The Ontario Hydro Network (OHN) is a provincial medium scale originating from data with regional scales of 1: 10,000 in Southern Ontario, 1: 20,000 in Northern Ontario and 1: 50,000 in the Far North. The shoreline is taken from the OHN - Waterbody data class. This data is used for cartographic purposes and web mapping services. This product requires the use of geographic information system (GIS) software. [Ontario Hydro Network (OHN) User Guide (Word)](https://www.sdc.gov.on.ca/sites/MNRF-PublicDocs/EN/CMID/OHN%20-%20UserGuide.docx)

With the changing climate conditions, marine traffic along Canada’s coastal regions has increased over the past couple of decades and the need to improve our state of preparedness for oil-spill-related emergencies is critical. Baseline coastal information, such as shoreline form, substrate, and vegetation type, is required for prioritizing operations, coordinating onsite spill response activities (i.e. Shoreline Cleanup Assessment Technique [SCAT]), and providing information for wildlife and ecosystem management. Between 2010 and 2016, georeferenced high-definition videography and photos were collected for various study sites along the north coast of Canada. The study areas include Beaufort Sea, Mackenzie Delta channels and Banks Island in the western Canadian Arctic and James Bay, Hudson Bay, Nunavik, Resolute Bay, Victoria Strait, Baffin Island and Coronation Gulf in the eastern Canadian Arctic.Data was collected during ice-free and low tide conditions (where applicable) between July and September. Low-altitude helicopter surveys were conducted at each study site to capture video of the shoreline characteristics. In addition to acquiring videography, ground-based observations were recorded in several locations for validation.Shoreline segmentation was then carried out by manual interpretation of the oblique videography and the photos aided by ancillary data. This involved splitting and classifying the shoreline vectors based on homogeneity of the upper intertidal zone. Detailed geomorphological information (i.e. shoreline type, substrate, slope, height, accessibility etc.) describing the upper intertidal, lower intertidal, supratidal and backshore zones was extracted from the video and entered into a geospatial database using a customized data collection form. In addition, biological characteristics like biobands, water features, fauna, human use etc. observed along the coast were recorded.The data was also validated through ground observations (when available) and a second interpreter QA (quality analysis) was performed on each dataset (excluding Nunavik) to ensure high quality and consistency. The final dataset contains segments ranging in length from 150 metres to 2500 metres. In total, from 2010 to 2016, within the 8 study sites, about 16,800 km of shoreline were segmented.

Extreme water level along the marine coastline is a result of a combination of storm surge, tides, and ocean waves. Future projections of climate change in the marine environment indicate that rising sea level and declining sea ice will cause changes in extreme water levels, which will impact Canada's coastlines and the infrastructure in these areas. Understanding these changes is essential for developing adaptation strategies that can minimize the harmful effects that may result.CAN-EWLAT is a science-based planning tool for climate change adaptation of coastal infrastructure related to future water-level extremes and changes in wave climate. The tool includes two main components: 1) vertical allowance and 2) wave climate. CAN-EWLAT was developed primarily for DFO Small Craft Harbours (SCH) locations, but it should prove useful for coastal planners dealing with infrastructure along Canada’s ocean coastlines.Cite this data as: Greenan B. Canadian Extreme Water Level Adaptation Tool (CAN-EWLAT) Published June 2022. Oceans Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S.

With the changing climate conditions, marine traffic along Canada’s coastal regions has increased over the past couple of decades and the need to improve our state of preparedness for oil-spill-related emergencies is critical. Baseline coastal information, such as shoreline form, substrate, and vegetation type, is required for prioritizing operations, coordinating onsite spill response activities (i.e. Shoreline Cleanup Assessment Technique [SCAT]), and providing information for wildlife and ecosystem management. Between 2013 and 2019, georeferenced high-definition videography and photos were collected for various study sites along the west coast. The study areas include the mainland, inlets, channels and islands along the BC coast starting from Kitimat in the north to Quadra Island in the south, including Haida Gwaii and North Vancouver Island in the west and Burrard Inlet in the extreme south.Data was collected during low tide conditions (where applicable) between July and September. Low-altitude helicopter surveys were conducted at each of the study site to capture video of the shoreline characteristics. In addition to acquiring videography, ground-based observations were recorded in several locations for validation.Shoreline segmentation was then carried out by manual interpretation of the oblique videography and the photos aided by ancillary data. This involved splitting and classifying the shoreline vectors based on homogeneity of the upper intertidal zone. Detailed geomorphological information (i.e. shoreline type, substrate, slope, height, accessibility etc.) describing the upper intertidal, lower intertidal, supratidal and backshore zones was extracted from the video and entered into a geospatial database using a customized data collection form. In addition, biological characteristics like biobands, water features, fauna, human use etc. observed along the coast were recorded.The data was also validated through ground samples (when available) and a second interpreter QA (quality analysis) was performed on the dataset to ensure high quality and consistency. The final dataset contains segments ranging in length from 150 metres (45 metres for study areas surveyed in 2018-19) to 2500 metres. In total, from 2013 to 2019, about 15,000 km of shoreline were segmented.

