/This layer provides access to a 1km cell sized raster of average annual evaporative loss from the land surface, measured in mm/year. Data are from the MOD16 Global Evapotranspiration Product, which is derived from MODIS imagery by a team of researchers at the University of Montana. This algorithm, which involves estimating land surface temperature and albedo and using them to solve the Penman-Monteith equation, is not valid over urban or barren land so these are shown as NoData, as is any open water. For all other pixels, the algorithm was used to estimate evapotranspiration for every 8-day period from 2000 to 2014 and these estimates have been averaged together to come up with the annual normal. You can also get access to the monthly totals using the MODIS Toolbox.What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "evapotranspiration" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "evapotranspiration" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

World Imagery provides one meter or better satellite and aerial imagery in many parts of the world and lower resolution satellite imagery worldwide. The map includes 15m TerraColor imagery at small and mid-scales (~1:591M down to ~1:72k) and 2.5m SPOT Imagery (~1:288k to ~1:72k) for the world. The map features 0.5m resolution imagery in the continental United States and parts of Western Europe from DigitalGlobe. Additional DigitalGlobe sub-meter imagery is featured in many parts of the world. In the United States, 1 meter or better resolution NAIP imagery is available in some areas. In other parts of the world, imagery at different resolutions has been contributed by the GIS User Community. In select communities, very high resolution imagery (down to 0.03m) is available down to ~1:280 scale. You can contribute your imagery to this map and have it served by Esri via the Community Maps Program. View the list of Contributors for the World Imagery Map.CoverageView the links below to learn more about recent updates and map coverage:What's new in World ImageryWorld coverage mapCitationsThis 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. A similar raster web map, Imagery with Labels, is also available.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 layer is a subset of Global Landcover 1992- 2020 Layer. This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years 1992-2020. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2020Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: Annual until 2020, no updates thereafterWhat can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Different Classifications Available to MapFive processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display.Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year.In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009.This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover.Land Cover ProcessingTo provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015.Source dataThe datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.phpCitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

This dataset contains both large (A0) printable maps of the Torres Strait broken into six overlapping regions, based on a clear sky, clear water composite Sentinel 2 composite imagery and the imagery used to create these maps. These maps show satellite imagery of the region, overlaid with reef and island boundaries and names. Not all features are named, just the more prominent features. This also includes a vector map of Ashmore Reef and Boot Reef in Coral Sea as these were used in the same discussions that these maps were developed for. The map of Ashmore Reef includes the atoll platform, reef boundaries and depth polygons for 5 m and 10 m.

This dataset contains all working files used in the development of these maps. This includes all a copy of all the source datasets and all derived satellite image tiles and QGIS files used to create the maps. This includes cloud free Sentinel 2 composite imagery of the Torres Strait region with alpha blended edges to allow the creation of a smooth high resolution basemap of the region.

The base imagery is similar to the older base imagery dataset: Torres Strait clear sky, clear water Landsat 5 satellite composite (NERP TE 13.1 eAtlas, AIMS, source: NASA).

Most of the imagery in the composite imagery from 2017 - 2021.


Method:
The Sentinel 2 basemap was produced by processing imagery from the World_AIMS_Marine-satellite-imagery dataset (01-data/World_AIMS_Marine-satellite-imagery in the data download) for the Torres Strait region. The TrueColour imagery for the scenes covering the mapped area were downloaded. Both the reference 1 imagery (R1) and reference 2 imagery (R2) was copied for processing. R1 imagery contains the lowest noise, most cloud free imagery, while R2 contains the next best set of imagery. Both R1 and R2 are typically composite images from multiple dates.

The R2 images were selectively blended using manually created masks with the R1 images. This was done to get the best combination of both images and typically resulted in a reduction in some of the cloud artefacts in the R1 images. The mask creation and previewing of the blending was performed in Photoshop. The created masks were saved in 01-data/R2-R1-masks. To help with the blending of neighbouring images a feathered alpha channel was added to the imagery. The processing of the merging (using the masks) and the creation of the feathered borders on the images was performed using a Python script (src/local/03-merge-R2-R1-images.py) using the Pillow library and GDAL. The neighbouring image blending mask was created by applying a blurring of the original hard image mask. This allowed neighbouring image tiles to merge together.

The imagery and reference datasets (reef boundaries, EEZ) were loaded into QGIS for the creation of the printable maps.

To optimise the matching of the resulting map slight brightness adjustments were applied to each scene tile to match its neighbours. This was done in the setup of each image in QGIS. This adjustment was imperfect as each tile was made from a different combinations of days (to remove clouds) resulting in each scene having a different tonal gradients across the scene then its neighbours. Additionally Sentinel 2 has slight stripes (at 13 degrees off the vertical) due to the swath of each sensor having a slight sensitivity difference. This effect was uncorrected in this imagery.


Single merged composite GeoTiff:
The image tiles with alpha blended edges work well in QGIS, but not in ArcGIS Pro. To allow this imagery to be used across tools that don't support the alpha blending we merged and flattened the tiles into a single large GeoTiff with no alpha channel. This was done by rendering the map created in QGIS into a single large image. This was done in multiple steps to make the process manageable.

