When the tenure data differs from the actual post locations on the ground, we use adjoining parcels to show that the area has no open ground.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)

Ecologically and Biologically Significant Areas (EBSAs) are areas within Canada's oceans that have been identified through formal scientific assessments as having special biological or ecological significance when compared with the surrounding marine ecosystem.Failure to define an area as an EBSA does not mean that it is unimportant ecologically. All areas serve ecological functions to some extent and require sustainable management. Rather, areas identified as EBSAs should be viewed as the most important areas where, with existing knowledge, regulators and marine users should be particularly risk averse to ensure ecosystems remain healthy and productive.Why are EBSAs identified?EBSA information is used to inform marine planning, including environmental assessment and the siting of marine-based activities, by:- Informing and guiding project-specific or regional environmental assessments;- Informing and guiding industries and regulators in their planning and operations, for example: EBSAs have been acknowledged and referred to (often as "Special Areas" or "Potentially Sensitive Areas") in oil and gas related assessments;- EBSA information has been provided to proponents of submarine cable projects to be used for route planning purposes;- Informing and guiding Integrated Oceans Management (IOM) process within five Large Ocean Management Areas (LOMAs) and twelve marine bioregions;- Serving as a basis for the identification of Areas of Interest (AOIs) and of Marine Protected Areas (MPAs) (individually and in the context of planning bioregional networks of MPAs).How are EBSAs identified?The process used to identify EBSAs is generally comprised of two phases. The first phase involves compiling scientific data and knowledge of a marine area's ecosystems - notably fish species, marine mammals, sea birds, marine flora, marine productivity, physical and chemical conditions and geology. "Knowledge" includes experiential knowledge of long-time uses of the areas. In some cases (e.g., in the Arctic), substantial efforts are taken to collect traditional knowledge on ecosystems and environmental conditions from community members, fish harvests, hunters and individuals whose knowledge of the study area complement often helps fill scientific data gaps.In the second phase, the available information for a marine area (e.g. a bioregion) is assessed against five nationally-established science-based criteria including:- Uniqueness: How distinct is the ecosystem of an area compared to surrounding ones?- Aggregation: Whether or not species populate or convene to the study area?- Fitness consequence: How critical the area is to the life history of the species that use it (e.g. is it a spawning or feeding ground)?- Naturalness: How pristine or disturbed by human activities is the study area?- Resilience: What is the ability of the ecosystem to bounce back if it is disturbed?Progress to date and next stepsEBSAs have been identified for large portions of Canada's Atlantic and Pacific Oceans as well as most of the Arctic oceans. EBSAs will continue to be identified in priority areas as resources become available to carry out the process. The boundaries or locations of existing EBSAs may be modified to reflect both new knowledge and changing environmental conditions.

This feature delineates enhanced vegetation, forest and cover attributes for the Whitehorse, Yukon area at a scale of 1:10,000. The field work for the inventory was carried out during the winter of 2004/2005 with the project delivered by the contractor in October 2005. Delineation was based on 1:10,000 black and white photography acquired by the City of Whitehorse in 2001. New mapping and DTM were available for this project based on that photography. Vegetation inventory polygons contain a variety of detailed information on, for example, the age and type of trees growing on the land base in the vicinity of Whitehorse.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)

