Proxied dataset of inshore lobster commercial fishing for 2012 - 2021 in the Newfoundland and Labrador region. Only lobster harvested from the Newfoundland and Labrador region are included, based on species sought. Commercial data for the inshore lobster fishery does not require a set of coordinates be provided for catch records. With zero georeferenced inshore lobster records, the inshore lobster fishery leaves a major data gap in one of Newfoundland and Labradors largest fisheries. The Gulf region created a lobster proxy mapping tool, which associated each commercial lobster record with the most likely 10km2 hexagon grid cell based on a number of weighted variables. The tool was adopted by the Newfoundland and Labrador region and altered to work with its own variables which include human use, habitat, accessibility, area/location, home port distance, traditional ecological knowledge and depth. Each hexagon represents the summed total weight of all records associated with a particular hexagon. The best available commercial data used in this model is derived from landings data and may not include catches that have resulted in cash/wharf sales. As a result, there are some areas of Newfoundland and Labrador that may be under represented in this dataset where wharf sales may be high. Therefore, this dataset should be viewed as a general estimation on lobster harvesting patterns within Newfoundland and Labrador.

Fetch is a proxy for wind-wave action and exposure. Estimates of fetch over a total of 39,938 km of the BC coastline were calculated at 50 m intervals, yielding 799,220 near shore fetch points. Fetch was calculated for five regions in Pacific Canada: Haida Gwaii (HG), North and Central Coast (NCC), Queen Charlotte and Johnstone Straits (QCS), Salish Sea (SoG), and West Coast Vancouver Island (WCVI). For all regions, a bearing interval of 5 degrees was used to generate fetch lines for each point along the shoreline, resulting in 72 fetch lines per point. A maximum fetch distance of 200 km was used to ensure the barrier effect of Haida Gwaii was captured. Supplementary information provided includes the fetch geometry calculator script and user guide (Gregr 2014) and a report on the fetch processing objectives, process, and results (Gregr 2015).

This point layer shows the locations of places of interest to Parks Canada, visitors, employees, or local residents. These are points that are not already mapped as Parks Canada facilities or components of facilties. Data is not necessarily complete - updates will occur weekly.

This dataset provides geospatial polygon boundaries for marine bivalve shellfish harvest area classification in Prince Edward Island, Canada. These data represent the five classification categories of marine bivalve shellfish harvest areas (Approved; Conditionally Approved; Restricted; Conditionally Restricted; and Prohibited) under the Canadian Shellfish Sanitation Program (CSSP). Data are collected by Environment and Climate Change Canada (ECCC) for the purpose of making applicable classification recommendations on the basis of sanitary and water quality survey results. ECCC recommendations are reviewed and adopted by Regional Interdepartmental Shellfish Committees prior to regulatory implementation by Fisheries and Oceans Canada (DFO). These geographic data are for illustrative purposes only; they show shellfish harvest area classifications when in Open Status. The classification may be superseded at any time by regulatory orders issued by DFO, which place areas in Closed Status, due to conditions such as sewage overflows or elevated biotoxin levels. For further information about the current status and boundary coordinates for areas under Prohibition Order, please contact your local DFO office. This dataset is 'Deprecated'. Please use updated source here. https://open.canada.ca/data/en/dataset/7aef69b5-3aaf-4d50-bb86-083031e6dc47

