imageryBaseMapsEarthCover
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This is a Mosaic of Canada which is made from 121 images captured by Canadian satellite RADARSAT-2. These images were acquired from May 1, 2013 to June 1, 2013. The color variation represents the changes in soil texture, roughness and the level of soil moisture. (Credit: RADARSAT-2 Data and Products © MacDonald, Dettwiler and Associates Ltd. (2013) - All Rights Reserved. RADARSAT is an official mark of the Canadian Space Agency.)
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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.
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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”.
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McElhanney Consulting Services Ltd (MCSL) has performed a LiDAR and Imagery survey in southern Saskatchewan. The acquisition was completed between the 16th and 25th of October, 2009. The survey consisted of approximately 790 square kilometers of coverage. While collecting the LiDAR data, we also acquired aerial photo in RGB and NIR modes consisting of 1649 frames each.
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The 1 cm resolution digital surface model (DSM) was created from unmanned aerial vehicle (UAV) imagery acquired from a single day survey, July 28th 2016, in Cambridge Bay, Nunavut. Five control points taken from a Global Differential Positioning System were positioned in the corners and the center of the vegetation survey. The DSM covering 525m2 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
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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.
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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.
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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.
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This publication contains a raster maps at 250 m resolution of the merchantable volume (m3/ha) of the mature Canadian forest available for harvesting in the next 20 years (2011 to 2031). The maps were produced from remote sensing products at a spatial resolution of 250 m on the MODIS pixel grid and 30 m on the Landsat pixel grid. More specifically, we used forest attribute data at the 250 m pixel for the years 2001 and 2011 (Beaudoin et al 2014 and 2018) combined with forest cover changes for the years 1985 to 2015 at 30 m (Guindon et al. 2017 and 2018). The map of mature forests in Canada was prepared at the forest management unit (FMU) level and therefore exclude private lands. To be considered mature (i.e. available for cutting in the next 20 years), the forest pixels of Beaudoin et al. (2018) was to have a merchantable volume per ha equal to or greater than 80% of the average merchantable volume of the pixels that were harvested between 2001 and 2011 per forest management unit. 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 in Canada’s managed forests. 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. Forecasting the spatial distribution of logging residues in Canada’s managed forests. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/dd94871a-9a20-47f5-825b-768518140f35
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The AAFC Infrastructure Flood Mapping in Saskatchewan 20 centimeter colour orthophotos is a collection of georeferenced color digital orthophotos with 20 cm pixel size. The imagery was delivered in GeoTIF and ECW formats. The TIF and ECW mosaics were delivered in the same 1 km x 1 km tiles as the LiDAR data, and complete mosaics for each area in MrSID format were also provided. The digital photos were orthorectified using the ground model created from the DTM Key Points. With orthorectification, only features on the surface of the ground are correctly positioned in the orthophotos. Objects above the surface of the ground, such as building rooftops and trees, may contain horizontal displacement due to image parallax experienced when the photos were captured. This is sometimes apparent along the cut lines between photos. For positioning of above-ground structures it is recommended to use the LiDAR point clouds for accurate horizontal placement.
Arctic SDI catalogue