The ‘Circa 1995 Landcover of the Prairies’dataset is a geospatial raster data layer portraying the rudimentaryland cover types of all grain-growing areas of Manitoba, Saskatchewan, Alberta and northeastern British Columbia at a 30-metre resolution for the 1995 timeframe. It is the collection of all the classified imagery (1993 to 1995) of the Western Grain Transition Payment Program (WGTPP) assembled into a single seamless raster data layer.

GeoAI are buildings, hydrography, forests, and roads automatically extracted using Deep Learning models applied to a source dataset, typically aerial or satellite images. The primary aim of GeoAI is to increase Canada's availability of high-resolution foundational geospatial data for both spatial and temporal coverage. ‌ The infrastructure and expertise put in place by NRCan enables a rapid, efficient, and scalable data creation process through the use of leading-edge technology and Artificial Intelligence models. Published datasets for a given source can be revisited at a later date as more accurate models are developed and put into production. For now, only static files are available, but as the series develops, new products and services will be added.

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

The “Soils of Canada, Derived” national scale thematic datasets display the distribution and areal extent of soil attributes such as drainage, texture of parent material, kind of material, and classification of soils in terms of provincial Detailed Soil Surveys (DDS) polygons, Soil Landscape Polygons (SLCs), Soil Order and Great Group. The relief and associated slopes of the Canadian landscape are depicted on the local surface form thematic dataset. The purpose of the “Soils of Canada, Derived” series is to facilitate the cartographic display and basic queries of the Soil Landscapes of Canada at a national scale. For more detailed or sophisticated analysis, users should investigate the full “Soil Landscapes of Canada” product.

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 collection is a legacy product that is no longer supported. It may not meet current government standards. Toporama is a digital topographic reference product using CanVec as source data. Developed by Natural Resources Canada (NRCan), Toporama covers the entire area of Canada's landmass and provides symbolic information in a geo-referenced raster format (GeoTIFF). The delimitation, content and representation of this product are similar to those of 1:50,000 scale topographical maps. Toporama is available in the following spatial reference systems: Universal Transverse Mercator (UTM) and geographic (latitude and longitude). Toporama is a product aimed at the general public that can be used by GPS system. The datasets in this collection present the version published in 2013.

Data represents surface water occurrence frequency (percentage), which describes the frequency for each grid appeared as water in the 30 years time period of 1991 to 2020. The data covers Canada’s entire landmass including all transboundary watersheds, and is at 30-meter spatial resolution. The surface water occurrence frequency is derived using the surface water model of Wang et al. (2023) from all-available monthly water data observed by the Landsat satellites (Pekel et al., 2016). Here, permanent waters are represented by 100%, and permanent land surfaces by 0%, of water occurrence for a 30-meter by 30-meter grid. References: Pekel, J.-F., A. Cottam, N. Gorelick, A.S. Belward, 2016, High-resolution mapping of global surface water and its long-term changes. Nature, 540, 418-422. Wang, S., J. Li, and H. A. J. Russell, 2023, Methods for Estimating Surface Water Storage Changes and Their Evaluations. Journal of Hydrometeorology, DOI: https://doi.org/10.1175/JHM-D-22-0098.1.

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).

IBL - Imagery, basemaps, and land cover (imageryBaseMapsEarthCover) Basemaps. For example, resources describing land cover, topographic maps, and classified and unclassified images

The Grassland Inventory provides a standardized, high-resolution land cover classification for the grassland ecosystems in Canada. Developed using a random forest classification on multi-temporal Sentinel-1 SAR and Sentinel-2 Optical imagery, the series differentiates intact native grasslands from high-disturbance, tame perennial forage systems; two classes that are spectrally and phenologically similar, yet critical to differentiate and quantify accurately for carbon and biodiversity modelling. Each release in the series includes a categorical 10 m land cover raster and a companion continuous likelihood layer representing model confidence in the native grassland class. As new classifications are added (semi-decadal) and geographic extent increased, the series will enable consistent temporal comparisons to track grassland dynamics and land cover change to support operational and research applications within AAFC and stakeholders.