A layer of points which delinate a change in shoreline type

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.

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 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.

The characterization of shoreline strips was carried out on 400 km of agricultural watercourses for the territory of 5 MRCs in Montérégie (Beauharnois-Salaberry, Haut-Richelieu, Jardins-de-Napierville, Jardins-de-Napierville, Marguerite-d'Youville, Vallée-du-Richelieu). The results obtained by photo-interpretation, based on the width of the sections of shoreline strips calculated from the high-water line and the embankment, were aggregated to produce global results by municipality. The project was carried out as part of the Regional Program for the Acquisition of Data on Wetlands and Water Environments (PRADMHH) and was funded by the Regions and Rurality Fund (FRR) of the Montérégie regional department of the MAMH.Criteria used to characterize the conformity of shoreline strips.Shoreline compliance (Criteria used according to the width of the shoreline)Non-compliant (The non-compliant shoreline has a total width of less than 3 meters)Nearly compliant (The nearly-compliant shoreline has a total width of 3 meters or more, but less than 1 meter wide on the embankment)Compliant (The compliant shoreline has a total width of three meters or more and a width of a minimum of one meter on the embankment)Exceptional (The exceptional shoreline has a total width of 5 meters or more and a width of 3 meters or more from the embankment)**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

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 was created to: * better understand the state of the shoreline * track changes over time * identify priority areas for restoration The dataset provides important information for fish and wildlife habitat assessments. This includes identification of significant fish and wildlife areas and habitats like wetlands, woodlands, spawning habitat and biological productive areas. The dataset contains 2 spatial files: * a vector linear layer representing shoreline reaches * another vector polygon layer capturing shoreline and near-shore structures

Examples include: * shoreline access * enhanced shoreline access (with a dock or pier) * boat launches This data was created to be used as part of the Fish ON-Line mapping application.

Seagrasses support multiple key ecosystem functions (e.g., sediment stabilization, carbon storage, nursery habitat) in nearshore environments across all three Canadian coastlines. Eelgrass (Zostera marina) is a seagrass species that has been designated as an Ecologically Significant Species (ESS) and is the most widely distributed and abundant seagrass in Canada. Given the considerable conservation and management interest in eelgrass habitats, large-scale data aggregation efforts have been initiated to generate comprehensive distribution maps of eelgrass coverage in Canadian coastal waters. However, extensive portions of the Newfoundland coastline have not yet been surveyed for the presence of eelgrass. We combine spatial data layers describing shoreline characteristics, relative exposure index (REI), depth, and freshwater inputs to delineate areas potentially suitable for eelgrass growth in Newfoundland. We determined that many nearshore environments could potentially support eelgrass habitats but confirming eelgrass presence will require further validation and ground-truthing. Incorporating distribution models that use environmental covariates to estimate probabilities of occurrence in nearshore areas could help to strengthen these predictions. Mapping eelgrass distributions provides baseline data essential to eelgrass monitoring and implementation of coastal marine conservation, informing emergency response, and is particularly important considering the consequences associated with the potential loss of ecosystem functions in the event of eelgrass decline. King, B.G.C., Gullage, L., Butt, K., Warren, M., Allard, K., Gregory, R.S., and Byrne, M.A. 2026. Using shoreline characteristics, relative exposure, depth, and freshwater inputs as surrogate measures to identify areas potentially suitable for eelgrass (Zostera marina) in Newfoundland. Can. Tech. Rep. Fish. Aquat. Sci. 3737: vii + 22 p.https://doi.org/10.60825/7thp-4425

The shallow, coastal regions of the world’s oceans are highly productive ecosystems providing important habitat for commercial, forage, endangered, and iconic species. Given the diversity of ecosystem services produced or supported by this ecosystem, a better understanding of its structure and function is central to developing an ecosystem-based approach to management. However this region termed the ‘white strip’ by marine geologists because of the general lack of high-resolution bathymetric data - is dynamic, highly variable, and difficult to access making data collection challenging and expensive. Since substrate is a key indicator of habitat in this important ecosystem, we created a continuous substrate map of Bottom Patches (BoPs) from the best available bottom type data using an approach that is simple, quantitative, and transparent making it amenable to iterative improvement as data quality and availability improve. To provide subsequent analyses (such as habitat models) with some confidence in the defined bottom type values, we developed a corresponding confidence surface based on the agreement of, and distance between observations. Such data are critical to assessments of species distributions and anthropogenic risk. Bottom patches (BoPs) have been created to represent bottom type for the entire Pacific Canadian coast from the high high water line to a depth of 50 metres (m). As a polygon representation, the BoPs describe patches of similar substrate prescribed by depth classes and the available field observations. In the areas where no observations are available, predicted bottom type values are used. The approach is described in Gregr et al. (2013), as a spatial framework for representing nearshore ecosystems. Accuracy of the bottom type depends on a multitude of factors but primarily the reliability and density of the bottom type observations. The horizontal accuracy of these data likely ranges from metres to 10s of metres because of the source data or data processing required. Areas with a higher data density, where the data show strong coherence, are understood to have higher accuracy. The BoPs use depth ribbons (polygons describing bathymetric ecozones) as an input. Depth ribbons for Pacific Canada were created from a high resolution (20 x 20 m2) bathymetry. Given the resolution of these data, processing was facilitated by dividing the Pacific Coast into 5 regions.The West Coast of Vancouver Island, extending from Cape Sutil in the North past Port San Juan to the South, includes a total of 110,313 BoP polygons. Bottom Patches for Queen Charlotte Strait and Strait of Georgia regions were combined for a total of 235,754 BoP polygons. The North Central Coast region, extending from the Alaskan border in the North to Cape Caution in the South, includes a total of 431,639 BoP polygons. The Haida Gwaii region includes a total of 86,825 BoP polygons.These data are intended for scientific research only. The developers (Fisheries and Oceans Canada, SciTech Environmental Consulting) are not responsible for damages resulting from any omissions or errors that may be contained in this dataset and expressly disclaims any warranty of fitness for any particular purpose. Developers shall not be liable for any losses, financial or otherwise, due to the use of these data. The user assumes the entire risk as to the suitability, results and performance of the dataset for their proposed use. Please credit SciTech and Fisheries and Oceans Canada as the source of the data in any maps, reports, or articles that are printed or published on paper or the Internet.