This collection is a legacy product that is no longer maintained.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)

Mackenzie Valley Air Photo Digital Orthotiles

This group of maps, which includes the CanMatrix and CanTopo collections, is now a legacy product that is no longer maintained. It may not meet current government standards. Natural Resources Canada's (NRCan) topographic raster maps provide a representation of the topographic phenomena of the Canadian landmass. Several editions of paper maps have been produced over time in order to offer improved products compared to their predecessors in terms of quality and the most up to date information possible. The georeferenced maps can be used in a Geographic Information System (GIS). In all cases, they accurately represent the topographical data available for the date indicated (validity date). The combination of CanMatrix and CanTopo data provides complete national coverage. • CanMatrix - Print Ready: Raster maps produced by scanning topographic maps at scales from 1:25 000 to 1:1 000 000. This product is not georeferenced. Validity dates: 1944 to 2005 (1980 on average). Available formats: PDF and TIFF • CanMatrix - Georeferenced: Raster maps produced by scanning topographic maps at scales of 1:50 000 and 1:250 000. These maps are georeferenced according to the 1983 North American Reference System (NAD 83). Validity dates: 1944 to 2005 (1980 on average). Available format: GeoTIFF • CanTopo: Digital raster maps produced mainly from the GeoBase initiative, NRCan digital topographic data, and other sources. Approximately 2,234 datasets (maps) at scale of 1:50 000, primarily covering northern Canada, are available. CanTopo datasets in GeoPDF and GeoTIFF format are georeferenced according to the 1983 North American Reference System (NAD 83). Validity dates: 1946 to 2012 (2007 on average). Available formats: PDF, GeoPDF, TIFF and GeoTIFF

Röð uppréttra loftmynda úr loftmyndasafni Náttúrufræðistofnunar sem unnar voru á árunum 2013 til 2018 hjá Jarðvísindastofnun HÍ, sem partur af tveimur verkefnum: 1 - Mælingar á jöklabreytingum úr sögulegum loftmyndum. Þetta verkefni var unnið af Joaquín M.C. Belart í M.Sc. og Ph.D. hjá Jarðvísindastofnun. Útvaldar loftmyndir frá 1945 til 1994 voru skannaðar hjá Landmælingum Íslands sérstaklega fyrir þetta verkefni. Vinnsla þessara loftmynda fór fram með því að nota "Ground Control Points" (GCP) sem teknir voru úr lidarmælingum á íslenskum jöklum. Úrvinnsla gagna úr Drangajökli fór fram með ERDAS hugbúnaðinum. Nánari upplýsingar um vinnsluna er að finna í Magnússon o.fl., 2016 (https://tc.copernicus.org/articles/10/159/2016/tc-10-159-2016.html). Úrvinnsla gagna frá öðrum jöklum var unnin með MicMac hugbúnaðinum, einnig með GCP teknir af lidar. Nánari upplýsingar um vinnsluna eru fáanlegar í Belart o.fl., 2019 (https://www.cambridge.org/core/journals/journal-of-glaciology/article/geodetic-mass-balance-of-eyjafjallajokull-ice-cap -for-19452014-processing-guidelines-and-relation-to-climate/9B715A9E0413A6345C2B151B1173E71D) og Belart o.fl., 2020 (https://www.frontiersin.org/articles/10.31630/feart/full.316390/feart. 2 - Mælingar á hraunmagni Heklugosanna á 20. öld. Þetta verkefni var unnið af Gro B.M. Pedersen sem hluti af verkefni þar sem unnið var að umhverfiskortlagningu og vöktun Íslands með fjarkönnun "Environmental Mapping and Monitoring of Iceland by Remote Sensing" (EMMIRS, fjármagnað af Rannís) á árunum 2015-2018. Loftmyndirnar af Heklu frá 1945 til 1992 voru skannaðar af Landmælingum Íslands. Vinnsla þessara mynda var gerð með ERDAS hugbúnaðinum og nánari upplýsingar um vinnsluna er hægt að nálgast í Pedersen o.fl., 2018 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017GL076887) --------------------------------------------------------------------------------------------------------------- A series of orthomosaics using the archives of aerial photographs from Náttúrufræðistofnun (Loftmyndasafn) created between 2013 and 2018 at the Institute of Earth Sciences, as part of two projects: 1 - Measurements of glacier changes from historical aerial photographs. This project was conducted by Joaquín M.C. Belart during his M.Sc. and his Ph.D. at the Institute of Earth Sciences. A selection of aerial photographs from 1945 to 1994 were scanned at Náttúrufræðistofnun specifically for this project. The processing of these aerial photographs was done using Ground Control Points (GCPs) extracted from lidar surveys of Icelandic glaciers. The processing of the data from Drangajökull ice cap was done using the ERDAS software. Further details on the processing are available in Magnússon et al., 2016 (https://tc.copernicus.org/articles/10/159/2016/tc-10-159-2016.html). The processing of the data from other glaciers was done using the MicMac software, also with GCPs extracted from lidar. Further details of the processing are available in Belart et al., 2019 (https://www.cambridge.org/core/journals/journal-of-glaciology/article/geodetic-mass-balance-of-eyjafjallajokull-ice-cap-for-19452014-processing-guidelines-and-relation-to-climate/9B715A9E0413A6345C2B151B1173E71D) and Belart et al., 2020 (https://www.frontiersin.org/articles/10.3389/feart.2020.00163/full) 2 - Measurements of the lava volumes of the Hekla eruptions in the 20th century. This project was conducted by Gro B.M. Pedersen as part of the Environmental Mapping and Monitoring of Iceland by Remote Sensing (EMMIRS, financed by Rannís) project between 2015-2018. The aerial photographs of Hekla from 1945 to 1992 were scanned by Náttúrufræðistofnun. The processing of these photographs was done using the ERDAS software, and further details of the processing are available in Pedersen et al., 2018 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017GL076887) References: Belart J.M.C., Magnússon E., Berthier E., Pálsson, F., Aðalgeirsdóttir, G., & Jóhannesson, T. (2019). The geodetic mass balance of Eyjafjallajökull ice cap for 1945–2014: Processing guidelines and relation to climate. Journal of Glaciology, 65(251), 395-409. doi:10.1017/jog.2019.16 Belart J.M.C., Magnússon E., Berthier E., Gunnlaugsson Á.Þ., Pálsson F., Aðalgeirsdóttir G., Jóhannesson T, Thorsteinsson T and Björnsson H (2020) Mass Balance of 14 Icelandic Glaciers, 1945–2017: Spatial Variations and Links With Climate. Front. Earth Sci. 8:163. doi: 10.3389/feart.2020.00163 Magnússon, E., Belart, J.M.C., Pálsson, F., Ágústsson, H., and Crochet, P.: Geodetic mass balance record with rigorous uncertainty estimates deduced from aerial photographs and lidar data – Case study from Drangajökull ice cap, NW Iceland, The Cryosphere, 10, 159–177, https://doi.org/10.5194/tc-10-159-2016, 2016. Pedersen, G. B. M., Belart, J. M. C., Magnússon, E., Vilmundardóttir, O. K., Kizel, F., Sigurmundsson, F. S., et al. (2018). Hekla volcano, Iceland, in the 20th century: Lava volumes, production rates, and effusion rates. Geophysical Research Letters, 45, 1805–1813. https://doi.org/10.1002/2017GL076887

