Forest Elevation(Ht) Mean 2015 Mean height of lidar first returns (m). Represents the mean canopy height. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Products relating the structure of Canada's forested ecosystems have been generated and made openly accessible. The shared products are based upon peer-reviewed science and relate aspects of forest structure including: (i) metrics calculated directly from the lidar point cloud with heights normalized to heights above the ground surface (e.g., canopy cover, height), and (ii) modelled inventory attributes, derived using an area-based approach generated by using co-located ground plot and ALS data (e.g., volume, biomass). Forest structure estimates were generated by combining information from lidar plots (Wulder et al. 2012) with Landsat pixel-based composites (White et al. 2014; Hermosilla et al. 2016) using a nearest neighbour imputation approach with a Random Forests-based distance metric. These products were generated for strategic-level forest monitoring information needs and are not intended to support operational-level forest management. All products have a spatial resolution of 30 m. For a detailed description of the data, methods applied, and accuracy assessment results see Matasci et al. (2018). When using this data, please cite as follows: Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018b. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment 216, 697-714. Matasci et al. 2018) Wulder et al. 2018) Geographic extent: Canada's forested ecosystems (~ 650 Mha) Time period: 1985–2011

PURPOSE: To gather localized high-quality data for mapping eelgrass distribution in bays and estuaries in the Gulf Region of Atlantic Canada. DESCRIPTION: Between 2018 and 2023, a total of 48 coastal sites in New Brunswick (NB), Prince Edward Island (PE), and Nova Scotia (NS) have been fully processed for eelgrass presence/absence and depth information. An additional 18 sites from the same region and time period (2018–2023) have data collected but not yet fully processed for depth and eelgrass classification. These sites will be incorporated into the dataset as processing is completed. PARAMETERS COLLECTED: Geographic coordinates, timestamp, submerged aquatic vegetation presence. NOTES ON QUALITY CONTROL: BioSonics Visual Aquatic was used to process raw dt4 files by delineating the bottom and submerged aquatic vegetation (SAV) heights. Initial delineation of the estuary bottom was performed using an automated algorithm within the software, followed by manual adjustments to refine the delineation as needed. An algorithm was then used to delineate vegetation, which was edited visually by referring to written ground-truthing notes and underwater photos taken with a GoPro underwater camera with GPS capacity. Expert advice within DFO was used to advise the analysts on best practices and subtleties in the echograms. All efforts were made to ensure vegetation mapped was eelgrass, but in some cases, such as where the acoustic response was not clear or ground-truthing notes were lacking, it is possible that other types of SAV were included. The processed data were exported from BioSonics Visual Aquatic aggregating sets of 10 pings that were in very close proximity. Grouped pings with a vegetation canopy height >= 0.1 m were assigned an eelgrass presence (i.e., "EG_Presence") value of "Y", while grouped pings with a height < 0.1 m were assigned a presence value of "N". SAMPLING METHODS: Acoustic data were collected during the summer or early fall season (varies depending on the site) by the Southern Gulf of St. Lawrence Coalition on Sustainability (Coalition-SGSL) in partnership with Fisheries and Oceans Canada (DFO) Gulf Region. At some sites, the Province of New Brunswick's Department of Agriculture, Aquaculture and Fisheries (NBDAAF) also collected data using their boat. BioSonics MX Aquatic Habitat Echosounder units with a single beam (8.7°) 204.8kHz transducer (mounting height varied depending on the boat used) was used for data collection by all parties. Positioning was achieved using the BioSonics internal GPS through 2020, then subsequently an external GPS unit (Hemisphere S631 RTK GPS) was used to improve positioning from 1-2m accuracy to ~20cm when differential was obtained. BioSonics Visual Acquisition software was used to collect the data. USE LIMITATION: This product is provided as-is and has not been accuracy-assessed against other data. Since there were no transect-independent ground-truthing points surveyed, the accuracy of any interpolated surfaces created from this data cannot be known. Not for use without inclusion of full metadata. The data products are supplied "as they stand" and DFO does not guarantee the integrity, the completeness, or the accuracy. There were issues with the internal GPS of the BioSonics unit, and their impact on positional accuracy has yet to be determined. Beginning in 2021, an external, higher precision GPS unit was used to increase accuracy. Use of various boats and surveyors, as well as analysts, can introduce some inconsistencies in the data collection and analysis between sites and years. Site-specific characteristics such as mixed submerged aquatic vegetation can complicate mapping efforts. Shallow areas can also be challenging to delineate accurately since the bottom and/or the vegetation can extend higher than the mounted transducer. In these cases, a best estimate was used by the analyst. Weather conditions such as wind can affect the accuracy of the results, as the transponder may pitch and roll with the boat, while increased sediment in the water can interfere with the signal. Single-beam acoustic data has a very small focus footprint, which varies with depth, so it should not be considered a comprehensive bottom mapping tool. However, it does provide valuable point data that can indicate presence of vegetation, canopy height, relative depth, and ground-truthing for other mapping techniques (e.g. aerial or satellite imagery). For example, at 1 m depth, the 8.6 degree single-beam used for this work has a footprint of approximately 0.0177 square metres, and at 2 m depth that footprint becomes 0.0711 square metres.

