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

OneSoil employs a proprietary machine learning (ML) model based on state-of-the-art instance segmentation to detect field boundaries. Utilizing raw Sentinel-2 data aggregated according to local vegetation season maps and an additional upscaling module to enhance boundary accuracy, we ensure precise results. OneSoil’s data preprocessing involves the utilization of their cloud detector module and local season mapping.

The Aquatic Invasive Species Surveillance Database is a compilation of fish community and habitat data from DFO’s Aquatic Invasive Species and Invasive Carp Program early detection surveillance efforts in Canadian waters of the Great Lakes basin. Data includes: sampling site location, date, fish species and counts, and associated habitat information. Annual project-specific details including purpose/objectives and study methodology are often reported in the DFO Canadian manuscript report of fisheries and aquatic sciences series.

This theme presents observations of invasive exotic species (IAS)transmitted and validated using the Sentinelle tool, an EEE detection system.An invasive exotic species is a plant, animal or microorganism (virus,bacteria or fungi) that are introduced outside of their natural range. Sonestablishment or its spread may pose a threat to the environment,the economy or society. The species listed are species of fauna and floraconcerning (or potentially worrying) for Quebec's biodiversity. Ellesinclude EEE present in Quebec and EEE not listed in Quebec atmonitor.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This data publication contains a set of files in which areas affected by fire or by harvest from 1984 to 2015 are identified at the level of individual 30m pixels on the Landsat grid. Details of the product development can be found in Guindon et al (2018). The change detection is based on reflectance-corrected yearly summer (July and August) Landsat mosaics from 1984 to 2015 created from individual scenes developed from USGS reflectance products (Masek et al, 2006; Vermote et al, 2006). Briefly, the change detection method uses a six-year temporal signature centered on the disturbance year to identify fire, harvest and no change. The signatures were derived from visually-interpreted disturbance or no-change polygons that were used to fit a decision tree model. The method detects about 91% of the areas harvested and 85% of the areas burned across Canada’s forests over the study period, but overestimates areas disturbed in the two initial and mostly in the two final years of the 1985 to 2015 time series. This is caused by the absence of appropriate pre-disturbance and post-disturbance data for the model-based detection and attribution. Disturbance coverage in those four years should therefore be used with caution. As in Guindon et al (2014), the method was designed to minimize commission errors and has a disturbance class attribution success rate of about 98%. The attribution success rate of disturbance year for fire is of about 69% for the exact year and of about 99% when attribution to the following year is also considered as a success. This common one-year lag is mostly due to the use of mid-summer Landsat mosaics for the analysis that will cause spring and fall events of the same year to be attributed to successive years. For example, a fire that occurred in the fall of 2004 (after July and August), will be detected and attributed to 2005, while for a fire that occurred in the spring of 2004 will be detected and attributed to 2004. The presence of clouds and shadows or image availability causes 10% of missing data annually and therefore can too delay the capture of events. The data provides uniform spatial and temporal information on fire and harvest across all provinces and territories of Canada and is intended for strategic-level analysis. Since no attention was given to other minor disturbances such as mining, road or flooding, the product should not be used for their identification. Finally, calibration datasets were developed for only three major forest pests (mountain pine beetle, eastern spruce budworm and forest tent caterpillar), but were folded within the “no-change” class in order to minimize commission errors for fire and harvest . Less common pests for which validation datasets are hard to develop were not considered and therefore could in rare circumstances generate false fire events. Considering that area having two (3.3%) to three disturbances (less than 1%) events are not common, only the most recent disturbance is provided, overlapping older disturbances in these rare case. ## Please cite this dataset as: Guindon, L., P. Villemaire, R. St-Amant, P.Y. Bernier, A. Beaudoin, F. Caron, M. Bonucelli and H. Dorion. 2017. Canada Landsat Disturbance (CanLaD): a Canada-wide Landsat-based 30-m resolution product of fire and harvest detection and attribution since 1984. https://doi.org/10.23687/add1346b-f632-4eb9-a83d-a662b38655ad ## Scientific article citation: The creation, validation and limitations of the CanLaD product are described in the Supplementary Material file associated with the following article: Guindon, L.; Bernier, P.Y.; Gauthier, S.; Stinson, G.; Villemaire, P.; Beaudoin, A. 2018. Missing forest cover gains in boreal forests explained. Ecosphere, 9 (1) Article e02094. doi:10.1002/ecs2.2094. ## Cited references: Masek, J.G., Vermote, E.F., Saleous N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T-K. (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters 3(1):68-72. http://dx.doi.org/10.1109/LGRS.2005.857030. Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment. http://dx.doi.org/10.1016/j.rse.2016.04.008.

