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.

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

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

Day programs provide employment, skills training, and day program opportunities for adults with disabilities in communities across Nova Scotia. They help people with disabilities develop meaningful relationships and expand skills that increase self-reliance and independence.

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. 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 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). It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). 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).

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)

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work. Each tutorial video is also accompanied by a written script, providing a step-by-step reference that users can follow alongside the video or consult afterwards.