This data publication contains two collections of raster maps of forest attributes across Canada, the first collection for year 2001, and the second for year 2011. The 2001 collection is actually an improved version of an earlier set of maps produced also for year 2001 (Beaudoin et al 2014, DOI: https://doi.org/10.1139/cjfr-2013-0401) that is itself available through the web site “http://nfi-nfis.org”. Each collection contains 93 maps of forest attributes: four land cover classes, 11 continuous stand-level structure variables such as age, volume, biomass and height, and 78 continuous values of percent composition for tree species or genus. The mapping was done at a spatial resolution of 250m along the MODIS grid. Briefly the method uses forest polygon information from the first version of photoplots database from Canada’s National Forest Inventory as reference data, and the non-parametric k-nearest neighbors procedure (kNN) to create the raster maps of forest attributes. The approach uses a set of 20 predictive variables that include MODIS spectral reflectance data, as well as topographic and climate data. Estimates are carried out on target pixels across all Canada treed landmass that are stratified as either forest or non-forest with 25% forest cover used as a threshold. Forest cover information was extracted from the global forest cover product of Hansen et al (2013) (DOI: https://doi.org/10.1126/science.1244693). The mapping methodology and resultant datasets were intended to address the discontinuities across provincial borders created by their large differences in forest inventory standards. Analysis of residuals has failed to reveal residual discontinuities across provincial boundaries in the current raster dataset, meaning that our goal of providing discontinuity-free maps has been reached. The dataset was developed specifically to address strategic issues related to phenomena that span multiple provinces such as fire risk, insect spread and drought. In addition, the use of the kNN approach results in the maintenance of a realistic covariance structure among the different variable maps, an important property when the data are extracted to be used in models of ecosystem processes. For example, within each pixel, the composition values of all tree species add to 100%. * Details on the product development and validation can be found in the following publication: Beaudoin, A., Bernier, P.Y., Villemaire, P., Guindon, L., Guo, X.-J. 2017. Tracking forest attributes across Canada between 2001 and 2011 using a kNN mapping approach applied to MODIS imagery, Canadian Journal of Forest Research 48: 85–93. DOI: https://doi.org/10.1139/cjfr-2017-0184 * Please cite this dataset as: 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 * This dataset contains these NFI forest attributes: ## LAND COVER : landbase vegetated, landbase non-vegetated, landcover treed, landcover non-treed ## TREE STRUCTURE : total above ground biomass, tree branches biomass, tree foliage biomass, stem bark biomass, stem wood biomass, total dead trees biomass, stand age, crown closure, tree stand heigth, merchantable volume, total volume ## TREE SPECIES : abies amabilis (amabilis fir), abies balsamea (balsam fir), abies lasiocarpa (subalpine fir), abies spp. (unidentified fir), acer macrophyllum (bigleaf maple), acer negundo (manitoba maple, box-elder), acer pensylvanicum (striped maple), acer rubrum (red maple), acer saccharinum (silver maple), acer saccharum (sugar maple), acer spicatum (mountain maple), acer spp. (unidentified maple), alnus rubra (red alder), alnus spp. (unidentified alder), arbutus menziesii (arbutus), betula alleghaniensis (yellow birch), betula papyrifera (white birch), betula populifolia (gray birch), betula spp. (unidentified birch), carpinus caroliniana (blue-beech), carya cordiformis (bitternut hickory), chamaecyparis nootkatensis (yellow-cedar), fagus grandifolia (american beech), fraxinus americana (white ash), fraxinus nigra (black ash), fraxinus pennsylvanica (red ash), juglans cinerea (butternut), juglans nigra (black walnut), juniperus virginiana (eastern redcedar), larix laricina (tamarack), larix lyallii (subalpine larch), larix occidentalis (western larch), larix spp. (unidentified larch), malus spp. (unidentified apple), ostrya virginiana (ironwood, hop-hornbeam), picea abies (norway spruce), picea engelmannii (engelmann spruce), picea glauca (white spruce), picea mariana (black spruce), picea rubens (red spruce), picea sitchensis (sitka spruce), picea spp. (unidentified spruce), pinus albicaulis (whitebark pine), pinus banksiana (jack pine), pinus contorta (lodgepole pine), pinus monticola (western white pine), pinus ponderosa (ponderosa pine), pinus resinosa (red pine), pinus spp. (unidentified pine), pinus strobus (eastern white pine), pinus sylvestris (scots pine), populus balsamifera (balsam poplar), populus grandidentata (largetooth aspen), populus spp. (unidentified poplar), populus tremuloides (trembling aspen), populus trichocarpa (black cottonwood), prunus pensylvanica (pin cherry), prunus serotina (black cherry), pseudotsuga menziesii (douglas-fir), quercus alba (white oak), quercus macrocarpa (bur oak), quercus rubra (red oak), quercus spp. (unidentified oak), salix spp. (unidentified willow), sorbus americana (american mountain-ash), thuja occidentalis (eastern white-cedar), thuja plicata (western redcedar), tilia americana (basswood), tsuga canadensis (eastern hemlock), tsuga heterophylla (western hemlock), tsuga mertensiana (mountain hemlock), tsuga spp. (unidentified hemlock), ulmus americana (white elm), unidentified needleaf, unidentified broadleaf, broadleaf species, needleaf species, unknown species

