The STAC API for NRCan's Earth Observation Database Management System (EODMS)..**This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

The documented occurrence data package contains 3 datasets that, in combination, help to provide generalized information about woodland caribou locations and survey areas in Saskatchewan. This information may assist users in their efforts to avoid or mitigate impacts to woodland caribou when operating in woodland caribou range. Generalized locations of caribou use have been provided to better reflect their large home ranges. Absence of a hexagon in an area should not be interpreted as absence of woodland caribou.Please read the Data Guide for important information about this product. Download survey boundaries, telemetry occurrence, and sightings/sign. Download the full package, including data guide here. The Woodland Caribou Documented Occurrence public data product is composed of three shapefiles/feature classes: 1. Woodland Caribou Occurrence - Sighting and Sign 2. Woodland Caribou Occurrence - Telemetry 3. Woodland Caribou Survey Boundaries The two occurrence datasets contain a grid of 18 sq km hexagons (tessellation). The inclusion of a hexagon in the dataset indicates that one or more animal sightings or sign, or telemetry points have been documented in that area. Importantly, lack of caribou occurrence (e.g. no hexagon) should not be interpreted as absence of woodland caribou. Rather, data may not have been collected in these areas or incidental or other observations have not been received.   The survey boundaries dataset displays the boundaries of woodland caribou surveys that were completed by or in collaboration with the Ministry of Environment from 2005 to 2024. Boundaries are from multiple sources, and include various types of surveys (fecal pellet collection or telemetry). These boundaries provide context when viewed alongside the woodland caribou occurrence datasets. We expect to see more occurrence locations in areas that have been surveyed. This information may provide context to areas with a seemingly higher number of occurrences.  For a full description of the data, please refer to the Data Guide document available for download on the Saskatchewan GeoHub.

From 1986 to 2000, a major ecological inventory program was carried out in the forests of southern Quebec in order to describe the diversity of forest ecosystems. In total, **28,425 ecological observation points (POE) ** were established on a territory covering 760,000 km2, located between 45° and 53° N latitude and 57° and 80° W longitude. The POE is a circular sampling unit that covers an area of 400 m². It collects data on the characteristics of forest stand (composition, structure), soil (texture, deposit, drainage), and topography, as well as location information. The coverage of each plant species in the plot is estimated visually. A detailed description of a soil profile is available in approximately 35% of POEs. The ecological classification elements of POEs (groups of indicator species, forest types, potential vegetation, ecological types, etc.) are determined using computerized identification keys using data on vegetation and the physical environment. The criteria used for this ecological classification are those presented in the guides for the recognition of ecological types. **The levels of the ecological classification system of the territory are also determined for each POE. ** **This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Groundwater samples have been collected in the hydrogeological unit, for various types of analysis. The dataset is not used to represent a particular phenomenon or observation but rather as a utility dataset to add context and reference to groundwater analysis. It represents a general description of the sample site and sample. Sampling methods vary according to the types of analysis.

A cross-country summary of the averages and extremes for the month, including precipitation totals, max-min temperatures, and degree days. This data is available from stations that produce daily data.

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%3DihubUncertainties: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).

The STAC API for NRCan's Earth Observation Database Management System (EODMS)..**This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

The STAC API for NRCan's Earth Observation Database Management System (EODMS)..**This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

40 Class - Canadian Ecological Domain Classification from Satellite Data. Satellite derived data including 1) topography, 2) landscape productivity based on photosynthetic activity, and 3) land cover were used as inputs to create an environmental regionalization of the over 10 million km2 of Canada’s terrestrial land base. The outcomes of this clustering consists of three main outputs. An initial clustering of 100 classes was generated using a two-stage multivariate classification process. Next, an agglomerative hierarchy using a log-likelihood distance measure was applied to create a 40 and then a 14 class regionalization, aimed to meaningfully group ecologically similar components of Canada's terrestrial landscape. For more information (including a graphical illustration of the cluster hierarchy) and to cite this data please use: Coops, N.C., Wulder, M.A., Iwanicka, D. 2009. An environmental domain classification of Canada using earth observation data for biodiversity assessment. Ecological Informatics, Vol. 4, No. 1, Pp. 8-22, DOI: https://doi.org/10.1016/j.ecoinf.2008.09.005. ( Coops et al. 2009).

100 Class - Canadian Ecological Domain Classification from Satellite Data. Satellite derived data including 1) topography, 2) landscape productivity based on photosynthetic activity, and 3) land cover were used as inputs to create an environmental regionalization of the over 10 million km2 of Canada’s terrestrial land base. The outcomes of this clustering consists of three main outputs. An initial clustering of 100 classes was generated using a two-stage multivariate classification process. Next, an agglomerative hierarchy using a log-likelihood distance measure was applied to create a 40 and then a 14 class regionalization, aimed to meaningfully group ecologically similar components of Canada's terrestrial landscape. For more information (including a graphical illustration of the cluster hierarchy) and to cite this data please use: Coops, N.C., Wulder, M.A., Iwanicka, D. 2009. An environmental domain classification of Canada using earth observation data for biodiversity assessment. Ecological Informatics, Vol. 4, No. 1, Pp. 8-22, DOI: https://doi.org/10.1016/j.ecoinf.2008.09.005. ( Coops et al. 2009).