Each pixel value corresponds to the difference (anomaly) between the mean “Best-Quality” Max-NDVI of the week specified (e.g. Week 18, 2000-2014) and the “Best-Quality” Max-NDVI of the same week in a specific year (e.g. Week 18, 2015). Max-NDVI anomalies < 0 indicate where weekly Max-NDVI is lower than normal. Anomalies > 0 indicate where weekly Max-NDVI is higher than normal. Anomalies close to 0 indicate where weekly Max-NDVI is similar to normal.

This dataset includes the extent of the boreal forest as well as the extent of managed boreal forest worldwide. The extent of boreal forest was produced from Brandt et al. (2013) and a modified version of Goudilin (1987). Managed forest was defined as suggested by IPCC (2003) using data from FAFS (2009), Gauthier et al. (2014), See et al. (2015) and AICC maps. The extent of managed forest mostly includes areas managed for wood production, areas protected from large-scale disturbances as well as formal protected areas. Both boreal forest extent and managed boreal forest extent are available in raster and vector data. Please cite this data product as: Boucher, D., D.G. Schepaschenko, S. Gauthier, P. Bernier, T. Kuuluvainen, A. Z. Shvidenko. 2024. World boreal forest and managed boreal forest extent. DOI: 10.23687/88d70716-2600-4995-8d5f-86f96e383abf These data were presented in the following article: Gauthier, S., P. Bernier, T. Kuuluvainen, A. Z. Shvidenko, D. G. Schepaschenko. 2015. Boreal forest health and global change. Science 349:819-822. DOI: 10.1126/science.aaa9092 References: J. P. Brandt, M. D. Flannigan, D. G. Maynard, I. D. Thompson, W. J. A. Volney, Environ. Rev. 21, 207–226 (2013) I. S. Goudilin, Landscape map of the USSR. Legend to the landscape map of the USSR. Scale 1:2 500 000. Moscow, Ministry of Geology of the USSR (1987) [in Russian]. Inter-governmental panel on climate change (IPCC). J. Penman, M. Gytarsky, T. Hiraishi, T. Krug, D. Kruger, et al., Eds., Good practice guidance for land use, land-use change and forestry (IPCC/NGGIP/IGES, Kanawaga, 2003) Federal Agency of Forest Service (FAFS), Forest Fund of the Russian Federation (state by 1 January 2009) (Federal Agency of Forest Service, Moscow, 2009) [in Russian] S. Gauthier et al., Environ. Rev. 22, 256–285 (2014). See et al., Harnessing the power of volunteers, the internet and Google Earth to collect and validate global spatial information using Geo-Wiki. Technological Forecasting and Social Change. (2015). doi:10.1016/j.techfore.2015.03.002 Alaska Interagency Coordination Center (AICC). Fire Information. https://fire.ak.blm.gov/content/maps/aicc/Large%20Maps/Alaska_Fire_Management_Options.pdf (the version of 2014 was used)

