The digital elevation models (DEM) are 2 m resolution raster elevation products that were generated from the Ontario Classified Point Cloud (Imagery-Derived) data. The point clouds were created via a pixel-autocorrelation process from the stereo aerial photography of the Geospatial Ontario (GEO) imagery program. The DEM does not represent a full ‘bare-earth’ elevation surface. There are areas where there are very few points classified as ground and interpolation has occurred across the resulting voids. Points classified as ground have not been assessed for accuracy to determine if they represent true ground features. Some features are still raised above ground surface, such as larger buildings, larger forest stands and other raised features. This data is for geospatial tech specialists, and is used by government, municipalities, conservation authorities and the private sector for land use planning and environmental analysis.

Species distribution models (SDMs) are tools that combine species observations of occurrence, abundance, or biomass with environmental variables to predict the distribution of a species in unsampled locations. To produce accurate predictions of occurrence, abundance or biomass distribution, a wide range of physical and/or biological variables is desirable. Such data is often collected over limited or irregular spatial scales, and require the application of geospatial techniques to produce continuous environmental surfaces that can be used for modelling at all spatial scales. Here we provide a review of 102 environmental data layers that were compiled for the entire spatial extent of Fisheries and Oceans Canada’s (DFO) Maritimes Region. Variables were obtained from a broad range of physical and biological data sources and spatially interpolated using geostatistical methods. For each variable we document the underlying data distribution, provide relevant diagnostics of the interpolation models and an assessment of model performance, and present the final standard error and interpolation surfaces. These layers have been archived in a common (raster) format at the Bedford Institute of Oceanography to facilitate future use. Based on the diagnostic summaries in this report, a subset of these variables has subsequently been used in species distribution models to predict the distribution of deep-water corals, sponges, and other significant benthic taxa in the Maritimes Region.Cite this data as: Beazley, Lindsay; Guijarro, Javier, Lirette; Camille; Wang, Zeliang; Kenchington, Ellen (2020). Characteristics of Environmental Data Layers for Use in Species Distribution Modelling in the Maritimes Region. Published July 2023. Ocean Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/34a917cb-a0e3-403c-91c7-af3dc20628b1

A tidal resource assessment dataset for the Quatsino Sound region, British Columbia, was developed, including temporal maximum, mean, and minimum velocity magnitudes, standard deviations, and power density. The dataset was generated using a high-resolution 2D depth-averaged hydrodynamic model based on the Telemac-Mascaret solver, with Natural Neighbor interpolation applied for raster creation. This newly published dataset is the first in a series of regional tidal energy maps for Canada. Developed by CanmetENERGY Ottawa in collaboration with partners, these maps aim to support effective project planning and development by providing comprehensive tidal resource data across the country.Disclaimer:Potential errors in the model results may arise from inherent limitations in the topo-bathymetric data accuracy, assumptions in boundary conditions, approximations within the numerical methods, and the input data used in the numerical modeling. These factors introduce uncertainties that can affect the overall model outcomes. The model is subject to the following conditions:• Topo-bathymetric data: Obtained from electronic navigational charts and the Canadian Hydrographic Service’s (CHS) NONNA-10 Bathymetric Data packages, consolidating CHS-managed digital bathymetric sources with a maximum resolution of 10 m.• Tidal and current harmonic components: Used as boundary conditions from the TPXO9 global tidal model.• Model calibration and validation: Performed using data from Acoustic Doppler Current Profilers (ADCP), surface elevations recorded at CHS tidal stations, and Lagrangian drifter measurements.• Interpolation method: Dataset outputs were generated with Natural Neighbor interpolation, which assumes smoothly varying data and may not capture sharp local gradients or features.• Modeled estimates: All values for velocity magnitudes, velocity standard deviations, and power density are modeled estimates and not direct field measurements.This dataset is intended for preliminary assessment of tidal projects only. It should not be the sole basis for making critical decisions or investments. We strongly recommend further validation and in-depth analysis. Users are responsible for conducting their own due diligence and additional research to verify the data's accuracy and relevance for specific applications.By accessing and using this dataset, users acknowledge and accept these disclaimers. The providers of this dataset explicitly absolve themselves of any responsibility or liability for any consequences arising from the use, reliance upon, or interpretation of this dataset. Users are advised that their use of the dataset is at their own risk, and they assume full responsibility for any actions or decisions made based on the information contained therein. This disclaimer is in accordance with applicable laws and regulations, and by accessing or utilizing the dataset, users agree to release the providers of this dataset from any legal claims, damages, or liabilities that may arise from such use.

