These products are derived from RGB (red/green/blue) images, a satellite processing technique that uses a combination of satellite sensor bands (also called channels) and applies a red/green/blue (RGB) filter to each of them. The result is a false-color image, i.e. an image that does not correspond to what the human eye would see, but offers high contrast between different cloud types and surface features. The on-board sensor of a weather satellite obtains two basic types of information: visible light data (reflected light) reflecting off clouds and different surface types, also known as "reflectance", and infrared data (emitted radiation) which are short-wave and long-wave radiation emitted by clouds and surface features. RGBs are specially designed to combine this type of satellite data, resulting in an information-rich final product.Other products are based on the enhancement of channel data for a single wavelength, also aimed at highlighting meteorological features of the observed surface or clouds, but in a simpler way since only a single wavelength is involved. This older approach is still useful today, as its simplicity makes image interpretation easier in some cases.

These products are derived from RGB (red/green/blue) images, a satellite processing technique that uses a combination of satellite sensor bands (also called channels) and applies a red/green/blue (RGB) filter to each of them. The result is a false-color image, i.e. an image that does not correspond to what the human eye would see, but offers high contrast between different cloud types and surface features. The on-board sensor of a weather satellite obtains two basic types of information: visible light data (reflected light) reflecting off clouds and different surface types, also known as "reflectance", and infrared data (emitted radiation) which are long-wave radiations emitted by clouds and surface features. RGBs are specially designed to combine this type of satellite data, resulting in an information-rich final product. Four types of products are currently generated from the GOES-West and GOES-East satellites: "NightIR" and "NightMicrophysics", at 2km resolution, are generated 24 hours a day with infrared channels, so are visible both night and day, and "NaturalColour" and "DayCloudConvection", at 1km resolution, which combine visible light channels with infrared channels; their higher resolution makes the latter two products more popular, but they are not available during most of the night (between 02UTC and 07UTC for GOES-Est, and between 06UTC and 11UTC for GOES-Ouest) given the absence of reflected sunlight. Other RGB products should be added gradually in the future to meet different needs.

The Indicator of Risk of Water Contamination by nitrogen (IROWC-N) estimates the risk of water contamination by nitrogen leaching on agricultural lands in Canada from 1981 to 2021. High nitrate level ( > 10 mg N/L) in drinking water may lead to various health impacts including methemoglobinemia (blue baby syndrome) and non-Hodgkin’s lymphoma. High nitrate levels in surface waters can also contribute to algal growth and eutrophication.

The European Green Crab (EGC) is a high-risk global invader that can devastate coastal marine ecosystems by displacing native species, degrading and disturbing native habitats (including eelgrass), and altering food webs. EGC has recently been detected in the Canadian portion of the Salish Sea. As EGC continue to establish in the region, identifying locations on which to focus limited monitoring resources is an ongoing problem given the vast amount of coastal habitat that could be occupied by the species. A variety of methods can be used to identify highly suitable habitats for EGC at a range of spatial scales. However, none have been evaluated in the context of informing EGC management, nor for the Canadian portion of the Salish Sea. Here we evaluate five individual methods developed to assess habitat suitability for EGC (i.e., MaxEnt, stochastic gradient boosted linear and logistic regression models, a rapid site selection tool, and a qualitative site assessment and ranking tool) and five derived models generated by multiplying the outputs of these individual models. Each model relied on slightly different environmental and habitat input variables affecting EGC invasion success. Thus, rather than identifying a single preferred model, we used a multi-model ensemble approach to identify sites that are expected to be most suitable for the species. The ensemble approach likely increases predictive power by including both environmental and habitat characteristics when identifying priority sites for early detection/monitoring for EGC in the Canadian waters of the Salish Sea. Finally, we describe how the models evaluated here, alone or in combination, could be used to identify additional sites either within the Salish Sea or into new areas.This dataset contains predicted habitat suitability from five models for European Green Crab at beaches in the Salish Sea (British Columbia, Pacific Region).

