The spatial representation for an Inventory Region or Compartment. Established by the Resource Inventory Branch, Inventory Regions are large drainages encompassing groups of small drainages also known as compartments. These were once used for planning purposes, similar to the current Planning Cells. They are now maintained in the files as reference for many administration boundaries

Fire weather refers to weather conditions that are conducive to fire. These conditions determine the fire season, which is the period(s) of the year during which fires are likely to start, spread and do sufficient damage to warrant organized fire suppression.The length of fire season is the difference between the start- and end-of-fire-season dates. These are defined by the Canadian Forest Fire Weather Index (FWI; http://cwfis.cfs.nrcan.gc.ca/) start-up and end dates. Start-up occurs when the station has been snow-free for 3 consecutive days, with noon temperatures of at least 12°C. For stations that do not report significant snow cover during the winter (i.e., less than 10 cm or snow-free for 75% of the days in January and February), start-up occurs when the mean daily temperature has been 6°C or higher for 3 consecutive days. The fire season ends with the onset of winter, generally following 7 consecutive days of snow cover. If there are no snow data, shutdown occurs following 7 consecutive days with noon temperatures lower than or equal to 5°C.Historical climate conditions were derived from the 1981–2010 Canadian Climate Normals. Future projections were computed using two different Representative Concentration Pathways (RCP). RCPs are different greenhouse gas concentration trajectories adopted by the Intergovernmental Panel on Climate Change (IPCC) for its fifth Assessment Report. RCP 2.6 (referred to as rapid emissions reductions) assumes that greenhouse gas concentrations peak between 2010-2020, with emissions declining thereafter. In the RCP 8.5 scenario (referred to as continued emissions increases) greenhouse gas concentrations continue to rise throughout the 21st century.Provided layer: the fire season length across Canada for a reference period (1981-2010).

This polygon layer showcases ultra-fine (2.5 km) short-range precipitation forecasts from the High Resolution Deterministic Prediction System (HRDPS), a convection-permitting model by Environment and Climate Change Canada. It identifies local-scale rainfall or snowfall patterns up to 48 hours, supporting urban flood forecasting, severe weather response, and detailed water resource planning.Convection-Permitting: The HRDPS can explicitly resolve thunderstorms and other small-scale weather events by running at ~2.5 km. Short-Range Focus: Typically provides forecasts out to 36–48 hours, updated several times daily. Local Impact: Valuable for pinpointing high-impact precipitation in complex terrain or urban environments, aiding emergency managers and hydrologists in short-lead-time decisions. Nested Model: Receives lateral boundary conditions from RDPS, maintaining consistency with regional forecasts while refining detail in local domains.

A Linear Safety Feature is one of a number of various appliances/appurtenances that have been installed or constructed either alongside or as an integral part of the road infrastructure to reduce the severity or potential of accidents. It is a Linear feature

This feature layer showcases ultra-fine (2.5 km) short-range precipitation forecasts from the High Resolution Deterministic Prediction System (HRDPS), a convection-permitting model by Environment and Climate Change Canada. It identifies local-scale rainfall or snowfall patterns up to 48 hours, supporting urban flood forecasting, severe weather response, and detailed water resource planning.Convection-Permitting: The HRDPS can explicitly resolve thunderstorms and other small-scale weather events by running at ~2.5 km. Short-Range Focus: Typically provides forecasts out to 36–48 hours, updated several times daily. Local Impact: Valuable for pinpointing high-impact precipitation in complex terrain or urban environments, aiding emergency managers and hydrologists in short-lead-time decisions. Nested Model: Receives lateral boundary conditions from RDPS, maintaining consistency with regional forecasts while refining detail in local domains.

Each pixel value corresponds to the difference (anomaly) between the mean “Best-Quality” Max-NDVI of the week specified (e.g. Week 18, 2000-2014) and the “Best-Quality” Max-NDVI of the same week in a specific year (e.g. Week 18, 2015). Max-NDVI anomalies < 0 indicate where weekly Max-NDVI is lower than normal. Anomalies > 0 indicate where weekly Max-NDVI is higher than normal. Anomalies close to 0 indicate where weekly Max-NDVI is similar to normal.

Data include a collection of annual land cover maps derived from MODIS 250 m spatial resolution remotely sensed imagery for the period 2000 to 2011. Processing of the time series was designed to reduce the occurrence of false change between maps. The method was based on change updating as described in Pouliot et al. (2011, 2013). Change detection accounted for both abrupt changes such as forest harvesting and more gradual changes such as recurrent insect defoliation. To determine the new label for a pixel identified as change, an evidential reasoning approach was used to combine spectral and contextual information. The 2005 MODIS land cover of Canada at 250 m spatial resolution described in Latifovic et al. (2012) was used as the base map. It contains 39 land cover classes, which for time series development was considered too detailed and was reduced to 25 and 19 class versions. The 19 class version corresponds to the North America Land Change Monitoring System (NALCMS) Level 2 legend as described in Latifovic et al. (2012). Accuracy assessment of time series is difficult due to the need to assess many maps. For areas of change in the time series accuracy was found to be 70% based on the 19 class thematic legend.This time series captures the spatial distribution of dominant land cover transitions. It is intended for use in modeling, development of remote sensing products such as leaf area index or land cover based albedo retrievals, and other exploratory analysis. It is not appropriate for use in any rigorous reporting or inventory assessments due to the accuracy of the land cover classification and uncertainty as to the capture of all relevant changes for an application.

Ministry of Transportation District Boundary is an exclusive area within a region that the Ministry organizational unit is responsible for. Each Ministry District is partitioned into one or more non-overlapping Contract Areas that cover the entire Ministry District

Hydro Features is composed of the network of Canadian surface waters. Hydro Features entities are: Watercourse, Water Linear Flow, Hydro Obstacle (falls, rapids\...), Waterbody (lake, watercourse\...), Permanent Snow and Ice, Water Well, and Spring. 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)

The purpose of this feature layer is to provide the 1997 overland flooding boundary in the Red River Valley.This dataset shows the extent of peak overland flooding in the Red River Valley in 1997 . Data is based on RADARSAT – 1 satellite imagery. During processing, the raw data set was resampled to 12.5 meter pixel resolution, then classified using PCI Geomatica software which is a specialized software designed to manipulate space born imagery. The final output depicting the flooding boundary is available as a TIFF or Shapefile. Launched in November 1995, RADARSAT-1 was a Canadian-led project which provided useful information to both commercial and scientific users in such fields as disaster management, agriculture, cartography, hydrology, forestry, oceanography, ice studies and coastal monitoring. Equipped with a powerful synthetic aperture radar (SAR) instrument, it acquired images of the Earth day or night, in all weather and through cloud cover, smoke and haze. As of March 2013, the satellite was declared non-operational and is no longer collecting data. Many applications were developed to take advantage of RADARSAT-1 capacity for detecting the presence of water. These included monitoring flooding and the build-up of river ice, and mapping the melting of snow-covered areas. When used for flood monitoring, RADARSAT-1 data helped assess the impact of flooding, predicted the extent and duration of floodwaters, analyzed the environmental impact of water diversion projects, and developed flood mitigation measures. Fields Included:FID : Internal feature numberNAME : Flooded area nameAREA_SQKM : Size of flooded area