This dataset represents lithogeochemistry of Saskatchewan samples.This dataset represents lithogeochemistry of Saskatchewan samples. This dataset represents the exhaustive mapping and sampling program of the Athabasca Group between 1975 and 1981 by the Saskatchewan Geological Survey (SGS), the results of which are contained in Ramaekers (1990). These samples are now stored at the Ministry of Energy and Resources, Subsurface Geological Laboratory in Regina, Saskatchewan. A selection of these samples was chosen to help characterize the background geochemical signature of the Athabasca Group and to identify anomalous regions. A total of 837 samples were chosen. All samples in this data set were processed at the Geoanalytical Laboratories at the Saskatchewan Research Council (SRC) in Saskatoon, Saskatchewan, an ISO/IEC 17025:2005 certified facility (i.e., meets the General Requirements for the Competence of Mineral Testing and Calibration Laboratories). Samples were crushed, split, agate ground, and then run with Sandstone Exploration Package ICPMS 1. The package produces three separate analysis types: inductively coupled plasma mass spectroscopy (ICP MS) partial digestion for trace elements; ICP MS total digestion for trace elements; and ICP–Optical Emission Spectrometry (ICP–OES) total digestion for major and minor elements. Details and detection limits are available on the SRC’s website. ICP total digestion: a 0.250 g pulp is gently heated in a mixture of ultrapure HF/HNO3/HClO4until dry and the residue dissolved in dilute ultrapure HNO3; ICP MS total digestion: a 0.250 g pulp is gently heated in a mixture of ultrapure HF/HNO3/HClO4until dry and the residue dissolved in dilute ultrapure HNO3; ICP MS partial digestion: a 2.00 g pulp is digested with 2.25 ml of 8:1 ultrapure HNO3:HCl for 1 hour at 95° C; Detection limits are from the SRC's 2011 Analytical Fee Schedule; null values indicate that elements are below the detection limit. NOTE: Attribute data headings ending with TD indicate Total Digestion, those ending with PD indicate Partial Digestion. Majors oxides are in percent; all other elements are in ppm. **Please Note – All published Saskatchewan Geological Survey datasets, including those available through the Saskatchewan Mining and Petroleum GeoAtlas, are sourced from the Enterprise GIS Data Warehouse. They are therefore identical and share the same refresh schedule. 

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.

