The Aquatic Invasive Species Surveillance Database is a compilation of fish community and habitat data from DFO’s Aquatic Invasive Species and Invasive Carp Program early detection surveillance efforts in Canadian waters of the Great Lakes basin. Data includes: sampling site location, date, fish species and counts, and associated habitat information. Annual project-specific details including purpose/objectives and study methodology are often reported in the DFO Canadian manuscript report of fisheries and aquatic sciences series.

Terrestrial gamma dose rates, reported as ambient dose equivalent in the unit nSv/hr, are presented for the past ninety days to present. Data is measured in real-time, typically from the spectroscopic dosimeters of the Fixed Point Surveillance (FPS) Network. Dosimeter stations of this network are located in population centres and other strategic locations across Canada. Real-time data provides insight into typical levels and fluctuations of radioactivity in the Canadian environment and may provide early detection of sources of radioactivity other than environmental background.As a reference, the Nuclear facilities and uranium mines and mills can be found in the Open Maps catalogue and added to the Real-time Environmental Radioactivity Monitoring map using the "+" (Add layer) button of the viewer’s “Layers” panel. The ESRI REST or WMS links found in the Nuclear facilities and uranium mines and mills metadata record is what’s needed to proceed. These links can be found here: https://open.canada.ca/data/en/dataset/6478153c-829f-4649-bd52-41f63b41021f.The Nuclear facilities and uranium mines and mills dataset provided by the Canadian Nuclear Safety Commission corresponds to nuclear licensees that operate across Canada. This includes uranium mines and mills, uranium processing facilities, nuclear power plants, research reactors, particle accelerators, and nuclear substances processing facilities. All of these facilities are licensed by the CNSC and are subject to strict regulatory oversight to ensure the safety of the public and the environment.

**PLEASE NOTE:** The Environmental Monitoring System will be replaced by the EnMoDS system on March 5th, 2026. The EMS results objects will not contain new data after Feb 26th, 2026. For recent data please refer to [EnMoDS spatial locations and location groups](https://catalogue.data.gov.bc.ca/dataset/f60fa4b5-e3d0-44ad-b5d8-778d29764e34). Environmental Monitoring Stations (EMS) spatial points coverage for the Province by LOCATION TYPES. The following spatial layers reference this as a data source: 1. Environmental Monitoring - All Stations 2. Environmental Monitoring Stations - Air Monitoring (Ambient Air Site) 3. Environmental Monitoring Stations - Air Monitoring (Air Permit) 4. Environmental Monitoring Stations - Water Sites (Water Monitoring) 5. Environmental Monitoring Stations - Water Sites (Water Permits) 6. Environmental Monitoring Stations - Water Sites (Well) 7. Environmental Monitoring Stations - Water Sites (Observation Well) 8. Environmental Monitoring Stations - Water Sites (Spring)

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 European Green Crab (EGC) is a high-risk global invader that can devastate coastal marine ecosystems by displacing native species, degrading and disturbing native habitats (including eelgrass), and altering food webs. EGC has recently been detected in the Canadian portion of the Salish Sea. As EGC continue to establish in the region, identifying locations on which to focus limited monitoring resources is an ongoing problem given the vast amount of coastal habitat that could be occupied by the species. A variety of methods can be used to identify highly suitable habitats for EGC at a range of spatial scales. However, none have been evaluated in the context of informing EGC management, nor for the Canadian portion of the Salish Sea. Here we evaluate five individual methods developed to assess habitat suitability for EGC (i.e., MaxEnt, stochastic gradient boosted linear and logistic regression models, a rapid site selection tool, and a qualitative site assessment and ranking tool) and five derived models generated by multiplying the outputs of these individual models. Each model relied on slightly different environmental and habitat input variables affecting EGC invasion success. Thus, rather than identifying a single preferred model, we used a multi-model ensemble approach to identify sites that are expected to be most suitable for the species. The ensemble approach likely increases predictive power by including both environmental and habitat characteristics when identifying priority sites for early detection/monitoring for EGC in the Canadian waters of the Salish Sea. Finally, we describe how the models evaluated here, alone or in combination, could be used to identify additional sites either within the Salish Sea or into new areas.This dataset contains predicted habitat suitability from five models for European Green Crab at beaches in the Salish Sea (British Columbia, Pacific Region).

