This map provides access to georeferenced TIFF images of legacy agriculture capability maps. Click on the map to view project and scanned map attributes for your point of interest. Scroll through all the options using the NEXT button and choose which maps and data to download using the hyperlinks. If you just want the map image download the .TIF file. For georeferenced maps (i.e., in order to use maps in a GIS and over lay them with other digital layers) download all the files in the folder. You can double click on the .TIF field to view it in your default image viewer. Select by Category from the main menu, to filter your search using scale, project name, map name or map sheet number. Use the Layer List from the main menu, to turn on other layers like the NTS 250K grid to see the map sheet locations and names. Data for this application is available for download from the BC Data Catalogue

The National Pollutant Release Inventory (NPRI) is Canada's public inventory of pollutant releases (to air, water and land), disposals and transfers for recycling. Each file contains the NPRI map layers in a KMZ format that you can use with virtual globe software such as Google Earth™. Data are available for the last two reporting years. You can filter the data by province or industry type. Select a facility to view a report that summarizes its pollutant releases, disposals and transfers. Please consult the following resources to enhance your analysis: - Guide on using and Interpreting NPRI Data: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/using-interpreting-data.html - Access additional data from the NPRI, including datasets and mapping products: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/tools-resources-data/exploredata.html

This dataset corresponds to daily snow cover percentage at 1km resolution grid over land areas of Canada from 2006-2010. The data are subsampled by 4km to reduce data volumes and considering the geolocation uncertainty of the input satellite imagery. The daily maps are generated by assimilation of daily cloud screened NOAA AVHRR satellite imagery and Canadian Meteorological Centre (CMC) snow depth analysis snow depth and density fields within an off-line version of the CMC daily snow depth model. The snow depth model is modified to include snowpack reflectance model and a surface radiative transfer scheme that relates vegetation and snowpack reflectance to top-of-canopy bi-directional reflectance. A logistic vegetation phenology model is used to parameterize temporal dynamics of canopy leaf area index. A per-pixel particle filter with a 30 day moving window is applied to assimilation observations corresponding to 1km resolution visible band directional reflectance and normalized difference vegetation index and 24km CMC daily snow depth and monthly snow density fields. The assimilation is forced using daily air temperature and precipitation fields. Validation of the datasets has been performed by comparison to MODIS snow cover maps and in-situ snow depth stations across Canada. Validation suggests similar accuracy to MODIS snow cover products over relatively flat terrain. Validation over mountainous regions is ongoing.

Flood extent polygons representing active floods (last three days) throughout Canada as monitored by Natural Resources Canada using satellite imagery for emergency response. In response to large flood events, Natural Resources Canada (NRCan), for the provision of emergency geomatics services, may be activated by Canada’s emergency management protocols. As new satellite imagery becomes available, NRCan will extract flood extent polygons and update the dataset in near real time (4 hours). This item contains the latest flood products generated in the past three days. For any data older than 72 hours, please refer to the Floods in Canada - Current Year entry. For this reason note that the web mapping service may not display data if flood polygons have not been published by the EGS in the past three days.The flood products generated are validated on a best effort basis. Various factors may affect the quality of the flood polygons. These factors include, but are not limited to, sensor type, image resolution, cloud cover or limitations of the flood polygon extraction method. In this layer, where possible, a symbology is applied to the flood polygons based on the underlying land use classification, or is simply unclassified and shows the raw flood extent. When using Web mapping services, to display a specific product, filter by date (UTC Date) and area of interest (AOI). Also, a link to download each product directly from the FTP site is available in the Resources section. This prepackaged and compressed product contains a Shape file, a PDF file and a KMZ file.Disclaimer: Emergency response authorities are the primary users of these satellite-derived open water flood extent map products. These products are generated to provide analysis and emergency response situational awareness and to facilitate decision-making during major flood events. The open water flood extent products are generated rapidly and limited time is available for editing and validation. The flood products reflect the open water flood conditions at the date/time of acquisition. While efforts are made to produce high quality products, near-real time products may contain errors due to the limited time available for vector editing and validation. Please note that current algorithms do not map flooded areas under the forest canopy and are not optimized for urban flood mapping. Limitation of Liability: Accordingly, the information contained on this website is provided on an “as is” basis and Natural Resources Canada makes no representations or warranties respecting the information, either expressed or implied, arising by law or otherwise, including but not limited to, effectiveness, completeness, accuracy or fitness for a particular purpose. Natural Resources Canada does not assume any liability in respect of any damage or loss based on the use of this website. In no event shall Natural Resources Canada be liable in any way for any direct, indirect, special, incidental, consequential, or other damages based on any use of this website or any other website to which this site is linked, including, without limitation, any lost profits or revenue or business interruption. Parent Collection:- Floods in Canada - Cartographic Product CollectionView Active Floods in Canada for more information, formats, contacts and metadata.