Off Road Vehicle (ORV) Trail Plan show the trails (whether individually or by zone) within the off-road vehicle management area or the part of the off-road vehicle management area for which a trail plan is developed, on which an off-road vehicle may be used. A red, yellow, green scheme is used to symbolize the level of restriction on the trail plan. Red: off road vehicles are prohibited; Yellow: off road vehicles are prohibited except where designated by an approved Off Road Management Area Trail Plan; Green: off road vehicles are not prohibitedDistributed 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@gov.yk. ca](mailto:geomatics.help@yukon.ca)

Coastal vegetated ecosystems are recognized for their role in cycling and storing carbon in the world’s oceans (i.e., blue carbon); however, high uncertainty in carbon sequestration rates is partly due to an absence of studies estimating carbon export to the deep sea. We modeled export from nearshore kelp forests and seagrass beds, showing variability by orders of magnitude across spatial scales (3 to hundreds of kilometers), kelp and seagrass species, seasons, and carbon forms, raising caution in using generalized export rates in blue carbon accounting. Our results also show rapid (20 to 30 days) and extensive export of neutrally buoyant dissolved organic carbon particles to the shelf break (up to 44% within 90 days), contrasting sinking particulate organic carbon particles that largely remained within 100-meter water depth in the nearshore. These results improve estimates of carbon sequestration by blue carbon ecosystems and reveal contrasting patterns of export relative to other regions of the globe. Cite this data as: Kira A. Krumhansl et al., Pathways of blue carbon export from kelp and seagrass beds along the Atlantic coast of Nova Scotia.Sci. Adv.11,eadw1952(2025).DOI: 10.1126/sciadv.adw1952

The purpose of the survey is to document and record habitat types and associated algae and marine invertebrate species in a variety of habitat types. Transect locations are randomly selected throughout the study area, which rotates between the north and south coasts of British Columbia on a biannual basis. Transects are laid perpendicular to the shoreline. A team of two divers swim the transect with data sheets to collect habitat, algae and marine invertebrate data as detailed below in the methods section. Data is keypunched in an MS Access database that can be queried for species observations and environmental information.This dataset includes three tables pulled from the original database containing observations by species, observations by quadrat, and additional header information for each observation. All three tables can be linked by the field HKey. Three lookup tables are included as well, one for algae, one for invertebrates, and one for substrates.

Phytoplankton species abundance (cell/L) and oceanographic conditions (temperature, salinity, chlorophylle-a (mg/m³) for some years and nutrient content (mmol/m³)) at stations of the Harmful Algae Monitoring Programme (HAMP) from1994 to 2016.The layer presents the station positions of the HAMP. Two files are attached to each station: one containing the cell counts and the second the oceanographic conditions.PurposeThe summer growth of many toxic and harmful microalgae species poses a serious threat for the public health and commercial or recreational exploitation of some marine species.The Department of Fisheries and Oceans (DFO) initiated the Harmful Algae Monitoring Programme (HAMP) in 1989 in order to complete the monitoring program for paralytic shellfish poisoning (PSP). Under the responsibility of Maurice-Lamontagne Institute scientists, the HAMP is to monitor, by means of a coastal station network, the natural occurrence of toxic and harmful algae in the St. Lawrence in order to determine their spatio-temporal distribution and the environmental conditions leading to their bloom.The network is made up of 11 coastal stations which are sampled every week from April to November and which are established along Quebec eastern shores. It extends from Tadoussac to Tête-à-la-Baleine on the St. Lawrence north shore and from Sainte-Flavie to Carleton on the south shore along the Gaspé peninsula. Another station is located in Havre-Aux-Maisons, Magdalen Islands.The HAMP was discontinued in 2010 but opportunistic samplings are still done at some stations.Additional informationThe sampling and analysis protocol is described in details in the following publication apart from the fact that the number of identified and counted species significantly has been increasing with time. Phytoplankton samples are preserved in a lugol solution.Blasco D., M. Levasseur, R. Gélinas, R. Larocque, A.D. Cembella, B. Huppertz et E. Bonneau.1998. Monitorage du phytoplancton toxique et des toxines de type IPM dans les mollusques du Saint-Laurent: 1989 à 1994. Rapp. stat. can. hydrogr. sci. océan. 15 1 : x i-117 p.

