A source protection area (SPA) is an area of land and water governed by a Source Protection Authority, an agency, person or body. This dataset defines the geographic boundaries where each SPA's terms of reference, assessment reports and source protection plans must be developed. *[SPA]: source protection area

Polygon features representing the geographical extent of a source area that contributes water to a drinking water system. A drinking water system is any domestic system servicing anything other than a single-family dwelling, as defined in the Drinking Water Protection Act. Source Area is a broad term used to describe any area that could potentially contribute water to the drinking water system; it does not mean that hydrologic analyses have been completed to determine likelihood of connectivity. These source areas most often represent the entire aquifer extent (for ground water systems) or the watershed upstream from the intake (for surface water systems). This is one of three Drinking Water Sources datasets (along with the DWS - Intakes/Wellheads points dataset, and DWS - Protection Zones polygons dataset). Note: these polygons have unique Source Areas IDs used to relate them to the Intakes/Wellhead points.

The Fish Pathogen Boundary Segment layer forms the boundary of the Fish Pathogen Management Zones and is made up of segments that were taken from the Ontario Road Network. This data class was created in order to store the road name information associated with each segment that makes up the fish pathogen management zone boundary. This product requires the use of geographic information system (GIS) software.

Kelp features were taken from digitized survey source fieldsheets produced by the Canadian Hydrographic Service (CHS). The area covered by this dataset encompasses various surveyed areas along the western coast of North America in British Columbia coastal waters. CHS has an extensive collection of hydrographic survey data in the form of field sheets based on over 100 years of surveying in Canada. Data has been collected using a wide range of methods and systems, from lead-line to modern day multi-transducer and multibeam systems. Positions have been established using the different types of terrestrial systems and methods available over many years - up to the latest advanced satellite positioning systems.Fieldsheets that had not been previously digitizted were imported into ESRI ArcMap and georeferenced directly to WGS84 using CHS georeferencing standards and principles (charts.gc.ca). In order to minimize error, a hierarchy of control points was used, ranging from high survey order control points to comparing conspicuous stable rock features apparent in satellite imagery. The georeferencing result was further validated against satellite imagery, CHS charts and fieldsheets, the CHS-Pacific High Water Line (charts.gc.ca), and adjacent and overlapping Fieldsheets. Finally, the kelp features were digitized, and corresponding chart information (category of kelp, scale, source, title, year, and comments) was added as attributes to each feature. When digitizing kelp features the points were located at the optical center of the feature being digitized. This dataset includes a point and a polygon layer. Kelp that is located on land is historically valid. Symbolized kelp is not always an exact location but indicates that kelp is present in the area. The symbol is a proxy.The kelp attribute field does not distinguish between different types of kelp. The field has three variables that are kelp, seaweed and Aquatic Plants. Seaweed is the general name for marine plants of the Algae class which grow in long narrow ribbons. (International Maritime Dictionary, 2nd Ed.)Kelp is one of an order (laminariales) of usually large, blade-shaped or vine-like brown algae. (IHO Dictionary, S-32, 5th Edition, 2611)Aquatic Plants – Aquatic plants are used as to represent vegetation in fresh water rivers and lakes.Geographically encompasses the kelp in the Western Coastal waters of North America (mainly Canada) and Temporally overlaps/continues from data extracted from the British Admiralties.

The Denali Fault spans over 200km of the Yukon Territory, and is a significant source of seismic hazard. This magnitude 7.4 earthquake scenario, centered near small communities along the Alaska Highway, visualizes the effects of a severe earthquake that could be produced by this fault.

Annual mapping of national level forest harvesting for Canada detected inclusive of 1985 to 2015 from Landsat satellite imagery. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). This dataset is composed of two layers: (1) binary harvest mask, and (2) year of harvest disturbance detection. The information outcomes represent 31 years of harvesting activity in Canada’s forests, derived from a single, consistent, spatially-explicit data source in an automated manner. Time series of Landsat data with 30-m spatial resolution were used to characterize national trends in stand replacing forest disturbances, including those attributed to harvest for the period 1985–2015 for Canada's 650 million hectare forested ecosystems (Hermosilla et al. 2016). See references below for an overview regarding the data, image processing, and time-series change detection methods applied, as well as information on independent accuracy assessment of the data. 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) For additional resources on the data used and methods applied, please see: 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., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., 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.( Hermosilla et al. 2017)