A digital magnetic anomaly database and map for the North American continent is the result of a joint effort by the Geological Survey of Canada (GSC), U. S. Geological Survey (USGS), and Consejo de Recursos Minerales of Mexico (CRM). The database and map represent a substantial upgrade from the previous compilation of magnetic anomaly data for North America, now over a decade old. This report presents three unique, gridded data sets used to make the magnetic anomaly map of North America. All three grids have 1-km spacing and are projected to the DNAG projection. These grids are provided in Geosoft binary grid format, with two files describing each of the grids (suffixes .grd and .gi). The first grids (NAmag_origmrg.grd and USmag_origmrg.grd) show the magnetic field at 1,000 m. above terrain. For the second grids (NAmag_hp500.grd and USmag_hp500.grd) we removed long-wavelength anomalies (500 km and greater) from the first grid. This grid was used for the published map. Although the North American merged grid represents a significant upgrade to older compilations, the existing patchwork of surveys is inherently unable to accurately represent anomalies with long (greater than roughly 150 km) wavelengths, particularly in the US and Canada (U.S. Magnetic- Anomaly Data Set Task Group, 1994). The lack of information about long wavelength anomalies is primarily related to datum shifts between merged surveys, caused by data acquisition at widely different times and by differences in merging procedures. Therefore, we removed anomalies with wavelengths greater than 500 km from the merged grid to reduce the effects caused by the spurious long wavelengths but still maintain the continuity of anomalies. The correction was accomplished by transforming the merged grid to the frequency domain, filtering the transformed data with a long-wavelength cutoff at 500 km, and subtracting the long-wavelength data grid from the merged grid. In addition to the 500-km high pass filter, an equivalent source method, based on long-wavelength characterization using satellite data (CHAMP satellite anomalies, Maus and others, 2002), was also used to correct for spurious shifts in the original magnetic anomaly grid (Ravat and others, 2002). These results are presented in the third grids (NAmag_CM.grd and USmag_CM.grd), in which the wavelengths longer than 500 km have been replaced by downward-continued satellite data.

[Note 02/2024: this resource is now decomissioned, link provides related maps, tools, and GIS layers.]The Dynamic Mapping Tool provides a spatial index to over 5,500 sites on streams and rivers in the U.S. and Canada where full year stream temperatures are currently being monitored by numerous agencies. You can filter stream temperature sites by state, agency, year and contact. The primary goal is to portray a comprehensive set of sites across all agencies to facilitate data sharing and avoid redundancies, as new monitoring sites are added to the regional network. Raw temperature data are not downloadable through this site, but typically reside with the local data stewards, whose contact information is displayed by clicking on a point in the map. In some instances, RMRS may have copies of the raw data and permission to distribute it, so we ask that you contact us before contacting the local data stewards. The map will be updated once each winter to maintain an accurate description of current monitoring locations. If interested in obtaining temperature data or adding temperature monitoring sites to this map, please contact Sherry Wollrab: 208.373.4371 or sherrywollrab@fs.fed.us.Resources in this dataset:Resource Title: Website Pointer for Stream Temperature Interactive Maps.File Name: Web Page, url: https://www.fs.usda.gov/rm/boise/AWAE/projects/stream_temp/maps.htmlThe Dynamic Mapping Tool provides a spatial index to over 5,500 sites on streams and rivers in the U.S. and Canada where full year stream temperatures are currently being monitored by numerous agencies. Users can filter stream temperature sites by state, agency, year and contact.

