This layer is the current fire year burn severity classification for large fires (greater than 100 ha). Burn severity mapping is conducted using best available pre- and post-fire satellite multispectral imagery acquired by the MultiSpectral Instrument (MSI) aboard the Sentinel-2 satellite or the Operational Land Imager (OLI) sensor aboard the Landsat-8 and 9 satellites. Every attempt is made to use cloud, smoke, shadow and snow-free imagery that was acquired prior to September 30th. However, in late fire seasons imagery acquired after September 30th may be used. This layer is considered an interim product for the 1-year-later burn severity dataset (WHSE_FOREST_VEGETATION.VEG_BURN_SEVERITY_SP). Mapping conducted during the following growing season benefits from greater post-fire image availability and is expected to be more representative of tree mortality. #### Methodology: • Select suitable pre- and post-fire imagery or create a cloud/snow/smoke-free composite from multiple images scenes • Calculate normalized burn severity ratio (NBR) for pre- and post-fire images • Calculate difference NBR (dNBR) where dNBR = pre NBR – post NBR • Apply a scaling equation (dNBR_scaled = dNBR*1000 + 275)/5) • Apply BARC thresholds (76, 110, 187) to create a 4-class image (unburned, low severity, medium severity, and high severity) • Mask out water bodies using a satellite-derived water layer • Apply region-based filters to reduce noise • Confirm burn severity analysis results through visual quality control • Produce a vector dataset and apply Euclidian distance smoothing

This mosaic is calculated over the North American domain with a horizontal spatial resolution of 1 km. This mosaic therefore includes all the Canadian and American radars available in the network and which can reach a maximum of 180 contributing radars. To better represent precipitation over the different seasons, this mosaic renders in mm/h to represent rain and in cm/h to represent snow. For the two precipitation types (rain and snow), we use two different mathematical relationships to convert the reflectivity by rainfall rates (mm/h rain cm/h for snow). This is a hybrid mosaic from DPQPE (Dual-Pol Quantitative Precipitation Estimation) for S-Band radars. For the US Nexrad radars, ECCC uses the most similar product from the US Meteorological Service (NOAA). This product displays radar reflectivity converted into precipitation rates, using the same formulas as the Canadian radars.

The Single Line Hydrography Network (SLNET) contains all captured single line representations of hydrographic features. In addition, single line representations of polygonal features and single line arbitrary network connectors are in the file.

These structure, isopach and zero edge files are part of a series of stratigraphic framework maps for the Saskatchewan Phanerozoic Fluids and Petroleum Systems (SPFPS) project.The series of stratigraphic framework maps for the Saskatchewan Phanerozoic Fluids and Petroleum Systems (SPFPS) project have been produced using 2 km equi-spaced modified grids generated from Golden Software’s Surfer 9 kriging algorithm. The dataset used to produce each of the maps in this series was created using data from several projects completed by the Ministry (Christopher, 2003; Saskatchewan Industry and Resources et al., 2004; Kreis et al., 2004; Marsh and Heinemann, 2006; Saskatchewan Ministry of Energy and Resources et al., 2007; Heinemann and Marsh, 2009); these data were validated and edited as required to facilitate correlations between the various regional projects. In addition, to minimize edge effects during contouring, the senior author also generated stratigraphic data from wells in adjacent jurisdictions.

High-resolution map of leading tree species distribution for Canada’s forested ecosystems (2019). Leading tree species map produced from a 2019 Landsat image composite, geographic and climate data, elevation derivatives, and remote sensing derived phenology following the framework described in Hermosilla et al. (2022). Regional classification models were generated based on Canada’s National Forest Inventory using a 150x150 km tiling system. The leading tree species are defined by representing the most voted tree species from the Random Forests classification models (i.e. the class with the highest class membership probability).The data represents leading tree species of Canada's forested ecosystems in 2019. An image compositing window of August 1 ± 30 days was used to generate the best-available-pixel (BAP) image composites utilized as source data for the classification.The science and methods developed to generate the information outcomes shown here, that track and characterize the history of Canada’s forests, were led by Canadian Forest Service of Natural Resources Canada, developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS), partnered with the University of British Columbia, augmented by processing capacity from Digital Research Alliance of Canada.For an overview on the data, image processing, and methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2022) https://doi.org/10.1016/j.rse.2022.113276When using this data, please cite as: Hermosilla, T., Bastyr, A., Coops, N.C., White, J.C., Wulder, M.A., 2022. Mapping the presence and distribution of tree species in Canada’s forested ecosystems. Remote Sensing of Environment 282, 113276.