This product provides population counts for 2001 and 2016 for 105 census subdivisions (CSDs) for which the forest sector is a major source of employment income—defined by Natural Resources Canada as 20% or more of total CSD income excluding government transfers. These files were produced by Statistics Canada, Environment, Energy and Transportation Statistics Division, 2018, special tabulation from the 2001 and 2016 Census of Population; Natural Resources Canada, Canadian Forest Services, Economic Analysis Division; Canada’s National Forest Inventory (NFI), 2016, Grouped kNN Map layers, http://tree.pfc.forestry.ca (accessed April 7, 2017). Data from the 2016 Census of Population were used to identify the 105 census subdivisions. Note that changes occur to the number and the boundaries of CSDs between censuses. Adjustments were made to CSD boundaries to account for changes.Some data were suppressed for data quality reasons or to meet the confidentiality requirements of the Statistics Act. Income data were available for 3,675 of 5,162 CSDs. This analysis may therefore underreport the total number of communities for which the forest sector is a major economic driver. Note that a decline in the percentage of forest sector income may be due to a decrease in forest sector income or an increase in income from other sources. The reference period for income data in the Census of Population is the calendar year prior to the census.The forest sector includes North American Industry Classification codes 113 – forestry and logging, 1153 – support activities for forestry and logging, 321 – wood product manufacturing and 322 – paper product manufacturing.

The Forest Change Type data described here is an update to previously posted open data. The date range for this data is 2012 to 2015. The Forest Change Type data for the prior period from 1985 to 2011 can be found here: https://opendata.nfis.org/mapserver/nfis-change_eng.html or https://gcgeo.gc.ca/geonetwork/search/eng search for “Forest Change” but you must be logged in to see the data. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The forest change data included in this product is national in scope (entire forested ecosystem) and represents the first wall-to-wall characterization of wildfire and harvest in Canada at a spatial resolution commensurate with human impacts. The information outcomes represent 25 years of stand replacing change in Canada’s forests, derived from a single, consistent spatially-explicit data source, derived in a fully automated manner. This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 1985–2010 for Canada's 650 million hectare forested ecosystems (https://authors.elsevier.com/sd/article/S0034425717301360 ). Landsat data has a 30m spatial resolution, so the change information is highly detailed and is commensurate with that of human impacts. These data represent annual stand replacing forest changes. The stand replacing disturbances types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2016; http://www.tandfonline.com/doi/full/10.1080/17538947.2016.1187673).The data available is, 1. a binary change/no-change; 2. Change year; and, 3. Change type. When using this data, please cite as: Hermosilla, T.,Wulder, M. A.,White, J. C.,Coops, N. C.,Hobart, G. W., (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. DOI: 10.1016/j.jag.2017.07.013 White, J.C., M.A. Wulder, T. Hermosilla, N.C. Coops, and G. Hobart. (2017). A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment. 192: 303-321. DOI: 10.1016/j.rse.2017.03.035.

This dataset provides a Canada-wide map of vegetation height and the delineation of the northern forest limit. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Vegetation height estimates were derived from ICESat-2 LiDAR observations, integrated with Landsat time series and topographic variables to model spatial patterns. The northern forest limit represents the transition between boreal forest and tundra, an ecologically significant zone for monitoring climate change impacts and biodiversity. Vegetation height was modeled for six time-periods including 1985-1995, 1990-2000, 1995-2005, 2000-2010, 2005-2015 and 2010-2021. Predictions for each time period represent the median conditions for that period. Predictions of height and the probability of canopy presence were generated using Random Forests models trained on spaceborne-lidar data collected by ICESat-2 from 2019-2021 and overlapping Landsat satellite imagery from 2010-2021. These Random Forests models were then applied to the entire archive of Landsat imagery, representing a period of ~35 years. This dataset provides spatially explicit prediction of vegetation height (m) along the Canadian northern forest limit at 30 m spatial resolution. Pixels with a low (< 50 %) probability of containing a vegetation canopy have been assigned a height of 0 m. The science and methods for this dataset were the result of a collaboration between the Canadian Forest Service of Natural Resources Canada, partnered with the Integrated Remote Sensing Studio (IRSS) in the Faculty of Forestry at the University of British Columbia. When using this data, please cite: Travers-Smith, H., Coops, N. C., Mulverhill, C., Wulder, M. A., Ignace, D., Lantz, T. C. (2024). Mapping vegetation height and identifying the northern forest limit across Canada using ICESat-2, Landsat time series and topographic data. Remote Sensing of Environment, 305, 114097. https://doi.org/10.1016/j.rse.2024.114097 (Travers-Smith et al. 2024). Additional details outlining application of the model to the time-series of Landsat data can be found here: Travers-Smith, H., Coops, N., Mulverhill, C., Wulder, M. A., Lantz, T. C., Ignace, D. (2025). Satellite observations reveal stable forest limits and shrub expansion across the Canadian forest-tundra ecotone. Environmental Research Letters, 20(10). https://doi.org/10.1088/1748-9326/adfc7f (Travers-Smith et al. 2025).

This deep water substrate bottom type model was created to aid in habitat modeling, and to complement the nearshore bottom patches. It was created from a combination of bathymetrically-derived layers in addition to bottom type observations. Using random forest classification, the relationship between observed substrates and bathymetric derivatives was estimated across the entire area of interest. The raster is categorized into: 1) Rock, 2) Mixed, 3) Sand, 4) Mud