The 'Data and Resources' box to the right includes a link to an FTP site where data, and a data dictionary, can be downloaded that provide access to compiled data from the primary ground-sampling programs managed by the Forest Analysis and Inventory Branch (FAIB) as well as a link to and Interactive Mapping App. FAIB ground-sampling programs include the Permanent Sample Plots (PSPs) that provide long term growth and yield information to support development and testing of growth-and-yield models. Active PSPs are the only plot type protected from harvesting. The Provincial Change Monitoring Inventory (CMI), Provincial Young Stand Monitoring (YSM) and National Forest Inventory (NFI) programs monitor the changes in growth, mortality, and forest health from statistically valid populations. Vegetation Resource Inventory (VRI) plots are used to audit and verify key spatial inventory attributes estimated during photo interpretation. The 'Ground Plot Data FTP' link contains tree- and plot-level compiled mensurational attributes for each ground plot across a series of repeated measurements. Both the PSP and non-PSP compilation outputs include a Data Dictionary that describes all the tables and attributes found in the downloadable files. The 'psp' dataset includes both inactive and active Permanent Sample Plot (PSP) data. The 'non-psp' dataset includes CMI, YSM, NFI, and VRI plots. The CMI, YSM and NFI plots are all located on a grid and only GENERALIZED COORDINATES are provided for these plot types. All PSP and VRI plots include REAL COORDINATES. The Interactive Mapping App provides a spatial view of FAIB ground plots with custom filters to enable selection of areas, BEC zones, species, TSA or plot types of interest. Once plots of interest are selected or filtered, an ‘export data’ button is available to download a plot summary file with limited attributes.

In response to large flood events, Natural Resources Canada (NRCan), for the provision of emergency geomatics services, may be activated by Canada’s emergency management protocols. As new satellite imagery becomes available, NRCan will extract flood extent polygons and update the dataset in near real time (4 hours). This item contains the flood products generated in the past year. For any data relating to previous years, please refer to the Floods in Canada – Archive entry. Please note that the web mapping service may not display data if flood polygons have not been published by the EGS for the current year. The flood products generated are validated on a best effort basis. Various factors may affect the quality of the flood polygons. These factors include, but are not limited to, sensor type, image resolution, cloud cover or limitations of the flood polygon extraction method. In this layer, where possible, a symbology is applied to the flood polygons based on the underlying land use classification, or is simply unclassified and shows the raw flood extent. When using Web mapping services, to display a specific product, filter by date (UTC Date) and area of interest (AOI). Also, a link to download each product directly from the FTP site is available in the Resources section. This prepackaged and compressed product contains a Shape file, a PDF file and a KMZ file.Disclaimer : Emergency response authorities are the primary users of these satellite-derived open water flood extent map products. These products are generated to provide analysis and emergency response situational awareness and to facilitate decision-making during major flood events. The open water flood extent products are generated rapidly and limited time is available for editing and validation. The flood products reflect the open water flood conditions at the date/time of acquisition. While efforts are made to produce high quality products, near-real time products may contain errors due to the limited time available for vector editing and validation. Please note that current algorithms do not map flooded areas under the forest canopy and are not optimized for urban flood mapping.Limitation of Liability : Accordingly, the information contained on this website is provided on an “as is” basis and Natural Resources Canada makes no representations or warranties respecting the information, either expressed or implied, arising by law or otherwise, including but not limited to, effectiveness, completeness, accuracy or fitness for a particular purpose. Natural Resources Canada does not assume any liability in respect of any damage or loss based on the use of this website. In no event shall Natural Resources Canada be liable in any way for any direct, indirect, special, incidental, consequential, or other damages based on any use of this website or any other website to which this site is linked, including, without limitation, any lost profits or revenue or business interruption.Parent Collection:- Floods in Canada - Cartographic Product CollectionVisit Floods in Canada - Current Year for more information, formats, contacts and metadata.