With the changing climate conditions, marine traffic along Canada’s coastal regions has increased over the past few decades and the need to improve our state of preparedness for oil-spill-related emergencies is critical. Baseline coastal information, such as shoreline form, substrate, and vegetation type, is required for prioritizing operations, coordinating onsite spill response activities (i.e., Shoreline Cleanup Assessment Technique [SCAT]), and providing information for wildlife and ecosystem management. Between 2011 and 2016, georeferenced high-definition videography and photos were collected for various study sites along the east coast. The study areas include Labrador, Bay of Fundy and Chedabucto Bay in Atlantic Canada.Data was collected during ice-free and low tide conditions (where applicable) between July and September. Low-altitude helicopter surveys were conducted at each study site to capture video of the shoreline characteristics. In addition to acquiring videography, ground-based observations were recorded in several locations for validation.Shoreline segmentation was then carried out by manual interpretation of the oblique videography and the photos aided by ancillary data. This involved splitting and classifying the shoreline vectors based on homogeneity of the upper intertidal zone. Detailed geomorphological information (i.e., shoreline type, substrate, slope, height, accessibility etc.) describing the upper intertidal, lower intertidal, supratidal and backshore zones was extracted from the video and entered into a geospatial database using a customized data collection form. In addition, biological characteristics like biobands, water features, fauna, human use etc. observed along the coast were recorded.The data was also validated through ground observations (when available) and a second interpreter QA (quality analysis) was performed on each dataset to ensure high quality and consistency. The final dataset contains segments ranging in length from 150 metres to 2500 metres. In total, from 2011 to 2016, within the 3 study sites, about 1,850 km of shoreline were mapped.

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

This layer is the current fire year burn severity classification for large fires (greater than 100 ha). Burn severity mapping is conducted using best available pre- and post-fire satellite multispectral imagery acquired by the MultiSpectral Instrument (MSI) aboard the Sentinel-2 satellite or the Operational Land Imager (OLI) sensor aboard the Landsat-8 and 9 satellites. Every attempt is made to use cloud, smoke, shadow and snow-free imagery that was acquired prior to September 30th. However, in late fire seasons imagery acquired after September 30th may be used. This layer is considered an interim product for the 1-year-later burn severity dataset (WHSE_FOREST_VEGETATION.VEG_BURN_SEVERITY_SP). Mapping conducted during the following growing season benefits from greater post-fire image availability and is expected to be more representative of tree mortality. #### Methodology: • Select suitable pre- and post-fire imagery or create a cloud/snow/smoke-free composite from multiple images scenes • Calculate normalized burn severity ratio (NBR) for pre- and post-fire images • Calculate difference NBR (dNBR) where dNBR = pre NBR – post NBR • Apply a scaling equation (dNBR_scaled = dNBR*1000 + 275)/5) • Apply BARC thresholds (76, 110, 187) to create a 4-class image (unburned, low severity, medium severity, and high severity) • Mask out water bodies using a satellite-derived water layer • Apply region-based filters to reduce noise • Confirm burn severity analysis results through visual quality control • Produce a vector dataset and apply Euclidian distance smoothing

Launched in 2017, the Cumulative Effects of Marine Shipping (CEMS) initiative is part of Canada’s $1.5 billion Oceans Protection Plan, which is providing economic opportunities to Canadians today, while protecting our coasts and waterways for future generations. The Cumulative Effects of Marine Shipping initiative is another way that the Government of Canada is protecting our coasts and waterways.https://tc.canada.ca/en/marine-transportation/marine-pollution-environmental-response/cumulative-effects-marine-shippingAs part of this initiative, Transport Canada is working with Indigenous partners and stakeholders in six pilot areas across Canada. Together, we are trying to understand the effects of marine shipping in various coastal areas. These pilot areas include:- North Coast British Columbia- South Coast British Columbia- St. Lawrence and Saguenay Rivers, Quebec- Bay of Fundy, New Brunswick and Nova Scotia- Placentia Bay, Newfoundland- Cambridge Bay, Nunavut

A synthesis of available background data on the types and extent of anthropogenic disturbances on this shoreline as a well as to understand the types and amount of remaining natural shoreline features.

This dataset contains the total annual releases of radionuclides released directly to the environment through direct discharge (i.e. releases to water) from the closed mine sites near Elliot Lake, Ontario, Canada.Note that there is no stack emissions for the Elliot Lake.