The land classification shown is derived from in-field investigation and is only undertaken for project investigation purposes. These polygons represent the most accurate and up-to-date information regarding agriculture land conditions at those specific locations.Distributed from [[GeoYukon](.underline)](https://yukon.ca/geoyukon) by the [[Government of Yukon](.underline)](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](.underline)](mailto:geomatics.help@yukon.ca)

The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 6 or 24 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar, satellite imagery and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 10 km. Data is only available for the surface level. Analysis data is made available four times a day for 6h intervals and twice a day for the 24h interval. A preliminary estimate is available approximately 1h after the end of the accumulation period, and revised 7h after in order to assimilate gauge data arriving later.

**NOTE** This dataset is going to be replaced by the Data Catalogue layer: [Forest Inventory Ground Plots - Public Access](https://catalogue.data.gov.bc.ca/dataset/6d6d115f-4cc2-4141-909e-3344b3a72bcf) This new layer links to the updated database for all Forest Analysis and Inventory Branch ground sample plots. Growth and Yield dataset is a provincial data set that comprised of Permanent Sample Plots (PSP). Researchers such as GY modellers and those wanting to know the position of all samples will use the all status view to better understand the spatial distribution of historic measurement data including samples that are currently destroyed or inactive

The Canadian Precipitation Analysis System (CaPA) produces a best estimate of 6 and 24 hour precipitation amounts. This objective estimate integrates data from in situ precipitation gauge measurements, radar QPEs and a trial field generated by a numerical weather prediction system. In order to produce the High Resolution Deterministic Precipitation Analysis (HRDPA) at a resolution of 2.5 km, CaPA is connected to the continental HRDPS for its trial field. CaPA-HRDPA produces four analyses of 6 hour amounts per day, valid at synoptic hours (00, 06, 12 and 18 UTC) and two 24 hour analysis valid at 06 and 12 UTC. A preliminary production is started 1 hour after valid time and a final one is launched 7 hours later. This translates into a production of 12 analyses per day.

This feature delineates forest health disturbances which includes Abiotic and Biotic forest health agents in the Yukon at a scale of 1:100,000. It is a management level forest health overview survey (as opposed to an operational level) - meaning that analysis and mapping are most effective close to the 1:100,000 scale and not larger. This Forest Health Overview surveys has been completed in various stages: 1 ) Starting with mapping the disturbance type, and severity from the Air using Fix wing aircraft on to hardcopy 1:100,000 scale maps ; 2 ) Transfering the Data to a clean Mylar for scanning and digitizing ; and, 3 ) Scanning and digitizing and populate data into GIS spatial database .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)

Between 2017 and 2024, underwater imaging sampling campaigns were conducted by Fisheries and Oceans Canada across a broad area of the shallow coastal zone of the St. Lawrence Estuary and Gulf. The sampling targeted the lower intertidal and subtidal zones, to a maximum depth of approximately 10 m, with an emphasis on eelgrass beds and macroalgae. These targeted surveys were primarily intended to produce ground-truth data for the mapping of estuarine and marine macrophytes of the Québec maritime region (Provencher-Nolet et al., 2025), supporting oil spill preparedness and response activities.This dataset summarizes information generated from the analysis of underwater video footage collected at 3,179 sampling stations using small boats equipped with a pole-mounted camera system, as described in Grégoire et al. (2025). The dataset documents multiple characteristics of coastal ecosystems, including the cover of erect vegetation, vegetation assemblages, dominant and minor vegetation, substrates, fauna, as well as the presence of encrusting algae, for each sampling station. The different attributes recorded during video analysis, along with certain identification criteria, are presented in the visual dictionary of Grégoire et al. (2022).

The Canadian Precipitation Analysis System (CaPA) produces a best estimate of 6 and 24 hour precipitation amounts. This objective estimate integrates data from in situ precipitation gauge measurements, radar QPEs and a trial field generated by a numerical weather prediction system. In order to produce the High Resolution Deterministic Precipitation Analysis (HRDPA) at a resolution of 2.5 km, CaPA is connected to the continental HRDPS for its trial field. CaPA-HRDPA produces four analyses of 6 hour amounts per day, valid at synoptic hours (00, 06, 12 and 18 UTC) and two 24 hour analyses valid at 06 and 12 UTC. A preliminary production is started 1 hour after valid time and a final one is launched 7 hours later. This translates into a production of 12 analyses per day.

