IBL - Imagery, basemaps, and land cover (imageryBaseMapsEarthCover) Basemaps. For example, resources describing land cover, topographic maps, and classified and unclassified images

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”.

CHS offers 500-metre bathymetric gridded data for users interested in the topography of the seafloor. This data provides seafloor depth in metres and is accessible for download as predefined areas.

Each pixel value corresponds to the quality control, cloud cover and snow fraction value for each pixel in the Best-Quality Max-NDVI product.

This layer represents Land use polygons as determined by a combination of analytic techniques, mostly using Landsat 5 image mosaics . BTM 1 was done on a federal satellite image base that was only accurate to about 250m. The images were geo-corrected, not ortho-corrected, so there is distortion in areas of high relief. This is not a multipart feature

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 10 cm to 50 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

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.

This data publication contains a set of files in which areas affected by fire or by harvest from 1984 to 2015 are identified at the level of individual 30m pixels on the Landsat grid. Details of the product development can be found in Guindon et al (2018). The change detection is based on reflectance-corrected yearly summer (July and August) Landsat mosaics from 1984 to 2015 created from individual scenes developed from USGS reflectance products (Masek et al, 2006; Vermote et al, 2006). Briefly, the change detection method uses a six-year temporal signature centered on the disturbance year to identify fire, harvest and no change. The signatures were derived from visually-interpreted disturbance or no-change polygons that were used to fit a decision tree model. The method detects about 91% of the areas harvested and 85% of the areas burned across Canada’s forests over the study period, but overestimates areas disturbed in the two initial and mostly in the two final years of the 1985 to 2015 time series. This is caused by the absence of appropriate pre-disturbance and post-disturbance data for the model-based detection and attribution. Disturbance coverage in those four years should therefore be used with caution. As in Guindon et al (2014), the method was designed to minimize commission errors and has a disturbance class attribution success rate of about 98%. The attribution success rate of disturbance year for fire is of about 69% for the exact year and of about 99% when attribution to the following year is also considered as a success. This common one-year lag is mostly due to the use of mid-summer Landsat mosaics for the analysis that will cause spring and fall events of the same year to be attributed to successive years. For example, a fire that occurred in the fall of 2004 (after July and August), will be detected and attributed to 2005, while for a fire that occurred in the spring of 2004 will be detected and attributed to 2004. The presence of clouds and shadows or image availability causes 10% of missing data annually and therefore can too delay the capture of events. The data provides uniform spatial and temporal information on fire and harvest across all provinces and territories of Canada and is intended for strategic-level analysis. Since no attention was given to other minor disturbances such as mining, road or flooding, the product should not be used for their identification. Finally, calibration datasets were developed for only three major forest pests (mountain pine beetle, eastern spruce budworm and forest tent caterpillar), but were folded within the “no-change” class in order to minimize commission errors for fire and harvest . Less common pests for which validation datasets are hard to develop were not considered and therefore could in rare circumstances generate false fire events. Considering that area having two (3.3%) to three disturbances (less than 1%) events are not common, only the most recent disturbance is provided, overlapping older disturbances in these rare case. ## Please cite this dataset as: Guindon, L., P. Villemaire, R. St-Amant, P.Y. Bernier, A. Beaudoin, F. Caron, M. Bonucelli and H. Dorion. 2017. Canada Landsat Disturbance (CanLaD): a Canada-wide Landsat-based 30-m resolution product of fire and harvest detection and attribution since 1984. https://doi.org/10.23687/add1346b-f632-4eb9-a83d-a662b38655ad ## Scientific article citation: The creation, validation and limitations of the CanLaD product are described in the Supplementary Material file associated with the following article: Guindon, L.; Bernier, P.Y.; Gauthier, S.; Stinson, G.; Villemaire, P.; Beaudoin, A. 2018. Missing forest cover gains in boreal forests explained. Ecosphere, 9 (1) Article e02094. doi:10.1002/ecs2.2094. ## Cited references: Masek, J.G., Vermote, E.F., Saleous N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T-K. (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters 3(1):68-72. http://dx.doi.org/10.1109/LGRS.2005.857030. Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment. http://dx.doi.org/10.1016/j.rse.2016.04.008.

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.