Point feature layer showing the locations of winter trail and recreation areas in Manitoba's provincial parks. Point feature layer showing the locations of winter trail and recreation areas in Manitoba's provincial parks. This layer is utilized in the interactive map Manitoba Parks - Winter Activity Trails Map and the web app Manitoba Parks - Winter Trails and Recreation Areas. The dataset includes the following fields (Alias (Name): Description) Park (Park): Name of the provincial park where the trail or recreation area is located Feature (Feature): Name of the trail or recreation area Activity (Activity): Contains a list of activities designated for the trail Length (Length): Length of the trail in kilometres Groom Date (Groom_Date): Date of the most recent grooming of the trail Rating (Rating): An overall rating of the trail or recreation area given the current conditions. (e.g., Excellent, Good, Fair, Poor, Closed, or Unknown) Trail Condition (Trail_Condition): This field contains a description of the current conditions of the trail or recreation area Additional Remarks (Additional_Remarks): Contains any other related information about the trail or recreation area PDF Link (PDF_Link): Contains a url link to a pdf map or website Ski (Ski): Indicates whether the activity is allowed and provides a way to filter the map features Skijor (Skijor): Indicates whether the activity is allowed and provides a way to filter the map features Walk (Walk): Indicates whether the activity is allowed and provides a way to filter the map features Skate Ski (Skate_Ski): Indicates whether the activity is allowed and provides a way to filter the map features Snowshoe (Snowshoe): Indicates whether the activity is allowed and provides a way to filter the map features Toboggan (Toboggan): Indicates whether the activity is allowed and provides a way to filter the map features Dog Sled (Dog_Sled): Indicates whether the activity is allowed and provides a way to filter the map features For more information about winter activities, visit the Manitoba Parks website.

This map data product enriches traditional spatial coverage with real-time mobility data inputs. It provides a dynamic view of the built environment, shaped by actual human movement tracked from anonymized mobile device signals. Unlike static mapping layers, this map data evolves continuously to reflect changes in traffic flow, density, and behavioral zones.

The product includes: •Road network overlays with usage patterns •Venue and POI footprints enhanced by footfall counts •Transit corridors annotated with dwell and throughput data •Area-level mobility clusters and hotspots

Perfect for building routing applications, geo-visualizations, or spatial dashboards, this map data integrates seamlessly with location data, foot traffic data, and geospatial data infrastructures.

Use it to: •Identify underutilized or congested corridors•Prioritize capital investments or public service deployment •Enhance OOH advertising insights •Create visual simulations for urban design reviews

All data is provided in standard mapping formats (shapefiles, GeoJSON, raster tiles) and can be filtered by country, region, POI category, or date range.

This map data is continuously refined to ensure high fidelity, spatial granularity, and machine-readability. By tying static cartography to real-time behavior, it bridges the gap between what’s mapped and what’s happening

Looking for a detailed FSA- or 3-digit postal code map of Canada? With Spotzi, you can explore this Canadian FSA boundaries data in our dashboards for free. Create a free account and unlock access to our powerful dashboard to analyze, segment, and target areas like never before.

Access the map instantly without payment or commitment. By registering a free Spotzi account, you also get access to advanced tools: radius filters, drivetime filters, build heatmaps, and even export postcode selections to use in your next marketing campaign. It's the easiest way to turn location data into action – no technical skills required.

