Level curves with an equidistance of 1 m derived from a lidar survey conducted in 2024.attributes:ID - Unique IDSubtype - Master (1) or secondary (2) level curve SCORE - Elevation value (m) The High Resolution Digital Elevation Model (m) product The High Resolution Digital Elevation Model (HRDM) product is available on the Open Government website.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Water quality and ecosystem health data used to conduct a cumulative effects assessment of Canadian Great Lakes nearshore waters in support of the Great Lakes Water Quality Agreement are included in this dataset. The data was collected by various government and non-government agencies and organizations and integrated into this dataset to allow the assessment to be conducted. By conducting a regular, systematic assessment of cumulative effects in the nearshore waters of the Great Lakes Environment and Climate Change Canada (ECCC) is able to identify areas of high quality and areas under stress. Knowledge of ecological thresholds, other Great Lakes assessments, stressor information, indicators and local and traditional ecological knowledge will be used to aid in: 1) the identification and mapping of high quality nearshore areas and areas that are or may become subject to high stress and; 2) the determination of factors and cumulative effects that are causing stress or threats. Cumulative effects impacting the nearshore and future threats to areas of high ecological value will be better understood and the knowledge shared will assist in priority setting for science and management at a meaningful and practical spatial scale within each Great Lake and connecting channel.

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).

The Air Quality Health Index (AQHI) is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk). The AQHI is calculated based on the relative risks of a combination of common air pollutants that are known to harm human health, including ground-level ozone, particulate matter, and nitrogen dioxide. The AQHI formulation captures only the short term or acute health risk (exposure of hour or days at a maximum). The formulation of the AQHI may change over time to reflect new understanding associated with air pollution health effects. The AQHI is calculated from data observed in real time, without being verified (quality control).

Forest Elevation(Ht) Mean 2015Mean height of lidar first returns (m). Represents the mean canopy height. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). 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) Wulder et al. 2018)Geographic extent: Canada's forested ecosystems (~ 650 Mha)Time period: 1985–2011

The High Resolution Digital Elevation Model Mosaic provides a unique and continuous representation of the high resolution elevation data available across the country. The High Resolution Digital Elevation Model (HRDEM) product used is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The mosaic is available for both the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) from web mapping services. It is part of the CanElevation Series created to support the National Elevation Data Strategy implemented by NRCan. This strategy aims to increase Canada's coverage of high-resolution elevation data and increase the accessibility of the products.Unlike the HRDEM product in the same series, which is distributed by acquisition project without integration between projects, the mosaic is created to provide a single, continuous representation of strategy data. The most recent datasets for a given territory are used to generate the mosaic. This mosaic is disseminated through the Data Cube Platform, implemented by NRCan using geospatial big data management technologies. These technologies enable the rapid and efficient visualization of high-resolution geospatial data and allow for the rapid generation of dynamically derived products. The mosaic is available from Web Map Services (WMS), Web Coverage Services (WCS) and SpatioTemporal Asset Catalog (STAC) collections. Accessible data includes the Digital Terrain Model (DTM), the Digital Surface Model (DSM) and derived products such as shaded relief and slope.The mosaic is referenced to the Canadian Height Reference System 2013 (CGVD2013) which is the reference standard for orthometric heights across Canada.Source data for HRDEM datasets used to create the mosaic is acquired through multiple projects with different partners.Collaboration is a key factor to the success of the National Elevation Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

The Atlas of Canada High Speed Internet Network map illustrates the availability of high-speed internet across Canada in 2024. High speed internet service is broken up into 4 download speeds in Megabits per second. 25 Mbps, 50 Mbps, 100 Mbps, and Gigabit (940 Mbps). It features an inset map and a bar graph that provide additional insights. The inset map shows the percentage of population in each province with access to 50 Mbps internet. The bar graph shows the percent of population without access 50 Mbps internet by year, dating back to 2018.The Atlas of Canada High Speed Internet Network map is shown at a scale of 1:6 000 000. It is part of the new Atlas of Canada series, which features a shared look and feel across all map products to ensure consistency in design and layout. This map supports the Communication and Transportation theme, one of five core themes featured in the renewed Atlas of Canada web presence at atlas.gc.ca. The common design helps make Atlas of Canada products easier to read, compare, and use together across the Atlas collectionUse the Atlas of Canada site to find places, digital maps, facts, geographical information and learning resources about Canada.Source: Canadian Radio-television and Telecommunications Commission (CRTC), 2025. Current trends - High-speed broadband. https://crtc.gc.ca/eng/publications/reports/PolicyMonitoring/ban.htmIf you have questions about CRTC data, e-mail info@crtc.gc.ca.Produced by the Canada Centre for Mapping and Earth Observation, the Geoscience and Earth Monitoring Sector, Natural Resources Canada. 2025.

To identify areas of high residency, fine-scale tracking data from individual animals and coarser, short-term movement patterns of herds were analyzed. Individual radio-tracking was assessed from 2001 to 2005 and herd visual tracking was assessed from 1989 to 2008. Data was collected by two research teams: GREMM (Groupe de recherche et d’éducation sur les mammifères marins) and Fisheries and Océans Canada (DFO). Areas of high residency were determined using net displacement speed of herds and they were defined as adjacent cells where 50% of the herds travelled at or below a threshold speed.Areas of high residency do not represent the general distribution of the beluga whale and no association between these areas and specific biological functions could be established. The exact delimitation of these areas can change according to the definition criteria used in the analysis. Therefore, the marginal cells are not necessarily indicative of lower habitat quality.Data source:Lefebvre, S., Michaud, R., Lesage, V. and Berteaux, D. (2012). Identifying high residency areas of the threatened St. Lawrence beluga whale from fine-scale movements of individuals and coarse-scale movements of herds. Mar. Ecol. Prog. Ser. 450: 243–257.

A spot height identifies the elevation (z value) above sea level of natural and man-made geographic features. It includes: * spot heights * vertical control points * water level/lake elevations This product requires the use of geographic information system (GIS) software.

This data set represents the digital Mineral Resource potential of the Province of Saskatchewan.This dataset represents the mineral resource potential of Saskatchewan. Methodologies used to determine a ranking system of low to high mineral potential areas. 6 being high mineral potential while 1 being low. The data was created as a file geodatabase feature class and output for public distribution. **Please Note – All published Saskatchewan Geological Survey datasets, including those available through the Saskatchewan Mining and Petroleum GeoAtlas, are sourced from the Enterprise GIS Data Warehouse.  They are therefore identical and share the same refresh schedule.