Landcover dataset created for the agricultural portion of Saskatchewan. Download: here A satellite imagery classification of Southern Saskatchewan based mainly on 1994 Landsat5 imagery. Developed by the Saskatchewan Research Council after 1997. Background: A group of Provincial and Federal Agencies formed a partnership in March of 1997 to share the cost of obtaining satellite imagery and interpreting this imagery to create a landcover dataset for the agricultural portion of Saskatchewan. The partnership included Saskatchewan Research Council (SRC), Saskatchewan Agriculture and Food (SAF), Saskatchewan Crop Insurance (SCI), Saskatchewan Property Management Corporation (SPMC), Environment Canada, the Prairie Farm Rehabilitation Administration (PFRA) and Saskatchewan Environment Resource Management (SERM). The University of Regina was also involved as an 'in kind' partner providing research services in the area of land cover classifications, accuracy assessment and data conversions. The Partnership Agreement required SRC (partner doing the bulk of data processing) to provide digital files for each of 328 1:50,000 NTS map sheets. The digital files included not only raw imagery, but also one file for each map sheet where the imagery was classified into 24 landcover types. The accuracy of this classification was to be demonstrated by SRC to be at least 90 per cent correct. In addition to the data processing done by SRC, SPMC provided the necessary positional control data (road intersection coordinates) and verified the positional accuracy of the final product. The other partners provided feedback to SRC on classification errors, which improved the overall accuracy of the final product. Classification Value No Data 0 Crop Land 1 Hay Crops (Forage) 2 Native Dominant Grass Lands 3 Tall Shrubs 4 Pasture (Seeded Grass Lands) 5 Hardwoods (Open Canopy) 6 Hardwoods (Closed Canopy) 7 Jack Pine (Closed Canopy) 8 Jack Pine (Open Canopy) 9 Spruce (Close Canopy) 10 Treed Rock 13 Recent Burns 14 Revegetating Burns 15 Cutovers 16 Water Bodies 17 Marsh 18 Herbaceous Fen 19 Mud/Sand/Saline 20 Shrub Fen (Treed Swamp) 21 Treed Bog 22 Open Bog 23 Slopes 25 Slopes 26 0. No Data 1. Crop Land - All lands dedicated to the production of annual cereal, oil seed and other specialty crops, and typically cultivated on an annual basis.  2. Hay Crops (Forage) - Alfalfa and alfalfa/tame grass mixtures.  3. Native Dominant Grass Lands - Native dominant grasslands/may contain tame grasses and herbs.  4. Tall Shrubs - Communities containing both low and tall shrub, snowberry, saskatoon, chokecherry, buffaloberry, and willow.  5. Pasture (Seeded Grass Lands) - Grassland dominated by tame grass species.  6. Hardwoods (Open Canopy) - Corresponds to Provincial Forest Inventory: over 75% hardwoods; 10-30% crown closure.  7. Hardwoods (Closed Canopy) - Corresponds to Provincial Forest Inventory: over 75% hardwoods; 30-100% crown closure.  8. Jack Pine (Closed Canopy) - Similar to Provincial Forest Inventory: 75% or greater Jack Pine; 30-100% crown closure.  9. Jack Pine (Open Canopy) - Similar to Provincial Forest Inventory: 75% or greater Jack Pine; 10-30% crown closure.  10. Spruce (Close Canopy) - Similar to Provincial Forest Inventory: 75% or greater Black and White Spruce; 10-30% crown closure. 11. Spruce: Open Canopy - Similar to Provincial Forest Inventory: 75% or greater Black and White Spruce; 10-30% crown closure. 12. Mixed Woods - All softwood/hardwood mixtures.  13. Treed Rock - Areas of exposed bedrock with generally less then 10% tree cover. Dominant species are Jack Pine and Black Spruce.  14. Recent Burns - All areas that have been recently burned over by wildfires.  15. Revegetating Burns - Burns with a regrowth of commercial timber generally 1-5 metres in height.  16. Cutovers - Areas where commercial timber has been completely or partially removed by logging operations.  17. Water Bodies - Consists of all open water - lakes, rivers, streams, ponds, and lagoons.  18. Marsh - Dominated by sedge and wetland grasses.  19. Herbaceous Fen - Fens dominated by herbaceous species.  20. Mud/Sand/Saline  21. Shrub Fen (Treed Swamp) - Fens dominated by shrubby species.  22. Treed Bog - Peat-covered or peat-filled depressions with a high water table and a surface carpet of moss, chiefly sphagnum. The bogs have 25% or more canopy by trees greater than one metre tall. The primary species is black spruce.  23. Open Bog - Peat-covered or peat-filled depressions with a high water table and a surface carpet of moss, chiefly sphagnum. 24. Farmstead - Farmstead types, towns, cities, Exposed areas with little or no vegetation or Cloud coverage.  25. Slopes - Steep Valley slopes or hill slopes where aspect and slope prohibit classification. 26. Slopes - Steep Valley slopes or hill slopes where aspect and slope prohibit classification.

