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

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)

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 ‘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.

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

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

CHS offers 500-metre bathymetric gridded data for users interested in the topography of the seafloor. This data provides seafloor depth in metres and is accessible for download as predefined areas.

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.

Each pixel value corresponds to the day-of-week (1-7) from which the Weekly Best-Quality NDVI retrieval is obtained (1 = Monday, 7 = Sunday).