__The link: * Access the data directory* is available in the section*Dataset description sheets; Additional information*__.As part of the provincial LiDAR sensor data acquisition project, a topographic humidity index or *Topographic Wetness Index* (TWI) was produced from the digital terrain model derived from aerial LiDAR (*Light Detection and Ranging*). The matrix layers thus produced provide information on the potential for water accumulation on the territory as a function of the slope and accumulation at a given pixel.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Past wind directions are mapped from stabilized sand dunes in Canada and the northern United States. The map shows the near-surface wind directions responsible for transporting sand when the dunes were active. The directions were mapped by interpreting the orientation of parabolic dunes from open-sourced Lidar (light detection and ranging) derived digital terrain models. The map also shows new dune areas that add to the existing knowledge of dune fields in North America. The interpreted wind directions provide insight into the past atmospheric circulation patterns that occurred during the deglaciation of North America and the transition to modern circulation patterns that occur today.

The Agriculture and Agri-Food Canada’s LiDAR Projects dataset was created from existing spatial data. It contains the footprints (outlines) of all the LiDAR data that is openly distributed by Agriculture and Agri-Food Canada.LiDAR (Light Detection And Ranging) is a method of acquiring survey points using optical remote sensing technology.The dataset indicates basic information about the location, source and properties of the data.

3D topographic representation of the territory in the form of a point cloud.LiDAR (Light Detection and Ranging) technology makes it possible to represent the Earth's surface topographically in three dimensions using a laser system mounted on board an aircraft. The very large number of 3D points recorded (up to 400,000 per second) makes it possible to obtain a multitude of details at the level of the ground and surface elements.LiDAR technology quickly, easily, and above all accurately provides the altitude of ground details and elements above ground, even in the presence of dense vegetation.The uses are: creation of a digital terrain model (MNT), creation of level curves, creation of level curves, volume calculation, planning, calculation of tree heights, mapping of building roofs, 3D modeling of cities, etc.Source: XEOS Imaging Inc.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The purpose of this dataset is to show end users where LiDAR data has been acquired by the Government of Manitoba.LiDAR (Light Detection and Ranging) is a remote sensing technology that uses lasers to collect accurate, continuous elevation data over relatively large areas. These data are essential for activities such as forestry, flood risk management, land use planning, and natural resources management. The Manitoba Government is increasingly acquiring LiDAR data across the province.This layer was created on August 5, 2009 by Manitoba Sustainable Development and was updated on August 9, 2021.To  download LiDAR data from the Manitoba Land Initiative (MLI) site, follow this link: https://mli2.gov.mb.ca/dems/index_external_lidar.html Fields included ( Alias (Field Name): Field description.) ObjectID (OBJECTID) - Automatically generated feature numberAcquired  (ACQUIRED) - LiDAR data capture dateContractor  (CONTRACTOR) - Contractor nameContributor  (CONTRIB) - Manitoba Government departmentName (NAME) - Dataset nameCellsize (CELLSIZE) - Raster DEM cell sizeMLI (MLI) - Data available for download on the Manitoba Land Initiative site

A chlorophyll fluorescence time series was collected at various locations around the coast of Vancouver Island, British Columbia, Canada for monitoring phytoplankton concentrations. A Wetlabs ECO fluorometer was deployed every few months on a schedule depending on season and sensor availability. The instrument hung by a chain attached to the side of the buoy, or dock, depending on location, and measured chlorophyll using the fluorescence emission at 695nm. The instrument also measured turbidity by detecting the scattered light at 700nm. The units had internal batteries and data storage and were programmed to make a group of 5 measurements every 30 minutes. A copper wiper covered the sampling window between groups of measurements to reduce fouling. Times are in UTC unless otherwise stated.

This file contains the location of all traffic lights managed by the City of Montreal, at least one crossing of which is equipped with a pedestrian light. The file contains the reference number of the intersection where the light is located, the names of the two streets that form the intersection, and the geographic coordinates of the center point of the intersection.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The Canadian Hydrospatial Network (CHN) is an analysis-ready geospatial network of features that help enable the modelling of surface water flow in Canada. The six main layers and feature types are: flowlines, waterbodies, catchments, catchment aggregates, work units, and hydro nodes. Where possible the CHN is derived from high resolution source data such as Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) and aerial imagery, to name a few. If existing provincial or territorial hydrographic networks meet the standards, they are incorporated into the CHN, otherwise automatic extraction methods are used on the high-resolution source data. To provide full network connectivity, if neither of these methods is possible in a region, the NHN is converted into the CHN until higher-resolution source data is available.Additional value-added attributes are included in the CHN to aid modelling, such as stream order and reach slope. The CHN physical model and features are also closely aligned and harmonized with the USGS 3DHP hydrographic network, which aids trans-border modelling. Where possible geonames (i.e. toponyms) are also added.The CHN is produced and disseminated by hydrologically connected geographic areas called work units. Work units can contain just one watershed, several small adjacent watersheds outletting into a large body of water, or be one of many parts of a larger watershed. In all cases, the features of a work unit are hydrologically connected. This is a more natural approach to data delivery, in comparison to data that is split into tiles. A generalized work unit index file is provided in the downloads to help users decide which files to download.For more information on the CHN please visit the project webpage: https://natural-resources.canada.ca/canadian-hydrospatial-network

__The link: * Access the data directory* is available in the section*Dataset description sheets; Additional information*__.Hydrographic derivatives from LiDAR (Light Detection and Ranging) were produced as part of the provincial LiDAR sensor data acquisition project. These products provide information on the geographical position of water flow beds on the territory as well as their nature (permanent or intermittent watercourse). These layers represent the path that water should take depending on the topography. It is therefore a potential flow bed that does not take into account the nature of the surface deposit or underground pipes. These vector layers are preliminary and do not replace reference hydrographic layers such as the Quebec Hydrographic Network Geobase (GRHQ). They are primarily used to support forest operations. These layers will also be improved over the coming years thanks to a collective effort by two ministries, namely the Ministry of Natural Resources and Forests (MRNF) and the Ministry of the Environment, the Fight against Climate Change, and Wildlife and Parks (MELCCFP).Data on potential flow beds are distributed, as of March 2022, by water drainage unit (UDH), a division that respects the natural boundaries of the watershed. This division uses the same codes and approximately the same spatial limits of the UDHs of the GRHQ. Data for each UDH is available in Geodatabase (GDB) or GeoPackage (GPKG) format.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

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