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.

Climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground.

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 2010. The attached dataset comprises the second 5-year update (first one in 2015) bringing the data up to 2020. The original disturbance mapping was based on 30-metre resolution Landsat-5 imagery from 2008-2010. Since then, anthropogenic disturbances within 51 caribou ranges across Canada were remapped every five years to create a nationally consistent, reliable and repeatable geospatial dataset that followed a common methodology. The ranges were defined by individual provinces and territories across Canada. 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. 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 30-metre multi-band imagery at a viewing scale of 1:50,000. The same concept was followed for the 2015 and 2020 disturbance mapping and any additional disturbance features that were observed since the original mapping date, were added. The 2015 database was used as a starting point for the 2020 database. Unlike the previous iteration, features were not removed in the mapping process which was a decision made in the name of time. Interpretation was carried out based on the most recent cloud free imagery available up to mid fall for a given year. Each disturbance feature type was represented in the database by a line or polygon depending on their geometric description. Linear disturbances included: roads, railways, powerlines, seismic exploration lines, pipelines, dams, air strips, as well as unknown features. Polygonal disturbances included: cutblocks, harvest (added in 2020), mines, built-up areas, well sites, agriculture, oil and gas facilities, 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 clearly visible in the Landsat imagery at the prescribed viewing scale. In comparison to the previous mapping protocol, one enhancement to the mapping process in 2020 was the addition of CFS harvest polygons (Ref: NRCan-CFS NTEMS; Wulder 2020) into the database prior to interpretation. This considerably reduced the digitizing time for polygons and accelerated the data collection process. The CFS harvest polygons were checked before inclusion, removing some which had been generated erroneously in their process. A 2nd interpreter quality-control phase was carried out to ensure high quality, complete and consistent data collection. Subsequently, the vector data of individual linear and polygonal disturbances were buffered by a 500-metre radius, representing their extended zone of impact upon boreal caribou herds. Additionally, forest fire polygons for the past forty years (CNFDB 1981-2020) were merged into the buffered 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.

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 is part of Environment and Climate Change Canada’s Shoreline Classification and Pre-Spill database. Shoreline classification data has been developed for use by the Environmental Emergencies Program of Environment and Climate Change Canada for environmental protection purposes. Marine and freshwater shorelines are classified according to the character (substrate and form) of the upper intertidal (foreshore) or upper swash zone (Sergy, 2008). This is the area where oil from a spill usually becomes stranded and where treatment or cleanup activities take place. The basic parameter that defines the shoreline type is the material that is present in the intertidal zone. The presence or absence of sediments is a key factor in determining whether oil is stranded on the surface of a substrate or can penetrate and/or be buried. This dataset contains thousands of linear shoreline segments ranging in length from 200 m and 2 km long. The entities represent the location of the segments and their geomorphological description. There exist further fields in the attribute table for this dataset. We are currently working on standardizing our shoreline segmentation datasets and the updated data will soon be uploaded to the catalog. Sergy, G. (2008). The Shoreline Classification Scheme for SCAT and Oil Spill Response in Canada. Proceedings of the 31stArctic and Marine Oil Spill Program Technical Seminar.Environment Canada, Ottawa, ON, Pp. 811-819.

Statistically downscaled multi-model ensembles of projected change (also known as anomalies) in mean temperature (°C) are available at a 10km spatial resolution for 1951-2100. Statistically downscaled ensembles are based on output from twenty-four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCM). Downscaled daily mean temperature was calculated by averaging downscaled daily minimum and maximum temperature. Daily minimum and maximum temperature from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). Historical gridded minimum and maximum temperature datasets of Canada (ANUSPLIN) were used as the respective downscaling targets. Projected change in mean temperature (°C) is with respect to the reference period of 1986-2005. Seasonal and annual averages of projected mean temperature change to 1986-2005 are provided. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the downscaled ensembles of mean temperature change are available for the historical time period, 1901-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in statistically downscaled minimum mean temperature (°C) for four time periods (2021-2040; 2041-2060; 2061-2080; 2081-2100), with respect to the reference period of 1986-2005, for RCP2.6, RCP4.5 and RCP8.5 are also available in a range of formats. The median projected change across the ensemble of downscaled CMIP5 climate models is provided. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.

Statistically downscaled multi-model ensembles of total precipitation are available at a 10km spatial resolution for 1951-2100. Statistically downscaled ensembles are based on output from twenty-four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCM). Daily precipitation (mm/day) from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). A historical gridded precipitation dataset of Canada (ANUSPLIN) was used as the downscaling target. The 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of downscaled total precipitation (mm/day) are available for the historical time period, 1951-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.

Gridded monthly, seasonal and annual mean temperature anomalies derived from daily minimum, maximum and mean surface air temperatures (degrees Celsius) is available at a 50km resolution across Canada. The Canadian gridded data (CANGRD) are interpolated from homogenized temperature (i.e., AHCCD datasets). Homogenized temperatures incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the difference between the temperature for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal temperature anomalies were computed for the years 1948 to 2017. The data will continue to be updated every year.

The atlas provides printable maps, Web Services and downloadable data files representing seabirds at-sea densities in eastern Canada. The information provided on the open data web site can be used to identify areas where seabirds at sea are found in eastern Canada. However, low survey effort or high variation in some areas introduces uncertainty in the density estimates provided. The data and maps found on the open data web site should therefore be interpreted with an understanding of this uncertainty. Data were collected using ships of opportunity surveys and therefore spatial and seasonal coverage varies considerably. Densities are computed using distance sampling to adjust for variation in detection rates among observers and survey conditions. Depending on conditions, seabirds can be difficult to identify to species level. Therefore, densities at higher taxonomic levels are provided. more details in the document: Atlas_SeabirdsAtSea-OiseauxMarinsEnMer.pdf. By clicking on "View on Map" you will visualize a example of the density measured for all species combined from April to July - 2006-2020. ESRI REST or WMS map services can be added to your web maps or opened directly in your desktop mapping applications. These are alternatives to downloading and provide densities for all taxonomical groups and species as well as survey effort.

Statistically downscaled multi-model ensembles of projected change (also known as anomalies) in minimum temperature (°C) are available at a 10km spatial resolution for 1951-2100. Statistically downscaled ensembles are based on output from twenty-four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCM). Daily minimum temperature from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). A historical gridded minimum temperature dataset of Canada (ANUSPLIN) was used as the downscaling target. Projected change in minimum temperature (°C) is with respect to the reference period of 1986-2005. Seasonal and annual averages of projected minimum temperature change to 1986-2005 are provided. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the downscaled ensembles of minimum temperature change are available for the historical time period, 1901-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in statistically downscaled mean minimum temperature (°C) for four time periods (2021-2040; 2041-2060; 2061-2080; 2081-2100), with respect to the reference period of 1986-2005, for RCP2.6, RCP4.5 and RCP8.5 are also available in a range of formats. The median projected change across the ensemble of downscaled CMIP5 climate models is provided. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.