The Big Picture 2002 is a legacy geographic information system (GIS) based landscape analysis. It is a partnership between the Nature Conservancy of Canada and the Ministry of Natural Resources’ Natural Heritage Information Centre. Many digital layers covering the Mixedwood Plains ecozone plus a portion of the Ontario Shield ecozone were compiled for this analysis. These layers include: * evaluated wetlands * forest cover * old growth forest * rare species and communities * waterbodies and watercourses * parks and protected areas * Areas of Natural and Scientific Interest (ANSIs) The data layers are overlaid in a GIS system and points are assigned to the features in each layer according to their conservation value. Core natural areas were identified by a combination of minimum size and a minimum point score per pixel. Potential linkages between these cores were computed by assigning scores to the landscape surrounding the cores based on the probable resistance to wildlife movement. We do not currently use this methodology for similar analyses.

Regional Deterministic Air Quality Analysis (RDAQA) is an objective analysis of surface pollutants that combines numerical forecasts from the Regional Air Quality Deterministic Prediction System (RAQDPS) with hourly observations from various monitoring networks in North America, including the Canadian measurement networks operated by the provinces, territories and certain cities, as well as the various American networks in the context of the AIRNow program administered by US/EPA (US Environmental Protection Agency). RDAQA analysis provides the best description of current air quality conditions, and is used to inform the public, meteorologists in the various Environment and Climate Change Canada forecasting offices, Health Canada and other users about the distribution of air pollutants near the ground, and the performance of forecasting models. Each hour, a preliminary product is available approximately one hour after the observation measurement time, while final and Firework products are available approximately two hours after the measurement time. The preliminary and final products contain analysis of the chemical constituents O3, SO2, NO, NO2, PM2.5 (fine particles with diameters of 2.5 micrometers or less) and PM10 (coarse particles with diameters of 10 micrometers or less), while the Firework product contains analysis of PM2.5 and PM10.

Offshore wind represents a potentially significant source of low-carbon energy for Canada, and ensuring that relevant, high-quality data and scientifically sound analyses are brought forward into decision-making processes will increase the chances of success for any future deployment of offshore wind in Canada. To support this objective, CanmetENERGY-Ottawa (CE-O), a federal laboratory within Natural Resources Canada (NRCan), completed a preliminary analysis of relevant considerations for offshore wind, with an initial focus on Atlantic Canada. To conduct the analysis, CE-O used geographic information system (GIS) software and methods and engaged with multiple federal government departments to acquire relevant data and obtain insights from subject matter experts on the appropriate use of these data in the context of the analysis. The purpose of this work is to support the identification of candidate regions within Atlantic Canada that could become designated offshore wind energy areas in the future.The study area for the analysis included the Gulf of St. Lawrence, the western and southern coasts of the island of Newfoundland, and the coastal waters south of Nova Scotia. Twelve input data layers representing various geophysical, ecological, and ocean use considerations were incorporated as part of a multi-criteria analysis (MCA) approach to evaluate the effects of multiple inputs within a consistent framework. Six scenarios were developed which allow for visualization of a range of outcomes according to the influence weighting applied to the different input layers and the suitability scoring applied within each layer.This preliminary assessment resulted in the identification of several areas which could be candidates for future designated offshore wind areas, including the areas of the Gulf of St. Lawrence north of Prince Edward Island and west of the island of Newfoundland, and areas surrounding Sable Island. This study is subject to several limitations, namely missing and incomplete data, lack of emphasis on temporal and cumulative effects, and the inherent subjectivity of the scoring scheme applied. Further work is necessary to address data gaps and take ecosystem wide impacts into account before deployment of offshore wind projects in Canada’s coastal waters. Despite these limitations, this study and the data compiled in its preparation can aid in identifying promising locations for further review.A description of the methodology used to undertake this study is contained in the accompanying report, available at the following link: https://doi.org/10.4095/331855. This report provides in depth detail into how these data layers were compiled and details any analysis that was done on the data to produce the final data layers in this package.