This collection is a legacy product that is no longer maintained. It may not meet current government standards. The correction matrices for the National Topographic Data Base (NTDB), also known under the acronym CORMAT, are products derived from the planimetric enhancement of NTDB data sets at the 1:50 000 scale. The correction matrix enables users to enhance the geometric accuracy of the less accurate NTDB. The matrix is a set of points arrayed on a regular 100-m grid. Each point describes the planimetric correction (DX, DY) to be applied at this location. The position of the points is given in UTM (Universal Transverse Mercator projection) coordinates based on the North American Datum of 1983 (NAD83) . Each file constitutes a rectangular area covering the entire corresponding NTDB data set. Its delimitation corresponds more or less to National Topographic System (NTS) divisions at the 1:50 000 scale. All NTDB data sets at the 1:50 000 scale whose original accuracy was less than 30 m can thus be geometrically corrected. A CORMAT data set contains a list of coordinates and the corresponding corrections to be applied in the form X Y DX DY. Related Products: [National Topographic Data Base (NTDB), 1944-2005](https://open.canada.ca/data/en/dataset/1f5c05ff-311f-4271-8d21-4c96c725c2af)

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).

The 2005 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 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 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

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.

BTM ( Baseline Thematic Mapping) Landsat Image Catalogue Acquisition Dates. This polygon coverage contains the date of capture of the Landsat images making up the seamless BC Landsat image catalogue. This is not a multipart feature

Organic soils in the boreal forest commonly store as much carbon as the vegetation above ground. While recent efforts through the National Forest Inventory has yielded new spatial datasets of forest structure across the vast area of Canada’s boreal forest, organic soils are poorly mapped. In this geospatial dataset, we produce a map primarily of forested and treed peatlands, those with more than 40 cm of peat accumulation and over 10% tree canopy cover. National Forest Inventory ground plots were used to identify the range of forest structure that corresponds to the presence of over 40 cm of peat soils. Areas containing that range of forest cover were identified using the National Forest Inventory k-NN forest structure maps and assigned a probability (0-100% as integer) of being a forested or treed peatland according to a statistical model. While this mapping product captures the distribution of forested and treed peatlands at a 250 m resolution, open, completely treeless peatlands are not fully captured by this mapping product as forest cover information was used to create the maps. The methodology used in the creation of this product is described in: Thompson DK, Simpson BN, Beaudoin A. 2016. Using forest structure to predict the distribution of treed boreal peatlands in Canada. Forest Ecology and Management, 372, 19-27. https://cfs.nrcan.gc.ca/publications?id=36751 This distribution uses an updated forest attribute layer current to 2011 from: Beaudoin A, Bernier PY, Villemaire P, Guindon L, Guo XJ. 2017. Species composition, forest properties and land cover types across Canada’s forests at 250m resolution for 2001 and 2011. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/ec9e2659-1c29-4ddb-87a2-6aced147a990 Additionally, this distribution varies slightly from the original published in 2016 in that here slope data is derived from the CDEM: https://open.canada.ca/data/en/dataset/7f245e4d-76c2-4caa-951a-45d1d2051333 The above peatland probability map was further processed to delineate bogs vs fens (based on mapped Larix content via the k-NN maps), as well as an approximation of the extent of open peatlands using EOSD data. The result is a 9-type peatland map with a more complete methodology as detailed in: Webster, K. L., Bhatti, J. S., Thompson, D. K., Nelson, S. A., Shaw, C. H., Bona, K. A., Hayne, S. L., & Kurz, W. A. (2018). Spatially-integrated estimates of net ecosystem exchange and methane fluxes from Canadian peatlands. Carbon Balance and Management, 13(1), 16. https://doi.org/10.1186/s13021-018-0105-5 In plain text, the legend for the 9-class map is as follows: value="0" label="not peat" alpha="0" value="1" label="Open Bog" alpha="255" color="#0a4b32" value="2" label="Open Poor Fen" alpha="255" color="#5c5430" value="3" label="Open Rich Fen" alpha="255" color="#792652" value="4" label="Treed Bog" alpha="255" color="#6a917b" value="5" label="Treed Poor Fen" alpha="255" color="#aba476" value="6" label="Treed Rich Fen" alpha="255" color="#af7a8f" value="7" label="Forested Bog" alpha="255" color="#aad7bf" value="8" label="Forested Poor Fen" alpha="255" color="#fbfabc" value="9" label="Forested Rich Fen" alpha="255" color="#ffb6db" This colour scale is given in qml/xml format in the resources below. The 9-type peatland map from Webster et al 2018 was further refined slightly following two simple conditions: (1) any 250-m raster cell with greater than 40% pine content is classified as upland (non-peat); (2) all 250-m raster cells classified as water or agriculture via the NRCan North American Land Cover Monitoring System (https://doi.org/10.3390/rs9111098) is also classified as non-peatland (value of zero in the 9-class map. This mapping scheme was used at a regional scale in the following paper: Thompson, D. K., Simpson, B. N., Whitman, E., Barber, Q. E., & Parisien, M.-A. (2019). Peatland Hydrological Dynamics as A Driver of Landscape Connectivity and Fire Activity in the Boreal Plain of Canada. Forests, 10(7), 534. https://doi.org/10.3390/f10070534 And is reproduced here at a national scale. Note that this mapping product does not fully capture all permafrost peatland features covered by open canopy spruce woodland with lichen ground cover. Nor are treeless peatlands near the northern treeline captured in the training data, resulting in unknown mapping quality in those regions.

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.

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.