Data include a collection of annual land cover maps derived from MODIS 250 m spatial resolution remotely sensed imagery for the period 2000 to 2011. Processing of the time series was designed to reduce the occurrence of false change between maps. The method was based on change updating as described in Pouliot et al. (2011, 2013). Change detection accounted for both abrupt changes such as forest harvesting and more gradual changes such as recurrent insect defoliation. To determine the new label for a pixel identified as change, an evidential reasoning approach was used to combine spectral and contextual information. The 2005 MODIS land cover of Canada at 250 m spatial resolution described in Latifovic et al. (2012) was used as the base map. It contains 39 land cover classes, which for time series development was considered too detailed and was reduced to 25 and 19 class versions. The 19 class version corresponds to the North America Land Change Monitoring System (NALCMS) Level 2 legend as described in Latifovic et al. (2012). Accuracy assessment of time series is difficult due to the need to assess many maps. For areas of change in the time series accuracy was found to be 70% based on the 19 class thematic legend. This time series captures the spatial distribution of dominant land cover transitions. It is intended for use in modeling, development of remote sensing products such as leaf area index or land cover based albedo retrievals, and other exploratory analysis. It is not appropriate for use in any rigorous reporting or inventory assessments due to the accuracy of the land cover classification and uncertainty as to the capture of all relevant changes for an application.

The MODIS Surface Albedo and Surface Reflectance Dataset (or simply Albedo) includes times series of 10-day composite products derived at 250-m spatial resolution over Canadian territory and neighboring areas produced at the Canada Centre for Remote Sensing (CCRS) since February 2000.The datasets contain spectral and broadband reflectance’s and albedo for MODIS bands B1-B7 designed primarily for land applications. The imagery for all spectral bands was downscaled and re-projected into the Lambert Conformal Conic (LCC) projection at 250-m spatial resolution. The area size is 5,700 km × 4,800 km. The specialized MODIS processing system was developed at CCRS to fully utilize the high quality of MODIS L2 swath imagery over the northern latitudes. As such, the CCRS Albedo product is different from the standard NASA product. The differences are related to temporal and spatial scaling, shape of kernel functions employed to fit data, as well as details of scene identification, atmospheric correction, and data fitting methodology.

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.

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.

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.