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These datasets show commercial fisheries catch weight landings of directed fisheries and bycatch from the Scotian Shelf, the Bay of Fundy, and Georges Bank from NAFO Divisions 4VWX and the Canadian portions of 5Y and 5Z. Atlantic Canadian inter-regional maps of four species (Atlantic Halibut, Bluefin Tuna, Redfish and Scallop) are also included from NAFO Divisions 4RST, 3KLMNOP, and 2GHJ. Five-year composite maps (2014–2018) that aggregate catches for each map series are publicly available. The maps aggregate catch weight (kg) per 10 km2 hexagon grid cell for selected species, species groupings and gear types to identify important fishing areas. These maps may be used for decision making in coastal and oceans management, including marine spatial planning, environmental emergency response operations and protocols, Marine Stewardship Council certification processes, marine protected area networks, and ecological risk assessment. These datasets have been filtered to comply with the Government of Canada's privacy policy. Privacy assessments were conducted to identify NAFO unit areas containing data with less than five vessel IDs, licence IDs or fisher IDs. If this threshold was not met, catch weight locations were withheld from these unit areas to protect the identity or activity of individual vessels or companies. Maps were created for the following species, species groupings and gear types: 1. Groundfish (all species) 2. Groundfish Bottom Trawl 3. Groundfish Gillnet 4. Groundfish Bottom Longline 5. Groundfish (quarterly composites Q1, Q2, Q3, Q4) 6. Atlantic Cod 7. Atlantic Cod, Haddock and Pollock 8. Flatfish 9. Atlantic Halibut 10. Greenland Halibut (Turbot) 11. Hagfish 12. Cusk 13. Dogfish 14. Redfish 15. Red Hake 16. Silver Hake 17. White Hake 18. Monkfish 19. Sculpin 20. Skate 21. Wolffish 22. Squid 23. Herring 24. Mackerel 25. Large Pelagics 26. Bluefin Tuna 27. Other Tuna 28. Swordfish 29. Porbeagle, Mako and Blue Shark 30. Snow Crab 31. Other Crab 32. Scallop 33. Scallop (quarterly composites Q1, Q2, Q3, Q4) 34. Offshore Clam 35. Shrimp 36. Offshore Lobster 37. Disputed Zone Area 38B Lobster 38. Whelk

This dataset contains two canopy height maps from forested ecosystems of Canada at 250m spatial resolution — one using information from the spaceborne LiDAR GEDI, and the other from ICESat-2. GEDI and ICESat-2 are particular in acquiring canopy height information in Canada — the former providing more accurate information of vegetation, yet not reaching full coverage in Canada, whilst the latter is not specifically designed to provide vegetation information but has a global coverage. We created wall-to-wall maps using ATL08 LiDAR product from the ICESat-2 satellite, and GEDI L2A from GEDI. The data were download for the mid growing season (June and August 2020). Points were filtered regarding solar background noise and atmospheric scattering, totaling 208,554 points from ICESat-2, and 1,249,354 points for GEDI after filtering and point thinning. These points were associated with 14 ancillary variables primarily corresponding to structure information, such as seasonal Sentinel-1 VV and VH polarization, seasonal Sentinel-2 red and NIR bands, and annual PALSAR-2 HH and HV polarization. Afterwards, the random forest algorithm was used to extrapolate LiDAR observations and develop regression models with the abovementioned spatially continuous variables. GEDI had a better performance than ICESat-2 with a mean difference (MD) of 0.9 m and 2.9 m in relation to ALS data used for validation, and a root mean square error (RMSE) of 4.2 m and 5.2 m, respectively. However, as both GEDI and ALS have no coverage in most of the hemi-boreal forests, ICESat-2 captures the tall canopy heights expected for these forests better than GEDI.

GapMaps Live is an easy-to-use location intelligence platform available across 25 countries globally that allows you to visualise your own store data, combined with the latest demographic, economic and population movement intel right down to the micro level so you can make faster, smarter and surer decisions when planning your network growth strategy.

With one single login, you can access the latest estimates on resident and worker populations, census metrics (eg. age, income, ethnicity), consuming class, retail spend insights and point-of-interest data across a range of categories including fast food, cafe, fitness, supermarket/grocery and more.

Some of the world's biggest brands including McDonalds, Subway, Burger King, Anytime Fitness and Dominos use GapMaps Live Map Data as a vital strategic tool where business success relies on up-to-date, easy to understand, location intel that can power business case validation and drive rapid decision making.

Primary Use Cases for GapMaps Live Map Data include:

1. Retail Site Selection - Identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers and where to find more of them.
3. Analyse your catchment areas at a granular grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)
6. Customer Profiling
7. Target Marketing
8. Market Share Analysis

Some of features our clients love about GapMaps Live Map Data include: - View business locations, competitor locations, demographic, economic and social data around your business or selected location - Understand consumer visitation patterns (“where from” and “where to”), frequency of visits, dwell time of visits, profiles of consumers and much more. - Save searched locations and drop pins - Turn on/off all location listings by category - View and filter data by metadata tags, for example hours of operation, contact details, services provided - Combine public data in GapMaps with views of private data Layers - View data in layers to understand impact of different data Sources - Share maps with teams - Generate demographic reports and comparative analyses on different locations based on drive time, walk time or radius. - Access multiple countries and brands with a single logon - Access multiple brands under a parent login - Capture field data such as photos, notes and documents using GapMaps Connect and integrate with GapMaps Live to get detailed insights on existing and proposed store locations.

