This dataset uses RADARSAT Constellation Mission (RCM) Synthetic Aperture Radar (SAR) satellite images to identify open water regions within ice-covered rivers during winter, with the aim to assess hydrokinetic resources near remote communities reliant on diesel fuel for electricity generation. The data is processed with the HyRASS, a machine learning-based SAR image processing and classification algorithm.Disclaimer:This dataset was designed to identify open water regions within ice-covered rivers for assessing hydrokinetic resources near remote communities reliant on diesel fuel for electricity generation and is subject to the following limitations: • This dataset was derived from RADARSAT Constellation Mission (RCM) Synthetic Aperture Radar (SAR) satellite images. While these images are generally reliable, they are subject to inherent limitations, including resolution constraints, potential distortion, and occasional inaccuracies in real-time conditions capture. • The HyRASS algorithm is designed to pinpoint open water areas using satellite images, with a particular emphasis on RCM quad polarization (QP) imagery. This specialization means that its effectiveness depends on the accessibility of this specific type of imagery. Consequently, the data it produces might not cover a broad spectrum of time periods. For more reliable results, it's essential to classify areas more regularly, ensuring that detected open water regions are consistent over time.This dataset is intended for preliminary assessment and should not be the sole basis for making critical decisions or investments related to hydrokinetic energy projects. Further validation and in-depth analysis are strongly recommended, and users should conduct their own due diligence and additional research to verify the data accuracy and relevance for specific applications. By accessing and using this dataset, users acknowledge and accept these disclaimers. The providers of this dataset explicitly absolve themselves of any responsibility or liability for any consequences arising from the use, reliance upon, or interpretation of this dataset. Users are advised that their use of the dataset is at their own risk, and they assume full responsibility for any actions or decisions made based on the information contained therein. This disclaimer is in accordance with applicable laws and regulations, and by accessing or utilizing the dataset, users agree to release the providers of this dataset from any legal claims, damages, or liabilities that may arise from such use.

Fire management zones help guide how wildfires are prioritized and managed and show where wildfires may be used to achieve ecological objectives. Zones are based on relatively constant factors, such as the level of human use or development and the presence of reliable fuel breaks.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 Remote Communities Energy Database (RCED) is a tool that aims to collect and show pertinent information about the generation and use of electricity and other energy sources for all remote communities of Canada. This data is collected from a variety sources, including Statistics Canada, Provincial and Territorial Governments, utilities, public reports and communities.The term “remote community” refers to any community not currently connected to the North American electrical grid nor to the piped natural gas network; and which is a permanent or long-term (five years or more) settlement with at least 10 dwellings.Disclaimer: Natural Resources Canada (NRCan) accepts no responsibility or liability for any errors, inaccuracies and/or omissions in the Remote Communities Energy Database (RCED). The content of the RCED is a compilation of data obtained from many sources and the data is subject to change without notice. Neither does NRCan warrant or guarantee that the information is accurate, complete or current at all times. In no event will NRCan or the Government of Canada and its employees be liable or responsible for any damages resulting from any such errors, inaccuracies or omissions nor will they be liable or responsible for any lost profits, loss of revenue or earnings, claims by third parties or for any economic, indirect, special, incidental, consequential or exemplary damage including but not limited to any demands or causes of action in contract, tort or otherwise including any liability for negligence.

The Remote Communities Energy Database is a public resource that provides pertinent factual information about the generation and use of electricity and other energy sources for all remote communities in Canada. Communities are identified as remote communities if they are not currently connected to the North-American electrical grid nor to the piped natural gas network; and is a permanent or long-term (5 years or more) settlement with at least 10 dwellings.The Remote Communities Energy Database is the only national data source on energy in remote communities that is publically available on one centralized site. The Remote Communities Energy Database allows users to search and conduct analyses of remote communities and their energy context. Users are also able download the data from the Remote Communities Energy Database dataset in CSV (i.e., excel compatible) format. This data is collected from a number of sources including the remote communities themselves, local utilities, provincial and territorial government’s, Indigenous and Northern Affairs Canada (INAC), Statistics Canada, Natural Resources Canada (NRCan) and various other stakeholders.

A National Forest Fire Behaviour Prediction (FBP) Fuel Type (FT) layer, representing fuel types as described in the Canadian Forest Fire Danger Rating System (CFFDRS) Canada-wide. This data is developed through the Spatialized Canadian National Forest Inventory (SCANFI) dataset, specifically the updated SCANFI version 2.0. The Fuel Type layer is produced at variable resolution to best meet the needs of users: 30m (default) and 100m (ideal for fire growth modelling).The purpose of this dataset is to characterize Canadian forests into Fuel Types for use in various applications, including wildland fire management, research, fuel management, and more. This layer is primarily developed to support national scale applications and uses but can be utilized for localized landbases and adapted as required.This data represents the most up-to-date national product and is updated annually prior to the start of the fire season to incorporate burned area and additional data from the previous year. This data will be updated annually and published online by April 30th of each year.Note: In this product Slash fuel types (S-1, S-2, and S-3) are not represented. Conifer Plantation (C-6) is not represented. Raster Grid Value 13 represents vegetated fuels without an effective match to an existing FBP Fuel Type under this ruleset. Grid Value 11 (corresponding to D-1 Leafless Aspen) is assumed to represent true Deciduous fuels as described in the FBP system.

A national map of Canadian Fire Behaviour Prediction (FBP) Fuel Types (FT) developed from public data sources. The resolution of the raster grid is 30m, classified from the Spatialized Canadian National Forest Inventory (SCANFI) dataset, ecozones of Canada, and the National Burned Area Composite (NBAC). The purpose of the dataset is to characterize Canadian forests into fuel types for use in Fire Behaviour Prediction calculations as well as for situational awareness of national fire potential.

Under the Regulation Respecting the Mandatory Reporting of Certain Contaminant Emissions into the Atmosphere (RDOECA), the Ministry of the Environment, the Fight against Climate Change, Wildlife and Parks collects data on greenhouse gases (GHGs) emitted by Quebec businesses in particular. Thus, any person or municipality operating an establishment that emits GHGs into the atmosphere in a quantity equal to or greater than 10,000 metric tons in CO2 equivalent (t eq. CO2) is required to report its emissions no later than June 1 of each year.The data is presented in separate files:* Total biogenic and CO2 emissions per establishment;* Emissions per establishment and per greenhouse gas and per establishment.Total emissions files include the total quantity of GHGs, the total quantity of GHGs excluding CO2 from biomass, the quantity of CO2 from the combustion of biomass, and the quantity of CO2 from other uses of biomass (for example fermentation).The emission files by establishment and by greenhouse gas include the quantity emitted of each of the GHGs in metric tons and t eq. CO2. Note that CO2 emissions include those from biomass.The data presented in this dataset includes emissions from mandatory and voluntary reporting.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Offshore Oil and Gias exporation Potential

Yukon Oil and Gas Dispositions. Created from the disposition abstracts and the Oil and Gas Land Division System. For more information visit [https://yukon.ca/en/doing-business/licensing/apply-oil-and-gas-rights#disposition-overview](https://yukon.ca:443/en/doing-business/licensing/apply-oil-and-gas-rights)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)

On July 6, a train of 72 cars, carrying 100 tons of crude oil each, exploded in Lac-Mégantic. A high-resolution aerial photograph was collected by Aéro-Photo after the train derailment. This aerial photograph was provided and georeferenced by Aéro-Photo (1961) inc. Purpose: This aerial photograph makes it possible to identify the impact radius of the accident. The image web service (WMS) is offered as an open service. However, to obtain the raw image, please contact Aéro-Photo (1961) inc.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**