List of schools offering French programs.

Satellite-based forest area consistent with FAO definitions for Canada. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The forest area is based on the Food and Agricultural Organization of the United Nations (FAO) definition. The FAO definition incorporates land use, whereby trees removed by fire and harvesting for instance, remain forest as the trees will return. The included map displays the current forest cover for year as noted (i.e. 2022), plus the satellite-based temporally informed forest area where tree cover has been temporarily lost due to stand replacing disturbances (i.e., fire, harvest). For an overview of the methods, data, image processing, as well as information on accuracy assessment see Wulder et al. (2020). Open Access: Wulder, M.A., T. Hermosilla, G. Stinson, F.A. Gougeon, J.C. White, D.A. Hill, B.P. Smiley. (2020). Satellite-based time series land cover and change information to map forest area consistent with national and international reporting requirements. Forestry: An International Journal of Forest Research 93(3), 331-34, https://doi.org/10.1093/forestry/cpaa0063 . ( Wulder et al. 2020)

Emerald Basin on the Scotia Shelf off Nova Scotia, Canada, is home to a globally unique population of the glass sponge Vazella pourtalesi. Through the analysis of both in situ photographs and trawl catch data from annual multispecies bottom-trawl surveys, we examined community composition, species density, and abundance of epibenthos and fish associated with V. pourtalesi compared to locations without this sponge. Using generalized linear models and analysis of similarities, the importance of V. pourtalesi in enhancing species density and abundance of the associated epibenthic community was assessed against that of the hard substrate on which it settles. Our results indicated that the megafaunal assemblage associated with V. pourtalesi was significantly different in composition and higher in species density and abundance compared to locations without V. pourtalesi. Analysis of similarity of trawl catch data indicated that fish communities associated with the sponge grounds are significantly different from those without V. pourtalesi, although no species were found exclusively on the sponge grounds. Our study provides further evidence of the role played by sponge grounds in shaping community structure and biodiversity of associated deep-sea epibenthic and fish communities. The mechanism for biodiversity enhancement within the sponge grounds formed by V. pourtalesi is likely the combined effect of both the sponge itself and its attachment substrate, which together comprise the habitat of the sponge grounds. We also discuss the role of habitat provision between the mixed-species tetractinellid sponges of the Flemish Cap and the monospecific glass sponge grounds of Emerald Basin. Please refer to the following citation for additional details on the data: Hawkes N, Korabik M, Beazley L, Rapp HT, Xavier JR, Kenchington E (2019) Glass sponge grounds on the Scotian Shelf and their associated biodiversity. Mar Ecol Prog Ser 614:91-109. https://doi.org/10.3354/meps12903 Cite this data as: Hawkes, Nickolas; Korabik, Michelle; Beazley, Lindsay; Rapp, Hans Tore; Xavier, Joana; Kenchington, Ellen (2019) Glass sponge grounds on the Scotian Shelf and their associated biodiversity. Published September 2023.Ocean Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/83c8e9af-ad3a-40bc-b1b7-d1ed4a069330

Since 1985, research surveys targeting scallops—primarily the sea scallop (Placopecten magellanicus) and, to a lesser extent, the Icelandic scallop (Chlamys islandica)—have been conducted by Fisheries and Oceans Canada (DFO) at one- or two-year intervals around the Magdalen Islands (fishing area 20A). The main objective of this survey is to assess the status of sea scallop stocks. The study area is situated south of the Magdalen Islands, where scallop beds are typically sampled at depths ranging from approximately 25 to 35 m. Sampling stations are randomly selected from a predetermined fixed grid, with sampling conducted along transects at these randomly assigned locations within the study area. Each station is sampled using a lined Digby scallop dredge (20 mm mesh size), towed for roughly 500 m across the seabed. This publication includes three files: the file biometriePetoncle_20, which contains detailed biometric data (species, size, weights and sex) from 1998 to 2024; the file taillePetoncle_20, which provides the size of the individuals sampled from 2009 to 2024; and the file traitPetoncle_20 which contains the abundances and densities per tow from 2009 to 2024. Data on abundances and densities per tow from 1998-2008 is available upon request. This dataset is updated every one to two years as data becomes available. A cleaning of aberrant data has been carried out. However, there is missing data in various columns of the dataset – use the data with caution. If you have any questions please contact DFO.DataManagementSAISB-GestionDonneesDAISS.MPO@dfo-mpo.gc.ca or the author. For certain time periods, associated species are identified and semi-quantitatively counted directly on the sorting table, and the results are presented in the following publications: - https://open.canada.ca/data/en/dataset/6529a4b0-f863-4568-ac71-1fa26cf68679 - https://open.canada.ca/data/en/dataset/71732ad5-5c70-4dbf-916d-a94e1380c53b 

This dataset provides various Ministry of Natural Resources and Forestry business areas with fundamental forest inventory information needed to meet their program mandates.