Description:Seasonal climatologies for Zooplankton biomass in seven size categories were calculated for the period 1990-2019. The data used were a subset of the Fisheries and Oceans Canada, Institute of Ocean Sciences Zooplankton Database. This dataset is incomplete and is regularly updated as analysis takes place. Methods:Data-Interpolating Variational Analysis (DIVA) was used for spatial. Seasonal averages were computed as the mean of yearly seasonal means. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain raster layers of seasonal Zooplankton climatology for the Canadian Pacific Exclusive Economic Zone (EEZ), with high spatial resolution of 1/300 degree.Uncertainties:Uncertainties are introduced when observational data are spatially interpolated to varying distances from the observation point. Climatological averages are calculated from these interpolated values. Caution should be used for data in inlets and nearshore as the interpolation does not perform well in these areas, data in some inlets have been masked.

Description:Seasonal climatologies (temperature, salinity, and sigma-t) of the Northeast Pacific Ocean were computed from historical observations including all available conductivity-temperature-depth (CTD), bottle, expendable bathy-thermograph (XBT), and Argo data in NOAA (http://www.argo.ucsd.edu/), Marine Environmental Data Service (MEDS), and Institute of Ocean Sciences archives over 1980 to 2010 period in spatial resolution ranging from approximately 100m to 70km.Methods:Calculations, including smooth and interpolation, were carried out in sixty-five subregions and up to fifty-two vertical levels from surface to 5000m. Seasonal averages were computed as the median of yearly seasonal values. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March.Uncertainties:Uncertainties are introduced when quality controlled observational data are spatially interpolated to varying distances from the observation point. Climatological averages are calculated from these interpolated values.

This catalog contains annual 30 m spatial resolution snow dynamics metrics for each snow-year from 2018-2019 to 2023-2024 for all of Canada. We gather all Landsat and Sentinel-2 images collected over Canada and identify the status of each pixel observation on the image collection date: snow (and ice), non-snow (i.e., land, water), unclear (i.e., clouds, shadows). We built an algorithm to calculate snow cover metrics for each pixel during each winter: start date of the first (and biggest) snow period [startF, startB], end date of the last (and biggest) snow period [endL, endB], number of days with snow cover in total (or in the biggest snow period) [lengthT, lengthB], number of snow periods (i.e., separated times with multiple confirmed snow observations) [periods], and a status classification (e.g., continuous snow, snow free) [status]. We do not obtain a clear observation every day because of satellite orbit frequencies and clouds. This means that timing-based metrics are identified by the middle date between two clear observations, with uncertainty quantified as half the length of the gap (i.e., ± days) [startF_u, startB_u, endL_u, endB_u, lengthT_u, lengthB_u].

Climate Normals and Averages are used to summarize or describe the average climatic conditions of a particular location. At the completion of each decade, Environment and Climate Change Canada updates its Climate Normals for as many locations and as many climatic characteristics as possible. The Climate Normals, Averages and Extremes offered here are based on Canadian climate stations with at least 15 years of data between 1981 to 2010.

Many geometrical schemes - or map projections - are used to represent the curved surface of the Earth on map sheets. Canada uses the Universal Transverse Mercator (UTM) system. It is called transverse because the strips run north-south rather than east-west along the equator. This data class shows a 1 km x 1 km UTM NAD83 grid that is a standard reference index for Ontario. It includes: * Military Grid Reference * Fire Base Map identifier * Fire Base Map Block * Atlas identifier

The raster maps depict a suite of forest attributes in 2001* and 2011 at 250 m by 250 m spatial resolution. The maps were produced using the k nearest neighbours method applied to MODIS imagery and trained from National Forest Inventory photo plot data. For detailed information about map production methods please refer to Beaudoin et al. (2018) "Tracking forest attributes across Canada between 2001 and 2011 using the k nearest neighbours mapping approach applied to MODIS imagery." Canadian Journal of Forest Research 48, 85-93. https://cfs.nrcan.gc.ca/publications?id=38979 The map datasets may be downloaded from https://nfi.nfis.org/downloads/nfi_knn2011.zip or https://open.canada.ca/data/en/dataset/ec9e2659-1c29-4ddb-87a2-6aced147a990 * Note: the forest composition (leading tree genus) map depicts forest attributes in 2001.How can this data be used?The resolution and accuracy of these map products are best suited for strategic-level forest reporting and informing policy and decision making at regional to national scales. As these maps also offer a coherent set of quantitative values for a large suite of forest attributes, they can be used as baseline information for modelling and in calculations such as merchantable forest volume or percentage of tree species. It is also possible to overlay these maps with other maps produced on the same pixel grid to make assessments of disturbance impacts, such as fire and harvests.

The level curves are derived from a LiDAR survey carried out in May 2014.The equidistance is 25 cm, the absolute planimetric accuracy (XY) isof approximately 40 cm and the absolute altimeter accuracy (Z) is approximately 20 cm.The urban sectors covered are the urban perimeters of Rouyn, Noranda,Granada, Évain (partially), Lac-Dufault and the airport.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**