The data in this dataset represent an amalgamation of sea turtle sighting, stranding, and entrapment events, mainly near Newfoundland and Labrador (NL), Canada.This document summarises the detection events data for Leatherback (Dermochelys coriacea), Loggerhead (Caretta caretta), and Green (Chelonia mydas) Turtles that has been collected from opportunistic and systematic survey sources, plus stranding and entrapment records, in the waters of NL from 1946 to 2023. To a much lesser extent there are also detection records for the southern Gulf of St. Lawrence. Scotian Shelf, and northeastern U.S. waters.These detection records are mostly derived from opportunistic reports, so there are rarely data for a report that includes measures of the observer effort expended to make the detection, and rarely associated imagery. During DFO aerial surveys there are measures of effort in most cases, enabling the turtle sightings reports to be used in habitat modelling (e.g., Mosnier et al. 2018).Most of the information variables (such as “Date”, “Latitude”, “Longitude”, “Number of Animals”) have been obtained from the detection report. In some cases data for variables such as “Location Reliability”, “ID Reliability”, “Platform”, and “Strand or Entrapment Outcome” were derived from interpretation of the comments associated with the report, if available. For description of the variables in the dataset see the Data Dictionary.References:Mosnier, A., Gosselin, J.-F., Lawson, J., Plourde, S., and Lesage, V. 2018. Predicting seasonal occurrence of leatherback turtles (Dermochelys coriacea) in eastern Canadian waters from turtle and sunfish (Mola mola) sighting data and habitat characteristics. Can. J. Zool. 97: 464-478. https://doi.org/10.1139/cjz-2018-0167

This is point GIS coverage consisting of either fire ignition locations or centroids of fire polygons where the exact location of fire ignition were unknown for fires within the Yukon, spanning a period from 1946 to present. Although the temporal scale of the coverage goes back to late 1940's, Yukon-wide fire detection capability was not fully developed until the 1960's. In addition to this, access to regular aerial mapping was not readily available until that same time period. As a result many fires in the 40's and 50's were simply not recorded or poorly mapped, particularly in the north. For that reason, care must be taken when drawing conclusions from these data as it relates to the early years. Starting 2020 on the data was automatically updated using iFMS.Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

PURPOSE: Adult salmon logbook data are collected annually from volunteer anglers on the Margaree River. These data are used as an input to the current model for predicting abundance of large and small salmon returns each season in the Margaree River (Breau and Chaput 2012). Logbook data used in the model come from in-season fishing. However, the dataset provided also includes early out-of-season fishing that was conducted by volunteer anglers under a scientific license issued by DFO. This early out-of-season early fishing began in 2015 as a pilot project and ran until 2023. DESCRIPTION: Tabularized data from logbooks of anglers in SFA 18A and 18B PHYSICAL SAMPLE DETAILS: LogbooksUSE LIMITATION: To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

The Canadian Nuclear Safety Commission (CNSC) is publishing a database with environmental monitoring results collected as part of the Eastern Athabasca Regional Monitoring Program. The samples are collected near communities located in northern Saskatchewan.

Harvest changes occurred from 1985 to 2022 displaying the year of greatest harvest disturbance. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The information outcomes represent 38 years of harvest activity in Canada's forests, derived from a single, consistent, spatially explicit data source in a fully automated manner. Time series of Landsat data with 30 m spatial resolution were used to characterize national trends in stand replacing forest disturbances caused by harvest 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. https://doi.org/10.1080/17538947.2016.1187673 ( 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).Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G. W. Hobart, (2017). Updating Landsat time series of surface-reflectance composites and forest change products with new observations. International Journal of Applied Earth Observation and Geoinformation. 63,104-111. https://doi.org/10.1016/j.jag.2017.07.013 (Hermosilla et al. 2017)