Forest Elevation(Ht) Covariance 2015 Coefficient of variation of first returns height (%). Represents the variability in canopy heights relative to 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) Geographic extent: Canada's forested ecosystems (~ 650 Mha) Time period: 1985–2011

Dominant Species Map 2015 The data represent dominant tree species for British Columbia forests in 2015, are based upon Landsat data and modeling, with results mapped at 30 m spatial resolution. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The map was generated with the Random Forests classifier that used predictor variables derived from Landsat time series including surface reflectance, land cover, forest disturbance, and forest structure, and ancillary variables describing the topography and position. Training and validation samples were derived from the Vegetation Resources Inventory (VRI), from a pool of polygons with homogeneous internal conditions and with low discrepancies with the remotely sensed predictions. Local models were applied over 100x100 km tiles that considered training samples from the 5x5 neighbouring tiles to avoid edge effects. An overall accuracy of 72% was found for the species which occupy 80% of the forested areas. Satellite data and modeling have demonstrated the capacity for up-to-date, wall-to-wall, forest attribute maps at sub-stand level for British Columbia, Canada. BC Species Likelihood 2015 The tree species class membership likelihood distribution data included in this product focused on the province of British Columbia, based upon Landsat data and modeling, with results mapped at 30 m spatial resolution. The data represent tree species class membership likelihood in 2015. The map was generated with the Random Forests classifier that used predictor variables derived from Landsat time series including surface reflectance, land cover, forest disturbance, and forest structure, and ancillary variables describing the topography and position. Training and validation samples were derived from the Vegetation Resources Inventory (VRI) selecting from a stratified pool of polygons with homogeneous internal conditions and with low discrepancies when related to remotely sensed information. Local models were applied over 100x100 km tiles that, to avoid edge effects, considered training samples from the 5x5 neighbouring tiles. An overall accuracy of 72% was found for the species which occupy 80% of the forested areas. As an element of the mapping process, we also obtain the votes received for each class by the Random Forest models. The votes can be understood as analogous to class membership likelihoods, providing enriched information on land cover class uncertainty for use in modeling. Tree species class membership likelihoods lower than 5% have been masked and converted to zero. When using this data, please cite as: Shang, C., Coops, N.C., Wulder, M.A., White, J.C., Hermosilla, T., 2020. Update and spatial extension of strategic forest inventories using time series remote sensing and modeling. International Journal of Applied Earth Observation and Geoinformation 84, 101956. DOI: 10.1016/j.jag.2019.101956 ( Shang et al. 2020).

Leaf area index (LAI) quantified the density of vegetation irrespective of land cover. LAI quantifies the total foliage surface area per groud surface area. LAI has been identified by the Global Climate Observing System as an essential climate variable required for ecosystem,weather and climate modelling and monitoring. This product consists of annual maps of the maximum LAI during a grownig season (June-July-August) at 100m resolution covering Canada's land mass.

This is a magnitude 5.0 earthquake scenario along the Burlington Toronto Structural Zone — a fault near Toronto and its surrounding region. This fault is not known to be active but demonstrates a plausible earthquake scenario for the Toronto region.

Used within the Travellers Road Information Portal Interactive Map to convey transportation related information in both official languages. This information includes a list of locations of carpool parking lots near dozens of highway interchanges throughout Ontario. This data is best viewed using Google Earth or similar Keyhole Markup Language (KML) compatible software. For instructions on how to use Google Earth, read the [Google Earth tutorial](http://www.google.com/earth/index.html) **.** This data set is now available via the Ontario 511 Developer API at *[KML]: Keyhole Markup Language This data is related to: * This data set is now available via the [Ontario 511 Developer API](https://511on.ca/developers/doc)