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We have found 61 datasets for the keyword " greenhouses". You can continue exploring the search results in the list below.
Datasets: 103,380
Contributors: 42
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61 Datasets, Page 1 of 7
Environment
ENV - Environment and conservation (environment)Environmental resources, protection, and conservation. For example, resources describing pollution, waste storage and treatment, environmental impact assessment, environmental risk, and nature reserves. )
Greenbelt specialty crop areas
The locations of the 2 specialty crop areas in the Greenbelt Plan: * the Holland Marsh * Niagara Peninsula Tender Fruit and Grape Lands
BC Parks, Ecological Reserves, and Protected Areas
This dataset contains parks and protected areas managed for important conservation values and are dedicated for the preservation of their natural environments for the inspiration, use and enjoyment of the public. Places of special ecological importance are designated as ecological reserves for scientific research and educational purposes. Source data is Tantalis. *April 18, 2018: Prior to this date this dataset had one spatial boundary per park per survey plan that intersected the boundary of that park. This resulted in multiple identical boundaries for each park that had more than one survey plan overlapping it’s boundaries. The change aggregated the park data so that there is just one boundary per park with the plan numbers concatenated into a single column where each different plan number is separated by a comma.
Renewable Energy Power Plants, 1 MW or more - North American Cooperation on Energy Information
Stations containing prime movers, electric generators, and auxiliary equipment for converting mechanical, chemical into electric energy with an installed capacity of 1 Megawatt or more generated from renewable energy, including biomass, hydroelectric, pumped-storage hydroelectric, geothermal, solar, and wind.Mapping Resources implemented as part of the North American Cooperation on Energy Information (NACEI) between the Department of Energy of the United States of America, the Department of Natural Resources of Canada, and the Ministry of Energy of the United Mexican States.The participating Agencies and Institutions shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics, if available, are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time and may differ from other official information. The Agencies and Institutions participants give no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data.Parent Collection:[North American Cooperation on Energy Information, Mapping Data](https://open.canada.ca/data/en/dataset/aae6619f-f9f3-435d-bc32-42decd58b674)
Greenhouse Gas Emissions Register
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).**
Rainbow Smelt - Feeding areas, breeding areas, concentration and specie present - FHAMIS
Feeding areas, breeding areas, concentration and specie present. Data extracted from the Fish Habitat Management Information System (FHAMIS) according to a literature review of documents produced between between 1977 and 2001
Terrestrial Protected Area Representation by Ecosection
Ecosection boundaries with percent protected, number of overlapping protected areas and other attributes added as a result of geoprocessing in the Protected Area System Overview (PASO) application. Protected area and park representation by ecosection provides a landscape context for natural resource planning processes such as; management plans, land use zoning, environmental risk assessment, landscape analysis, habitat supply, and management of high priority species. Ecosections are distinguished from each other by enduring characteristics such as minor physiographic and macroclimatic or oceanographic variations. For more information on ecosections and the Ecoregion Classification System see: http://www.env.gov.bc.ca/ecology/ecoregions/index.html. For important warnings about using this data for spatial analysis see the Data Quality section of the metadata
High primary production areas in the Estuary and the Gulf of St. Lawrence
This layer represents areas where primary production is considered to be high. Primary production includes microscopic algal blooms, named phytoplankton, a food resource at the base of the food web of marine ecosystems. The knowledge of these zones can serve as a proxy to identify areas of the St. Lawrence where productivity is higher at different times of the year. Impacting his component may influence the rest of the life cycle in the affected area. Data were generated from the Gulf of St. Lawrence Biogeochemical Model (GSBM) developed by Dr. Diane Lavoie. This model makes it possible to calculate, using 10 variables, the primary production in each cell of the grid of the model. This calculation was done at a monthly resolution and a threshold was then applied to the data to keep only those cells where the estimated concentrations exceeded 20 mg C / m-2. This level of primary production is considered high.Additional InformationMonthly mean primary production (mg C m-2) in