Training Schedule is a listing of all in-class training provided to our clients. It lists the location, name of training, duration, and dates.

The Essential Skills Playbook projects map is developed to highlight projects featured as part of the “Essential Skills Playbook” published by the Office of Literacy and Essential Skills program (OLES) at Employment and Social Development Canada (ESDC). The playbook is developed as a showcase for sharing promising practices, case studies and partnerships, based on OLES projects that were funded through grants and contributions generally dating back to 2012. This map allows users to visualize OLES-funded projects and explore various data variables such as the targeted groups, essential skills, and industry sectors of each project.

Driver Training Schools licenced to provide theory and/or practical lessons.

A listing of training institutions within Nova Scotia providing accredited early education degrees and diplomas.

List and geolocation of specialized training organizations in the arts supported by the Operational Assistance program of the Ministry of Culture and Communications in 2015-2016 (https://www.mcc.gouv.qc.ca/index.php?id=1645).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Dominant Species Map 2015The 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. 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 2015The 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).

High-resolution annual forest land cover maps for Canada's forested ecosystems (1984-2022). The annual time series of forest land cover maps are national in scope (entire 650 million hectare forested ecosystem) and represent a wall-to-wall land cover characterization yearly from 1984 to 2022. These time-series land cover maps were produced from annual time-series of Landsat image composites, forest change information, and ancillary topographic and hydrologic data following the framework described in Hermosilla et al. (2022), which builds upon the approach introduced in Hermosilla et al. (2018). The methodological innovations included (i) a refined training pool derived from existing land cover products using airborne and spaceborne measures of forest structure; (ii) selection of training samples proportionally to the land cover distribution using a distance-weighted approach; and (iii) generation of regional classification models using a 150x150 km tiling system. Maps are post-processed using disturbance information to ensure logical class transitions over time using a Hidden Markov Model. Hidden Markov Models assess individual year class likelihoods to reduce variability and possible noise in year-on-year class assignments (for instances when class likelihoods are similar).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. Vol. 268, No. 112780. https://doi.org/10.1016/j.rse.2021.112780. ( Hermosilla et al. 2022)Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G. W. Hobart, (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.DOI: 10.1080/07038992.2018.1437719 ( Hermosilla et al. 2018).

The Department of National Defence has designated Firing Practice and Exercise Areas off the coasts of Canada. Activities in these areas may include bombing practice from aircraft, air-to-air, air-to-sea or ground firing, and anti-aircraft firing, etc. In Atlantic Canada, the Nova Scotia Area includes sea area employments for sub-surface operations and firing exercises (FIREX). The Gulf of St. Lawrence Area, excluding the French territorial waters of Saint-Pierre et Miquelon, includes sea area employments for sub-surface operations and underwater demolition training. For full details, see the Notices to Mariners, Section F, National Defence Military Notices, available online: https://www.notmar.gc.ca/publications/annual-annuel/section-f/f35-en.pdf.Legal Constraints: Users should be aware that the polygons depicting firing practice and exercise areas are intended for illustration only and should not be used for navigational or legal purposes.

In 2010 the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) continued the process of generating annual crop inventory digital maps using satellite imagery. Focusing on the Prairie Provinces, a Decision Tree (DT) based methodology was applied using both optical (AWiFS, Landsat-5, DMC) and radar (RADARSAT-2) based satellite imagery, and having a final spatial resolution of 56m. Methods were also developed to enhance the optical classification with RADARSAT-2 imagery, addressing issues associated with cloud cover. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues. The overall process for Crop Inventory Map includes: satellite data acquisition; field data acquisition for classification training and accuracy assessment; and, operational implementation of the classification methodology.

This data is used for referencing spatial and tabular Forest Resource Processing Facility information. Each facility has one or more processing sites, each dedicated to processing resources for a specific purpose. For example, a pulp, paper and paperboard facility has one processing site to produce pulp, and another one dedicated to producing paper. All facilities that use 1,000 cubic metres or more of forest resources in one year must have a facility licence. This data class has been remodeled in 2014 to make it more flexible as a stand-alone product.