The 'Data and Resources' box to the right includes a link to an FTP site where data, and a data dictionary, can be downloaded that provide access to compiled data from the primary ground-sampling programs managed by the Forest Analysis and Inventory Branch (FAIB) as well as a link to and Interactive Mapping App. FAIB ground-sampling programs include the Permanent Sample Plots (PSPs) that provide long term growth and yield information to support development and testing of growth-and-yield models. Active PSPs are the only plot type protected from harvesting. The Provincial Change Monitoring Inventory (CMI), Provincial Young Stand Monitoring (YSM) and National Forest Inventory (NFI) programs monitor the changes in growth, mortality, and forest health from statistically valid populations. Vegetation Resource Inventory (VRI) plots are used to audit and verify key spatial inventory attributes estimated during photo interpretation. The 'Ground Plot Data FTP' link contains tree- and plot-level compiled mensurational attributes for each ground plot across a series of repeated measurements. Both the PSP and non-PSP compilation outputs include a Data Dictionary that describes all the tables and attributes found in the downloadable files. The 'psp' dataset includes both inactive and active Permanent Sample Plot (PSP) data. The 'non-psp' dataset includes CMI, YSM, NFI, and VRI plots. The CMI, YSM and NFI plots are all located on a grid and only GENERALIZED COORDINATES are provided for these plot types. All PSP and VRI plots include REAL COORDINATES. The Interactive Mapping App provides a spatial view of FAIB ground plots with custom filters to enable selection of areas, BEC zones, species, TSA or plot types of interest. Once plots of interest are selected or filtered, an ‘export data’ button is available to download a plot summary file with limited attributes.

Flood extent polygons from the last three days in selected Canadian regions that have been designated for observation, monitored by Natural Resources Canada using satellite imagery for emergency response. Coverage is not comprehensive nationwide. In response to large flood events, Natural Resources Canada (NRCan), for the provision of emergency geomatics services, may be activated by Canada’s emergency management protocols. As new satellite imagery becomes available, NRCan will extract flood extent polygons and update the dataset in near real time (4 hours). This item contains the latest flood products generated in the past three days. For any data older than 72 hours, please refer to the Floods in Canada - Current Year entry. Note that the web mapping service may not display data if flood polygons have not been published by the EGS in the past three days. The flood products generated are validated on a best effort basis. Various factors may affect the quality of the flood polygons. These factors include, but are not limited to, sensor type, image resolution, cloud cover or limitations of the flood polygon extraction method. In this layer, where possible, a symbology is applied to the flood polygons based on the underlying land use classification, or is simply unclassified and shows the raw flood extent. When using Web mapping services, to display a specific product, filter by date (UTC Date) and area of interest (AOI). Also, a link to download each product directly is available in the Resources section. This prepackaged and compressed product contains a Shape file, a PDF file and a KMZ file. Disclaimer: Emergency response authorities are the primary users of these satellite-derived open water flood extent map products. These products are generated to provide analysis and emergency response situational awareness and to facilitate decision-making during major flood events. The open water flood extent products are generated rapidly and limited time is available for editing and validation. The flood products reflect the open water flood conditions at the date/time of acquisition. While efforts are made to produce high quality products, near-real time products may contain errors due to the limited time available for vector editing and validation. Please note that current algorithms do not map flooded areas under the forest canopy and are not optimized for urban flood mapping. Limitation of Liability: Accordingly, the information contained on this website is provided on an “as is” basis and Natural Resources Canada makes no representations or warranties respecting the information, either expressed or implied, arising by law or otherwise, including but not limited to, effectiveness, completeness, accuracy or fitness for a particular purpose. Natural Resources Canada does not assume any liability in respect of any damage or loss based on the use of this website. In no event shall Natural Resources Canada be liable in any way for any direct, indirect, special, incidental, consequential, or other damages based on any use of this website or any other website to which this site is linked, including, without limitation, any lost profits or revenue or business interruption. Parent Collection: - Floods in Canada - Cartographic Product Collection

