AAFC’s Canadian Ag-Land Monitoring System (CALMS), operational since 2009, was developed by AAFC’s Earth Observation Service (EOS) to deliver weekly NDVI-based maps of crop condition in near-real-time. The CALMS uses data collected by the Moderate Resolution Imaging Spectro-radiometer (MODIS), a sensor mounted onboard NASA’s Terra satellite that has been acquiring data since February 2000. The state-of-the-art radiometric, spectral and spatial resolutions of MODIS Terra make it particularly well-suited for large-scale vegetation mapping and assessment. Crop condition (NDVI) maps are generated weekly by AAFC throughout Canada’s growing season, the period defined as the six-month period stretching from the start of Julian week 12 (end of March) to the end of Julian week 44 (late October). Weeks of the year are defined according to the ISO 8601 week-numbering standard, where weeks start on a Monday and end the following Sunday. CALMS products are generated in the MODIS native Integrated Sinusoidal (ISIN) projection for the region covering the twelve MODIS tiles h09v03 to h14v03 and h09v04 to h14v04.

Landcover dataset created for the northern part of Saskatchewan based on a combination of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM+) data representing circa 2000 conditions. Download: here It is a priority of the Saskatchewan and Canadian government to assess and monitor the health and sustainability of Canada's Forest. The North Digital Land Cover Classification (NDLC) will provide Saskatchewan's contribution to Canada's Earth Observation for Sustainable Development of Forests (EOSD) initiative, helping Canada fulfill it's obligation to the Kyoto Protocol. The NDLC supports the mission and directives of the Saskatchewan provincial government by providing an essential dataset which will enable researchers, natural resource managers and government to assess the health and sustainability of our forests, perform research in the area of climate change, manage natural resources and create policy. The NDLC will be based on a combination of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM+) data representing circa 2000 conditions. The NDLC is being produced through a collaboration of federal, provincial, and territorial governments, agencies and industry. Classification Value Background 0 Agriculture 1 Not Assigned 2 Pasture Upland Herbaceous Graminoid 3 Not Assigned 4 Not Assigned 5 Hardwood Open Canopy 6 Hardwood Closed Canopy 7 Jack Pine Closed Canopy 8 Jack Pine Open Canopy 9 Spruce Closed Canopy 10 Spruce Open Canopy 11 Mixed Hardwoods/Softwoods, Softwood/Hardwood Open and Closed Canopy 12 Treed Rock 13 Recent Burn 14 Revegetating/Regenerating Burn 15 Cutovers 16 Water 17 Marsh 18 Herbaceous Fen 19 Mud Sand Saline 20 Shrub Fen 21 Treed Bog 22 Open Bog 23 Not Assigned 24 Settlements/Roads 25 Barren Land 26 Mixed Softwoods Open and Closed 27 Cloud/Shadow/Haze 28 Unclassified 29 0. Background: Where pixels values are equal to 0 in all channels of satellite image data.  1. Agriculture: Cropland and agricultural clearing areas  2. Not Assigned: Empty Class  3. Pasture Upland Herbaceous Graminoid: Lands containing known pastures, tame or native grasses and herbaceous vegetation. May contain low-lying shrubs with less then 10% tree cover. 4. Not Assigned: Empty Class  5. Not Assigned: Empty Class  6. Hardwood Open Canopy: Trembling Aspen, White Birch, Balsam Poplar composes greater than 75% of species by area, Crown Closure: greater than 10% and less than or equal to 55% (SE crown closure classes A and B).  