the first 50 meters of the simulated surface with the three-dimensional CANOPA-GSBM numerical model over a period of 13 years (1998-2010).The Gulf of St. Lawrence Biogeochemical Model (GSBM) simulates biogeochemical cycles of oxygen, carbon and nitrogen, and the biological components that determine the dynamics of the planktonic ecosystem. The model has 10 state variables. The NPZD (nutrients, primary production, zooplankton, detritus) model includes both simplified herbivorous and microbial food chains typical of bloom and post-bloom conditions. The export of biogenic matter at depth is mediated by the herbivorous food web (nitrate, large phytoplankton (diatoms), mesozooplankton, particulate organic matter), while the microbial food web (ammonium, small phytoplankton, microzooplankton, dissolved organic matter) is mainly responsible for nutrient recycling in the euphotic zone. Nitrate is also supplied by rivers. The tight coupling between small phytoplankton growth and microzooplankton grazing, autochtonous nitrogen release and (dissolved organic nitrogen) DON remineralization to ammonium (NH4+) is used to represent the dynamic of the microbial food chain. Biological transfer functions are derived from bulk formulations using mean parameters found in the literature. Biological variables are calculated in nitrogen units and algal biomass and production converted to Chl a and carbon units using fixed stoichiometric ratios. Detrital particulate organic nitrogen (PON) gets fragmented to dissolved organic nitrogen (DON) as it sinks toward the bottom. The phytoplankton growth rate is a function of light and nutrient availability. The available light for phytoplankton growth is a function of sea-ice cover, Chl a and colored dissolved organic matter (CDOM). The GSBM biogeochemical model, coupled with the CANOPA regional circulation model, was used to produce the Chl a layer. The grid of the model is 1/12° horizontally (about 6 x 8 km), 46 layers vertical and covers the Gulf of St. Lawrence, Scotian Shelf and Gulf of Maine regions. The vertical resolution is variable (between 6 m close to the surface to 90 m at depths of about 500 m). This model includes tidal forcing and the freshwater supply of the St. Lawrence River and the many rivers in the region, as well as atmospheric forcing (temperature, wind, etc.) produced by an independent model (National Center for Environmental Prediction (NCEP) Climate Forecast System Version 2). In addition, the circulation model is coupled with a model of sea ice that reproduces the seasonality of the ice cover in the region. The temperature and salinity fields are produced freely by the model and only constrained by monthly climatologies of these conditions at the boundaries of the model domain. The simulation was carried out over a part of the period covering the Zonal Monitoring Program (AZMP) from 1998 to 2010.
Shorezone Observed Habitat Polygons
The Observed Habitat Polygons show the various types of particular habitat that have been observed or calculated by biologists as well as an expectation of different species found in the habitats. Each bioarea has several observed habitats, and it is the combination of the bioarea and habitat observed number that identifies each unique observed habitat.
Sponge significant concentration areas in the Gulf of St. Lawrence
Identification of significant concentrations of sponges in the Gulf of St. Lawrence biogeographic unit using Kernel density estimation (KDE).This method was applied to create a modelled biomass surface for each taxa and an aerial expansion method was permitted to identify significant concentrations. Only geo-referenced biomass data have been used to identify the “hot spots”. The borders of the areas were refined using knowledge of null catches and species distribution models. Predictive models were produced using a random forest machine-learning technique. For more details, please refer to this report: Kenchington, E., L. Beazley, C. Lirette, F.J. Murillo, J. Guijarro, V. Wareham, K. Gilkinson, M. Koen Alonso, H. Benoît, H. Bourdages, B. Sainte-Marie, M. Treble, and T. Siferd. 2016. Delineation of Coral and Sponge Significant Benthic Areas in Eastern Canada Using Kernel Density Analyses and Species Distribution Models. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/093. vi + 178 p.http://waves-vagues.dfo-mpo.gc.ca/Library/40577806.pdfThe present layer only contains the analysis results for sponges. Purpose:As part of the Canada's commitment to the identification and protection of sensitive benthic marine ecosystems, maps of the location of significant concentrations of corals and sponges on the east coast of Canada were produced through quantitative analyses of research vessel trawl survey data, supplemented with other data sources where available. The taxa analyzed are sponges (Porifera), large and small gorgonian corals (Alcyonacea), and sea pens (Pennatulacea). However, only the sponges (Porifera) and sea pens (Pennatulacea) have been considered in the analysis concerning the Gulf of St. Lawrence biogeographic unit.
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