Note: To visualize the data in the viewer, zoom into the area of interest. The National Air Photo Library (NAPL) of Natural Resources Canada archives over 6 million aerial photographs covering all of Canada, some of which date back to the 1920s. This collection includes Time Series of aerial orthophoto mosaics over a selection of major cities or targeted areas that allow the observation of various changes that occur over time in those selected regions. These mosaics are disseminated through the Data Cube Platform implemented by NRCan using geospatial big data management technologies. These technologies enable the rapid and efficient visualization of high-resolution geospatial data and allow for the rapid generation of dynamically derived products. The data is available as Cloud Optimized GeoTIFF (COG) for direct access and as Web Map Services (WMS) or Web Coverage Services (WCS) with a temporal dimension for consumption in Web or GIS applications. The NAPL mosaics are made from the best spatial resolution available for each time period, which means that the orthophotos composing a NAPL Time Series are not necessarily coregistrated. For this dataset, the spatial resolutions are: 75 cm for the year 1960 and 50 cm for the year 1974. The NAPL indexes and stores federal aerial photography for Canada, and maintains a comprehensive historical archive and public reference centre. The Earth Observation Data Management System (EODMS) online application allows clients to search and retrieve metadata for over 3 million out of 6 million air photos. The EODMS online application enables public and government users to search and order raw Government of Canada Earth Observation images and archived product managed by NRCan such as aerial photos and satellite imagery. To access air photos, you can visit the EODMS web site: https://eodms-sgdot.nrcan-rncan.gc.ca/index-en.html

In 2020, 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) 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, Manitoba, & Quebec; point observations from the PEI Department of Environment, Water and Climate Change; the Ontario Ministry of Agriculture, Food and Rural Affairs; and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Charlottetown, Fredericton, and Guelph. Due to COVID-19 travel restrictions, complete sampling coverages in NL, NS, NB and BC were not possible, as a result the general agriculture class (120) is found in these provinces in areas where there was no ground data collected.

Description: Spatial information on ecologically important species is needed to support marine spatial planning initiatives in British Columbia’s (BC) marine environment. For data deficient taxa, such as shark species, species distribution models that integrate presence-absence data from different sources can be used to predict their coastwide distributions. Here we provide spatial estimates of the distribution of Blue Shark (Prionace glauca), Salmon Shark (Lamna ditropis), Pacific Sleeper Shark (Somniosus pacificus) and Bluntnose Sixgill Shark (Hexanchus griseus). These estimates were generated using spatial generalized linear mixed effects models and are based on data from two scientific surveys and the commercial hook and line, midwater trawl and bottom trawl fisheries. For each species, we provide predicted probability of occurrence and prediction uncertainty at a 3 km resolution for the British Columbia coast, and parameter estimates for model covariates (depth, slope, year, data source). Results show variable predicted distributions across species, with Blue Shark and Pacific Sleeper Shark showing higher probability of presence along the continental slope, while Salmon Shark show low probability of occurrence coastwide and Bluntnose Sixgill Shark show the highest probability of occurrence in the Strait of Georgia. The results from this study can support ongoing marine spatial planning initiatives in the BC and support the conservation and management of these important species. Methods: Data Sources The species distribution models (SDMs) are based on data from two fishery independent scientific surveys and from the commercial hook and line fishery, which are all conducted within Canadian Pacific waters. The scientific surveys include the Fisheries and Oceans Canada (DFO) hard bottom longline surveys and the International Pacific Halibut Commission (IPHC) fishery-independent setline survey. The study area is bound by the outer convex hull of these three data sources. Other DFO research surveys, such as the groundfish synoptic bottom trawl surveys, midwater trawl surveys and sablefish trap surveys were investigated as potential data sources, but were found to have insufficient presence observations for the species of interest to warrant their inclusion in the analysis. For more information on the details of the source data please refer to Proudfoot et al. 2024. Modelling Approach and Comparison For each species, we fit a suite of generalized linear mixed effects models (GLMMs) using the sdmTMB package (Anderson et al. 2022). For each species, we fit four models, each with a different set of fixed effects/environmental predictors. Additionally, we compared the predictive power of four models for each species, with each model having a different combination of environmental predictors (i.e., slope, depth, slope + depth, none). A summary of the candidate models is provided in Table 2 of Proudfoot et al. 2024. For each species, we selected the model with the highest predictive accuracy (assessed using the predicted log likelihood based on the cross-validation) as the best fit. Spatial Species Distribution Predictions We made predictions of species occurrence using the selected model and a 3 km resolution spatial prediction grid. Our predictions were made for the entire BC coast, and species distribution predictions were made using models fit to the full dataset, as opposed to models fit using cross-validation. We made predictions with year set to 2014 (the approximate midpoint of the dataset) and type set to IPHC (the dataset with the most even spatial distribution of data points). Uncertainties: Because limited survey and commercial catch data exists for deep areas off the continental shelf, predictions in these areas are likely more uncertain than predictions on the shelf. To illustrate this, uncertainty (standard deviation derived from the 500 simulated values from the joint precision matrix of selected models) was mapped across the full study area for each species. Additionally, because these models are based on data that likely do not span the full spatiotemporal extent of the species’ habitat (i.e., mid depths, surface waters, and data across all seasons may not be captured), these results illustrate a snapshot of occurrence but do not account for more complex migration and movement patterns undertaken by these species.

