This dataset represents the location of the physical kilometre post reference signs along Yukon highways . Missing kilometre values indicate that there is no sign present, and the values do not indicate exact distances.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)

The Location Markers layer represents the location of the physical kilometre post reference signs along Yukon highways within the City of Whitehorse. The values do not indicate exact distances.Data was modeled using the NENA NG9-1-1 GIS Data Template (NENA-REF-006. 2 -202 2 ).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)

The Landmark Kilometre Inventory spatial layer is used primarily by the Ministry of Transportation and Infrastructure (MOT) to represent routes against which accident data and traffic counts are collected. This method describes roads by a four number designation referred to as a segment (i.e. 0375) This spatial layer was created based on Digital Road Atlas (DRA) centerline using linear referencing

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)

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)

100 year peak flow isolines in cubic metres per second (m3/s) for 100 square kilometre watersheds and 100 year return period

10 year peak flow isolines in cubic metres per second (m3/s) for 100 square kilometre watersheds and 10 year return period

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