This data includes the projected capacity, energy potential and cost of possible hydro sites throughout the Yukon. Other sites will be added as the data becomes available.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)

This service provides routeing measures. These include established (mandatory) direction of traffic flow, recommended direction of traffic flow, separation lines, separation zones, limits of restricted routeing measure, limits of routeing measures, precautionary areas, archipelagic sea lanes (axis line and limit beyond which vessels shall not navigate) and fairways designated by regulatory authority.

Forest Lorey's Height 2015Lorey's mean height. Average height of trees weighted by their basal area (m). Products relating the structure of Canada's forested ecosystems have been generated and made openly accessible. The shared products are based upon peer-reviewed science and relate aspects of forest structure including: (i) metrics calculated directly from the lidar point cloud with heights normalized to heights above the ground surface (e.g., canopy cover, height), and (ii) modelled inventory attributes, derived using an area-based approach generated by using co-located ground plot and ALS data (e.g., volume, biomass). Forest structure estimates were generated by combining information from lidar plots (Wulder et al. 2012) with Landsat pixel-based composites (White et al. 2014; Hermosilla et al. 2016) using a nearest neighbour imputation approach with a Random Forests-based distance metric. These products were generated for strategic-level forest monitoring information needs and are not intended to support operational-level forest management. All products have a spatial resolution of 30 m. For a detailed description of the data, methods applied, and accuracy assessment results see Matasci et al. (2018). When using this data, please cite as follows: Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018b. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment 216, 697-714. Matasci et al. 2018)Geographic extent: Canada's forested ecosystems (~ 650 Mha)Time period: 1985–2011

Level below which soil or rock is saturated with water, in the well and at the time the level has been measured, expressed in m above the sea level. Groundwater depth is measured on the field, using a water level meters. The depth is then subtracted from the elevation of the measurement site to obtain the water level elevation. The dataset is a general description of the measurement site including location and well elevation. It features a series of points of the surface elevation of the groundwater body.

Forest Percentage Above 2m 2015Percentage of first returns above 2 m (%). Represents canopy cover. Products relating the structure of Canada's forested ecosystems have been generated and made openly accessible. The shared products are based upon peer-reviewed science and relate aspects of forest structure including: (i) metrics calculated directly from the lidar point cloud with heights normalized to heights above the ground surface (e.g., canopy cover, height), and (ii) modelled inventory attributes, derived using an area-based approach generated by using co-located ground plot and ALS data (e.g., volume, biomass). Forest structure estimates were generated by combining information from lidar plots (Wulder et al. 2012) with Landsat pixel-based composites (White et al. 2014; Hermosilla et al. 2016) using a nearest neighbour imputation approach with a Random Forests-based distance metric. These products were generated for strategic-level forest monitoring information needs and are not intended to support operational-level forest management. All products have a spatial resolution of 30 m. For a detailed description of the data, methods applied, and accuracy assessment results see Matasci et al. (2018). When using this data, please cite as follows: Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018b. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment 216, 697-714. Matasci et al. 2018)Geographic extent: Canada's forested ecosystems (~ 650 Mha)Time period: 1985–2011

Forest Gross Stem Volume 2015Gross stem volume. Individual tree gross volumes are calculated using species-specific allometric equations. In the measured ground plots, gross total volume per hectare is calculated by summing the gross total volume of all trees and dividing by the area of the plot (units = m3ha-1). Products relating the structure of Canada's forested ecosystems have been generated and made openly accessible. The shared products are based upon peer-reviewed science and relate aspects of forest structure including: (i) metrics calculated directly from the lidar point cloud with heights normalized to heights above the ground surface (e.g., canopy cover, height), and (ii) modelled inventory attributes, derived using an area-based approach generated by using co-located ground plot and ALS data (e.g., volume, biomass). Forest structure estimates were generated by combining information from lidar plots (Wulder et al. 2012) with Landsat pixel-based composites (White et al. 2014; Hermosilla et al. 2016) using a nearest neighbour imputation approach with a Random Forests-based distance metric. These products were generated for strategic-level forest monitoring information needs and are not intended to support operational-level forest management. All products have a spatial resolution of 30 m. For a detailed description of the data, methods applied, and accuracy assessment results see Matasci et al. (2018). When using this data, please cite as follows: Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018b. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment 216, 697-714. Matasci et al. 2018)Geographic extent: Canada's forested ecosystems (~ 650 Mha)Time period: 1985–2011

This data contains the memorials and monuments located in communities across the country. Currently, more than 8,000 memorials are included in this data. This data is regularly updated as we continue to receive information on Canadian military memorials.

