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 0.34 cm resolution orthomosaic was created from unmanned aerial vehicle (UAV) imagery acquired from a single day survey, July 28th 2016, in Cambridge Bay, Nunavut. Five control points taken from a Global Differential Positioning System were positioned in the corners and the center of the vegetation survey. The orthomosaic covering 525m2 was produced by Canada Centre for Remote Sensing /Canada Centre for Mapping and Earth Observation. The UAV survey was completed in collaboration with the Canadian High Arctic Research Station (CHARS) for northern vegetation monitoring research. For more information, refer to our current Arctic vegetation research: Fraser et al; "UAV photogrammetry for mapping vegetation in the low-Arctic" Arctic Science, 2016, 2(3): 79-102. http://www.nrcresearchpress.com/doi/abs/10.1139/AS-2016-0008.

The MODIS surface albedo dataset was produced by the Canada Center for Remote Sensing (CCRS), Natural Resources Canada. The dataset represents the solar shortwave broadband surface albedo and it is at a 10-day interval covering the entire Canadian landmass as well as northern USA, Alaska, and the Greenland. The dataset was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the TERRA satellite which provides a global coverage every 1-2 days in 36 spectral bands ranging from visible to infrared and to thermal wavelengths between 405 and 14,385 nm, and was available since 2000. For the estimation of surface albedo, the first seven spectral bands of B1 to B7 ranging from 459 nm to 2155 nm were used. B1 and B2 have a 250 meter resolution and B3 to B7 have a 500 meter resolution. A downscaling method using a regression and normalization scheme was employed to downscale the bands B3 to B7 to 250 meter resolution while preserving radiometric properties of the original data. To obtain clear-sky observations from MODIS, composite images for a 10 day period were generated by using a series of advanced algorithms (Luo et al., 2008). The 10-day composites of B1-B7 reflectance were then used to retrieve spatially continuous spectral albedo by using a combined land/snow BRDF (Bi-directional Reflectance Distribution Function) model. In that method, the modified RossThick-LiSparse BRDF model (Maignan et al., 2004) for land and Kokhanovsky and Zege’s model (2004) for snow are linearly combined for mixed surface conditions. They are weighted by snow fraction (0.0 ~ 1.0). The seven spectral albedo were then converted into the shortwave broadband surface albedo using the empirical MODIS polynomial conversion equation of Liang et al. (1999). The data product is in LCC (Lambert Conformal Conic) projection with a 250m pixel resolution. There are 36 albedo images per year. A dataset representing the pixel state (e.g. cloud/shadow, snow/ice, water, land, et al.) was also generated for each 10-day corresponding to the surface albedo product. References: Kokhanovsky, A. A. and Zege, E. P., 2004, Scattering Optics of Snow, Applied Optics, 43, 1589-1602, doi:10.1364/AO.43.001589, 20. Liang, S., Strahler, A.H., Walthall, C., 1999. Retrieval of land surface albedo from satellite observations: a simulation study. J. Appl. Meteorol. 38, 712–725. Luo, Y., Trishchenko, A.P., Khlopenkov, K.V., 2008. Developing clear-sky, cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America. Remote Sens. Environ. 112, 4167–4185. Maignan, F., F.M. Bréon and R. Lacaze, 2004, Bidirectional reflectance of Earth targets : evaluation of analytical models using a large set of spaceborne measurements with emphasis with the hot spot, Remote Sens. Environ., 90, 210-220.

