Each pixel value corresponds to the day-of-week (1-7) from which the Weekly Best-Quality NDVI retrieval is obtained (1 = Monday, 7 = Sunday).

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 layer represents Land use polygons as determined by a combination of analytic techniques, mostly using Landsat 5 image mosaics . BTM 1 was done on a federal satellite image base that was only accurate to about 250m. The images were geo-corrected, not ortho-corrected, so there is distortion in areas of high relief. This is not a multipart feature

Note: To visualize the data in the viewer, zoom into the area of interest. The National Air Photo Library (NAPL) of Natural Resources Canada archives over 6 million aerial photographs covering all of Canada, some of which date back to the 1920s. This collection includes Time Series of aerial orthophoto mosaics over a selection of major cities or targeted areas that allow the observation of various changes that occur over time in those selected regions. These mosaics are disseminated through the Data Cube Platform implemented by NRCan using geospatial big data management technologies. These technologies enable the rapid and efficient visualization of high-resolution geospatial data and allow for the rapid generation of dynamically derived products. The data is available as Cloud Optimized GeoTIFF (COG) files for direct access and as Web Map Services (WMS) or Web Coverage Services (WCS) with a temporal dimension for consumption in Web or GIS applications. The NAPL mosaics are made from the best spatial resolution available for each time period, which means that the orthophotos composing a NAPL Time Series are not necessarily coregistered. For this dataset, the spatial resolutions vary from 10 cm to 50 cm. The NAPL indexes and stores federal aerial photography for Canada, and maintains a comprehensive historical archive and public reference centre. The Earth Observation Data Management System (EODMS) online application allows clients to search and retrieve metadata for over 3 million out of 6 million air photos. The EODMS online application enables public and government users to search and order raw Government of Canada Earth Observation images and archived products managed by NRCan such as aerial photos and satellite imagery. To access air photos, you can visit the EODMS web site: https://eodms-sgdot.nrcan-rncan.gc.ca/index-en.html

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 third generation of high resolution 10-m wetland inventory map of Canada, covering an approximate area of one billion hectares, was generated using multi-year (2016-2020), multi-source imagery (Sentinel-1, Sentinel-2, ALOS PALSAR-2, and SRTM) Earth Observation (EO) data as well as environmental features. Over 8800 wetland polygons were processed within an object-based random forest classification scheme on the Google Earth Engine cloud computing platform. The average overall accuracy of 90.5% is an increase of 4.7% over CWIM2. CWIM Versions: The Canadian Wetland Inventory Map (CWIM) is an extension of work started at Memorial University to produce a Newfoundland and Labrador wetland inventory during 2015-2018 which was significantly funded by Environment and Climate Change Canada. The first national CWIM was produced 2018-2019 as a collaboration between Memorial University, C-CORE, and Natural Resources Canada. Dr. Brian Brisco was instrumental in connecting ground truth from multiple sources to the project and providing guidance. Version 2 was produced in 2020 which included more training data and processing by Canada’s ecozones rather than provinces to take advantage of the commonality of landscape ecological features within ecozones to improve the accuracy. Version 3 produced in 2021 continued adding more data sources to further improve accuracy specifically an overestimation of wetland area as well as introducing a confidence map. Version 3A completed in 2022 updates only the arctic ecozones due to their relatively lower accuracy and added hydro-physiographic data layers. Currently work is underway to create a northern circumpolar wetland inventory map to be published in 2025. Paper on Newfoundland and Labrador Wetland Inventory: Mahdianpari, M.; Salehi, B.; Mohammadimanesh, F.; Homayouni, S.; Gill, E. The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform. Remote Sens. 2019, 11, 43. https://doi.org/10.3390/rs11010043 Paper on CWIM1: Mahdianpari, M., Salehi, B., Mohammadimanesh, F., Brisco, B., Homayouni, S., Gill, E., … Bourgeau-Chavez, L. (2020). Big Data for a Big Country: The First Generation of Canadian Wetland Inventory Map at a Spatial Resolution of 10-m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform. Canadian Journal of Remote Sensing, 46(1), 15–33. https://doi.org/10.1080/07038992.2019.1711366 Paper on CWIM2: Mahdianpari, M., Brisco, B., Granger, J. E., Mohammadimanesh, F., Salehi, B., Banks, S., … Weng, Q. (2020). The Second Generation Canadian Wetland Inventory Map at 10 Meters Resolution Using Google Earth Engine. Canadian Journal of Remote Sensing, 46(3), 360–375. https://doi.org/10.1080/07038992.2020.1802584 Paper on CWIM3: M. Mahdianpari et al., "The Third Generation of Pan-Canadian Wetland Map at 10 m Resolution Using Multisource Earth Observation Data on Cloud Computing Platform," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 8789-8803, 2021, doi: 10.1109/JSTARS.2021.3105645. Paper on Arctic ecoregion enhancement for CWIM3A: Michael Merchant, et al., ”Leveraging google earth engine cloud computing for large-scale arctic wetland mapping,” in International Journal of Applied Earth Observation and Geoinformation, vol. 125, 2023, https://doi.org/10.1016/j.jag.2023.103589.