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.

A hydrogeological unit is defined as any soil or rock unit or zone that by virtue of its hydraulic properties has a distinct influence on the storage or movement of groundwater. It is considered the main dataset from the GGP point of view. Hydrogeological units are ranked into five levels (from largest to smallest): 1) hydrogeological region, 2) hydrogeological context, 3) aquifer system, 4) hydrostratigraphic unit, and 5) aquifer. Here are formal definitions for these different types of hydrogeologic units. - Hydrogeological region Hydrogeological regions are areas in which the properties of sub-surface water, or groundwater, are broadly similar in geology, climate and topography. There are 9 such regions identified in Canada (ref?). - Hydrogeological context Hydrogeological contexts are units of reporting, conceptually narrower than regions, and are additionally delineated by physiographic and hydrogeological aspects. - Aquifer system ""A heterogeneous body of intercalated permeable and poorly permeable material that functions regionally as a water-yielding hydraulic unit; it comprises two or more permeable beds [aquifers] separated at least locally by aquitards [confining units] that impede groundwater movement but do not greatly affect the regional hydraulic continuity of the system"" (Poland et al., 1972). - Hydrostratigraphic unit (HSU) ""Body of sediment and/or rock characterized by ground water flow that can be demonstrated to be distinct under both unstressed (natural) and stressed (pumping) conditions, and is distinguishable from flow in other HSUs"" (Noyes et al.) - Aquifer ""A formation, group of formations, or part of a formation that contains sufficient saturated permeable material to yield significant quantities of water to wells and springs"" (Lohman et al, 1972, p. 21). The rank attribute is used to specify the scope of the described unit. The general principle behind this specification is to allow the same data structure to apply to various types of hydrogeological units, from the local aquifer to the almost continental hydrogeological region. The dataset includes properties such as identification, physiography, geology, aquifer description and properties, water balance, groundwater use and risk. It features numerical values or a general description when no values are available. The description can also be used to add context to the numerical values. For each property, metadata identifying the source of the original data, links to similar data in GIN, and description of the processes, algorithms or methodology used to obtain these datasets will be available to complement the data. This dataset is designed to capture and represent a set of synthesized information pertaining to hydrogeological units through maps and succinct table reports. Some attributes (or properties) of the dataset are irrelevant depending of the rank of the unit. In general, this dataset is organised to include multiple properties associated with aquifers and larger hydrogeologic units. These properties are grouped into categories, which include identification, physiography, geology, aquifer description, water balance, groundwater use and risk. The numerical values associated with each of the properties can be used to create thematic maps; hence, the importance of using standardized units of measurement and definitions for these properties. When numerical values are not available, a general description may be supplied instead. The description can also be used to add context to the numerical values. Because this dataset is the cornerstone of the national view on groundwater, supplemental contextual information (metadata) must be part of the data. Thus, for each property, metadata identifying the source of the original data, links to similar data in GIN, and a description of the processes, algorithms or methodology used to obtain these datasets will be available to complement the data.

GeoAI are buildings, hydrography, forests, and roads automatically extracted using Deep Learning models applied to a source dataset, typically aerial or satellite images. The primary aim of GeoAI is to increase Canada's availability of high-resolution foundational geospatial data for both spatial and temporal coverage.‌The infrastructure and expertise put in place by NRCan enables a rapid, efficient, and scalable data creation process through the use of leading-edge technology and Artificial Intelligence models. Published datasets for a given source can be revisited at a later date as more accurate models are developed and put into production. For now, only static files are available, but as the series develops, new products and services will be added.