CHS offers 500-metre bathymetric gridded data for users interested in the topography of the seafloor. This data provides seafloor depth in metres and is accessible for download as predefined areas.

An interpretation of bedrock geology, topography and other sources of information that shows the potential for karst formations. This is a reconnaissance level map for all of British Columbia

The Nova Scotia Hydrographic Network is an enhanced version of the Nova Scotia Topographic Database's Water Features theme. This dataset includes network spines for connectivity of water flow and various attribution for flow direction, priority of water flow and toponymic objects where applicable.

Canada’s landmass is very diversified and comprises 7 distinctive areas called physiographic regions, each of which has its own unique topography and geology. Physiographic regions are large areas that share similar relief and landforms shaped by common geomorphic processes and geological history. Physiographic regions are often used to describe Canada’s geography to show regional differences in climate, vegetation, population and the economy.This dataset collection contains three interrelated datasets mapping the location of Canada’s 7 different physiographic regions, their 21 subregions and many divisions (landforms).

The data shared are spatially explicit projections of wildfire burn probability across Canada’s forested ecozones under multiple future climate scenarios at a 30-m spatial resolution. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Four future climate scenarios were used to examine the spatiotemporal distribution of burn probability in the 21st century based on climate, vegetation, and topographic conditions ( Mulverhill et al. 2024). Projected burn probability is provided for four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) and four future time periods, including 2021-2040, 2041-2060, 2061-2080, and 2081-2100, along with a baseline period representing average climate conditions and burn probability between 1991 and 2020. Outputs represent the probability that the conditions (climate, vegetation, topography) of a given pixel resemble those of historically burned areas. All non-climate variables were held static; therefore, projections represent burn probability under future climate scenarios given contemporary (2020) forest conditions. When using this dataset, please cite Mulverhill et al. (2025), as below.Mulverhill, C., Coops, N. C., Wulder, M. A., Hermosilla, T., White, J. C., & Bater, C. W. (2025). Projected Future Changes in Burn Probability in Canada’s Forests and Communities Under Different Climate Change Scenarios. Canadian Journal of Remote Sensing, 51(1). https://doi.org/10.1080/07038992.2025.2560347(Mulverhill et al. 2025).For a detailed description of the source data and methods applied to the baseline period to enable the Mulverhill et al. (2025) projections, see:Mulverhill, C., Coops, N.C., Wulder, M.A., White, J.C., Hermosilla, T., and Bater, C.W. 2024. “Multidecadal mapping of status and trends in annual burn probability over Canada’s forested ecosystems.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 209 pp. 279–295. https://doi.org/10.1016/j.isprsjprs.2024.02.006(Mulverhill et al. 2024).

14 Class - Canadian Ecological Domain Classification from Satellite Data. Satellite derived data including 1) topography, 2) landscape productivity based on photosynthetic activity, and 3) land cover were used as inputs to create an environmental regionalization of the over 10 million km2 of Canada’s terrestrial land base. The outcomes of this clustering consists of three main outputs. An initial clustering of 100 classes was generated using a two-stage multivariate classification process. Next, an agglomerative hierarchy using a log-likelihood distance measure was applied to create a 40 and then a 14 class regionalization, aimed to meaningfully group ecologically similar components of Canada's terrestrial landscape. For more information (including a graphical illustration of the cluster hierarchy) and to cite this data please use: Coops, N.C., Wulder, M.A., Iwanicka, D. 2009. An environmental domain classification of Canada using earth observation data for biodiversity assessment. Ecological Informatics, Vol. 4, No. 1, Pp. 8-22, DOI: https://doi.org/10.1016/j.ecoinf.2008.09.005. ( Coops et al. 2009).

100 Class - Canadian Ecological Domain Classification from Satellite Data. Satellite derived data including 1) topography, 2) landscape productivity based on photosynthetic activity, and 3) land cover were used as inputs to create an environmental regionalization of the over 10 million km2 of Canada’s terrestrial land base. The outcomes of this clustering consists of three main outputs. An initial clustering of 100 classes was generated using a two-stage multivariate classification process. Next, an agglomerative hierarchy using a log-likelihood distance measure was applied to create a 40 and then a 14 class regionalization, aimed to meaningfully group ecologically similar components of Canada's terrestrial landscape. For more information (including a graphical illustration of the cluster hierarchy) and to cite this data please use: Coops, N.C., Wulder, M.A., Iwanicka, D. 2009. An environmental domain classification of Canada using earth observation data for biodiversity assessment. Ecological Informatics, Vol. 4, No. 1, Pp. 8-22, DOI: https://doi.org/10.1016/j.ecoinf.2008.09.005. ( Coops et al. 2009).

40 Class - Canadian Ecological Domain Classification from Satellite Data. Satellite derived data including 1) topography, 2) landscape productivity based on photosynthetic activity, and 3) land cover were used as inputs to create an environmental regionalization of the over 10 million km2 of Canada’s terrestrial land base. The outcomes of this clustering consists of three main outputs. An initial clustering of 100 classes was generated using a two-stage multivariate classification process. Next, an agglomerative hierarchy using a log-likelihood distance measure was applied to create a 40 and then a 14 class regionalization, aimed to meaningfully group ecologically similar components of Canada's terrestrial landscape. For more information (including a graphical illustration of the cluster hierarchy) and to cite this data please use: Coops, N.C., Wulder, M.A., Iwanicka, D. 2009. An environmental domain classification of Canada using earth observation data for biodiversity assessment. Ecological Informatics, Vol. 4, No. 1, Pp. 8-22, DOI: https://doi.org/10.1016/j.ecoinf.2008.09.005. ( Coops et al. 2009).

The distribution of Base - Small Scale Topographic data to the public is provided by AltaLIS Ltd., distributing agent for the Alberta Data Partnerships Ltd. (ADP), formerly Spatial Data Warehouse Ltd. (SDW), on behalf of the Government of Alberta. The Base - Small Scale Topographic data includes a vector mapping database created by Alberta Environment and Parks and includes the following: 1:250 000 Base, 1:1 000 000 Base and 1:2 000 000 Base.

TOPEX (acronym which means “topographical exposure”) is an index of topographic exposure. It reflects the influence of local topography on the degree of exposure to wind and on its behavior (Ruel et al. 2002). TOPEX data are presented in the form of a matrix file (raster) whose spatial resolution is 50 m with a projection in Nad 1983 Quebec Lambert. This raster covers the entire territory of Quebec located approximately south of 52°40' and west of 61°10' and extends beyond the borders of Quebec by 75 to 125 m (in order to have values on the entire Quebec territory covered).TOPEX values were calculated using the Digital Terrain Model (DTM) from the NASA Shuttle Radar Topography Mission (SRTM). This MNT is provided in a WGS 84 projection (EPSG: 4326) with a resolution of one arcsecond (+/- 30 m). A mosaic of the SRTM tiles was created to then project the MNT into Lambert and resample it at 50 m.TOPEX can be used as an input in a windfall vulnerability assessment system. However, it should be borne in mind that this index does not take into account the wind channeling effect that may occur in certain places and that can influence windfall. In addition, other factors must be taken into consideration in order to carry out a complete analysis of the risk of windfall. RUEL, J.-C., S. J. MITCHELL and M. DORNIER, 2002. A GIS based approach to map wind exposure for windthrow hazard rating. Northern Journal of Applied Forestry, 19 (4): 183-187.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**