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)

BiologicEcologic ISO Feature Dataset symbolization and publication. September 5, 2017.

The Canadian Aquatic Biomonitoring Network (CABIN) is an aquatic biomonitoring program for assessing the health of fresh water ecosystems in Canada. Benthic macroinvertebrates are collected at a site location and their counts are used as an indicator of the health of that water body. CABIN is based on the network of networks approach that promotes inter-agency collaboration and data-sharing to achieve consistent and comparable reporting on fresh water quality and aquatic ecosystem conditions in Canada. The program is maintained by Environment and Climate Change Canada (ECCC) to support the collection, assessment, reporting and distribution of biological monitoring information. A set of nationally standardized CABIN protocols are used for field collection, laboratory work, and analysis of biological monitoring data. A training program is available to certify participants in the standard protocols. There are two types of sites in the CABIN database (reference and test). Reference sites represent habitats that are closest to “natural” before any human impact. The data from reference sites are used to create reference models that CABIN partners use to evaluate their test sites in an approach known as the Reference Condition Approach (RCA). Using the RCA models, CABIN partners match their test sites to groups of reference sites on similar habitats and compare the observed macroinvertebrate communities. The extent of the differences between the test site communities and the reference site communities allows CABIN partners to estimate the severity of the impacts at those locations. CABIN samples have been collected since 1987 and are organized in the database by study (partner project). The data is delineated by the 11 major drainage areas (MDA) found in Canada and each one has a corresponding study, habitat and benthic invertebrate data file. Links to auxiliary water quality data are provided when available. Visits may be conducted at the same location over time with repeat site visits being identified by identical study name / site code with different dates. All data collected by the federal government is available on Open Data and more partners are adding their data continually. The csv files are updated monthly. Contact the CABIN study authority to request permission to access non open data.

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)

This dataset is used is used to determine the significance or status of wetland classes and certain other natural heritage features. It is also used to set targets for Wilderness Class Provincial parks, State of the Forest reporting and to study natural disturbance regimes.

**Attention: there is a new version of this product (SCANFI v2)**SCANFI v2 can be found here: https://doi.org/10.23687/07653869-f303-46c2-a04e-9ab479b73cbfThis data publication contains a set of 30m resolution raster files representing 2020 Canadian wall-to-wall maps of broad land cover type, forest canopy height, degree of crown closure and aboveground tree biomass, along with species composition of several major tree species. The Spatialized CAnadian National Forest Inventory data product (SCANFI) was developed using the newly updated National Forest Inventory photo-plot dataset, which consists of a regular sample grid of photo-interpreted high-resolution imagery covering all of Canada’s non-arctic landmass. SCANFI was produced using temporally harmonized summer and winter Landsat spectral imagery along with hundreds of tile-level regional models based on a novel k-nearest neighbours and random forest imputation method. A full description of all methods and validation analyses can be found in Guindon et al. (2024). As the Arctic ecozones are outside NFI’s covered areas, the vegetation attributes in these regions were predicted using a single random forest model. The vegetation attributes in these arctic areas could not be rigorously validated. The raster file « SCANFI_aux_arcticExtrapolationArea.tif » identifies these zones.SCANFI is not meant to replace nor ignore provincial inventories which could include better and more regularly updated inputs, training data and local knowledge. Instead, SCANFI was developed to provide a current, spatially-explicit estimate of forest attributes, using a consistent data source and methodology across all provincial boundaries and territories. SCANFI is the first coherent 30m Canadian wall-to-wall map of tree structure and species composition and opens novel opportunities for a plethora of studies in a number of areas, such as forest economics, fire science and ecology.# Limitations1- The spectral disturbances of some areas disturbed by pests are not comprehensively represented in the training set, thus making it impossible to predict all defoliation cases. One such area, severely impacted by the recent eastern spruce budworm outbreak, is located on the North Shore of the St-Lawrence River. These forests are misrepresented in our training data, there is therefore an imprecision in our estimates.2- Attributes of open stand classes, namely shrub, herbs, rock and bryoid, are more difficult to estimate through the photointerpretation of aerial images. Therefore, these estimates could be less reliable than the forest attribute estimates.3- As reported in the manuscript, the uncertainty of tree species cover predictions is relatively high. This is particularly true for less abundant tree species, such as ponderosa pine and tamarack. The tree species layers are therefore suitable for regional and coarser scale studies. Also, the broadleaf proportion are slightly underestimated in this product version.4- Our validation indicates that the areas in Yukon exhibit a notably lower R2 value. Consequently, estimates within these regions are less dependable. 5- Urban areas and roads are classified as rock, according to the 2020 Agriculture and Agri-Food Canada land-use classification map. Even though those areas contain mostly buildings and infrastructure, they may also contain trees. Forested urban parks are usually classified as forested areas. Vegetation attributes are also predicted for forested areas in agricultural regions.Updates of this dataset will eventually be available on this metadata page.# Details on the product development and validation can be found in the following publication:Guindon, L., Manka, F., Correia, D.L.P., Villemaire, P., Smiley, B., Bernier, P., Gauthier, S., Beaudoin, A., Boucher, J., and Boulanger, Y. 2024. A new approach for Spatializing the Canadian National Forest Inventory (SCANFI) using Landsat dense time series. Can. J. For. Res. https://doi.org/10.1139/cjfr-2023-0118# Please cite this dataset as: Guindon L., Villemaire P., Correia D.L.P., Manka F., Lacarte S., Smiley B. 2023. SCANFI: Spatialized CAnadian National Forest Inventory data product. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/18e6a919-53fd-41ce-b4e2-44a9707c52dc # The following raster layers are available:• NFI land cover class values: Land cover classes include Water, Rock, Bryoid, Herbs, Shrub, Treed broadleaf, Treed mixed and Treed conifer• Aboveground tree biomass (tonnes/ha): biomass was derived from total merchantable volume estimates produced by provincial agencies• Height (meters): vegetation height• Crown closure (%): percentage of pixel covered by the tree canopy• Tree species cover (%): estimated as the proportion of the canopy covered by each tree species: o Balsam fir tree cover in percentage (Abies balsamea) o Black spruce tree cover in percentage (Picea mariana) o Douglas fir tree cover in percentage (Pseudotsuga menziesii) o Jack pine tree cover in percentage (Pinus banksiana) o Lodgepole pine tree cover in percentage (Pinus contorta) o Ponderosa pine tree cover in percentage (Pinus ponderosa) o Tamarack tree cover in percentage (Larix laricina) o White and red pine tree cover in percentage (Pinus strobus and Pinus resinosa) o Broadleaf tree cover in percentage (PrcB) o Other coniferous tree cover in percentage (PrcC)

