Area of influence of the various city centers in Saguenay**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

1 kilometer buffer zone around railway stations and rail yard areas. **Collection context** Buffer zone of 1000 meters. **Collection method** Applying a stamp using geoprocessing. **Attributes** * `Id` (`long`): Identifier * `BUFF_DIST` (`double`): Distance * `ORIG_FID` (`long`): FID For more information, consult the metadata on the Isogeo catalog (OpenCatalog link).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

In 2015, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from the BC Ministry of Agriculture and our regional AAFC colleagues.

This record contains data on beluga whale locations in the Mackenzie estuary reported in Harwood, L.A. and P. Norton (1996). Aerial survey data from the southeast Beaufort Sea, Mackenzie River estuary and west. Amundsen Gulf, July 1992. Canadian Data Report of Fisheries and Aquatic Sciences No. 964

Description: These commercial whale watching data are comprised of two datasets. First, the ‘whale_watching_trips_jun_sep_british_columbia’ data layer summarizes commercial whale watching trips that took place in 2019, 2020 and 2021 during the summer months (June to September). The second data layer, ‘wildlife_viewing_events_jun_sep_british_columbia’ contains estimated wildlife viewing events carried out by commercial whale watching vessels for the same years (2019, 2020 and 2021) and months (June to September). Commercial whale watching trips and wildlife viewing events are summarized using the same grid, and they can be related using the unique cell identifier field ‘cell_id’. The bulk of this work was carried out at University of Victoria and was funded by the Marine Environmental Observation, Prediction and Response (MEOPAR) Network under the ‘Whale watching AIS Vessel movement Evaluation’ or WAVE project (2018 – 2022). The aim of the WAVE project was to increase the understanding of whale watching activities in Canada’s Pacific region using vessel traffic data derived from AIS (Automatic Identification System). The work was finalized by DFO Science in the Pacific Region. These spatial data products of commercial whale watching operations can be used to inform Marine Spatial Planning, conservation planning activities, and threat assessments involving vessel activities in British Columbia. Methods: A list of commercial whale watching vessels based in British Columbia and Washington State and their corresponding MMSIs (Maritime Mobile Service Identity) was compiled from the whale watching companies and Marine Traffic (www.marinetraffic.com). This list was used to query cleaned CCG AIS data to extract AIS positions corresponding to commercial whale watching vessels. A commercial whale watching trip was defined as a set of consecutive AIS points belonging to the same vessel departing and ending in one of the previously identified whale watching home ports. A classification model (unsupervised Hidden Markov Model) using vessel speed as the main variable was developed to classify AIS vessel positions into wildlife-viewing and non wildlife viewing events. Commercial whale watching trips in the south and north-east of Vancouver Island were limited to a duration of minimum 1 hour and maximum 3.5 hours. For trips in the west coast of Vancouver island the maximum duration was set to 6 hours. Wildlife-viewing events duration was set to minimum of 10 minutes to a maximum of 1 hour duration. For more information on methodology, consult metadata pdf available with the Open Data record. References: Nesdoly, A. 2021. Modelling marine vessels engaged in wildlife-viewing behaviour using Automatic Identification Systems (AIS). Available from: https://dspace.library.uvic.ca/handle/1828/13300. Data Sources: Oceans Network Canada (ONC) provided encoded AIS data for years 2019, 2020 and 2021, within a bounding box including Vancouver Island and Puget Sound used to generate these products. This AIS data was in turn provided by the Canadian Coast Guard (CCG) via a licensing agreement between the CCG and ONC for the non-commercial use of CCG AIS Data. More information here: https://www.oceannetworks.ca/science/community-based-monitoring/marine-domain-awareness-program/ Molly Fraser provided marine mammal sightings data collected on board a whale watching vessels to develop wildlife-viewing events classification models. More information about this dataset here: https://www.sciencedirect.com/science/article/pii/S0308597X20306709?via%3Dihub Uncertainties: The main source of uncertainty is with the conversion of AIS point locations into track segments, specifically when the distance between positions is large (e.g., greater than 1000 meters).

Results of the visual inspection carried out in 2019 of the condition of all alleys located on the territory of the City of Montreal.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

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).**

The SiIGsol-100m maps make it possible to document the spatial evolution of the properties of the mineral part of the soil at a spatial resolution of 100 m. These values were simulated by combining point observation data and spatially explicit covariates, derived from remote sensing data, using automated learning methods. These values should not be interpreted as an observation, but rather as the most likely value for a given distribution of observations and set of predictor variables. Soil property values are simulated for all non-urbanized land surfaces. All attributes whose value is likely to change with the depth are available in 6 layers, which make it possible to translate the evolution of properties with depth. The depth limits used come from the GlobalSoilMap.net project mapping standard: 00-05 cm, 05-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm. Map products are currently divided into 6 variables; the sand fraction, the silt fraction, the clay fraction, the pH, the cation exchange capacity and the proportion of organic matter. They cover the entire territory of the Province of Québecois located south of the 55th parallel.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

In 2009 the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) began the process of generating annual crop inventory digital maps using satellite imagery. Focusing on the Prairie Provinces, a Decision Tree (DT) based methodology was applied using both optical (AWiFS, Landsat-5) and radar (RADARSAT-2) based satellite imagery, and having a final spatial resolution of 56m. Methods were also developed to enhance the optical classification with RADARSAT-2 imagery, addressing issues associated with cloud cover. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues. The overall process for Crop Inventory Map includes: satellite data acquisition; field data acquisition for classification training and accuracy assessment; and, operational implementation of the classification methodology. The initial methodology was developed in partnership with AAFC Research Branch, and supported in part by the Canadian Space Agency. The long-term objective of this endeavour is to expand from the Prairies and produce an annual crop inventory of the entire agricultural extent of Canada.

The National Ecological Framework for Canada's "Surficial Geology by Ecodistrict” dataset contains tables that provide surficial geology information with the ecodistrict framework polygons. It provides codes that characterize surficial geology (unconsolidated geologic materials) and their English and French-language descriptions as well as information about the area and percentage of the polygon that the material occupies.