Each pixel value corresponds to the mean historical “Best-quality” Max-NDVI value for a given week, as calculated from the previous 20 years in the MODIS historical record (i.e. does not include data from the current year). These data are also often referred to as “weekly baselines” or “weekly normals”.

This collection is a legacy product that is no longer maintained. It may not meet current government standards. The correction matrices for the National Topographic Data Base (NTDB), also known under the acronym CORMAT, are products derived from the planimetric enhancement of NTDB data sets at the 1:50 000 scale. The correction matrix enables users to enhance the geometric accuracy of the less accurate NTDB. The matrix is a set of points arrayed on a regular 100-m grid. Each point describes the planimetric correction (DX, DY) to be applied at this location. The position of the points is given in UTM (Universal Transverse Mercator projection) coordinates based on the North American Datum of 1983 (NAD83) . Each file constitutes a rectangular area covering the entire corresponding NTDB data set. Its delimitation corresponds more or less to National Topographic System (NTS) divisions at the 1:50 000 scale. All NTDB data sets at the 1:50 000 scale whose original accuracy was less than 30 m can thus be geometrically corrected. A CORMAT data set contains a list of coordinates and the corresponding corrections to be applied in the form X Y DX DY. Related Products: [National Topographic Data Base (NTDB), 1944-2005](https://open.canada.ca/data/en/dataset/1f5c05ff-311f-4271-8d21-4c96c725c2af)

Röð uppréttra loftmynda úr loftmyndasafni Náttúrufræðistofnunar sem unnar voru á árunum 2013 til 2018 hjá Jarðvísindastofnun HÍ, sem partur af tveimur verkefnum: 1 - Mælingar á jöklabreytingum úr sögulegum loftmyndum. Þetta verkefni var unnið af Joaquín M.C. Belart í M.Sc. og Ph.D. hjá Jarðvísindastofnun. Útvaldar loftmyndir frá 1945 til 1994 voru skannaðar hjá Landmælingum Íslands sérstaklega fyrir þetta verkefni. Vinnsla þessara loftmynda fór fram með því að nota "Ground Control Points" (GCP) sem teknir voru úr lidarmælingum á íslenskum jöklum. Úrvinnsla gagna úr Drangajökli fór fram með ERDAS hugbúnaðinum. Nánari upplýsingar um vinnsluna er að finna í Magnússon o.fl., 2016 (https://tc.copernicus.org/articles/10/159/2016/tc-10-159-2016.html). Úrvinnsla gagna frá öðrum jöklum var unnin með MicMac hugbúnaðinum, einnig með GCP teknir af lidar. Nánari upplýsingar um vinnsluna eru fáanlegar í Belart o.fl., 2019 (https://www.cambridge.org/core/journals/journal-of-glaciology/article/geodetic-mass-balance-of-eyjafjallajokull-ice-cap -for-19452014-processing-guidelines-and-relation-to-climate/9B715A9E0413A6345C2B151B1173E71D) og Belart o.fl., 2020 (https://www.frontiersin.org/articles/10.31630/feart/full.316390/feart. 2 - Mælingar á hraunmagni Heklugosanna á 20. öld. Þetta verkefni var unnið af Gro B.M. Pedersen sem hluti af verkefni þar sem unnið var að umhverfiskortlagningu og vöktun Íslands með fjarkönnun "Environmental Mapping and Monitoring of Iceland by Remote Sensing" (EMMIRS, fjármagnað af Rannís) á árunum 2015-2018. Loftmyndirnar af Heklu frá 1945 til 1992 voru skannaðar af Landmælingum Íslands. Vinnsla þessara mynda var gerð með ERDAS hugbúnaðinum og nánari upplýsingar um vinnsluna er hægt að nálgast í Pedersen o.fl., 2018 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017GL076887) --------------------------------------------------------------------------------------------------------------- A series of orthomosaics using the archives of aerial photographs from Náttúrufræðistofnun (Loftmyndasafn) created between 2013 and 2018 at the Institute of Earth Sciences, as part of two projects: 1 - Measurements of glacier changes from historical aerial photographs. This project was conducted by Joaquín M.C. Belart during his M.Sc. and his Ph.D. at the Institute of Earth Sciences. A selection of aerial photographs from 1945 to 1994 were scanned at Náttúrufræðistofnun specifically for this project. The processing of these aerial photographs was done using Ground Control Points (GCPs) extracted from lidar surveys of Icelandic glaciers. The processing of the data from Drangajökull ice cap was done using the ERDAS software. Further details on the processing are available in Magnússon et al., 2016 (https://tc.copernicus.org/articles/10/159/2016/tc-10-159-2016.html). The processing of the data from other glaciers was done using the MicMac software, also with GCPs extracted from lidar. Further details of the processing are available in Belart et al., 2019 (https://www.cambridge.org/core/journals/journal-of-glaciology/article/geodetic-mass-balance-of-eyjafjallajokull-ice-cap-for-19452014-processing-guidelines-and-relation-to-climate/9B715A9E0413A6345C2B151B1173E71D) and Belart et al., 2020 (https://www.frontiersin.org/articles/10.3389/feart.2020.00163/full) 2 - Measurements of the lava volumes of the Hekla eruptions in the 20th century. This project was conducted by Gro B.M. Pedersen as part of the Environmental Mapping and Monitoring of Iceland by Remote Sensing (EMMIRS, financed by Rannís) project between 2015-2018. The aerial photographs of Hekla from 1945 to 1992 were scanned by Náttúrufræðistofnun. The processing of these photographs was done using the ERDAS software, and further details of the processing are available in Pedersen et al., 2018 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017GL076887) References: Belart J.M.C., Magnússon E., Berthier E., Pálsson, F., Aðalgeirsdóttir, G., & Jóhannesson, T. (2019). The geodetic mass balance of Eyjafjallajökull ice cap for 1945–2014: Processing guidelines and relation to climate. Journal of Glaciology, 65(251), 395-409. doi:10.1017/jog.2019.16 Belart J.M.C., Magnússon E., Berthier E., Gunnlaugsson Á.Þ., Pálsson F., Aðalgeirsdóttir G., Jóhannesson T, Thorsteinsson T and Björnsson H (2020) Mass Balance of 14 Icelandic Glaciers, 1945–2017: Spatial Variations and Links With Climate. Front. Earth Sci. 8:163. doi: 10.3389/feart.2020.00163 Magnússon, E., Belart, J.M.C., Pálsson, F., Ágústsson, H., and Crochet, P.: Geodetic mass balance record with rigorous uncertainty estimates deduced from aerial photographs and lidar data – Case study from Drangajökull ice cap, NW Iceland, The Cryosphere, 10, 159–177, https://doi.org/10.5194/tc-10-159-2016, 2016. Pedersen, G. B. M., Belart, J. M. C., Magnússon, E., Vilmundardóttir, O. K., Kizel, F., Sigurmundsson, F. S., et al. (2018). Hekla volcano, Iceland, in the 20th century: Lava volumes, production rates, and effusion rates. Geophysical Research Letters, 45, 1805–1813. https://doi.org/10.1002/2017GL076887

