This dataset provides the results obtained by Health Canada’s Canadian Radiological Monitoring Network (CRMN) for the gross alpha and beta activity concentrations in drinking water, given in units of becquerels per liter (Bq/L). More information about the CRMN network can be found on the Health Canada website (see link below). Although water quality is a matter of provincial jurisdiction, the CRMN, in collaboration with the city of Ottawa, has been conducting a targeted program to monitor the radiological content of drinking water from two water treatment plants in Ottawa, ON. The Guidelines for Canadian Drinking Water Quality recommend screening levels of 0.5 Bq/L and 1.0 Bq/L for gross alpha and gross beta activity, respectively. The screening levels are set to reflect the most restrictive Maximum Acceptable Concentrations (MACs) for specific radionuclides in drinking water. If the screening levels are not exceeded, compliance with the guidelines can be inferred. The screening levels set out in the Guidelines for Canadian Drinking Water Quality are calculated based on annual averages of radionuclides in drinking water. Short-term exposure to levels above those recommended by these guidelines does not indicate a health risk. The measured gross alpha and gross beta activity concentrations presented here are well below the screening levels set by the Guidelines for Canadian Drinking Water Quality, with only one exception to date. This occurred February 28, 2011, and was attributable to the flushing of lead pipes at the water treatment plant. It resulted in a spike of naturally occurring lead radionuclides that was dealt with immediately by the City of Ottawa. The map shows the approximate sampling location for each monitoring station. Stations are found within the associated location range.

Forest Gross Stem Volume 2015Gross stem volume. Individual tree gross volumes are calculated using species-specific allometric equations. In the measured ground plots, gross total volume per hectare is calculated by summing the gross total volume of all trees and dividing by the area of the plot (units = m3ha-1). 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

__The link: *Access the data directory* is available in the section*Dataset Description Sheets; Additional Information*__.Two products are available to know the gross commercial volume of a tree according to its diameter at chest height (DHP) and its height. Their contents and the way of using them are different, but they both make it possible to obtain gross commercial volume values per tree. The first product is the **LIN3 cubing rate**. It is presented in the form of a table where the values of the gross market volume can be extracted directly. These are local rates, i.e. the height used in the general rate equation (volume prediction models) is predicted by height-DHP relationships developed per survey unit.The second product offers increased precision. It is presented in the form of several tables where the values of the gross commercial volume do not appear directly. Rather, the content of these tables is used to apply **models for predicting the height and gross commercial volume** of a tree. Height prediction models are also developed locally at the scale of survey units. The use of the product requires consultation of the document [“Models for predicting the height and gross commercial volume of trees - Method and use”] (https://mrnf.gouv.qc.ca/nos-publications/modele-prediction-hauteur-volume/).__ 📣 Recommendation of the Forest Inventory Directorate: __ it is preferable to use models to predict height and gross market volume in territories where they are available. A new height prediction model is available when a territory obtains results from forest compilations. In the absence of these models, it is still possible to use the LIN3 cubing rate.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Organic soils in the boreal forest commonly store as much carbon as the vegetation above ground. While recent efforts through the National Forest Inventory has yielded new spatial datasets of forest structure across the vast area of Canada’s boreal forest, organic soils are poorly mapped. In this geospatial dataset, we produce a map primarily of forested and treed peatlands, those with more than 40 cm of peat accumulation and over 10% tree canopy cover. National Forest Inventory ground plots were used to identify the range of forest structure that corresponds to the presence of over 40 cm of peat soils. Areas containing that range of forest cover were identified using the National Forest Inventory k-NN forest structure maps and assigned a probability (0-100% as integer) of being a forested or treed peatland according to a statistical model. While this mapping product captures the distribution of forested and treed peatlands at a 250 m resolution, open, completely treeless peatlands are not fully captured by this mapping product as forest cover information was used to create the maps. The methodology used in the creation of this product is described in:Thompson DK, Simpson BN, Beaudoin A. 2016. Using forest structure to predict the distribution of treed boreal peatlands in Canada. Forest Ecology and Management, 372, 19-27. https://cfs.nrcan.gc.ca/publications?id=36751 This distribution uses an updated forest attribute layer current to 2011 from:Beaudoin A, Bernier PY, Villemaire P, Guindon L, Guo XJ. 2017. Species composition, forest properties and land cover types across Canada’s forests at 250m resolution for 2001 and 2011. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/ec9e2659-1c29-4ddb-87a2-6aced147a990 Additionally, this distribution varies slightly from the original published in 2016 in that here slope data is derived from the CDEM: https://open.canada.ca/data/en/dataset/7f245e4d-76c2-4caa-951a-45d1d2051333 The above peatland probability map was further processed to delineate bogs vs fens (based on mapped Larix content via the k-NN maps), as well as an approximation of the extent of open peatlands using EOSD data. The result is a 9-type peatland map with a more complete methodology as detailed in: Webster, K. L., Bhatti, J. S., Thompson, D. K., Nelson, S. A., Shaw, C. H., Bona, K. A., Hayne, S. L., & Kurz, W. A. (2018). Spatially-integrated estimates of net ecosystem exchange and methane fluxes from Canadian peatlands. Carbon Balance and Management, 13(1), 16. https://doi.org/10.1186/s13021-018-0105-5 In plain text, the legend for the 9-class map is as follows:value="0" label="not peat" alpha="0"value="1" label="Open Bog" alpha="255" color="#0a4b32"value="2" label="Open Poor Fen" alpha="255" color="#5c5430"value="3" label="Open Rich Fen" alpha="255" color="#792652"value="4" label="Treed Bog" alpha="255" color="#6a917b"value="5" label="Treed Poor Fen" alpha="255" color="#aba476"value="6" label="Treed Rich Fen" alpha="255" color="#af7a8f"value="7" label="Forested Bog" alpha="255" color="#aad7bf"value="8" label="Forested Poor Fen" alpha="255" color="#fbfabc"value="9" label="Forested Rich Fen" alpha="255" color="#ffb6db"This colour scale is given in qml/xml format in the resources below. The 9-type peatland map from Webster et al 2018 was further refined slightly following two simple conditions: (1) any 250-m raster cell with greater than 40% pine content is classified as upland (non-peat); (2) all 250-m raster cells classified as water or agriculture via the NRCan North American Land Cover Monitoring System (https://doi.org/10.3390/rs9111098) is also classified as non-peatland (value of zero in the 9-class map. This mapping scheme was used at a regional scale in the following paper: Thompson, D. K., Simpson, B. N., Whitman, E., Barber, Q. E., & Parisien, M.-A. (2019). Peatland Hydrological Dynamics as A Driver of Landscape Connectivity and Fire Activity in the Boreal Plain of Canada. Forests, 10(7), 534. https://doi.org/10.3390/f10070534 And is reproduced here at a national scale. Note that this mapping product does not fully capture all permafrost peatland features covered by open canopy spruce woodland with lichen ground cover. Nor are treeless peatlands near the northern treeline captured in the training data, resulting in unknown mapping quality in those regions.