Wildfire change magnitude 1985-2022. Spectral change magnitude for wildfires that occurred from 1985 to 2022. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The wildfire change magnitude included in this product is expressed via differenced Normalized Burn Ratio (dNBR), computed as the variation between the spectral values before and after a given change event. Higher dNBR values are related to higher burn severity. Time series of Landsat data with 30-m spatial resolution were used to characterize national trends in stand replacing forest disturbances caused by wildfire for the period 1985-2022 for Canada's 650 million-hectare forested ecosystems. When using this data, please cite as: Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, 2016. Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth 9(11), 1035-1054. (Hermosilla et al. 2016). See references below for an overview on the data processing, metric calculation, change attribution and time series change detection methods applied, as well as information on independent accuracy assessment of the data. Hermosilla, T., Wulder, M. A., White, J. C., Coops, N.C., Hobart, G.W., 2015. An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment 158, 220-234. (Hermosilla et al. 2015a). Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., 2015. Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sensing of Environment 170, 121-132. (Hermosilla et al. 2015b).

The Probabilistic Seismic Risk Model for Canada introduces a structured framework of indicators that profile the physical, social and economic dimensions of earthquake risk at the neighborhood scale. Risk metrics include measures of building damage and collapse probability, life safety and expected economic losses. An overall risk rating is also provided which aggregates the physical, social, and economic dimensions of risk. The probabilistic assessment reports information based on both the ‘total’ impact as well as the ‘percentage’ impact. It considers only damage to buildings, and their inhabitants, from earthquake shaking, and therefore does not include damage to critical infrastructure or vehicles. Losses from secondary hazards, such as aftershocks, liquefaction, landslides, or fire following are also not currently included. The information is provided at the approximate scale of Census dissemination areas, and is intended to support planning and emergency management activities in earthquake prone regions. This project is run by the Geological Survey of Canada's Public Safety Geoscience Program. For inquiries related to Canada's Seismic Risk Model, please contact Tiegan E. Hobbs at tiegan.hobbs@nrcan-rncan.gc.ca. If you are looking for our end-user interface or any associated documentation, please visit www.RiskProfiler.ca.

A research survey on Stimpson's surfclam (Mactromeris polynyma) was conducted from June 15 to June 26 2017 in the Estuary of the St. Lawrence River on the Forestville deposit (Fishing Area 1A). The primary objective of this survey was to investigate the spatial distribution of pre-commercial (< 80 mm) and commercial (≥ 80 mm) sizes of Stimpson's surfclams as well as to assess the abundance and diversity of benthic species associated with the sandy habitat of the Stimpson's surfclam. Only benthic species data associated with Stimpson's surfclam habitat are presented in this dataset. Data were collected according to a systematic sampling design consisting of 77 stations, between 7 and 45 m depth. Stations were spaced 200 m apart and dispersed along a total of 18 transects perpendicular to the bathymetry. Transects were parallel and spaced 500 m apart. Specimens were collected using a hydraulic dredge of the "New England" type with a total length of 2.29 meters and a total width of 1.68 meters, of which 1.35 meters was knife width. The dredge was lined with a 19 millimeter mesh Vexar™ to harvest small individuals. The hauls were made at a speed of 0.2-0.3 knots for a duration of 2 to 3 minutes. Start and end positions were recorded to calculate the distance traveled at each tow using the geosphere library in R. The average tow distance was approximately 25 m. The area covered at each stroke was the product of the width of the dredge blade and the distance. The three files provided (DarwinCore format) are complementary and are linked by the "eventID" key. The "event_information" file includes generic event information, including date and location. The "additional_information_event_and_occurrence" file includes sample size, sampling protocol and sampling effort, among others. The "taxon_occurrence" file includes the taxonomy of the species observed, identified to the species or lowest possible taxonomic level. For abundance and biomass estimates, contact Virginie Roy (virginie.roy@dfo-mpo.gc.ca). For quality controls, all taxonomic names were checked against the World Register of Marine Species (WoRMS) to match recognized standards. The WoRMS match was placed in the "scientificNameID" field of the occurrence file. Special cases were noted in "identificationRemarks" and selected specimens were confirmed using field photos. Data quality checks were performed using the R obistools and worrms libraries. All sampling locations were spatially validated.

The data contains the following mapping layers from the Oak Ridges Moraine Conservation Plan: * Oak Ridges Moraine Conservation Plan outer boundary * Oak Ridges Moraine Conservation Plan designations, including: * natural core area * natural linkage area * countryside area * rural settlement * Palgrave Estates residential community * settlement area The data also contains associated policy designation mapping.

Individual facilities with a specific geographic location for each reporting BC Greenhouse Gas Emissions. Part of the set of data for Industrial Facility Greenhouse Gas Emission Summaries as currently published for 2010, 2011, 2012 and 2013 by the Director under authority of s.29 of the Reporting Regulation (under authority of the Greenhouse Gas Emission Reduction (Cap and Trade) Act with full confidentiality processes completed.

This dataset provides wall-to-wall maps of forest structure across Canada's 650 million hectare forested ecosystems for the year 2022, generated at a spatial resolution of 30 m. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Structure estimates include key attributes such as canopy height, canopy cover, and aboveground biomass, derived using a combination of airborne lidar and Landsat-based spectral composites. Structure models were trained using the - lidar-plot framework - (Wulder et al. 2012), which integrates co-located airborne lidar data and ground plot measurements with Landsat time-series composites (Hermosilla et al. 2016). A Nearest Neighbour imputation approach was applied to estimate structural attributes across the full extent of Canada's forested area. These nationally consistent products are intended to support strategic-level forest monitoring and assessment and are not designed for operational forest management. For further details on the methods, accuracy assessment, and source data, see Matasci et al. (2018). Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment, 216, 697-714. https://doi.org/10.1016/j.rse.2018.07.024 (Matasci et al. 2018)