When the tenure data differs from the actual post locations on the ground, we use adjoining parcels to show that the area has no open ground.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)

Weather Elements on Grid (WEonG) based on the High Resolution Deterministic Prediction System (HRDPS) is a post-processing system designed to compute the weather elements required by different forecast programs (public, marine, aviation, air quality, etc.). This system amalgamates numerical and post-processed data using various diagnostic approaches. Hourly concepts are produced from different algorithms using outputs from the pan-Canadian High Resolution Deterministic Prediction System (HRDPS-NAT).

List of locations of B.C. public and B.C. private degree granting and non-B.C. degree granting post-secondary institutions.

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

Land authorizations representing the road right of way for road activities. The spatial data includes polygon data for approved and post-construction road rights of way collected on or after October 30, 2006. This dataset is updated nightly.

Land authorizations for areas on which well or facility activities can occur. This dataset contains spatial data collected on or after October 30, 2006. The spatial data includes approved and post-construction land areas associated with well or facility activities. This dataset is updated nightly.

Post-disturbance forest recovery data for Canada's forested ecosystems, representing a total area of ~650 million ha, captures the return of forests following wildfire and harvest that occurred between 1986 and 2012. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). These spatially-explicit outputs represent the rate of spectral recovery: the rate at which a pixel returns to 80% of its pre-disturbance value (White et al. 2017) within the observation period (1985-2017) using the Y2R or Years-to-Recovery metric derived from Landsat times series data. Baseline rates of spectral recovery (Y2R) were defined for each of Canada's 12 forested ecozones. These baselines were then used to identify spatial clusters of recovering pixels on the landscape where Y2R were either significantly faster or slower than their ecozonal baseline. Finally, areas that were disturbed by wildfire and harvest (1986-2012), but which had not recovered by the end of the observation period (2017) are also provided. Note that these areas are still recovering, but they had not yet recovered according to our metric of spectral recovery, by the end of the time series in 2017. For an overview of the methods, the validation of the Y2R metric, and interpretation of the derived trends, see White et al. (2022) and White et al. (2017).White, J.C., Hermosilla, T., Wulder, M.A., Coops, N.C., 2022. Mapping, validating, and interpreting spatio-temporal trends in post-disturbance forest recovery. Remote Sensing of Environment, 271, 112904. https://doi.org/10.1016/j.rse.2022.112904 ( White et al. 2022)White, J.C., Wulder, M.A., Hermosilla, T., Coops, N.C., Hobart, G.W. 2017. A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment, 194, pp. 303-321. DOI: https://doi.org/10.1016/j.rse.2017.03.035 .( White et al. 2017)

This project was completed by the Shellfish Section in the Newfoundland and Labrador Science Branch of Fisheries and Oceans Canada (DFO), in collaboration with industry partners. The Coastal Environmental Baseline program supported the Placentia Bay portion of project work for an ongoing industry-DFO collaborative post-season trap survey for Snow Crab that was initiated in 2003 and has occurred each year. This survey is conducted by Snow Crab harvesters accompanied by at-sea observers and takes place in NAFO Divisions 2J3KLNOP4R. Historically the survey focused on commercial fishing grounds but began transitioning to a partly random stratified design in 2017. Since 2018, approximately 50% of survey stations are randomly allocated while 50% remain fixed. At each station, six (for inshore stations) or ten (for offshore stations) commercial traps are set in a fleet. To gather data on non-commercial sized Snow Crab, including females, many fleets also include one small-mesh trap. The coverage of small-mesh traps has been expanding in recent years with the aim of one small-mesh trap for every station in the coming years. Biological sampling is undertaken on at least one commercial trap and the small-mesh trap at each station. The data from this survey is incorporated into the annual stock assessment for Snow Crab in the Newfoundland and Labrador region. This record contains trap locations for Placentia Bay, and information on the types of data collected. More detailed information can be found in Pantin et al. (2022).https://publications.gc.ca/collections/collection_2023/mpo-dfo/fs70-5/Fs70-5-2022-076-eng.pdf

This dataset provides the location of Yukon's education facilities ranging from elementary to post-seondary education. The data also includes contact information and general programming information about each facility.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: [geomatics.help@yukon.ca](mailto:%20geomatics.help@yukon.ca)

A placer claim is a parcel of land located or granted for placer mining. A claim also includes any ditches or water rights used for mining the claim and all other things belonging to or used in the working of the claim for mining purposes. A placer claim is a rectangular plot of ground. All angles of a claim must be right angles, except in the case where a boundary line of a previously located claim is adopted as common to both locations. The length of a regular placer claim from post one to post two is 500 feet along a baseline. Co-discovery claims must not exceed 1250 feet each and single discovery claims must not exceed 1500 feet.Distributed from the [GeoYukon](https://yukon.ca/geoyukon) digital map data collection by [Yukon government](https://yukon.ca/maps) . Discover more digital map data and interactive maps at Yukon government's [Open Maps](https://metadata-yukon.hub.arcgis.com) collection.For more information: [Geomatics.Help@yukon.ca](mailto:Geomatics.Help@yukon.ca)