In this dataset, we share maps of annual dominant tree species (also known as leading tree species) from 1984-2022 covering the entirety of Canada's 650 Mha forested ecosystems using Landsat time-series imagery at a 30-m spatial resolution. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Classifications are based on regionally representative Random Forests model using local training samples from Canada's National Forest Inventory (Hermosilla et al., 2024). Descriptive metrics provide information on spectral, geographic, climatic, and topographic characteristics. Initial annual tree species classifications were subjected to a time series post-classification process using the forward-backward Hidden Markov Model to improve the temporal consistency of tree species transitions within the time series. Assessment of the annual species maps using independent validation data resulted in an overall accuracy of 86.1% ± 0.14% (95%-confidence interval). These data allow consistent comparison of trends and rates of change in tree species composition nationally and across regions using a common time frame, spatial resolution, and analytical approach.Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Bater, C.W., Hobart, G.W., 2024. Characterizing long-term tree species dynamics in Canada's forested ecosystems using annual time series remote sensing data. Forest Ecology and Management, 122313. https://doi.org/10.1016/j.foreco.2024.122313 (Hermosilla et al. 2024)

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)

Wall-to-wall map of water bodies across Canada's forested ecosystems for the year 2022, derived from the "water" class of the annual Virtual Land Cover of Engine (VLCE) product. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The VLCE maps are based on Landsat image time-series composites and represent annual land cover classifications from 1984 to 2022 at a spatial resolution of 30 m. The classification process integrates forest change information and ancillary topographic and hydrologic variables, applying a regional modeling framework based on a 150x150 km tiling system ( Hermosilla et al., 2022). Training data are drawn from multiple land cover sources and selected proportionally to land cover distributions using a distance-weighted approach. Classifications are refined over time using a Hidden Markov Model to ensure consistency and reduce classification noise between years.Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C. 2022. Land cover classification in an era of big and open data: Optimizing localized implementation and training data selection to improve mapping outcomes. Remote Sensing of Environment. 268, 112780. https://doi.org/10.1016/j.rse.2021.112780. ( Hermosilla et al., 2022)Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W. 2018. Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series. Canadian Journal of Remote Sensing. 44(1) 67-87. https://doi.org/10.1080/07038992.2018.1437719.( Hermosilla et al., 2018)

The Shorezone Biobanding Polygons are an area representation of the various types of biota (flora and fauna) and their distribution, or lack thereof, found in the shoreunit.