The Automatic Identification System (AIS) is a global, satellite-based and terrestrial-based ship tracking system that uses shipborne equipment to remotely track vessel identification and positional information and is typically required on vessels of 300 gross tonnage or more on an international voyage, of 500 gross tonnage or more not on an international voyage, and passenger ships of all sizes. AIS tracking technologies are primarily used in support of real-time maritime domain awareness and for maritime security and safety of life at sea. This report describes a geographic information system (GIS) analysis of 2019 AIS data to produce yearly and monthly vessel density maps of all vessel classes combined and yearly density maps of each vessel class. The year 2019 was selected to portray shipping densities in a pre-COVID 19 pandemic depiction of the maritime transport sector in the Northwest Atlantic. Vessel density map applications include use in spatial analysis and decision support for marine spatial planning.

The “Gross and Effective Drainage Area Boundaries of the AAFC Watersheds Project - 2013” dataset is a geospatial data layer containing line features representing boundaries associated with the ‘incremental gross drainage areas’ of the Agriculture and Agri-Food Canada (AAFC) Watersheds Project. The project is subdivided by hydrometric gauging station. The maximum area that could contribute runoff to each station, less that of its upstream neighbour(s) is called its ‘incremental gross drainage area’. Two types of boundary are provided: ‘gross’ and ‘effective’. ‘Gross’ boundaries separate adjacent incremental gross drainage areas. ‘Effective’ boundaries delimit, within each incremental gross drainage area, the separation between areas that supply runoff, based on average runoff, from those that don’t.

The "Areas of Non-Contributing Drainage within Total Gross Drainage Areas of the AAFC Watersheds Project - 2013" dataset is a geospatial data layer containing polygon features representing the areas within the “total gross drainage areas” of each gauging station of the Agriculture and Agri-Food Canada (AAFC) Watersheds Project that DO NOT contribute to average runoff. A “total gross drainage area” is the maximum area that could contribute runoff for a single gauging station – the “areas of non-contributing drainage” are those parts of that “total gross drainage area” that DO NOT contribute to average runoff. For each “total gross drainage area” there can be none to several unconnected “areas of non-contributing drainage”.These polygons may overlap with those from other gauging stations’ “total gross drainage area”, as upstream land surfaces form part of multiple downstream gauging stations’ “total gross drainage areas”.

An abiotic damage event is a non-biological event -- such as wind or an ice storm -- that has damaged areas of forested land. Abiotic damage event information is mainly used to: * generate summary maps for these events at a general or provincial scale * monitor the extent of damage for forest fire prevention purposes * calculate gross timber volume loss estimates caused by these events This product requires the use of geographic information system (GIS) software.

The Automatic Identification System (AIS) is a global, satellite-based and terrestrial-based ship tracking system that uses shipborne equipment to remotely track vessel identification and positional information and is typically required on vessels of 300 gross tonnage or more on an international voyage, of 500 gross tonnage or more not on an international voyage, and passenger ships of all sizes. AIS tracking technologies are primarily used in support of real-time maritime domain awareness and for maritime security and safety of life at sea. This report describes a geographic information system (GIS) analysis of 2019 AIS data to produce yearly and monthly vessel density maps of all vessel classes combined and yearly density maps of each vessel class. The year 2019 was selected to portray shipping densities in a pre-COVID 19 pandemic depiction of the maritime transport sector in the Northwest Atlantic. Vessel density map applications include use in spatial analysis and decision support for marine spatial planning. In 2023 the process was applied to the years 2013 through to 2022 and were made available using the same processes that were applied to the original 2019 datasets.

The Automatic Identification System (AIS) is a global, satellite-based and terrestrial-based ship tracking system that uses shipborne equipment to remotely track vessel identification and positional information and is typically required on vessels of 300 gross tonnage or more on an international voyage, of 500 gross tonnage or more not on an international voyage, and passenger ships of all sizes. AIS tracking technologies are primarily used in support of real-time maritime domain awareness and for maritime security and safety of life at sea. This report describes a geographic information system (GIS) analysis of 2019 AIS data to produce yearly and monthly vessel density maps of all vessel classes combined and yearly density maps of each vessel class. The year 2019 was selected to portray shipping densities in a pre-COVID 19 pandemic depiction of the maritime transport sector in the Northwest Atlantic. Vessel density map applications include use in spatial analysis and decision support for marine spatial planning.In 2023 the process was applied to the years 2013 through to 2022 and were made available using the same processes that were applied to the original 2019 datasets.