7. Hardwood Closed Canopy: Trembling Aspen, White Birch, Balsam Poplar composes greater than 75% of species by area, Crown Closure: greater than 55% (SE crown closure classes C and D).  8. Jack Pine Closed Canopy: Jack Pine composes greater than 75% of species by area, Crown Closure: greater than 55% (SE crown closure classes C and D).  9. Jack Pine Open Canopy: Jack Pine composes greater than 75% of species by area, Crown Closure: greater than 10 and less than or equal to 55% (SE crown closure classes C and D).  10. Spruce Closed Canopy: White Spruce, Black Spruce composes greater than 75% of species by area, Crown Closure: greater than 55% (SE crown closure classes C and D).  11. Spruce Open Canopy: White Spruce, Black Spruce composes greater than 75% of species by area, Crown Closure: greater than 10 and less than or equal to 55% (SE crown closure classes C and D).  12. Mixed Hardwoods/Softwoods, Softwood/Hardwood Open and Closed Canopy: An area of hardwood and softwood combinations in which neither hardwood nor softwood account for greater than 75% of species by area and where the crown closure is greater than 10%.  13. Treed Rock: Forest vegetation less than 10%.  14. Recent Burn: An area showing evidence of recent burning natural or prescribed and there is little to no regeneration or revegetation visible.  15. Revegetating/Regenerating Burn: An area showing evidence of natural or prescribed burning and where regeneration or revegetation is visible.  16. Cutovers: An area of deforestation, vegetated and non-vegetated. Ancillary data required to correctly classify due to the anthropogenic land cover/land use class.  17. Water: These areas include lakes, rivers, streams, and reservoirs.  18. Marsh: A periodically wet or continually flooded but non peat-forming area supporting grasses, sedges and reeds.  19. Herbaceous Fen: A wetland area consisting of nutrient rich water and decomposing peat supporting vascular and nonvascular plants grasses, sedges, and reeds.  20. Mud Sand Saline: Water saturated soil, salt water and sand containing no vegetation.  21. Shrub Fen: A wetland area consisting of nutrient rich water and decomposing peat supporting low shrubs, forbs, grass, moss, and a sparse tree cover.  22. Treed Bog: A wetland area consisting of decomposing peat moss, lichen and shrubs with 10% to 25% tree cover of stunted black spruce and tamarack.  23. Open Bog: A wetland area consisting of low nutrient water and decomposing peat moss, lichen, and sparse tree cover.  24. Not Assigned: Empty Class  25. Settlements/Roads: Anthropogenic land cover consisting of urban, commercial, industrial, major roads, highways, surface mines, gravel pits and spoil piles.  26. Barren Land: With the exception of the settlements and Roads class, any area of exposed rock, soil or non-vegetated land.  27. Mixed Softwoods Open and Closed: Jack Pine/Spruce, Spruce/Jack Pine Open and Closed, an area of softwood combinations in which neither Jack Pine or Spruce account for greater than 75% of species by area and where crown closure is greater than 10%.  28. Cloud/Shadow/Haze: An area of cloud, shadow, haze.  29. Unclassified: An area of unidentifiable land cover, indicates no work done/not classified, wrong information, missing data and possible new class greater than 3 pixels. 