This archive contains native resolution and super resolution (SR) Landsat imagery, derivative lake shorelines, and previously-published lake shorelines derived airborne remote sensing, used here for comparison. Landsat images are from 1985 (Landsat 5) and 2017 (Landsat 8) and are cropped to study areas used in the corresponding paper and converted to 8-bit format. SR images were created using the model of Lezine et al (2021a, 2021b), which outputs imagery at 10x-finer resolution, and they have the same extent and bit depth as the native resolution scenes included. Reference shoreline datasets are from Kyzivat et al. (2019a and 2019b) for the year 2017 and Walter Anthony et al. (2021a, 2021b) for Fairbanks, AK, USA in 1985. All derived and comparison shoreline datasets are cropped to the same extent, filtered to a common minimum lake size (40 m2 for 2017; 13 m2 for 1985), and smoothed via 10 m morphological closing. The SR-derived lakes were determined to have F-1 scores of 0.75 (2017 data) and 0.60 (1985 data) as compared to reference lakes for lakes larger than 500 m2, and accuracy is worse for smaller lakes. More details are in the forthcoming accompanying publication.

All raster images are in cloud-optimized geotiff (COG) format (.tif) with file naming shown in Table 1. Vector shoreline datasets are in ESRI shapefile format (.shp, .dbf, etc.), and file names use the abbreviations LR for low resolution, SR for high resolution, and GT for “ground truth” comparison airborne-derived datasets.

Landsat-5 and Landsat-8 images courtesy of the U.S. Geological Survey

For an interactive map demo of these datasets via Google Earth Engine Apps, visit: https://ekyzivat.users.earthengine.app/view/super-resolution-demo

Table 1: File naming scheme based on region, with some regions requiring two-scene mosaics.

Region

Landsat ID

Mosaic name

Yukon Flats Basin

LC08_L2SP_068014_20170708_20200903_02_T1

LC08_20170708_yflats_cog.tif

“

LC08_L2SP_068013_20170708_20201015_02_T1

“

Old Crow Flats

LC08_L2SP_067012_20170903_20200903_02_T1

-

Mackenzie River Delta

LC08_L2SP_064011_20170728_20200903_02_T1

LC08_20170728_inuvik_cog.tif

“

LC08_L2SP_064012_20170728_20200903_02_T1

“

Canadian Shield Margin

LC08_L2SP_050015_20170811_20200903_02_T1

LC08_20170811_cshield-margin_cog.tif

“

LC08_L2SP_048016_20170829_20200903_02_T1

“

Canadian Shield near Baker Creek

LC08_L2SP_046016_20170831_20200903_02_T1

-

Canadian Shield near Daring Lake

LC08_L2SP_045015_20170723_20201015_02_T1

-

Peace-Athabasca Delta

LC08_L2SP_043019_20170810_20200903_02_T1

-

Prairie Potholes North 1

LC08_L2SP_041021_20170812_20200903_02_T1

LC08_20170812_potholes-north1_cog.tif

“

LC08_L2SP_041022_20170812_20200903_02_T1

“

Prairie Potholes North 2

LC08_L2SP_038023_20170823_20200903_02_T1

-

Prairie Potholes South

LC08_L2SP_031027_20170907_20200903_02_T1

-

Fairbanks

LT05_L2SP_070014_19850831_20200918_02_T1

-

References:

Kyzivat, E. D., Smith, L. C., Pitcher, L. H., Fayne, J. V., Cooley, S. W., Cooper, M. G., Topp, S. N., Langhorst, T., Harlan, M. E., Horvat, C., Gleason, C. J., & Pavelsky, T. M. (2019b). A high-resolution airborne color-infrared camera water mask for the NASA ABoVE campaign. Remote Sensing, 11(18), 2163. https://doi.org/10.3390/rs11182163

