imageryBaseMapsEarthCover
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BTM ( Baseline Thematic Mapping) Landsat Image Catalogue Acquisition Dates. This polygon coverage contains the date of capture of the Landsat images making up the seamless BC Landsat image catalogue. This is not a multipart feature
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The ‘Circa 1995 Landcover of the Prairies’dataset is a geospatial raster data layer portraying the rudimentaryland cover types of all grain-growing areas of Manitoba, Saskatchewan, Alberta and northeastern British Columbia at a 30-metre resolution for the 1995 timeframe. It is the collection of all the classified imagery (1993 to 1995) of the Western Grain Transition Payment Program (WGTPP) assembled into a single seamless raster data layer.
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The MODIS Surface Albedo and Surface Reflectance Dataset (or simply Albedo) includes times series of 10-day composite products derived at 250-m spatial resolution over Canadian territory and neighboring areas produced at the Canada Centre for Remote Sensing (CCRS) since February 2000.The datasets contain spectral and broadband reflectance’s and albedo for MODIS bands B1-B7 designed primarily for land applications. The imagery for all spectral bands was downscaled and re-projected into the Lambert Conformal Conic (LCC) projection at 250-m spatial resolution. The area size is 5,700 km × 4,800 km. The specialized MODIS processing system was developed at CCRS to fully utilize the high quality of MODIS L2 swath imagery over the northern latitudes. As such, the CCRS Albedo product is different from the standard NASA product. The differences are related to temporal and spatial scaling, shape of kernel functions employed to fit data, as well as details of scene identification, atmospheric correction, and data fitting methodology. The data is hosted on AWS; users can follow the tutorial (https://github.com/OpsCCRS/AWS-Open-Data-Registry-Preparation/blob/main/CCRSMODISAlbedo/get-to-know-adataset-CCRSMODISAlbedo.ipynb) to access the data or via the URL in Data Resources for each year.
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Our Imagery Base Maps and Mosaics of a number of Raster Datasets. This includes the ASTER DEM, CDED and Shaded Relief Datasets. As well as a number of mosaics, including SPOT, RapidEye, Landsat, and MVI Landcover data.
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The Grassland Inventory provides a standardized, high-resolution land cover classification for the grassland ecosystems in Canada. Developed using a random forest classification on multi-temporal Sentinel-1 SAR and Sentinel-2 Optical imagery, the series differentiates intact native grasslands from high-disturbance, tame perennial forage systems; two classes that are spectrally and phenologically similar, yet critical to differentiate and quantify accurately for carbon and biodiversity modelling. Each release in the series includes a categorical 10 m land cover raster and a companion continuous likelihood layer representing model confidence in the native grassland class. As new classifications are added (semi-decadal) and geographic extent increased, the series will enable consistent temporal comparisons to track grassland dynamics and land cover change to support operational and research applications within AAFC and stakeholders.
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This publication contains a raster maps at 250 m resolution of the merchantable volume (m3/ha) of the mature Canadian forest available for harvesting in the next 20 years (2011 to 2031). The maps were produced from remote sensing products at a spatial resolution of 250 m on the MODIS pixel grid and 30 m on the Landsat pixel grid. More specifically, we used forest attribute data at the 250 m pixel for the years 2001 and 2011 (Beaudoin et al 2014 and 2018) combined with forest cover changes for the years 1985 to 2015 at 30 m (Guindon et al. 2017 and 2018). The map of mature forests in Canada was prepared at the forest management unit (FMU) level and therefore exclude private lands. To be considered mature (i.e. available for cutting in the next 20 years), the forest pixels of Beaudoin et al. (2018) was to have a merchantable volume per ha equal to or greater than 80% of the average merchantable volume of the pixels that were harvested between 2001 and 2011 per forest management unit. A scientific article gives additional details on the methodology: Barrette J, Paré D, Manka F, Guindon L, Bernier P, Titus B. 2018. Forecasting the spatial distribution of logging residues in Canada’s managed forests. Can. J. For. Res. 48: http://www.nrcresearchpress.com/doi/10.1139/cjfr-2018-0080 Reference for this dataset: Barrette J, Paré D, Manka F, Guindon L, Bernier P, Titus B. 2018. Forecasting the spatial distribution of logging residues in Canada’s managed forests. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/dd94871a-9a20-47f5-825b-768518140f35
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Note: To visualize the data in the viewer, zoom into the area of interest. The National Air Photo Library (NAPL) of Natural Resources Canada archives over 6 million aerial photographs covering all of Canada, some of which date back to the 1920s. This collection includes Time Series of aerial orthophoto mosaics over a selection of major cities or targeted areas that allow the observation of various changes that occur over time in those selected regions. These mosaics are disseminated through the Data Cube Platform implemented by NRCan using geospatial big data management technologies. These technologies enable the rapid and efficient visualization of high-resolution geospatial data and allow for the rapid generation of dynamically derived products. The data is available as Cloud Optimized GeoTIFF (COG) files for direct access and as Web Map Services (WMS) or Web Coverage Services (WCS) with a temporal dimension for consumption in Web or GIS applications. The NAPL mosaics are made from the best spatial resolution available for each time period, which means that the orthophotos composing a NAPL Time Series are not necessarily coregistered. For this dataset, the spatial resolutions vary from 150 cm to 200 cm. The NAPL indexes and stores federal aerial photography for Canada, and maintains a comprehensive historical archive and public reference centre. The Earth Observation Data Management System (EODMS) online application allows clients to search and retrieve metadata for over 3 million out of 6 million air photos. The EODMS online application enables public and government users to search and order raw Government of Canada Earth Observation images and archived products managed by NRCan such as aerial photos and satellite imagery. To access air photos, you can visit the EODMS web site: https://eodms-sgdot.nrcan-rncan.gc.ca/index-en.html
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The 2015 AAFC Land Use is a culmination and curated metaanalysis of several high-quality spatial datasets produced between 1990 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.
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Topographic maps produced by Natural Resources Canada conform to the National Topographic System (NTS) of Canada. Indexes are available in three standard scales: 1:1,000,000, 1:250,000 and 1:50,000. The area covered by a given mapsheet is determined by its latitude and longitude. 1:1,000,000 mapsheets are identified by a combination of three numbers (e.g. 098). 1:250,000 mapsheets are identified by a combination of numbers, and letters ranging from A through P (e.g. 098C). Sixteen smaller segments (1 to 16) form blocks used for 1:50,000 mapping (e.g. 098C03).
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The 2000 AAFC Land Use is a culmination and curated metaanalysis of several high-quality spatial datasets produced between 1990 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.
Arctic SDI catalogue