The blue whale (Balaenopterus musculus) is a wide-ranging cetacean that can be found in all oceans, inhabiting coastal and oceanic habitats. In the North Atlantic, little is known about blue whale distribution and genetic structure, and if whether animals found in Icelandic waters, the Azores, or Northwest Africa are part of the same population as those from the Northwest Atlantic. In the Northwest Atlantic, seasonal movements of blue whales and habitat use, including the location of breeding and wintering areas, are poorly understood. The behaviour of remotely-monitored animals can be inferred from a time series of location data. This is because animals tend to demonstrate stochasticity in their movement paths as a result of spatial variation in environmental characteristics, such as topography or prey density (Curio 1976; Gardner et al. 1989; Turchin 1991; Wiens et al. 1993). Predators are expected to decrease travel speed and/or increase turning frequency and turning angle when a suitable resource, e.g., food patch, is encountered (Turchin 1991), otherwise known as area-restricted search (ARS). In contrast, animals in transit or travelling tend to move at faster and more regular speeds, with infrequent and smaller turning angles (Kareiva and Odell 1987; Turchin 1998). Based on satellite telemetry to track the seasonal movements of 24 blue whales from eastern Canada in 2002 and from 2010 to 2015, it was possible to estimate trajectories and locations where ARS behaviour of blue whales was inferred at a 4h time interval. To assess blue whale movements and behavior, a Bayesian switching statespace model (SSSM) was applied to Argos-derived telemetry data (Jonsen et al. 2005; Jonsen et al. 2013). An SSSM essentially estimates animal location at fixed time intervals, movement parameters and behavioral patterns. Two important sources of uncertainty can be measured separately: estimation error resulting from inaccurate observations (Argos location error) and process variability linked to the stochasticity of the movement process (behavior mode estimation) (Jonsen et al. 2003; Patterson et al. 2008). The points visible on land are the result of errors in the Argos geographic position calculation. They have been deliberately left unchanged to assess the performance of the model, which was able to clean up some positions, but not all. Lesage, V., Gavrilchuk, K., Andrews, R.D., and Sears, R. 2016. Wintering areas, fall movements and foraging sites of blue whales satellite-tracked in the Western North Atlantic. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/078. v + 38 p.

To assess the current distribution of kelp beds and other macroalgae in Nova Scotia and Southwest New Brunswick, subtidal drop camera surveys were performed from 2022-2023 at 140 sites. For each site, a GoPro HERO 10 camera was towed along a deep (7-12m) and shallow (3-5m) depth contour until 20 images were acquired per depth. Where possible, species were identified from photos, with particular care given to kelps (defined here as orders Laminariales and Tilopteridales) and fucoids (order Fucales). Crust-forming algae was not counted. Percent cover was calculated using a 10x10 point grid overlaid on each image and recording the dominant cover type at each point. The depth (in meters) of each photo after correcting for tide height ranged from ~0.5m to ~12m. Depths were corrected to chart datum (lowest astronomical tide) using tide predictions from the nearest tide station, taken from tides.gc.ca. Sampling was performed between July and October. Cite this data as: Krumhansl K, Brooks C, Lowen B, DiBacco C, (2025). Camera Surveys of the Subtidal Flora of Nova Scotia and Southwest New Brunswick 2022-2023. Version 1.7. Fisheries and Oceans Canada. Samplingevent dataset. https://ipt.iobis.org/obiscanada/resource?r=camera_surveys_of_the_subtidal_flora_of_nova_scotia_2022-2023&v=1.7 For additional information please see: Krumhansl K.A., Brooks C.M., Lowen B., O’Brien J., Wong M., DiBacco C. Loss, resilience and recovery of kelp forests in a region of rapid ocean warming. Annals of Botany 2024 Mar 8; 133(1):73-92. Brooks C.M., Krumhansl K.A. 2023. First record of the Asian Antithamnion sparsum Tokida, 1932 (Ceramiales, Rhodophyta) in Nova Scotia, Canada. BioInvasions Records 12(3):745-725.

The Prairie Soil Zones file shows the general distribution of major soil zones across the Prairie region of Canada. Soil zones (based on the Canadian System of Soil Classification) are named based on the dominant soil classification of the soils in each zone. Data extent is limited to the Agricultural Zone as defined in Soil Landscapes of Canada v 3.0 (Lefebvre et al. 2005).