Each pixel value corresponds to the mean historical “Best-quality” Max-NDVI value for a given week, as calculated from the previous 20 years in the MODIS historical record (i.e. does not include data from the current year). These data are also often referred to as “weekly baselines” or “weekly normals”.

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)

Each pixel value corresponds to the quality control, cloud cover and snow fraction value for each pixel in the Best-Quality Max-NDVI product.

Our Imagery Base Maps and Mosaics of a number of Raster Datasets.  This includes the ASTER DEM, CDED and Shaded Relief Datasets.  As well as a number of mosaics, including SPOT, RapidEye, Landsat, and MVI Landcover data.

Land cover classification image for the Aspen Parkland ecoregion of Saskatchewan with a spatial resolution of 10m. The goal of this land cover classification was to distinguish native from tame grasslands. The classification was based on Sentinel-1 and Sentinel-2 imagery using machine learning analysis in the Google Earth Engine platform. The classification was conducted on imagery acquired in 2022, and the classification model was built with field data collected in 2020 - 2022. There are eight classes in total: native grassland, tame grassland, mixed/altered grassland, cropland, shrubs, trees, water, and urban area. Download: here The Prairie Landscape Inventory (PLI) aims to develop improved methods of assessing land cover and land use for conservation. Native grassland has historically been one of the hardest to map at-risk ecosystems because of the challenges in distinguishing native grassland from tame grassland land cover using remotely sensed imagery. This classification distinguishes native grassland from tame grassland and will provide valuable information for habitat suitability for native grassland species, identifying high biodiversity potential and invasion risk potential. The classification map has eight (8) classes:  1. Cropland This class represents all cultivated areas with crop commodities, including corn, pulse, soybeans, canola, grains, and summer-fallow. 2. Native grassland This class represents the native grassland areas that are composed of at least 75% native grass, sedge and forb species, such as the needle grasses and wheatgrasses along with June grass and blue grama grass. Unbroken grassland that is invaded by species like Kentucky bluegrass, crested wheatgrass or smooth brome, such that native cover is less than 75%, is not considered native for the purpose of this project.  3. Mixed/altered grassland This class represents a grassland with a mix of less than 75% native grass, sedge and forb species or less than 75% tame species. These are grassland areas that do not fit into either of the native or tame grassland definitions.  4. Tame grassland This class represents the tame grassland areas that are composed of at least 75% seeded or planted species with introduced grasses and forb species such as crested wheatgrass, smooth brome, Kentucky bluegrass, alfalfa, and sweet clover. 5. Water This class represents permanent water locations such as lakes and rivers. 6. Shrubs This class represents the sites dominated by woody vegetation of relatively low height (generally +/-2 meters) with shrub canopy typically >20% of total vegetation cover. 7. Trees This class represents the coniferous/deciduous trees, mixed-wood area, and other trees >2 meters height with tree canopy typically >20% of total vegetation cover. 9. Urban area This class represents both urban municipalities and buffered roads. Urban municipalities was used to mask the urban/developed area class of the Annual Crop Inventory 2021 (Agriculture Agri-Food Canada).   Colour Classes: Value Label Red Green Blue 1 Cropland 255 255 190 2 Native grassland 168 168 0 3 Mixed/altered grassland 199 215 158 4 Tame grassland 245 202 122 5 Water 190 232 255 6 Shrubs 205 102 153 7 Trees 66 128 53 9 Urban area 128 128 128 Accuracy metrics This model has an overall accuracy of 73 per cent. The table below summarizes the user’s accuracy, producer’s accuracy, and F1-score of the model on the validation dataset. Class User’s accuracy (%) Producer’s accuracy (%) F1-score Cropland 91.2 94.5 0.93 Native grassland 74.8 73.1 0.74 Mixed grassland 44.7 44.1 0.44 Tame grassland 67.9 72.8 0.70 Water 94.8 91.3 0.93 Shrubs 61.2 51.1 0.56 Trees 89.7 94.6 0.92

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.

Each pixel value corresponds to the best quality maximum NDVI recorded within that pixel over the week specified. Poor quality pixel observations are removed from this product. Observations whose quality is degraded by snow cover, shadow, cloud, aerosols, and/or low sensor zenith angles are removed (and are assigned a value of “missing data”). In addition, negative Max-NDVI values, occurring where R reflectance > NIR reflectance, are considered non-vegetated and assigned a value of 0. This results in a Max-NDVI product that should (mostly) contain vegetation-covered pixels. Max-NDVI values are considered high quality and span a biomass gradient ranging from 0 (no/low biomass) to 1 (high biomass).

Each pixel value corresponds to the actual number (count) of valid Best-quality Max-NDVI values used to calculate the mean weekly values for that pixel. Since 2020, the maximum number of possible observations used to create the Mean Best-Quality Max-NDVI for the 2000-2014 period is n=20. However, because data quality varies both temporally and geographically (e.g. cloud cover and snow cover in spring; cloud near large water bodies all year), the actual number (count) of observations used to create baselines can vary significantly for any given week and year.