Coal Agreement feature class contains provincial extent polygon features representing Coal applications, agreements, leases, and licences, with varying term dates and conditions. These applications and subsequent agreements give the holder the right to explore Coal.

This map illustrates the distribution of soil parent material textures in the agricultural region of Alberta. Soil texture is defined by the relative proportions of the sand, silt and clay particles present. Soil textures are identified by classes using the Soil Texture Triangle illustrated below. The Soil Texture Triangle identifies the textural class of a soil at the intersection of the percent sand (x-axis) and the percent clay (y-axis). The percent silt of the soil is the remainder to add up to 100 percent. This resource was created in 2002 using ArcGIS.

Wildfire ignition density rasters separated by cause (human, natural (lightning)) for mean and normalized mean summaries from 1980 to 2023.

Coal Agreement feature class contains provincial extent polygon features representing Coal applications, agreements, leases, and licences, with varying term dates and conditions. These applications and subsequent agreements give the holder the right to explore Coal.

The mapping depicts the relative abundance of relict (buried glacier) ice preserved in upper permafrost at a national scale. The mapping is updated and based on modelling by O'Neill et al. (2019) (https://doi.org/10.5194/tc-13-753-2019). The mapping offers an improved depiction of ground ice in Canada at a broad scale, incorporating current knowledge on the associations between geological and environmental conditions and ground ice type and abundance. It provides a foundation for hypothesis testing related to broad-scale controls on ground ice formation, preservation, and melt.

The mapping depicts a first-order estimate of the combined volumetric percentage of excess ice in the top 5 m of permafrost from segregated, wedge, and relict ice. The estimates for the three ice types are based on modelling by O'Neill et al. (2019) (https://doi.org/10.5194/tc-13-753-2019), and informed by available published values of ground ice content and expert knowledge. The mapping offers an improved depiction of ground ice in Canada at a broad scale, incorporating current knowledge on the associations between geological and environmental conditions and ground ice type and abundance. It provides a foundation for hypothesis testing related to broad-scale controls on ground ice formation, preservation, and melt.

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

Provincially significant peatlands are a type of protected area designated under The Peatlands Stewardship Act and The Provincially Significant Peatlands Regulation.Provincially significant peatlands are a type of protected area designation under The Peatlands Stewardship Act and The Provincially Significant Regulation. Peatlands designated as provincially significant are legally protected from resource development activities, including peat harvesting, mining, hydro-electric development, logging, and agricultural activities. See The Peatlands Stewardship Act for additional details https://www.gov.mb.ca/nrnd/forest/land-management/peatlands/index.html. 

::: (style="text-align:Left;")Footprints for all imagery in the Yukon Composite 150 cm Imagery Service. The Yukon Composite is a composite imagery basemap created from the most recent medium resolution SPOT-6/7 satellite images from the Government of Yukon satellite imagery repository.Distributed from GeoYukon by the Government of Yukon. Discover more digital map data and interactive maps from Yukon's digital map data collection. For more information: :::

Leaf area index (LAI) quantified the density of vegetation irrespective of land cover. LAI quantifies the total foliage surface area per ground surface area. LAI has been identified by the Global Climate Observing System as an essential climate variable required for ecosystem, weather and climate modelling and monitoring. This product consists of a national scale coverage (Canada) of monthly maps of the maximum LAI during a growing season (May-June-july-August-September) at 20m.References:L. Brown, R. Fernandes, N. Djamai, C. Meier, N. Gobron, H. Morris, C. Canisius, G. Bai, C. Lerebourg, C. Lanconelli, M. Clerici, J. Dash. Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States IISPRS J. Photogramm. Remote Sens., 175 (2021), pp. 71-87, https://doi.org/10.1016/j.isprsjprs.2021.02.020. https://www.sciencedirect.com/science/article/pii/S0924271621000617Richard Fernandes, Luke Brown, Francis Canisius, Jadu Dash, Liming He, Gang Hong, Lucy Huang, Nhu Quynh Le, Camryn MacDougall, Courtney Meier, Patrick Osei Darko, Hemit Shah, Lynsay Spafford, Lixin Sun, 2023.Validation of Simplified Level 2 Prototype Processor Sentinel-2 fraction of canopy cover, fraction of absorbed photosynthetically active radiation and leaf area index products over North American forests,Remote Sensing of Environment, Volume 293, https://doi.org/10.1016/j.rse.2023.113600.https://www.sciencedirect.com/science/article/pii/S0034425723001517