Kyzivat, E.D., L.C. Smith, L.H. Pitcher, J.V. Fayne, S.W. Cooley, M.G. Cooper, S. Topp, T. Langhorst, M.E. Harlan, C.J. Gleason, and T.M. Pavelsky. 2019a. ABoVE: AirSWOT Water Masks from Color-Infrared Imagery over Alaska and Canada, 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1707

Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021a). Super-resolution surface water mapping on the Canadian shield using planet CubeSat images and a generative adversarial network. Canadian Journal of Remote Sensing, 47(2), 261–275. https://doi.org/10.1080/07038992.2021.1924646

Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021b). Super-resolution surface water mapping on the canadian shield using planet CubeSat images and a generative adversarial network. Canadian Journal of Remote Sensing, 47(2), 261–275. https://doi.org/10.1080/07038992.2021.1924646

Walter Anthony, K.., Lindgren, P., Hanke, P., Engram, M., Anthony, P., Daanen, R. P., Bondurant, A., Liljedahl, A. K., Lenz, J., Grosse, G., Jones, B. M., Brosius, L., James, S. R., Minsley, B. J., Pastick, N. J., Munk, J., Chanton, J. P., Miller, C. E., & Meyer, F. J. (2021a). Decadal-scale hotspot methane ebullition within lakes following abrupt permafrost thaw. Environ. Res. Lett, 16, 35010. https://doi.org/10.1088/1748-9326/abc848

Walter Anthony, K., and P. Lindgren. 2021b. ABoVE: Historical Lake Shorelines and Areas near Fairbanks, Alaska, 1949-2009. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1859

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2024 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2024. Key Properties Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryAnalysis: Optimized for analysisClass Definitions: ValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and class isolation for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer displays all classes.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent year is displayed. To discover and isolate specific years for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer.Zonal Statistics is a common tool used for understanding the composition of a specified area by reporting the total estimates for each of the classes. GeneralIf you are new to Sentinel-2 LULC, the Sentinel-2 Land Cover Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide.Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

Notes: The Canadian Aquatic Biomonitoring Network (CABIN) program is used to assess the ecological health of wadeable streams by using data from a wide range of sites that are minimally impacted by humans to establish the natural variability of healthy benthic invertebrate communities within a large watershed. The data collected at these sites (known as “potential reference”) are used to build CABIN models, which can be used to evaluate the aquatic health at sites where there are concerns with the aquatic ecosystem (known as “test sites”). The differences between the benthic macroinvertebrates at the test site compared with the range of conditions found at similar reference sites provides information about the level of impacts within a watershed. Once the sites have been incorporated into a CABIN model, and the model has been approved by ECCC, the site designation is changed from “potential reference” to “reference”.

This dataset is comprised of all CABIN sites in BC sampled by the BC Ministry of Environment and Climate Change Strategy and Environment and Climate Change Canada. The data has been clipped to only include BC and formatted for use with the BC government applications. More information and source data is available here: https://open.canada.ca/data/en/dataset/13564ca4-e330-40a5-9521-bfb1be767147 Suggested Filters:BC CABIN Sites - Reference Status: STATUS = 'Reference'BC CABIN Sites - Potential Reference Status: STATUS = 'Potential Reference'BC CABIN Sites - Test Status: STATUS = 'Test'BC CABIN Sites - Unknown Status: STATUS IS NULLSee how to apply filters.WMS GetCapabilities URL: DataBC also offers access to this data in OGC WMS format. WMS is useful when the map author does not require custom popups, styling, or analytic capabilities for the layer. ArcGIS Online authors may want to use WMS, instead of this ArcGIS Server layer, in the following scenarios: Where they want to use existing Data Custodian approved styling, and/or They only need simple identify and map rendering functionality.Copy the: WMS GetCapabilities URL to add this web item to an ArcGIS Online Map or Scene Viewer. In some cases, multiple Styles are listed in the GetCapabilities and can be added as WMS Custom parameters. For more information on how to use a WMS layer see - ESRI's OGC ArcGIS Online HelpBC Data Catalogue Metadata URL: https://catalogue.data.gov.bc.ca/dataset/822901ec-6d11-4f19-b60a-742cd2b47fcf

This data set contains distribution information for all terrestrial and aquatic reptiles, crocodilians, and turtles occurring in the United States and Canada.