Fraction of absorbed photosynthetically active radiation (fAPAR) quantified the absorbed by green foliage. fAPAR has been identified by the Global Climate Observing System as an essential climate variable required for ecosystem, weather and climate modelling and monitoring. This product consists of a national scale coverage (Canada) of monthly maps of fAPAR during a growing season (May-June-July-August-September) at 20m resolution. References: L. Brown, R. Fernandes, N. Djamai, C. Meier, N. Gobron, H. Morris, C. Canisius, G. Bai, C. Lerebourg, C. Lanconelli, M. Clerici, J. Dash. Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States IISPRS J. Photogramm. Remote Sens., 175 (2021), pp. 71-87, https://doi.org/10.1016/j.isprsjprs.2021.02.020. https://www.sciencedirect.com/science/article/pii/S0924271621000617 Richard Fernandes, Luke Brown, Francis Canisius, Jadu Dash, Liming He, Gang Hong, Lucy Huang, Nhu Quynh Le, Camryn MacDougall, Courtney Meier, Patrick Osei Darko, Hemit Shah, Lynsay Spafford, Lixin Sun, 2023. Validation of Simplified Level 2 Prototype Processor Sentinel-2 fraction of canopy cover, fraction of absorbed photosynthetically active radiation and leaf area index products over North American forests, Remote Sensing of Environment, Volume 293, https://doi.org/10.1016/j.rse.2023.113600. https://www.sciencedirect.com/science/article/pii/S0034425723001517

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

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.

The AAFC Infrastructure Flood Mapping in Saskatchewan 20 centimeter colour orthophotos is a collection of georeferenced color digital orthophotos with 20 cm pixel size. The imagery was delivered in GeoTIF and ECW formats. The TIF and ECW mosaics were delivered in the same 1 km x 1 km tiles as the LiDAR data, and complete mosaics for each area in MrSID format were also provided. The digital photos were orthorectified using the ground model created from the DTM Key Points. With orthorectification, only features on the surface of the ground are correctly positioned in the orthophotos. Objects above the surface of the ground, such as building rooftops and trees, may contain horizontal displacement due to image parallax experienced when the photos were captured. This is sometimes apparent along the cut lines between photos. For positioning of above-ground structures it is recommended to use the LiDAR point clouds for accurate horizontal placement.

The Moderate Resolution Imaging Spectroradiometer (MODIS ) is one of the most sophisticated sensors that is used in a wide range of applications related to land, ocean and atmosphere. It has 36 spectral channels with spatial resolution varying between 250 m and 1 km at nadir. MODIS channels 1 (B1, visible) and 2 (B2, near infrared) are available at 250 m spatial resolution, an additional five channels for terrestrial applications (bands B3 to B7) are available at 500 m spatial resolution, the other twenty-nine channels not included in this data set capture images with a spatial resolution of 1 km. The MODIS record begins in March 2000 and extends to present with daily measurements over the globe. This level 3 product for Canada was created from the following original Level 1 (1B) MODIS data (collection 5): a) MOD02QKM - Level 1B 250 m swath data, 5 min granules; b ) MOD02HKM - level 1B , 500 m swath data, 5 min granules; c) MOD03 - level 1 geolocation information, 1 km swath data, 5 min granules. All these data are available from the DAAC Earth Observing System Data Gateway (NASA http://ladsweb.nascom.nasa.gov/data/search.html). The terrestrial channels MODIS (B3 to B7) at 500 m spatial resolution were reduced to 250 m with an adaptive regression system and normalization described in Trishchenko et al. (2006, 2009), and the data were mapped using a Lambert Conformal Conic (LCC ) projection (Khlopenkov et al., 2008). These data were combined to form pan-Canadian images using a technique for detection of clear sky, clouds and cloud shadows with a maximum interval of 10 days (Luo et al., 2008). Atmospheric and sun-sensor geometry corrections have not been applied. For each date, data include forward and backward scattering observations as separate files. This allows data to be optimized for a given application. For general use, data from either forward or backward scattering or both should be used. Future release of the MODIS time series will correct the forward and backward scattering geometry to provide a single best observation for each pixel.

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.

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

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.

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) 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 coregistrated. For this dataset, the spatial resolutions are: 100 cm for the year 1947 and 50 cm for the year 1967. 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

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).