The 1 cm resolution digital surface model (DSM) was created from unmanned aerial vehicle (UAV) imagery acquired from a single day survey, July 28th 2016, in Cambridge Bay, Nunavut. Five control points taken from a Global Differential Positioning System were positioned in the corners and the center of the vegetation survey. The DSM covering 525m2 was produced by Canada Centre for Remote Sensing /Canada Centre for Mapping and Earth Observation. The UAV survey was completed in collaboration with the Canadian High Arctic Research Station (CHARS) for northern vegetation monitoring research. For more information, refer to our current Arctic vegetation research: Fraser et al; "UAV photogrammetry for mapping vegetation in the low-Arctic" Arctic Science, 2016, 2(3): 79-102. http://www.nrcresearchpress.com/doi/abs/10.1139/AS-2016-0008

Leaf area index (LAI) quantified the density of vegetation irrespective of land cover. LAI quantifies the total foliage surface area per groud surface area. LAI has been identified by the Global Climate Observing System as an essential climate variable required for ecosystem,weather and climate modelling and monitoring. This product consists of annual maps of the maximum LAI during a grownig season (June-July-August) at 100m resolution covering Canada's land mass.

This group of maps, which includes the CanMatrix and CanTopo collections, is now a legacy product that is no longer maintained. It may not meet current government standards. Natural Resources Canada's (NRCan) topographic raster maps provide a representation of the topographic phenomena of the Canadian landmass. Several editions of paper maps have been produced over time in order to offer improved products compared to their predecessors in terms of quality and the most up to date information possible. The georeferenced maps can be used in a Geographic Information System (GIS). In all cases, they accurately represent the topographical data available for the date indicated (validity date). The combination of CanMatrix and CanTopo data provides complete national coverage. • CanMatrix - Print Ready: Raster maps produced by scanning topographic maps at scales from 1:25 000 to 1:1 000 000. This product is not georeferenced. Validity dates: 1944 to 2005 (1980 on average). Available formats: PDF and TIFF • CanMatrix - Georeferenced: Raster maps produced by scanning topographic maps at scales of 1:50 000 and 1:250 000. These maps are georeferenced according to the 1983 North American Reference System (NAD 83). Validity dates: 1944 to 2005 (1980 on average). Available format: GeoTIFF • CanTopo: Digital raster maps produced mainly from the GeoBase initiative, NRCan digital topographic data, and other sources. Approximately 2,234 datasets (maps) at scale of 1:50 000, primarily covering northern Canada, are available. CanTopo datasets in GeoPDF and GeoTIFF format are georeferenced according to the 1983 North American Reference System (NAD 83). Validity dates: 1946 to 2012 (2007 on average). Available formats: PDF, GeoPDF, TIFF and GeoTIFF

Mackenzie Valley Air Photo Digital Orthotiles

The VMap1 collection is a legacy product that is no longer supported. It may not meet current government standards. VMap1 is a vector digital topographic reference product developed by Natural Resources Canada (NRCan) and The Department of National Defence (DND). VMap1 complies with international military specifications vector map, level 1. There are 24 VMap1 libraries covering the Canadian territory. The National Topographic Data Base (NTDB) at scale of 1:250 000 is the main source used to populate the Canadian VMap1 Libraries. Administrative Boundaries from Statistics Canada are used to add international borders, provincial and Indian reserve limits. NRCan paper maps at scale of 1:250 000 and the information in the Canadian Geographical Names Data Base (CGNDB) are used to capture the names. The JOG (Joint Operations Graphic) paper maps were used by DND for the production of libraries 37, 38 and 66. Topographic features mainly from the NTDB have not been updated. VMap1 is published once and no product revision is planned.

Vegetation biophysical parameters correspond to physical properties of vegetation structure (e.g. density, height, biomass), biochemistry (e.g. chlorophyll and water content) or energy exchange (e.g. albedo, temperature). These parameters have been identified by the Global Climate Observing System as an essential climate variable required for ecosystem, weather and climate modelling and monitoring. The Canada wide products are derived from systematically acquired satellite imagery with spatial resolution from 10m to 30m and provided as monthly temporal or peak-season composites due to cloud cover. Products are derived applying algorithms developed at Canada Centre for Remote Sensing (NRCan) to Copernicus Sentinel 2 satellite imagery. Select a related product first to view content.

The Canada Centre for Mapping and Earth Observation (CCMEO) has created a 30m resolution radar mosaic of Canada's landmass from the RADARSAT Constellation Mission (RCM). This product highlights different types of radar interaction with the surface, which can assist the interpretation and study of land cover on a national scale. The national mosaic is made up of 3222 RCM images acquired between August 2023 and February 2024. (Credit: RADARSAT Constellation Mission imagery © Government of Canada [2024]. RADARSAT is an official mark of the CSA.)