Land Features entities are: Island, Shoreline, Wooded Area, Saturated soil, Landform Feature (esker, sand\...), and Cut Line. CanVec is a digital cartographic reference product of Natural Resources Canada (NRCan). It originates from the best available data sources covering Canadian territory, offers quality topographical information in vector format, and complies with international geomatics standards. CanVec is a multi-source product coming mainly from the National Topographic Data Base (NTDB), the Mapping the North process conducted by the Canada Center for Mapping and Earth Observation (CCMEO), the Atlas of Canada data, the GeoBase initiative, and the data update using satellite imagery coverage (e.g. Landsat 7, Spot, Radarsat, etc.).Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

These structure, isopach and zero edge files are part of a series of stratigraphic framework maps for the Saskatchewan Phanerozoic Fluids and Petroleum Systems (SPFPS) project.The series of stratigraphic framework maps for the Saskatchewan Phanerozoic Fluids and Petroleum Systems (SPFPS) project have been produced using 2 km equi-spaced modified grids generated from Golden Software’s Surfer 9 kriging algorithm. The dataset used to produce each of the maps in this series was created using data from several projects completed by the Ministry (Christopher, 2003; Saskatchewan Industry and Resources et al., 2004; Kreis et al., 2004; Marsh and Heinemann, 2006; Saskatchewan Ministry of Energy and Resources et al., 2007; Heinemann and Marsh, 2009); these data were validated and edited as required to facilitate correlations between the various regional projects. In addition, to minimize edge effects during contouring, the senior author also generated stratigraphic data from wells in adjacent jurisdictions.

These structure, isopach and zero edge files are part of a series of stratigraphic framework maps for the Saskatchewan Phanerozoic Fluids and Petroleum Systems (SPFPS) project.The series of stratigraphic framework maps for the Saskatchewan Phanerozoic Fluids and Petroleum Systems (SPFPS) project have been produced using 2 km equi-spaced modified grids generated from Golden Software’s Surfer 9 kriging algorithm. The dataset used to produce each of the maps in this series was created using data from several projects completed by the Ministry (Christopher, 2003; Saskatchewan Industry and Resources et al., 2004; Kreis et al., 2004; Marsh and Heinemann, 2006; Saskatchewan Ministry of Energy and Resources et al., 2007; Heinemann and Marsh, 2009); these data were validated and edited as required to facilitate correlations between the various regional projects. In addition, to minimize edge effects during contouring, the senior author also generated stratigraphic data from wells in adjacent jurisdictions.

These structure, isopach and zero edge files are part of a series of stratigraphic framework maps for the Saskatchewan Phanerozoic Fluids and Petroleum Systems (SPFPS) project.The series of stratigraphic framework maps for the Saskatchewan Phanerozoic Fluids and Petroleum Systems (SPFPS) project have been produced using 2 km equi-spaced modified grids generated from Golden Software’s Surfer 9 kriging algorithm. The dataset used to produce each of the maps in this series was created using data from several projects completed by the Ministry (Christopher, 2003; Saskatchewan Industry and Resources et al., 2004; Kreis et al., 2004; Marsh and Heinemann, 2006; Saskatchewan Ministry of Energy and Resources et al., 2007; Heinemann and Marsh, 2009); these data were validated and edited as required to facilitate correlations between the various regional projects. In addition, to minimize edge effects during contouring, the senior author also generated stratigraphic data from wells in adjacent jurisdictions.

The Government of Canada acquired a national image coverage from the Systeme Pour l'Observation de la Terre (SPOT 4 - 5) satellites that includes four multispectral bands in the visible to shortwave infrared region at 20m spatial resolution. Five years from 2005 - 2010 were necessary to image all of Canada under clear-sky conditions, while acquisition anniversary dates were less important provided the data were imaged during the snow-free period. These data were downloaded from the GeoBase Orthoimage 2005 - 2010 dataset (http://www.geobase.ca/geobase/en/data/imagery/imr/description.html) and used to map 2005 - 2010 land cover south of treeline. Northern Canada has not currently been remapped since circa 2000 due to technical challenges associated with land cover variability and image acquisition dates relative to short summers. This land cover product includes 16 generic classes based on plant functional and a minimum mapping unit of 20m. Radiometric normalization was applied to balance images acquired near mid-summer during the 'peak-of-season' temporal window. The combined Enhancement and Classification by Progressive Generalization methods were used to classify large-area balanced mosaics over twenty mapping zones. Image interpretation was guided using high resolution imagery and other content in Google Earth. Knowledge of land cover spectral signatures, field experience and published reports were also used to assist interpretation in many regions. Remaining images acquired outside the peak-of-season window in early spring or late fall were subsequently classified using decision trees trained on data from overlapping classified peak-of-season images. Accuracy was assessed using ground truth data acquired during several field campaigns conducted with other government departments such as Parks Canada and the Geological Survey. This sample was enhanced using points interpreted in Google Earth as described above to provide a more even spatial coverage of Canada. Overall accuracy assessed at 71% using 1566 reference points, more than half of which were acquired in the field. When assessed using only land cover that was homogeneous within 3 by 3 pixels to account for potential geolocation errors, accuracy increased to 85% for 349 points that were biased towards easily classified classes such as water.