Electoral districts for the 2021 municipal election. **Collection context** Creation of districts in collaboration with the legal services and the electoral data of the Chief Electoral Officer (DGE). Balancing of districts according to anthropogenic constraints and number of voters. **Collection method** Analysis and creation with computer-aided mapping software. **Attributes** * `DISTRIC_NAME` (`varchar`): District name * `NO` (`long`): Number * `AREA` (`varchar`): Area * `COUNCIL_NAME` (`varchar`): Counsellor name * `SOURCE` (`varchar`): Source * `DATE_CREAT` (`date`): Creation date * `DATE_MODIF` (`date`): Date of modification * `USER_MODIF` (`varchar`): Modified by For more information, consult the metadata on the Isogeo catalog (OpenCatalog link).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Geometric and conventional representation of the hydrographic network. The 3D hydrographic layer is represented by several natural or physical elements associated with the presence of water. These elements form part of the layers in the digital cartographic compilation.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The assessment of the status of eelgrass (Zostera marina) beds at the bay-scale in turbid, shallow estuaries is problematic. The bay-scale assessment (i.e., tens of km) of eelgrass beds usually involves remote sensing methods such as aerial photography or satellite imagery. These methods can fail if the water column is turbid, as is the case for many shallow estuaries on Canada’s eastern seaboard. A novel towfish package was developed for the bay-scale assessment of eelgrass beds irrespective of water column turbidity. The towfish consisted of an underwater video camera with scaling lasers, sidescan sonar and a transponder-based positioning system. The towfish was deployed along predetermined transects in three northern New Brunswick estuaries. Maps were created of eelgrass cover and health (epiphyte load) and ancillary bottom features such as benthic algal growth, bacterial mats (Beggiatoa) and oysters. All three estuaries had accumulations of material reminiscent of the oomycete Leptomitus, although it was not positively identified in our study. Tabusintac held the most extensive eelgrass beds of the best health. Cocagne had the lowest scores for eelgrass health, while Bouctouche was slightly better. The towfish method proved to be cost effective and useful for the bay-scale assessment of eelgrass beds to sub-meter precision in real time. Cite this data as: Vandermeulen H. Data of: Bay Scale Assessment of Eelgrass Beds Using Sidescan and Video -Tabusintac 2008. Published: March 2021. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/d1c58bc6-69d4-47b2-bb19-988f88233900

In 2019, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Sentinel-2) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the PEI Department of Environment, Water and Climate Change and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Kentville, Charlottetown, Fredericton, and Guelph.

Polygonal layer of electoral districts for the 2025 election. New balancing of voters increases the number of districts from 11 to 10 for the 2025 election. **Collection context** Review committee to balance the districts according to the data of the Chief Electoral Officer. **Collection method** Analysis of voters by address using cartographic analysis software. Update by computer-aided mapping. **Attributes** * `ID_SEC_DISTRICT_ELEC` (`integer`): Identifier * `DISTRICT_NAME` (`varchar`): District name * `NO` (`integer`): Number * `AREA` (`varchar`): Area * `ADVISOR_NAME` (`varchar`): Recommended * `SOURCE` (`varchar`): Source * `DATE_CREATION` (`smalldatetime`): Created on * `DATE_MODIFICATION` (`smalldatetime`): Modified on * `USER_MODIFICATION` (`varchar`): Modified by For more information, consult the metadata on the Isogeo catalog (OpenCatalog link).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Monthly mean currents from Bedford Institute of Oceanography North Atlantic Model (BNAM) results were averaged over 1990 to 2015 period to create monthly mean climatology for the Northwest Atlantic Ocean, which can be considered as a representation of the climatological state of the Northwest Atlantic Ocean. The BNAM model is eddy-resolving, NEMO-based ice-ocean coupled North Atlantic Ocean model developed at the Bedford Institute of Oceanography (BIO) to support DFO monitoring programs. The data available here is monthly climatology for eight selected depths (surface, 110 m, 156 m, 222 m, 318 m, 541 m, 1062 m, bottom) in 1/12 degree spatial resolution. The data for each month from 1990 until present for the entire model domain ( 8°–75°N latitude and 100°W–30°E longitude) and various depths is available upon request. The 1990-2017 model hindcast result is compared with observational data from surface drifter and satellite altimetry. The model demonstrates good skill in simulating surface currents, winter convection events in the Labrador Sea, and the Atlantic Meridional Overturning Circulation as observed at 26.5°N and 41°N. Model results have been used to interpret changes in the Labrador Current and observed warming events on the Scotian Shelf, and are reported through the annual AZMP Canadian Science Advisory Secretariat Process. When using data please cite following: Wang, Z., Lu, Y., Greenan, B., Brickman, D., and DeTracey, B., 2018. BNAM: An eddy resolving North Atlantic Ocean model to support ocean monitoring. Can. Tech. Rep. Hydrogr. Ocean. Sci. 327: vii + 18p

