This service shows the average owner estimated value of dwelling for Canada by 2016 census division. The data is from the Census Profile, Statistics Canada Catalogue no. 98-316-X2016001.Value (owner estimated) of private dwelling refers to the dollar amount expected by the owner if the asset were to be sold.In the context of dwelling, it refers to the value of the entire dwelling, including the value of the land it is on and of any other structure, such as a garage, which is on the property. If the dwelling is located in a building which contains several dwellings, or a combination of residential and business premises, all of which the household owns, the value is estimated as a portion of the market value that applies only to the dwelling in which the household resides. For additional information refer to 'Value (owner estimated)' in the 2016 Census Dictionary.For additional information refer to 'Value (owner estimated)' in the 2016 Census Dictionary.To have a cartographic representation of the ecumene with this socio-economic indicator, it is recommended to add as the first layer, the “NRCan - 2016 population ecumene by census division” web service, accessible in the data resources section below.

This service shows the average owner estimated value of dwelling for Canada by 2016 census subdivision. The data is from the Census Profile, Statistics Canada Catalogue no. 98-316-X2016001.Value (owner estimated) of private dwelling refers to the dollar amount expected by the owner if the asset were to be sold.In the context of dwelling, it refers to the value of the entire dwelling, including the value of the land it is on and of any other structure, such as a garage, which is on the property. If the dwelling is located in a building which contains several dwellings, or a combination of residential and business premises, all of which the household owns, the value is estimated as a portion of the market value that applies only to the dwelling in which the household resides. For additional information refer to 'Value (owner estimated)' in the 2016 Census Dictionary.For additional information refer to 'Value (owner estimated)' in the 2016 Census DictionaryTo have a cartographic representation of the ecumene with this socio-economic indicator, it is recommended to add as the first layer, the “NRCan - 2016 population ecumene by census subdivision” web service, accessible in the data resources section below.

Predicted silt percentage (%) at a defined depth range (0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm).

Predicted soil pH in CaCl2 at a defined depth range (0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm). The degree of acidity or alkalinity of a soil expressed in terms of the pH scale.

This service shows the median total income of households in 2015 for Canada by 2016 census division.The data is from the Census Profile, Statistics Canada Catalogue no. 98-316-X2016001.Total income refers to the sum of certain incomes (in cash and, in some circumstances, in kind) of the statistical unit during a specified reference period. The median income of a specified group is the amount that divides the income distribution of that group into two halves. For additional information refer to 'Total income' in the 2016 Census Dictionary.For additional information refer to 'Total income' in the 2016 Census Dictionary.To have a cartographic representation of the ecumene with this socio-economic indicator, it is recommended to add as the first layer, the “NRCan - 2016 population ecumene by census division” web service, accessible in the data resources section below.

Predicted bulk density (g/cm3) at defined depth ranges (0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm). The mass of dry soil per unit bulk volume.

Predicted cation exchange capacity (meq/100g) at a defined depth range (0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm). The interchange of a cation in solution and another cation on the surface of any surface-active material such as clay colloid or organic colloid.

This dataset provides wall-to-wall maps of forest structure across Canada's 650 million hectare forested ecosystems for the year 2022, generated at a spatial resolution of 30 m.It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Structure estimates include key attributes such as canopy height, canopy cover, and aboveground biomass, derived using a combination of airborne lidar and Landsat-based spectral composites. Structure models were trained using the - lidar-plot framework - (Wulder et al. 2012), which integrates co-located airborne lidar data and ground plot measurements with Landsat time-series composites (Hermosilla et al. 2016). A Nearest Neighbour imputation approach was applied to estimate structural attributes across the full extent of Canada's forested area. These nationally consistent products are intended to support strategic-level forest monitoring and assessment and are not designed for operational forest management.For further details on the methods, accuracy assessment, and source data, see Matasci et al. (2018).Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018. 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. https://doi.org/10.1016/j.rse.2018.07.024 (Matasci et al. 2018)

Forest Gross Stem Volume 2015Gross stem volume. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). 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 = m3/ha). 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 dataset identifies potential low head hydropower development sites across Canada, based on a comprehensive review of existing studies and resource databases. Low head hydropower refers to sites where the vertical drop (head) is 15 meters or less. Sites included in this dataset also have an estimated generation capacity of 50 megawatts (MW) or less.The original database was developed by Hatch Ltd. for Natural Resources Canada (NRCan) as part of a national low head hydropower market assessment. It integrates data from multiple sources, including provincial inventories, prior Hatch studies, and legacy fieldwork records. To enhance data completeness, NRCan subsequently revised the database by supplementing missing site names, river names, and geographic coordinates where possible.The finalized dataset comprises 2,629 potential low head hydropower sites distributed across Canada. It is provided in the form of provincial and territorial shapefiles, each containing detailed site-level attributes. Attributes include site name, river name, site ID, source of information, local map number, geographic coordinates (X and Y), site region, presence of a dam, grid connection status, Indigenous affiliation, catchment area, mean annual flow, rated discharge, gross head, penstock length and diameter, estimated installed capacity, dedicated transmission line length, site comments, and Indigenous reserve name.Disclaimer:This dataset was developed to support preliminary assessments of low head hydropower potential in Canada and is subject to the following limitations:• The original database was compiled from various secondary sources, including data provided by third-party contributors and Hatch Ltd.’s own records. Some of these sources date back several decades. The data provided has not necessarily undergone independent verification for its accuracy, completeness, or validity.• NRCan undertook a data revision process involving manual review and geolocation using publicly available mapping tools (e.g., Google Maps) to address missing information. Where geographic coordinates could not be reliably determined, this is explicitly indicated in the dataset.• Site conditions may have changed over time due to natural processes or human activities. NRCan does not accept responsibility for any impact such changes may have on the accuracy or validity of the dataset’s observations, conclusions, or recommendations.• The inclusion of a site in this dataset does not imply technical, economic, or environmental feasibility. Any potential development would require further investigation, including detailed field studies, environmental assessments, and comprehensive technical and financial evaluations.This dataset is provided for informational purposes only and is not intended to serve as the sole basis for investment, planning, or development decisions. Users are strongly advised to undertake independent due diligence, including site-specific assessments and technical evaluations, prior to making any decisions based on the dataset.By accessing or using this dataset, users acknowledge and accept these terms and limitations. The dataset providers expressly disclaim any responsibility or liability for consequences resulting from the use, interpretation, or reliance on the information contained herein. Use of this dataset is entirely at the user’s own risk, and users assume full responsibility for any actions or decisions made based on its contents.This disclaimer is provided in accordance with applicable laws and regulations. By utilizing the dataset, users agree to release the dataset providers from any legal claims, damages, or liabilities that may arise from such use.