This inventory, conducted from September 26th to October 3th, 2019, aimed to describe the community structure of macroalgae and benthic macroinvertebrates of five small estuaries of the Upper North Shore of Quebec, namely Barthélemy Bay and the Colombier, Mistassini, Franquelin and Saint-Nicolas rivers. This inventory is part of a doctoral study of Valentine Loiseau on the global changes in the St. Lawrence system, mainly the study of marine benthic communities in response to changes of salinity, to ensure proper management of the environment in the face of future changes. The main objective is to describe the structure and the levels of specific diversities of mediolittoral communities of benthic macroinvertebrates and macroalgae along a salinity gradient. These five small estuaries were selected because of their similar size, hard substrates and easy access. Three levels of hypoosmotic stress (low, medium, high) and one control level (seawater) were used for each of the selected estuaries, with eight quadrats per stress level. Quadrat positions were randomly selected but had to meet two criteria: (1) regular height in the foreshore to control the influence of other stresses (temperature, exposure); and (2) presence of at least one macroalga to maintain homogeneity. A percentage cover by macroalgal and macroinvertebrate species was estimated, and then all organisms were weighed by species and size group. The salinity of the nearest water point was measured at mid-tide with a portable refractometer and a Castaway-type CTD (Conductivity-Temperature-Density) probe. The inventory was done using a stratified random sampling design and the sampling unit was a quadrat measuring 25 x 25 cm. The three files provided (DarwinCore format) are complementary and are linked by the "eventID" key. The "event_information" file includes the generic information of the quadrat, including date and location. The "additional_information_event_and_occurrence" file includes salinity and substrate type of the quadrat, as well as the total weight of all individuals of the same species caught in the quadrat extrapolated to one square metre of surface. For nudibranchs and barnacles, weight was estimated from the size of the individuals so that they were not removed from the environment. The "taxon_occurrence" file includes the taxonomic inventory of macroalgal and benthic macroinvertebrate species observed in the quadrat, identified to the lowest possible species or taxonomic level and biomass by identified species.For quality control, organisms were identified on the field using the following guide: Chabot, Robert et Anne Rossignol. 2003. Algues et faune du littoral du Saint-Laurent maritime : Guide d'identification. Institut des Sciences de la mer de Rimouski, Rimouski ; Pêches et Océans Canada (Institut Maurice-Lamontagne), Mont-Joli. 113 pages. The taxonomy was checked against the World Register of Marine Species (WoRMS) to match recognized standards and using the R obistools and worrms libraries. The WoRMS match was placed in the "scientificNameID" field of the occurrence file. All sample locations were spatially validated. This project was funded by DFO Coastal Environmental Baseline Program under Canada’s Oceans Protection Plan. This initiative aims to acquire environmental baseline data contributing to the characterization of important coastal areas and to support evidence-based assessments and management decisions for preserving marine ecosystems.

Sightings data were collected by the Mingan Island Cetacean Study (MICS) from 1980 to 2008 with annual surveys realised in the Gulf of St. Lawrence between the end of may and early november. Surveys were conducted using inflatable boats enabling the close approaches necessary to photograph and biopsy blue whales.The aim of this project was to provide additional information for designating blue whale critical habitat as required under the Canadian Species at Risk Act.For more details consult the following report:Ramp, C. and Sears, R. 2013. Distribution, densities, and annual occurrence of individual blue whales (Balaenoptera musculus) in the Gulf of St. Lawrence, Canada from 1980-2008. DFO Can. Sci. Advis. Sec. Res. Doc. 2012/157. vii + 37 p.http://www.dfo-mpo.gc.ca/csas-sccs/Publications/ResDocs-DocRech/2012/2012_157-eng.htmlData of blue whale sightings, collected by the MICS, have been analysed per km of effort in 3 x 3 km grid cells in the Gulf of St. Lawrence for the 2000-2008 period.