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.

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).

Um er að ræða vefkort sem var útbúið með því að staðsetja og klippa saman hin svokölluðu Herforingjaráðskort. Eftirfarandi lýsing á Herforingjaráðskortum er tekin af vef Landsbókasafns: Á síðasta áratug 19. aldar varð dönskum yfirvöldum ljóst að þau kort sem til voru af Íslandi stæðust ekki þær kröfur sem gera þyrfti í samfélagi þess tíma. Bestu kort af Íslandi sem buðust voru í stórum dráttum byggð á strandmælingum danska sjóhersins sem fram fóru á árunum 1801-1818 annars vegar og hins vegar á kortum Björns Gunnlaugssonar sem byggð voru á fyrrnefndum strandmælingum og eigin mælingum Björns á árunum 1831-1843. Á fjárlögum 1899 voru veittar 5000 krónur og skyldi hefja nýjar þríhyrninga- og strandmælingar á Reykjanesi. Árið 1900 var gefin út í Danmörku tilskipun um að sendur skyldi leiðangur til Íslands til að mæla hér grunnlínu og hnattstöðu. Síðan var ætlunin að mæla þríhyrninganet út frá nýju grunnlínunni. Hingað voru sendir danskir liðsforingjar og sumarið 1900 var unnin ýmis undirbúningsvinna. Árið 1902 höfðu fjárveitingar verið auknar svo að rétt þótti að hefjast handa. Byrjað var á Hornafirði og mælt vestur ströndina og um lágsveitir Suðurlands en uppsveitum og hálendi frestað. Verkinu var svo haldið áfram tvö næstu árin en féll niður 1905 vegna fjárskorts og annarra anna hjá Landmælingadeild danska herforingjaráðsins (Generalstabens topografiske Afdeling) er tókst verkið á hendur. Eftir eins árs bið var þráðurinn tekinn upp að nýju enda bættist nú við fjárstyrkur úr ríkissjóði Dana. Á árunum 1906-1914 var unnið öll sumur, nema 1909, þegar ekkert var aðhafst. Var þá lokið byggðamælingum sunnanlands og mælt um Vesturland, norður og austur um Húnaflóa. Árangurinn var 117 kortblöð af þriðjungi landsins, suður- og vesturhluta, í mælikvarða 1:50.000 (auk nokkurra sérkorta af afmörkuðum svæðum). Þau eru gjarnan nefnd herforingjaráðskortin í höfuðið á þeim sem stóðu fyrir gerð þeirra.

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.

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.

The dataset includes two data products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) imager operated by the US National Oceanic and Atmospheric Administration (NOAA) onboard Suomi National Polar-Orbiting Partnership (SNPP) satellite: 1) Normalized Difference Vegetation Index (NDVI) 2) Snow Mask (Snow) with supplementary information about data quality and scene identification Each product, NDVI and Snow, has been derived at two spatial resolutions: 1) I-band resolution for 250-m spatial grid (VIIRS image bands I1 and I2) 2) M-band resolution for 500-m spatial grid (VIIRS moderate resolution bands M5 and M7) Datasets are produced with a daily temporal frequency, i.e. one file per day. The study area with the size of 5,700 km × 4,800 km covers Canada and neighboring regions (Trishchenko, 2019). The VIIRS time series are produced from VIIRS /SNPP imagery at CCRS from January 1, 2017.

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

The 1 cm resolution vegetation digital height model was extracted using a bare earth model and digital surface model (DSM) derived from unmanned aerial vehicle (UAV) imagery acquired from a single day survey on July 28th 2016, in Cambridge Bay, Nunavut. The mapping product covers 525m2 and 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