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.

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.

The 2000 AAFC Land Use is a culmination and curated metaanalysis of several high-quality spatial datasets produced between 1990 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.

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

The 2020 AAFC Land Use is a culmination and curated metaanalysis of several high-quality spatial datasets produced between 1990 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.

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.

The “Land Cover for Agricultural Regions of Canada (circa 2000), Date Index” dataset is a geospatial data layer containing polygon features representing the Landsat scene number, associated dates and other products that were incorporated into the thematic land cover classification which is contained within the AAFC Landcover (circa 2000) product.

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.

The 2010 AAFC Land Use is a culmination and curated metaanalysis of several high-quality spatial datasets produced between 1990 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.