STE_TEI_ATTRIBUTE_POLYS_SP contains Terrestrial Ecosystem Information (TEI) polygons with key and amalgamated (concatenated) attributes derived from the RISC (Resource Inventory Standards Committee) standard attributes. These describe the physical and biological characteristics of ecosystems at a landscape level. TEI currently includes Terrestrial Ecosystem Mapping (TEM), Predictive Ecosystem Mapping (PEM), Sensitive Ecosystems Inventory (SEI), Terrain Mapping (TER) and Soil Mapping (SOIL). Mapping methods include manual air photo interpretation and modeling supported by limited field checking. Current version: v11 (published on 2024-10-03) Previous versions: v10 (published on 2023-11-14), v9 (published on 2023-03-01), v8 (published on 2016-09-01)

Terrestrial Ecosystem Information Project Boundaries contains boundaries (study areas) and attributes describing each project (project level metadata), plus links to the locations of other data associated with the project (e.g., reports, polygon datasets, plotfiles, legends). TEI inventories describe the physical and biological attributes of ecosystems. TEI currently includes Terrestrial Ecosystem Mapping (TEM), Predictive Ecosystem Mapping (PEM), Sensitive Ecosystems Inventory (SEI), Terrain Mapping (TER), Soil Mapping (SOIL), Species Distribution Mapping (SDM) and Wildlife Habitat Ratings (WHR) projects. Current version: v11 (published on 2024-10-03) Previous versions: v10 (published on 2023-11-14), v9 (published on 2023-03-01), v8 (published on 2016-09-01)

The “Terrestrial Ecozones of Canada” dataset provides representations of ecozones. An ecozone is the top level of the four levels of ecosystems that the National Ecological Framework for Canada defines. The framework divides Canada into 15 terrestrial ecozones that define its ecological mosaic on a sub-continental scale. Ecozones represent an area of the earth’s surface as large and very generalized ecological units. These units are characterized by interactive and adjusting abiotic and biotic factors.

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)

SEI_Polygons contains Sensitive Ecosystems Inventory polygons with key and amalgamated (concatenated) attributes derived from the RISC (Resource Inventory Standards Committee) standard attributes. SEI identifies and maps rare and fragile terrestrial ecosystems. Ecosystems mapped may include (but are not limited to) older forests, woodlands, coastal bluffs, herbaceous and sparsely vegetated ecosystems, grasslands, riparian ecosystems and wetlands. SEI methods include manual air photo interpretation or theming of other Ecosystem Mapping, each supported by selective field checking. This layer is derived from the STE_TEI_ATTRIBUTE_POLYS_SP layer by filtering on the PROJECT_TYPE attribute. Project types include: SEI, TEMSEI, TEMSET, and SEIWHR. Current version: v11 (published on 2024-10-03) Previous versions: v10 (published on 2023-11-14), v9 (published on 2023-03-01), v8 (published on 2016-09-01)

The “Terrestrial Ecodistricts of Canada” dataset provides representations of ecodistricts. An ecodistrict is a subdivision of an ecoregion and is characterized by distinctive assemblages of relief, landforms, geology, soil, vegetation, water bodies and fauna. For example, the Jeddore Lake ecodistrict (no. 473) is one of five ecodistricts within the Maritime Barrens ecoregion.

The “Terrestrial Ecoregions of Canada” dataset provides representations of ecoregions. An ecoregion is a subdivision of an ecoprovince and is characterized by distinctive regional ecological factors, including climate, physiography, vegetation, soil, water, and fauna. For example, the Maritime Barrens ecoregion (no. 114) is one of nine ecoregions within the Newfoundland ecoprovince.

Sensitive Ecosystems Inventory (SEI) project boundaries (study areas) contains attributes describing each project (project level metadata), and includes links to the locations of other data associated with the project (e.g. reports, polygon datasets, plot files). SEI identifies and maps rare and fragile terrestrial ecosystems in a given area for the purpose of encouraging land-use decisions that will ensure the continued integrity of these ecosystems. This layer is derived from the STE_TEI_PROJECT_BOUNDARIES_SP layer by filtering on the PROJECT_TYPE attribute. Project types include: SEI, TEMSEI, TEMSET, TEMSEW and SEIWHR. Current version: v11 (published on 2024-10-03) Previous versions: v10 (published on 2023-11-14), v9 (published on 2023-03-01), v8 (published on 2016-09-01)

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).

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).