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)

This publication contains vector data (shapefile) of the post-harvest forest residues in Canada for the bioenergy/bioproducts sector in oven-dry tonnes per year (ODT/yr) over the next 20 years. The maps were produced using different remote sensing products. We used forest attribute data at 250 m MODIS for the years 2001 and 2011 (Beaudoin et al. 2014 and 2018) combined with forest cover changes for the years 1985 to 2015 contained in the CanLaD dataset at 30 m Landsat(Guindon et al. 2017 and 2018). Results of available biomass (in the form of harvest residues) were reported at the 10 km x 10 km scale, while the map of mature forests in Canada was prepared at the forest management unit (FMU) level. Briefly, our methodology consisted of three steps: 1- create a map of mature forests for the year 2011, based on 2001-2010 average cut volumes within FMUs; 2- develop an annual cut rate from the area harvested within FMUs from 1985 to 2015 and; 3- define the amount of biomass in the form of forest residues available for the bioenergy sector. The biomass of branches and leaves of forest attribute data was used as a proxy to define the biomass of forest residues available. Nationally, the average biomass of forest residues available after harvest is 26 ± 16 ODT/ha, while the total annual availability for all managed forests in Canada was 21 million ODT/yr. A scientific article gives additional details on the methodology: Barrette J, Paré D, Manka F, Guindon L, Bernier P, Titus B. 2018. Forecasting the spatial distribution of logging residues across the Canadian managed forest. Can. J. For. Res. 48: http://www.nrcresearchpress.com/doi/10.1139/cjfr-2018-0080 Reference for this dataset: Barrette J, Paré D, Manka F, Guindon L, Bernier P, Titus B. 2018. Maps forecasting the availability of logging residues in Canada. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/5072c495-240c-42a3-ad55-c942ab37c32a

Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) sensors were used to generate the circa 2010 Mosaic of Canada at 30 m spatial resolution. All scenes were processed to Standard Terrain Correction Level 1T by the United States Geological Survey (USGS). Further processing performed by the Canada Centre for Remote Sensing included conversion of sensor measurements to top of atmosphere reflectance, cloud and cloud shadow detection, re-projection, selection of best measurements, mosaic generation ,noise removal and quality control. To provide a clear sky measurement for each location in Canada, data from the years 2009, 2010, and 2011 were used, but 2010 was preferentially selected. Bands 3 (0.63-0.69 µm), 4 (0.76-0.90 µm), 5 (1.55-1.75 µm), and 7 (2.08-2.35 µm) are provided in this version as significant atmosphere effects strongly limit the quality of the blue (0.45-0.52 µm) and green (0.52-0.60 µm) bands. Multi-criteria compositing was used for the selection of the most representative pixel. For ETM+ onboard Landsat 7 a scan line malfunction caused missing lines of data in all scenes collected after May 2003. Atmosphere and target variability between scenes cause these lines to have significant radiometric differences in some cases. A Fourier transformation approach was applied to correct this occurrence. This mosaic was developed for land cover and biophysical mapping applications across Canada. Other applications of these data are also possible, but should consider the temporal and spectral limitations of the product. Research to enhance the spatial, spectral and temporal aspects are in development for future versions of moderate resolution products from historical Landsat sensors, Landsat 8, and Sentinel 2 data.

The “Land Cover for Agricultural Regions of Canada, circa 2000” is a thematic land cover classification representative of Circa 2000 conditions for agricultural regions of Canada. Land cover is derived from Landsat5-TM and/or 7-ETM+ multi-spectral imagery by inputting imagery and ground reference training data into a Decision-Tree or Supervised image classification process. Object segmentation, pixel filtering, and/or post editing is applied as part of the image classification. Mapping is corrected to the GeoBase Data Alignment Layer. National Road Network (1:50,000) features and other select existing land cover products are integrated into the product. UTM Zone mosaics are generated from individual 30 meter resolution classified scenes. A spatial index is available indicating the Landsat imagery scenes and dates input in the classification. This product is published and compiled by Agriculture and Agri-Food Canada (AAFC), but also integrates products mapped by other provincial and federal agencies; with appropriate legend adaptations. This release includes UTM Zones 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, and 22 for corresponding agricultural regions in Newfoundland, Prince Edward Island, Nova Scotia, New Brunswick, Québec, Ontario, Manitoba, Saskatchewan, Alberta and British Columbia covering approximately 370,000,000 hectares of mapped area. Mapped classes include: Water, Exposed, Built-up, Shrubland, Wetland, Grassland, Annual Crops, Perennial Crops and Pasture, Coniferous, Deciduous and Mixed forests. However, emphasis is placed on accurately delineating agricultural classes, including: annual crops (cropland and specialty crops like vineyards and orchards), perennial crops (including pastures and forages), and grasslands.