PURPOSE:Provide early detection of newly arrived Aquatic Invasive Species (AIS) and determine the spread, establishment and spatial distribution of existing AIS within marine waters of the southern Gulf of St. Lawrence (sGSL), DFO Gulf Region boundaries (northern and eastern coastal shores of NB, Gulf shore of NS, and PEI shoreline). DESCRIPTION:DFO Science monitors for AIS in the Gulf Region. The data collected from DFO's biofouling monitoring program provides an overview of the distribution and abundance of Aquatic Invasive Species (AIS) in the Gulf Region. This information can be used by the general public, scientists and DFO managers.Monitoring program targeting aquatic invasive species (AIS). Native biofouling species are not included in this dataset. Botrylloides violaceus: Violet tunicateBotryllus schlosseri: Golden star tunicateCiona intestinalis: Vase tunicateStyela clava: Clubbed tunicateCaprella mutica*: Japanese skeleton shrimpMembranipora membranacea: Coffin box bryozoanCarcinus maenas*: European green crabCodium fragile*: Oyster thief alga*indicates species that are not included as percent cover as they are not accurately captured by the sampling method, but are included as detections.Included here is a dataset of detection and percent cover data of AIS, as well as a monitoring station dataset. Environmental data collected, including from temperature loggers, are stored but not included here. PARAMETERS COLLECTED:Air and water temperature, salinity, depth, dissolved oxygen, weather conditions, list of biofouling AIS, percent cover of AIS on PVC plates.NOTES ON QUALITY CONTROL:Each sample and species is processed and identified in a standardized fashion using standardized DFO Science AIS protocols and taxonomic references. Data is manually entered into DFO Gulf Region's AIS Science biofouling database and randomly verified for accuracy.SAMPLING METHODS:Biofouling monitoring is conducted using PVC collector plates that are deployed in the water column approximately 1 meter below the sea surface in the spring of each year. Biofouling organisms settle on these plates which are collected in the fall of the same year. Abundances of AIS are given as percent plate cover. Physico-chemical data including temperature, conductivity, and depth as well as weather conditions are noted at each geo-referenced biofouling monitoring site during initial deployment and at time of retrieval. All biofouling organisms settled on the underside of the PVC plates are noted and percent cover of each AIS is estimated.USE LIMITATION:To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

A Linear Safety Feature is one of a number of various appliances/appurtenances that have been installed or constructed either alongside or as an integral part of the road infrastructure to reduce the severity or potential of accidents. It is a Linear feature

River ice roughness products from the last three days in selected Canadian regions that have been designated for observation, monitored by Natural Resources Canada using satellite imagery for emergency response. Coverage is not comprehensive nationwide. In order to mitigate ice jam induced flood risks, Natural Resources Canada emergency geomatics service (EGS) may be activated by Canada’s emergency management authorities. As new satellite imagery becomes available, NRCan will produce river ice roughness maps and update the dataset in near real time (4 hours). This item contains the latest river ice roughness products generated in the past three days. For any data older than 72 hours, please refer to the [River Ice in Canada - Current Year](https://open.canada.ca/data/en/dataset/8ca6f047-ddef-43d7-81c2-47654f4c69bd) entry. The river ice product is generated and validated on a best effort basis. Various factors may affect the quality of the river ice roughness maps. Those factors include but are not limited to: environmental condition at the time of acquisition, image resolution or the limitations of the methodology used. To view a specific product in Web Services, filter the data by date (UTC Date) and area of interest (AOI). A link to download specific EGS products is available in the Resources section.Disclaimer:Emergency response authorities are the primary users of these satellite-derived river ice roughness map products. These products are generated to provide analysis and emergency response situational awareness and to facilitate decision-making during major flood events. The river ice roughness products are generated rapidly and limited time is available for editing and validation. The river ice roughness products reflect the river ice surface roughness conditions at the date/time of acquisition. While efforts are made to produce high quality products, near-real time products may contain errors due to the limited time available for validation and the limited availability of ground truthing data. Limitation of Liability:Accordingly, the information contained on this website is provided on an “as is” basis and Natural Resources Canada makes no representations or warranties respecting the information, either expressed or implied, arising by law or otherwise, including but not limited to, effectiveness, completeness, accuracy or fitness for a particular purpose. Natural Resources Canada does not assume any liability in respect of any damage or loss based on the use of this website. In no event shall Natural Resources Canada be liable in any way for any direct, indirect, special, incidental, consequential, or other damages based on any use of this website or any other website to which this site is linked, including, without limitation, any lost profits or revenue or business interruption.Parent Collection:- **[River Ice State in Canada - Cartographic Product Collection](https://open.canada.ca/data/en/dataset/d1fcb44f-5f86-4957-bdb4-e6fd1aa69283)**