The Oceans Act (1997) commits Canada to maintaining biological diversity and productivity in the marine environment. A key component of this is to identify areas that are considered ecologically or biologically significant. Fisheries and Oceans Canada (DFO) Science has developed guidance on the identification of Ecologically or Biologically Significant Areas (EBSAs) (DFO 2004) and has endorsed the scientific criteria of the Convention on Biological Diversity (CBD) for identifying ecologically or biologically significant marine areas as defined in Annex I of Decision IX/20 of its 9th Conference of Parties. These criteria were applied to the Newfoundland and Labrador (NL) Shelves Bioregion in two separate data-driven processes. The first process focused on the area north of the Placentia Bay-Grand Banks (PBGB) Large Ocean Management Area (LOMA) (DFO 2013). The second process focused on the PBGB area (DFO 2019), where EBSAs had previously been identified using a more Delphic approach (Templeman 2007). In both cases, an EBSA Steering Committee, comprised of experts in oceanography, ecosystem structure and function, taxa-specific life histories and Geographic Information Systems (GIS) guided the process by advising or aiding in the identification, collection, processing and analysis of data layers, as well as participating in the final selection of candidate EBSAs (Wells et al. 2017, Ollerhead et al. 2017, Wells et al. 2019). All information was compiled in a GIS and a hierarchical approach was used to review individual data layers and groupings of data layers. Peer review meetings were held for both processes, during which candidate EBSAs were reviewed and the final EBSAs were agreed upon and delineated. In the northern study area, a total of fifteen EBSAs were identified and described; three of these areas are primarily coastal areas; seven are in offshore areas; four EBSAs straddle coastal and offshore areas; and one is a transitory EBSA that follows the southern extent of pack ice. In the PBGB study area, fourteen EBSAs were identified in two different categories: seven based on coastal data and seven based on offshore data. In comparing the new PBGB EBSAs to those identified in 2007, nine of them overlap spatially and are based on similar features; however, there were some variations in the boundaries. Two of the EBSAs that were identified in 2007 were no longer considered EBSAs in 2017, but portions of both of these areas were captured in part by other EBSAs. Five new EBSAs were identified in areas not previously considered.References:DFO, 2004. Identification of Ecologically and Biologically Significant Areas. DFO Can. Sci. Advis. Sec. Ecosystem Status Rep. 2004/006.DFO. 2013. Identification of additional Ecologically and Biologically Significant Areas (EBSAs) within the Newfoundland and Labrador Shelves Bioregion. DFO Can. Sci. Advis. Sec. Sci. Advis. Rep. 2013/048.DFO. 2019. Re-evaluation of the Placentia Bay-Grand Banks Area to Identify Ecologically and Biologically Significant Areas . DFO Can. Sci. Advis. Sec. Sci. Advis. Rep. 2019/040.Ollerhead, L.M.N., Gullage, M., Trip, N., and Wells, N. 2017. Development of Spatially Referenced Data Layers for Use in the Identification and Delineation of Candidate Ecologically and Biologically Significant Areas in the Newfoundland and Labrador Shelves Bioregion. DFO Can. Sci. Advis. Sec. Res. Doc. 2017/036. v + 38 pTempleman, N.D. 2007. Placentia Bay-Grand Banks Large Ocean Management Area Ecologically and Biologically Significant Areas. Can. Sci. Advis. Sec. Res. Doc. 2007/052: iii + 15 p.Wells, N.J., Stenson, G.B., Pepin, P., and Koen-Alonso, M. 2017. Identification and Descriptions of Ecologically and Biologically Significant Areas in the Newfoundland and Labrador Shelves Bioregion. DFO Can. Sci. Advis. Sec. Res. Doc. 2017/013. v + 87 p.Wells, N., K. Tucker, K. Allard, M. Warren, S. Olson, L. Gullage, C. Pretty, V. Sutton-Pande and K. Clarke. 2019. Re-evaluation of the Placentia Bay-Grand Banks Area of the Newfoundland and Labrador Shelves Bioregion to Identify and Describe Ecologically and Biologically Significant Areas. DFO Can. Sci. Advis. Sec. Res. Doc. 2019/049. viii + 138 p.

BiologicEcologic ISO Feature Dataset symbolization and publication. September 5, 2017.

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. 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)

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. 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)

Terrain contours (TRNCNT) is a vector delineation of areas of equivalent elevation, in 5 m classes, as contour lines.Download: Here The Saskatchewan Ministry of Environment, Forest Service Branch, has developed a forest resource inventory (FRI) which meets a variety of strategic and operational planning information needs for the boreal plains. Such needs include information on the general land cover, terrain, and growing stock (height, diameter, basal area, timber volume and stem density) within the provincial forest and adjacent forest fringe. This inventory provides spatially explicit information as 10 m or 20 m raster grids and as vectors polygons for relatively homogeneous forest stands or naturally non-forested areas with a 0.5 ha minimum area and a 2.0 ha median area.  Terrain contours (TRNCNT) is a vector delineation of areas of equivalent elevation, in 5 m classes, as contour lines.  For more information, see the Forest Inventory Standard of the Saskatchewan Environmental Code, Forest Inventory Chapter.

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. 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)

A point file showing a collection of specific GPS spatial points recorded during the video taping of the shoreline. The points are represented by a specific latitude and longitude taken at a specific date and time. Each are associated with a specific BIOSLIDE at a specific SHOREUNIT in the Shorezone data