The “Land Cover for Agricultural Regions of Canada, circa 2000” is a thematic land cover classification representative of Circa 2000 conditions for agricultural regions of Canada. Land cover is derived from Landsat5-TM and/or 7-ETM+ multi-spectral imagery by inputting imagery and ground reference training data into a Decision-Tree or Supervised image classification process. Object segmentation, pixel filtering, and/or post editing is applied as part of the image classification. Mapping is corrected to the GeoBase Data Alignment Layer. National Road Network (1:50,000) features and other select existing land cover products are integrated into the product. UTM Zone mosaics are generated from individual 30 meter resolution classified scenes. A spatial index is available indicating the Landsat imagery scenes and dates input in the classification. This product is published and compiled by Agriculture and Agri-Food Canada (AAFC), but also integrates products mapped by other provincial and federal agencies; with appropriate legend adaptations. This release includes UTM Zones 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, and 22 for corresponding agricultural regions in Newfoundland, Prince Edward Island, Nova Scotia, New Brunswick, Québec, Ontario, Manitoba, Saskatchewan, Alberta and British Columbia covering approximately 370,000,000 hectares of mapped area. Mapped classes include: Water, Exposed, Built-up, Shrubland, Wetland, Grassland, Annual Crops, Perennial Crops and Pasture, Coniferous, Deciduous and Mixed forests. However, emphasis is placed on accurately delineating agricultural classes, including: annual crops (cropland and specialty crops like vineyards and orchards), perennial crops (including pastures and forages), and grasslands.

Each pixel value corresponds to the best quality maximum NDVI recorded within that pixel over the week specified. Poor quality pixel observations are removed from this product. Observations whose quality is degraded by snow cover, shadow, cloud, aerosols, and/or low sensor zenith angles are removed (and are assigned a value of “missing data”). In addition, negative Max-NDVI values, occurring where R reflectance > NIR reflectance, are considered non-vegetated and assigned a value of 0. This results in a Max-NDVI product that should (mostly) contain vegetation-covered pixels. Max-NDVI values are considered high quality and span a biomass gradient ranging from 0 (no/low biomass) to 1 (high biomass).

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.

Since 1988, the governments of Canada and Quebec have been working together to conserve, restore, protect and develop the St. Lawrence River under the St. Lawrence Action Plan (SLAP). One of the projects identified under the theme of biodiversity conservation is the development of an integrated plan for the conservation of the natural environments and biodiversity of the St. Lawrence River. The identification of priority sites for conservation has been the first step of this planning exercise. Conservation planning of natural environments requires a reliable, accurate and up-to-date image of the spatial distribution of ecosystems in the study area. In order to produce an Atlas of Priority Sites for Conservation in the St. Lawrence Lowlands, an updated cartography of the land cover of this vast territory was undertaken. This project required obtaining reliable information on the natural environments of the St. Lawrence Lowlands. Although several land cover mapping projects have been conducted for specific types of habitats, it was particularly important to obtain a homogeneous product that would cover the entire territory and that would provide the most detailed information on its various thematic components: agricultural, aquatic, human-modified and forest environments, wetlands as well as old fields and bare ground. The methodology used to produce the land cover mapping of the St. Lawrence Lowlands thus relied mainly on combining and enhancing the best existing products for each theme. This project was made in collaboration with MDDELCC as part of the St. Lawrence Action Plan (SLAP).