As part of a scientific assessment of critical habitat for boreal woodland caribou (Environment Canada 2011, see full reference in accompanying documentation), Environment Canada's Landscape Science and Technology Division was tasked with providing detailed anthropogenic disturbance mapping, across known caribou ranges, as of 2015. This data comprises a 5-year update to the mapping of 2008-2010 disturbances, and allows researchers to better understand the attributes that have a known effect on caribou population persistence. The original disturbance mapping was based on 30-metre resolution Landsat-5 imagery from 2008 -2010. The mapping process used in 2010 was repeated using 2015 Landsat imagery to create a nationally consistent, reliable and repeatable geospatial dataset that followed a common methodology. The methods developed were focused on mapping disturbances at a specific point of time, and were not designed to identify the age of disturbances, which can be of particular interest for disturbances that can be considered non-permanent, for example cutblocks. The resultant datasets were used for a caribou resource selection function (habitat modeling) and to assess overall disturbance levels on each caribou ranges. Anthropogenic disturbances within 51 caribou ranges across Canada were mapped. The ranges were defined by individual provinces and territories across Canada. Disturbances were remapped across these ranges using 2015 Landsat-8 satellite imagery to provide the most up-to-date data possible. As with the 2010 mapping project, anthropogenic disturbance was defined as any human-caused disturbance to the natural landscape that could be visually identified from Landsat imagery with 30-metre multi-band imagery at a viewing scale of 1:50,000. A minimum mapping unit MMU of 2 ha (approximately 22 contiguous 30-metre pixels) was selected. Each disturbance feature type was represented in the database by a line or polygon depending on their geometric description. Polygonal disturbances included: cutblocks, mines, reservoirs, built-up areas, well sites, agriculture, oil and gas facilities, as well as unknown features. Linear disturbances included: roads, railways, powerlines, seismic exploration lines, pipelines, dams, air strips, as well as unknown features. For each type of anthropogenic disturbance, a clear description was established (see Appendix 7.2 of the science assessment) to maintain consistency in identifying the various disturbances in the imagery by the different interpreters. Features were only digitized if they were visible in the Landsat imagery at the prescribed viewing scale. A 2nd interpreter quality-control phase was carried out to ensure high quality, complete and consistent data collection. For this 2015 update an additional, separate higher-resolution database was created by repeating the process using 15-metre panchromatic imagery. For the 30-metre database only, the line and poly data were buffered by a 500-metre radius, representing their extended zone of impact upon boreal caribou herds. Additionally, forest fire polygons were merged into the anthropogenic footprint in order to create an overall disturbance footprint. These buffered datasets were used in the calculation of range disturbance levels and for integrated risk assessment analysis.

This dataset provides marine bacteriological water quality data for bivalve shellfish harvest areas in Quebec, Canada. Shellfish harvest area water temperature and salinity data are also provided as adjuncts to the interpretation of fecal coliform density data. The latter is the indicator of fecal matter contamination monitored annually by Environment and Climate Change Canada (ECCC) within the framework of the Canadian Shellfish Sanitation Program (CSSP). The geospatial positions of the sampling sites are also provided. These data are collected by ECCC for the purpose of making recommendations on the classification of shellfish harvest area waters. ECCC recommendations are reviewed and adopted by Regional Interdepartmental Shellfish Committees prior to regulatory implementation by Fisheries and Oceans Canada (DFO). This dataset is 'Deprecated'. Please use updated source here. https://open.canada.ca/data/en/dataset/6417332a-7f37-49bd-8be9-ce0402deed2a

Hunting districts as presented in the Compendium of Migratory Bird Hunting Regulations: Quebec https://www.canada.ca/fr/environnement-changement-climatique/services/chasse-oiseaux-migrateurs-gibier/reglementation-resumes-provinciaux-territoriaux/quebec.html These boundaries are presented for information purposes only and have no legal value.

In 2021, the Canada Coast Guard (CCG) and Fisheries and Oceans Canada updated its administrative boundaries following the creation a new Arctic region. There are now 4 administrative regions in CCG (Western, Arctic, Central and Atlantic). DFO and Coast Guard Arctic Regions developed these regions in partnership with the people they serve; this important decision will lead to stronger programs and services to better meet the unique needs of our Arctic communities. DFO and CCG operations and research cover Canada's land and waters to the international boundaries (EEZ) and are in no way limited to the boundaries drawn in the map.

The joint Natural Resources Canada/Department of Fisheries and Oceans Marine Spatial Planning Program requires the highest resolution marine based bathymetric elevation data and adjacent land based topographic elevation data that are available. This digital elevation model of Canada's west coast compiles the best data available from multiple government agencies to create a regional model gridded at 10 meter spacing. The transitions between the marine and terrestrial areas are seamless creating a continuous surface of elevations for scientific research and mapping.