The rendered map was cut into twenty 1 x 1 degree georeferenced PNG images using the Atlas feature of QGIS. This process baked in the alpha blending across neighbouring Sentinel 2 scenes. The PNG images were then merged back into a large GeoTiff image using GDAL (via QGIS), removing the alpha channel. The brightness of the image was adjusted so that the darkest pixels in the image were 1, saving the value 0 for nodata masking and the boundary was clipped, using a polygon boundary, to trim off the outer feathering. The image was then optimised for performance by using internal tiling and adding overviews. A full breakdown of these steps is provided in the README.md in the 'Browse and download all data files' link.

The merged final image is available in export\TS_AIMS_Torres Strait-Sentinel-2_Composite.tif.


Source datasets:
Complete Great Barrier Reef (GBR) Island and Reef Feature boundaries including Torres Strait Version 1b (NESP TWQ 3.13, AIMS, TSRA, GBRMPA), https://eatlas.org.au/data/uuid/d2396b2c-68d4-4f4b-aab0-52f7bc4a81f5

Geoscience Australia (2014b), Seas and Submerged Lands Act 1973 - Australian Maritime Boundaries 2014a - Geodatabase [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, https://dx.doi.org/10.4225/25/5539DFE87D895

Basemap/AU_GA_AMB_2014a/Exclusive_Economic_Zone_AMB2014a_Limit.shp
The original data was obtained from GA (Geoscience Australia, 2014a). The Geodatabase was loaded in ArcMap. The Exclusive_Economic_Zone_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

Geoscience Australia (2014a), Treaties - Australian Maritime Boundaries (AMB) 2014a [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, http://dx.doi.org/10.4225/25/5539E01878302
Basemap/AU_GA_Treaties-AMB_2014a/Papua_New_Guinea_TSPZ_AMB2014a_Limit.shp
The original data was obtained from GA (Geoscience Australia, 2014b). The Geodatabase was loaded in ArcMap. The Papua_New_Guinea_TSPZ_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

AIMS Coral Sea Features (2022) - DRAFT
This is a draft version of this dataset. The region for Ashmore and Boot reef was checked. The attributes in these datasets haven't been cleaned up. Note these files should not be considered finalised and are only suitable for maps around Ashmore Reef. Please source an updated version of this dataset for any other purpose.
CS_AIMS_Coral-Sea-Features/CS_Names/Names.shp
CS_AIMS_Coral-Sea-Features/CS_Platform_adj/CS_Platform.shp
CS_AIMS_Coral-Sea-Features/CS_Reef_Boundaries_adj/CS_Reef_Boundaries.shp
CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth5m_Coral-Sea.shp
CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth10m_Coral-Sea.shp

Murray Island 20 Sept 2011 15cm SISP aerial imagery, Queensland Spatial Imagery Services Program, Department of Resources, Queensland
This is the high resolution imagery used to create the map of Mer.

World_AIMS_Marine-satellite-imagery
The base image composites used in this dataset were based on an early version of Lawrey, E., Hammerton, M. (2024). Marine satellite imagery test collections (AIMS) [Data set]. eAtlas. https://doi.org/10.26274/zq26-a956. A snapshot of the code at the time this dataset was developed is made available in the 01-data/World_AIMS_Marine-satellite-imagery folder of the download of this dataset.


Data Location:
This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\TS_AIMS_Torres-Strait-Sentinel-2-regional-maps. On the eAtlas server it is stored at eAtlas GeoServer\data\2020-2029-AIMS.


Change Log:
2025-05-12: Eric Lawrey
Added Torres-Strait-Region-Map-Masig-Ugar-Erub-45k-A0 and Torres-Strait-Eastern-Region-Map-Landscape-A0. These maps have a brighten satellite imagery to allow easier reading of writing on the maps. They also include markers for geo-referencing the maps for digitisation.

2025-02-04: Eric Lawrey
Fixed up the reference to the World_AIMS_Marine-satellite-imagery dataset, clarifying where the source that was used in this dataset. Added ORCID and RORs to the record.

2023-11-22: Eric Lawrey
Added the data and maps for close up of Mer.
- 01-data/TS_DNRM_Mer-aerial-imagery/
- preview/Torres-Strait-Mer-Map-Landscape-A0.jpeg
- exports/Torres-Strait-Mer-Map-Landscape-A0.pdf
Updated 02-Torres-Strait-regional-maps.qgz to include the layout for the new map.

2023-03-02: Eric Lawrey
Created a merged version of the satellite imagery, with no alpha blending so that it can be used in ArcGIS Pro. It is now a single large GeoTiff image. The Google Earth Engine source code for the World_AIMS_Marine-satellite-imagery was included to improve the reproducibility and provenance of the dataset, along with a calculation of the distribution of image dates that went into the final composite image. A WMS service for the imagery was also setup and linked to from the metadata. A cross reference to the older Torres Strait clear sky clear water Landsat composite imagery was also added to the record.