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

Landcover dataset created for the northern part of Saskatchewan based on a combination of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM+) data representing circa 2000 conditions. Download: here It is a priority of the Saskatchewan and Canadian government to assess and monitor the health and sustainability of Canada's Forest. The North Digital Land Cover Classification (NDLC) will provide Saskatchewan's contribution to Canada's Earth Observation for Sustainable Development of Forests (EOSD) initiative, helping Canada fulfill it's obligation to the Kyoto Protocol. The NDLC supports the mission and directives of the Saskatchewan provincial government by providing an essential dataset which will enable researchers, natural resource managers and government to assess the health and sustainability of our forests, perform research in the area of climate change, manage natural resources and create policy. The NDLC will be based on a combination of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM+) data representing circa 2000 conditions. The NDLC is being produced through a collaboration of federal, provincial, and territorial governments, agencies and industry. Classification Value Background 0 Agriculture 1 Not Assigned 2 Pasture Upland Herbaceous Graminoid 3 Not Assigned 4 Not Assigned 5 Hardwood Open Canopy 6 Hardwood Closed Canopy 7 Jack Pine Closed Canopy 8 Jack Pine Open Canopy 9 Spruce Closed Canopy 10 Spruce Open Canopy 11 Mixed Hardwoods/Softwoods, Softwood/Hardwood Open and Closed Canopy 12 Treed Rock 13 Recent Burn 14 Revegetating/Regenerating Burn 15 Cutovers 16 Water 17 Marsh 18 Herbaceous Fen 19 Mud Sand Saline 20 Shrub Fen 21 Treed Bog 22 Open Bog 23 Not Assigned 24 Settlements/Roads 25 Barren Land 26 Mixed Softwoods Open and Closed 27 Cloud/Shadow/Haze 28 Unclassified 29 0. Background: Where pixels values are equal to 0 in all channels of satellite image data.  1. Agriculture: Cropland and agricultural clearing areas  2. Not Assigned: Empty Class  3. Pasture Upland Herbaceous Graminoid: Lands containing known pastures, tame or native grasses and herbaceous vegetation. May contain low-lying shrubs with less then 10% tree cover. 4. Not Assigned: Empty Class  5. Not Assigned: Empty Class  6. Hardwood Open Canopy: Trembling Aspen, White Birch, Balsam Poplar composes greater than 75% of species by area, Crown Closure: greater than 10% and less than or equal to 55% (SE crown closure classes A and B).  7. Hardwood Closed Canopy: Trembling Aspen, White Birch, Balsam Poplar composes greater than 75% of species by area, Crown Closure: greater than 55% (SE crown closure classes C and D).  8. Jack Pine Closed Canopy: Jack Pine composes greater than 75% of species by area, Crown Closure: greater than 55% (SE crown closure classes C and D).  9. Jack Pine Open Canopy: Jack Pine composes greater than 75% of species by area, Crown Closure: greater than 10 and less than or equal to 55% (SE crown closure classes C and D).  10. Spruce Closed Canopy: White Spruce, Black Spruce composes greater than 75% of species by area, Crown Closure: greater than 55% (SE crown closure classes C and D).  11. Spruce Open Canopy: White Spruce, Black Spruce composes greater than 75% of species by area, Crown Closure: greater than 10 and less than or equal to 55% (SE crown closure classes C and D).  12. Mixed Hardwoods/Softwoods, Softwood/Hardwood Open and Closed Canopy: An area of hardwood and softwood combinations in which neither hardwood nor softwood account for greater than 75% of species by area and where the crown closure is greater than 10%.  13. Treed Rock: Forest vegetation less than 10%.  14. Recent Burn: An area showing evidence of recent burning natural or prescribed and there is little to no regeneration or revegetation visible.  15. Revegetating/Regenerating Burn: An area showing evidence of natural or prescribed burning and where regeneration or revegetation is visible.  16. Cutovers: An area of deforestation, vegetated and non-vegetated. Ancillary data required to correctly classify due to the anthropogenic land cover/land use class.  17. Water: These areas include lakes, rivers, streams, and reservoirs.  18. Marsh: A periodically wet or continually flooded but non peat-forming area supporting grasses, sedges and reeds.  19. Herbaceous Fen: A wetland area consisting of nutrient rich water and decomposing peat supporting vascular and nonvascular plants grasses, sedges, and reeds.  20. Mud Sand Saline: Water saturated soil, salt water and sand containing no vegetation.  21. Shrub Fen: A wetland area consisting of nutrient rich water and decomposing peat supporting low shrubs, forbs, grass, moss, and a sparse tree cover.  22. Treed Bog: A wetland area consisting of decomposing peat moss, lichen and shrubs with 10% to 25% tree cover of stunted black spruce and tamarack.  23. Open Bog: A wetland area consisting of low nutrient water and decomposing peat moss, lichen, and sparse tree cover.  24. Not Assigned: Empty Class  25. Settlements/Roads: Anthropogenic land cover consisting of urban, commercial, industrial, major roads, highways, surface mines, gravel pits and spoil piles.  26. Barren Land: With the exception of the settlements and Roads class, any area of exposed rock, soil or non-vegetated land.  27. Mixed Softwoods Open and Closed: Jack Pine/Spruce, Spruce/Jack Pine Open and Closed, an area of softwood combinations in which neither Jack Pine or Spruce account for greater than 75% of species by area and where crown closure is greater than 10%.  28. Cloud/Shadow/Haze: An area of cloud, shadow, haze.  29. Unclassified: An area of unidentifiable land cover, indicates no work done/not classified, wrong information, missing data and possible new class greater than 3 pixels. 

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).

This collection is a legacy product that is no longer supported. It may not meet current Government standards. The National Topographic Data Base (NTDB) comprises digital vector data sets that cover the entire Canadian landmass. The NTDB includes features such as watercourses, urban areas, railways, roads, vegetation, and relief. The organizational unit for the NTDB is the National Topographic System (NTS), based on the North American Datum of 1983 (NAD83). Each file (data set) consists of one NTS unit at either the 1:50,000 or 1:250,000 scale. Related Products: [NTDB Correction Matrices, 2003-2009](https://ouvert.canada.ca/data/en/dataset/b6d0c19c-27e3-4392-b21f-49b1eec95653)

Data include a collection of annual land cover maps derived from MODIS 250 m spatial resolution remotely sensed imagery for the period 2000 to 2011. Processing of the time series was designed to reduce the occurrence of false change between maps. The method was based on change updating as described in Pouliot et al. (2011, 2013). Change detection accounted for both abrupt changes such as forest harvesting and more gradual changes such as recurrent insect defoliation. To determine the new label for a pixel identified as change, an evidential reasoning approach was used to combine spectral and contextual information. The 2005 MODIS land cover of Canada at 250 m spatial resolution described in Latifovic et al. (2012) was used as the base map. It contains 39 land cover classes, which for time series development was considered too detailed and was reduced to 25 and 19 class versions. The 19 class version corresponds to the North America Land Change Monitoring System (NALCMS) Level 2 legend as described in Latifovic et al. (2012). Accuracy assessment of time series is difficult due to the need to assess many maps. For areas of change in the time series accuracy was found to be 70% based on the 19 class thematic legend. This time series captures the spatial distribution of dominant land cover transitions. It is intended for use in modeling, development of remote sensing products such as leaf area index or land cover based albedo retrievals, and other exploratory analysis. It is not appropriate for use in any rigorous reporting or inventory assessments due to the accuracy of the land cover classification and uncertainty as to the capture of all relevant changes for an application. NOTE: To see this entire product in the map viewer, use a base map in the "World" section (EPSG: 3857).

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.

Each pixel value corresponds to the actual number (count) of valid Best-quality Max-NDVI values used to calculate the mean weekly values for that pixel. Since 2020, the maximum number of possible observations used to create the Mean Best-Quality Max-NDVI for the 2000-2014 period is n=20. However, because data quality varies both temporally and geographically (e.g. cloud cover and snow cover in spring; cloud near large water bodies all year), the actual number (count) of observations used to create baselines can vary significantly for any given week and year.