Eelgrass (Zostera marina) is important to waterfowl such as Atlantic Brant (Branta bernicla hrota), Canada Goose (Branta canadensis), American Black Duck (Anas rubripes), Common Goldeneye (Bucephala clangula) and Barrow's Goldeneye (Bucephala islandica). In New Brunswick eelgrass can be found along the Gulf of St. Lawrence, in protected harbours. Within this dataset are the results of eelgrass land-cover classifications using either satellite or aerial photography for seven harbours: Bouctouche (46 30’N, 64 39’W); Miscou (47.90 N, -64.55 W); Neguac (47.25 N, -65.03 W); Richibucto (46.70 N, -64.80 W); Saint-Simon (47.77 N, -64.76 W); Tracadie (47.55 N, -64.88 W); and Cocagne (46.370 N, -64.600 W). Information on each dataset is provided: 1. Bouctouche This dataset contains results from an eelgrass classification for Bouctouche Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 688 sites. Two-thirds of these sites were used to assist in image classification, while the remainder were used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 83.7% of the time in a fuzzy accuracy assessment technique, whereby those classes that were ‘off’ by one class, e.g. Good Quality eelgrass classed as Medium Quality, were given half credit towards the overall accuracy. Of 187 sites that were within the classification area, 131 were correct, 51 were "one-off", and 5 were incorrect [(131 + (51/2))/ 187 = 0.837]. 2. Miscou True colour aerial photography at 57 centimetre resolution was collected on August 20th and 24th, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 103 sites. From these sites 70% were used to assist in image classification, while the remainder were used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 96.7% of the time (30/31 = 0.967). 3. Neguac This dataset contains results from an eelgrass classification for Neguac Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 126 sites. Two-thirds of these sites were used to assist in image classification, while the remainder were used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 81% of the time in a fuzzy accuracy assessment technique, whereby those classes that were ‘off’ by one class, e.g. Good Quality eelgrass classed as Medium Quality, were given half credit towards the overall accuracy. Of 39 sites that were within the classification area, 27 were correct, 9 were "one-off", and 3 were incorrect [(27 + (9/2))/ 39 = 0.81]. 4. Richibucto Eelgrass classification in Richibucto Harbour, New Brunswick. Derived from a Quickbird satellite image collected on August 28, 2007 at as close to low-tide as possible. Quickbird's ground resolution is 2.4 m. Classification was objected-oriented using Definiens software. Accuracy was 81.5%. Data used for accuracy and training was collected along transects using a differential GPS positioned towfish holding sidescan sonar, and a video camera that was later transcribed as XY points to describe eel-grass presence. 5. Saint-Simon An eelgrass distribution map was classified from remotely sensed imagery in Shippagan Harbour, New Brunswick. Derived from a Quickbird satellite image collected on July 27, 2007 at as close to low-tide as possible. Classification was objected-oriented using Definiens software. Data used for accuracy and training was collected along transects using a differential GPS positioned towfish holding sidescan sonar, and a video camera that was later transcribed as XY points to describe eel-grass presence. 6. Tracadie This dataset contains results from an eelgrass classification for Tracadie Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 101 sites. Approximately two-thirds of these sites were used to assist in image classification, while the remainder was used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 79.3% of the time in a fuzzy accuracy assessment technique, whereby those classes that were ‘off’ by one class, e.g. Good Quality eelgrass classed as Medium Quality, were given half credit towards the overall accuracy. Of 29 sites that were within the classification area, 18 were correct, 10 were "one-off", and 1 was incorrect [(18 + (10/2))/ 29 = 0.793]. 7. Cocagne Visible orthorectified aerial photography was used to classify polygons containing eelgrass in Cocagne Harbour. Field data for image training and validation were collected along transects in summer 2008 using a dGPS positioned towfish holding sidescan sonar and a video camera that was later transcribed as XY geographic points to describe eelgrass presence and a qualitative description of density. The area was flown for photography on September 24, 2008. eCognition Developer 8 software was used to segment the imagery, essentially polygons. Polygons were then classified manually for the presence of eelgrass. Using field data revealed eelgrass presence to be mapped correctly 87.2% of the time.