The third generation of high resolution 10-m wetland inventory map of Canada, covering an approximate area of one billion hectares, was generated using multi-year (2016-2020), multi-source imagery (Sentinel-1, Sentinel-2, ALOS PALSAR-2, and SRTM) Earth Observation (EO) data as well as environmental features. Over 8800 wetland polygons were processed within an object-based random forest classification scheme on the Google Earth Engine cloud computing platform. The average overall accuracy of 90.5% is an increase of 4.7% over CWIM2. CWIM Versions: The Canadian Wetland Inventory Map (CWIM) is an extension of work started at Memorial University to produce a Newfoundland and Labrador wetland inventory during 2015-2018 which was significantly funded by Environment and Climate Change Canada. The first national CWIM was produced 2018-2019 as a collaboration between Memorial University, C-CORE, and Natural Resources Canada. Dr. Brian Brisco was instrumental in connecting ground truth from multiple sources to the project and providing guidance. Version 2 was produced in 2020 which included more training data and processing by Canada’s ecozones rather than provinces to take advantage of the commonality of landscape ecological features within ecozones to improve the accuracy. Version 3 produced in 2021 continued adding more data sources to further improve accuracy specifically an overestimation of wetland area as well as introducing a confidence map. Version 3A completed in 2022 updates only the arctic ecozones due to their relatively lower accuracy and added hydro-physiographic data layers. Currently work is underway to create a northern circumpolar wetland inventory map to be published in 2025. Paper on Newfoundland and Labrador Wetland Inventory: Mahdianpari, M.; Salehi, B.; Mohammadimanesh, F.; Homayouni, S.; Gill, E. The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform. Remote Sens. 2019, 11, 43. https://doi.org/10.3390/rs11010043 Paper on CWIM1: Mahdianpari, M., Salehi, B., Mohammadimanesh, F., Brisco, B., Homayouni, S., Gill, E., … Bourgeau-Chavez, L. (2020). Big Data for a Big Country: The First Generation of Canadian Wetland Inventory Map at a Spatial Resolution of 10-m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform. Canadian Journal of Remote Sensing, 46(1), 15–33. https://doi.org/10.1080/07038992.2019.1711366 Paper on CWIM2: Mahdianpari, M., Brisco, B., Granger, J. E., Mohammadimanesh, F., Salehi, B., Banks, S., … Weng, Q. (2020). The Second Generation Canadian Wetland Inventory Map at 10 Meters Resolution Using Google Earth Engine. Canadian Journal of Remote Sensing, 46(3), 360–375. https://doi.org/10.1080/07038992.2020.1802584 Paper on CWIM3: M. Mahdianpari et al., "The Third Generation of Pan-Canadian Wetland Map at 10 m Resolution Using Multisource Earth Observation Data on Cloud Computing Platform," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 8789-8803, 2021, doi: 10.1109/JSTARS.2021.3105645. Paper on Arctic ecoregion enhancement for CWIM3A: Michael Merchant, et al., ”Leveraging google earth engine cloud computing for large-scale arctic wetland mapping,” in International Journal of Applied Earth Observation and Geoinformation, vol. 125, 2023, https://doi.org/10.1016/j.jag.2023.103589.

This publication contains vector data (shapefile) of the post-harvest forest residues in Canada for the bioenergy/bioproducts sector in oven-dry tonnes per year (ODT/yr) over the next 20 years. The maps were produced using different remote sensing products. We used forest attribute data at 250 m MODIS for the years 2001 and 2011 (Beaudoin et al. 2014 and 2018) combined with forest cover changes for the years 1985 to 2015 contained in the CanLaD dataset at 30 m Landsat(Guindon et al. 2017 and 2018). Results of available biomass (in the form of harvest residues) were reported at the 10 km x 10 km scale, while the map of mature forests in Canada was prepared at the forest management unit (FMU) level. Briefly, our methodology consisted of three steps: 1- create a map of mature forests for the year 2011, based on 2001-2010 average cut volumes within FMUs; 2- develop an annual cut rate from the area harvested within FMUs from 1985 to 2015 and; 3- define the amount of biomass in the form of forest residues available for the bioenergy sector. The biomass of branches and leaves of forest attribute data was used as a proxy to define the biomass of forest residues available. Nationally, the average biomass of forest residues available after harvest is 26 ± 16 ODT/ha, while the total annual availability for all managed forests in Canada was 21 million ODT/yr. 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 across the Canadian managed forest. 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. Maps forecasting the availability of logging residues in Canada. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/5072c495-240c-42a3-ad55-c942ab37c32a

The 2005 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.