Spiny dogfish (Squlaus acanthias), is a species found in Atlantic Canadian waters which is encountered mostly in commercial fisheries. Pop-up Satellite Archival Tags (PSAT) from Wildlife Computers were applied to spiny dogfish from 2008 to 2009 to collect data on depth (pressure), temperature and ambient light level (for position estimation). Deployments were conducted in Canada on commercial fishing vessels from August to October. Wildlife Computers PSAT Mk10 (N=6) were used and 3 of 6 tags reported. One tag was found washed up on shore and was returned. The spiny dogfish tagged ranged in size from 80 cm to 96 cm Fork Length (curved); all 6 were female. Time at liberty ranged from 75 – 234 days and the 43 tags that reported remained on the sharks for the programmed duration. Raw data transmitted from the PSAT’s after release was processed through Wildlife Computers software (GPE3) to get summary files, assuming a maximum swimming speed of 2m/s, NOAA OI SST V2 High Resolution data set for SST reference and ETOPO1-Bedrock dataset for bathymetry reference. The maximum likelihood position estimates are available in .csv and .kmz format and depth and temperature profiles are also in .csv format. Other tag outputs as well as metadata from the deployments can be obtained upon request from: warren.joyce@dfo-mpo.gc.ca or heather.bowlby@dfo-mpo.gc.ca.

The canopy is defined by the projection on the ground of the tops (crown) of trees (including leaves, branches, and trunks), which is visible from the sky. Any vegetation with a height greater than 2 m was taken into account. The mapping of the canopy was carried out using deep learning methods, based on variables (resolution of 1 m) calculated from raw airborne lidar data from 2010 to 2020. It covers the six census metropolitan areas (CMAs) of Quebec: Gatineau, Montreal, Quebec, Saguenay, Sherbrooke and Trois-Rivières. For more details on the creation of the map as well as the benefits, limitations, and potential uses, consult the * [Technote] (https://www.donneesquebec.ca/recherche/dataset/canopee-des-six-rmr-du-quebec/resource/3d6ed9d1-5760-43f8-95ed-b391b98a30bd) * (simplified version) and/or the * [methodological report] (https://www.donneesquebec.ca/recherche/dataset/canopee-des-six-rmr-du-quebec/resource/09155478-3b52-4865-bda4-3ad4189fe0a5) * (full version). The production of these data was coordinated by the National Institute of Public Health of Quebec (INSPQ) and carried out by the forest remote sensing laboratory of the Sainte-Foy Forestry Education and Research Center (CERFO). NOTE: The canopy height per pixel is available in the raster files that you can download (see the data download guide in the “Documentation” section).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The West Nile virus (WNV) activity zone corresponds to the territory where WNV cases have been documented by human, animal, and entomological (mosquito) surveillance. This zone indicates where there is a higher probability of the virus being present in Quebec based on historical data. All surveillance data was aggregated to form the WNV's area of activity over the study period, by merging the 2 km resolution buffer zones and the municipalities of each mosquito case or batch. Outside of this area, the presence of WNV remains possible, but the virus has not been detected by surveillance. This can be explained, among other things, by the movements of infected birds and mosquitoes over varying distances. The climatic zone favorable to the transmission of WNV by Culex pipiens (one of the main vectors of the virus) highlights the territory where the estimated seasonal average temperature could be conducive to the transmission of WNV in Quebec. This zone is defined by a seasonal average temperature (calculated from April to September) greater than or equal to 14°C. The indicator was calculated for historical records 1989-2018 (current distribution) and for the horizons of 2030, 2050 and 2080 according to the greenhouse gas emissions scenarios SSP2-4.5 and SSP3-7.0 (future distribution). Seasonal mean temperatures were calculated during the WNV's active period (i.e. April to September) by adding up the daily maximum and minimum temperatures and then dividing them by two. These temperatures were generated with a resolution of 10 km x 10 km covering the whole of Quebec for time horizons and greenhouse gas emission scenarios. The final value for seasonal mean temperatures used is the 50th percentile. For more information on the area of activity of the WNV or the climatic zones favorable to the transmission of WNV by Culex pipiens, you can consult the [Mapping of the current and future distribution of the West Nile virus in Quebec in the context of climate change] (https://www.inspq.qc.ca/publications/3693) OR the INSPQ website [Current and future distribution maps of zoonoses in Quebec] (https://www.inspq.qc.ca/zoonoses/cartes).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This data set presents the places of interest in the City of Montreal according to the classification carried out as part of the Montreal on Foot (MàP) initiative in 2020. The Montréal à Pied project aims to improve orientation and pedestrian paths throughout Montreal. Although the data concern the territory of the boroughs, places of interest may be located on the territory of linked cities for a better coherence of geographic information.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**