This layer details Important Areas (IAs) relevant to important geographic features in the Strait of Georgia (SOG) ecoregion. This data was mapped to inform the selection of marine Ecologically and Biologically Significant Areas (EBSA). Experts have indicated that these areas are relevant based upon their high ranking in one or more of three criteria (Uniqueness, Aggregation, and Fitness Consequences). The distribution of IAs within ecoregions is used in the designation of EBSAs. Canada’s Oceans Act provides the legislative framework for an integrated ecosystem approach to management in Canadian oceans, particularly in areas considered ecologically or biologically significant. DFO has developed general guidance for the identification of ecologically or biologically significant areas. The criteria for defining such areas include uniqueness, aggregation, fitness consequences, resilience, and naturalness. This science advisory process identifies proposed EBSAs in Canadian Pacific marine waters, specifically in the Strait of Georgia (SOG), along the west coast of Vancouver Island (WCVI, southern shelf ecoregion), and in the Pacific North Coast Integrated Management Area (PNCIMA, northern shelf ecoregion). Initial assessment of IAs in PNCIMA was carried out in September 2004 to March 2005 with spatial data collection coordinated by Cathryn Clarke. Subsequent efforts in WCVI and SOG were conducted in 2009, and may have used different scientific advisors, temporal extents, data, and assessment methods. WCVI and SOG IA assessment in some cases revisits data collected for PNCIMA, but should be treated as a separate effort. Other datasets in this series detail IAs for birds, cetaceans, coral and sponges, fish, invertebrates, and other vertebrates. Though data collection is considered complete, the emergence of significant new data may merit revisiting of IAs on a case by case basis.

The National Ecological Framework for Canada's "Land and Water Area by Province/Territory and Ecoregion” dataset provides land and water area values by province or territory for the Ecoregion framework polygon, in hectares. It includes codes and their English and French descriptions for a polygon’s province or territory, total area, land-only area and large water body area.

This dataset is a contribution to the development of a kelp distribution vector dataset. Bull kelp (Nereocystis leutkeana) and giant kelp (Macrocystis pyrifera) are important canopy-forming kelp species found in marine nearshore habitats on the West coast of Canada. Often referred to as a foundation species, beds of kelp form structural underwater forests that offer habitat for fishes and invertebrates. Despite its far-ranging importance, kelp has experienced a decline in the west coast of North America. The losses have been in response to direct harvest, increase in herbivores through the removal of predators by fisheries or diseases, increase in water turbidity from shoreline development as well as sea temperature change, ocean acidification, and increased storm activates. Understanding these impacts and the level of resilience of different kelp populations requires spatiotemporal baselines of kelp distribution. The area covered by this dataset includes the BC coast and extends to portions of the Washington and Alaska coasts. This dataset was created using 137 British Admiralty (BA) charts, including insets, with scales ranging from 1:6,080 to 1:500,000, created between 1858 and 1956. All surveys were based on triangulation, in which a sextant or theodolite was used to determine latitude and angles, while a chronometer was used to help determine longitude. First, each BA chart was scanned by the Canadian Hydrographic Service (CHS) using the CHS Colortrac large format scanner, and saved as a Tagged Image Format at 200 DPI, which was deemed sufficient resolution to properly visualize all the features of interest. Subsequently, the scanned charts were imported into ESRI ArcMap and georeferenced directly to WGS84 using CHS georeferencing standards and principles (charts.gc.ca). In order to minimize error, a hierarchy of control points was used, ranging from high survey order control points to comparing conspicuous stable rock features apparent in satellite imagery. The georeferencing result was further validated against satellite imagery, CHS charts and fieldsheets, the CHS-Pacific High Water Line (charts.gc.ca), and adjacent and overlapping BA charts. Finally, the kelp features were digitized, and corresponding chart information (scale, chart number, title, survey start year, survey end year, and comments) was added as attributes to each feature. Given the observed differences in kelp feature representation at different scales, when digitizing kelp features, polygons were used to represent the discrete observations, and as such, they represent presence of kelp and not kelp area. Polygons were created by tracing around the kelp feature, aiming to keep the outline close to the stipe and blades. The accuracy of the location of the digitized kelp features was defined using a reliability criterion, which considers the location of the digitized kelp feature (polygon) in relation to the local depth in which the feature occurs. For this, we defined a depth threshold of 40 m to represent a low likelihood of kelp habitat in areas deeper than the threshold. An accuracy assessment of the digitized kelp features concluded that 99% of the kelp features occurred in expected areas within a depth of less than 40 m, and only about 1% of the features occurred completely outside of this depth.

Canada’s landmass is very diversified and comprises 7 distinctive areas called physiographic regions, each of which has its own unique topography and geology. Physiographic regions are large areas that share similar relief and landforms shaped by common geomorphic processes and geological history. Physiographic regions are often used to describe Canada’s geography to show regional differences in climate, vegetation, population and the economy. This dataset collection contains three interrelated datasets mapping the location of Canada’s 7 different physiographic regions, their 21 subregions and many divisions (landforms).