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We have found 13 datasets for the keyword " neb". You can continue exploring the search results in the list below.
Datasets: 106,031
Contributors: 42
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13 Datasets, Page 1 of 2
Oil and Gas Seismic Lines
Data for seismic surveys prior to 1999 were obtained from National Energy Board (NEB) as points data (end points and bends) in Excel file format with basic information only. The NEB dataset was not comprehensive. Seismic lines coordinates' data for some of the earliest surveys recorded during 1960s and 1970s were recorded in NAD27 and contained location errors. In addition some data were not converted to NAD83 datum currently in use therefore resulting in even larger error. Starting at 1999 seismic lines location data are provided to the Government of Yukon by exploration companies as part of their statutory reporting requirements.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)
High resolution forest change for Canada (Change Year) 1985-2011
High resolution forest change for Canada (Change Year) 1985-2011The forest change data included in this product is national in scope (entire forested ecosystem) and represents the first wall-to-wall characterization of wildfire and harvest in Canada at a spatial resolution commensurate with human impacts. The information outcomes represent 27 years of stand replacing change in Canada’s forests, derived from a single, consistent spatially-explicit data source, derived in a fully automated manner. This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 1985–2011 for Canada's 650 million hectare forested ecosystems (https://authors.elsevier.com/sd/article/S0034425717301360 ). Landsat data has a 30m spatial resolution, so the change information is highly detailed and is commensurate with that of human impacts. These data represent annual stand replacing forest changes. The stand replacing disturbances types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2016; http://www.tandfonline.com/doi/full/10.1080/17538947.2016.1187673).The data available is, 1. a binary change/no-change; 2. Change year; and, 3. Change type. When using this data, please cite as: White, J.C., M.A. Wulder, T. Hermosilla, N.C. Coops, and G. Hobart. (2017). A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment. 192: 303-321. DOI: 10.1016/j.rse.2017.03.035. https://authors.elsevier.com/sd/article/S0034425717301360Geographic extent: Canada's forested ecosystems (~ 650 Mha)Time period: 1985–2011
2020-21 Grants and Contributions
Data provided shows grants and contributions provided to Canadian firms by National Research Council (NRC) and its Industrial Research Assistance Program (IRAP) between April 1, 2020 and March 31, 2021.
Difference in fire season length - Short-term (2011-2040) under RCP 8.5 compared to reference period
Fire weather refers to weather conditions that are conducive to fire. These conditions determine the fire season, which is the period(s) of the year during which fires are likely to start, spread and do sufficient damage to warrant organized fire suppression.The length of fire season is the difference between the start- and end-of-fire-season dates. These are defined by the Canadian Forest Fire Weather Index (FWI; http://cwfis.cfs.nrcan.gc.ca/) start-up and end dates. Start-up occurs when the station has been snow-free for 3 consecutive days, with noon temperatures of at least 12°C. For stations that do not report significant snow cover during the winter (i.e., less than 10 cm or snow-free for 75% of the days in January and February), start-up occurs when the mean daily temperature has been 6°C or higher for 3 consecutive days. The fire season ends with the onset of winter, generally following 7 consecutive days of snow cover. If there are no snow data, shutdown occurs following 7 consecutive days with noon temperatures lower than or equal to 5°C.Historical climate conditions were derived from the 1981–2010 Canadian Climate Normals. Future projections were computed using two different Representative Concentration Pathways (RCP). RCPs are different greenhouse gas concentration trajectories adopted by the Intergovernmental Panel on Climate Change (IPCC) for its fifth Assessment Report. RCP 2.6 (referred to as rapid emissions reductions) assumes that greenhouse gas concentrations peak between 2010-2020, with emissions declining thereafter. In the RCP 8.5 scenario (referred to as continued emissions increases) greenhouse gas concentrations continue to rise throughout the 21st century.Provided layer: difference in projected fire season length for the short-term (2011-2040) under the RCP 8.5 (continued emissions increases) compared to reference period across Canada.
2025-26 Grants and Contributions
Data provided shows grants and contributions provided to Canadian firms by National Research Council (NRC) and its Industrial Research Assistance Program (IRAP) between April 1, 2025 and March 31, 2026.
2019-20 Grants and Contributions
Data provided shows grants and contributions provided to Canadian firms by National Research Council (NRC) and its Industrial Research Assistance Program (IRAP) between April 1, 2019 and March 31, 2020.
2018-19 Grants and Contributions
Data provided shows grants and contributions provided to Canadian firms by National Research Council (NRC) and its Industrial Research Assistance Program (IRAP) between April 1, 2018 and March 31, 2019.
High resolution forest change for Canada (2012-2015) (Change Year)
The Forest Change Type data described here is an update to previously posted open data. The date range for this data is 2012 to 2015. The Forest Change Type data for the prior period from 1985 to 2011 can be found here: https://opendata.nfis.org/mapserver/nfis-change_eng.html or https://gcgeo.gc.ca/geonetwork/search/eng search for “Forest Change” but you must be logged in to see the data. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The forest change data included in this product is national in scope (entire forested ecosystem) and represents the first wall-to-wall characterization of wildfire and harvest in Canada at a spatial resolution commensurate with human impacts. The information outcomes represent 25 years of stand replacing change in Canada’s forests, derived from a single, consistent spatially-explicit data source, derived in a fully automated manner. This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 1985–2010 for Canada's 650 million hectare forested ecosystems (https://authors.elsevier.com/sd/article/S0034425717301360 ). Landsat data has a 30m spatial resolution, so the change information is highly detailed and is commensurate with that of human impacts. These data represent annual stand replacing forest changes. The stand replacing disturbances types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2016; http://www.tandfonline.com/doi/full/10.1080/17538947.2016.1187673).The data available is, 1. a binary change/no-change; 2. Change year; and, 3. Change type. When using this data, please cite as: Hermosilla, T.,Wulder, M. A.,White, J. C.,Coops, N. C.,Hobart, G. W., (2017). Updating Landsat time series of surface-reflectance composites and forest change products with new observations. International Journal of Applied Earth Observation and Geoinformation. 63: 104-111. DOI: 10.1016/j.jag.2017.07.013 White, J.C., M.A. Wulder, T. Hermosilla, N.C. Coops, and G. Hobart. (2017). A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment. 192: 303-321. DOI: 10.1016/j.rse.2017.03.035.
BEC Map
The current and most detailed version of the approved corporate provincial digital Biogeoclimatic Ecosystem Classification (BEC) Zone/Subzone/Variant/Phase map (version 12, September 2, 2021). Use this version when performing GIS analysis regardless of scale. This mapping is deliberately extended across the ocean, lakes, glaciers, etc to facilitate intersection with a terrestrial landcover layer of your choice
Weekly Best-Quality Maximum - NDVI Anomalies
Each pixel value corresponds to the difference (anomaly) between the mean “Best-Quality” Max-NDVI of the week specified (e.g. Week 18, 2000-2014) and the “Best-Quality” Max-NDVI of the same week in a specific year (e.g. Week 18, 2015). Max-NDVI anomalies < 0 indicate where weekly Max-NDVI is lower than normal. Anomalies > 0 indicate where weekly Max-NDVI is higher than normal. Anomalies close to 0 indicate where weekly Max-NDVI is similar to normal.
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