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.

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.

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.

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.

The 2010 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.

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.

The “Land Cover for Agricultural Regions of Canada (circa 2000), Date Index” dataset is a geospatial data layer containing polygon features representing the Landsat scene number, associated dates and other products that were incorporated into the thematic land cover classification which is contained within the AAFC Landcover (circa 2000) product.

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

Organic soils in the boreal forest commonly store as much carbon as the vegetation above ground. While recent efforts through the National Forest Inventory has yielded new spatial datasets of forest structure across the vast area of Canada’s boreal forest, organic soils are poorly mapped. In this geospatial dataset, we produce a map primarily of forested and treed peatlands, those with more than 40 cm of peat accumulation and over 10% tree canopy cover. National Forest Inventory ground plots were used to identify the range of forest structure that corresponds to the presence of over 40 cm of peat soils. Areas containing that range of forest cover were identified using the National Forest Inventory k-NN forest structure maps and assigned a probability (0-100% as integer) of being a forested or treed peatland according to a statistical model. While this mapping product captures the distribution of forested and treed peatlands at a 250 m resolution, open, completely treeless peatlands are not fully captured by this mapping product as forest cover information was used to create the maps. The methodology used in the creation of this product is described in: Thompson DK, Simpson BN, Beaudoin A. 2016. Using forest structure to predict the distribution of treed boreal peatlands in Canada. Forest Ecology and Management, 372, 19-27. https://cfs.nrcan.gc.ca/publications?id=36751 This distribution uses an updated forest attribute layer current to 2011 from: Beaudoin A, Bernier PY, Villemaire P, Guindon L, Guo XJ. 2017. Species composition, forest properties and land cover types across Canada’s forests at 250m resolution for 2001 and 2011. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/ec9e2659-1c29-4ddb-87a2-6aced147a990 Additionally, this distribution varies slightly from the original published in 2016 in that here slope data is derived from the CDEM: https://open.canada.ca/data/en/dataset/7f245e4d-76c2-4caa-951a-45d1d2051333 The above peatland probability map was further processed to delineate bogs vs fens (based on mapped Larix content via the k-NN maps), as well as an approximation of the extent of open peatlands using EOSD data. The result is a 9-type peatland map with a more complete methodology as detailed in: Webster, K. L., Bhatti, J. S., Thompson, D. K., Nelson, S. A., Shaw, C. H., Bona, K. A., Hayne, S. L., & Kurz, W. A. (2018). Spatially-integrated estimates of net ecosystem exchange and methane fluxes from Canadian peatlands. Carbon Balance and Management, 13(1), 16. https://doi.org/10.1186/s13021-018-0105-5 In plain text, the legend for the 9-class map is as follows: value="0" label="not peat" alpha="0" value="1" label="Open Bog" alpha="255" color="#0a4b32" value="2" label="Open Poor Fen" alpha="255" color="#5c5430" value="3" label="Open Rich Fen" alpha="255" color="#792652" value="4" label="Treed Bog" alpha="255" color="#6a917b" value="5" label="Treed Poor Fen" alpha="255" color="#aba476" value="6" label="Treed Rich Fen" alpha="255" color="#af7a8f" value="7" label="Forested Bog" alpha="255" color="#aad7bf" value="8" label="Forested Poor Fen" alpha="255" color="#fbfabc" value="9" label="Forested Rich Fen" alpha="255" color="#ffb6db" This colour scale is given in qml/xml format in the resources below. The 9-type peatland map from Webster et al 2018 was further refined slightly following two simple conditions: (1) any 250-m raster cell with greater than 40% pine content is classified as upland (non-peat); (2) all 250-m raster cells classified as water or agriculture via the NRCan North American Land Cover Monitoring System (https://doi.org/10.3390/rs9111098) is also classified as non-peatland (value of zero in the 9-class map. This mapping scheme was used at a regional scale in the following paper: Thompson, D. K., Simpson, B. N., Whitman, E., Barber, Q. E., & Parisien, M.-A. (2019). Peatland Hydrological Dynamics as A Driver of Landscape Connectivity and Fire Activity in the Boreal Plain of Canada. Forests, 10(7), 534. https://doi.org/10.3390/f10070534 And is reproduced here at a national scale. Note that this mapping product does not fully capture all permafrost peatland features covered by open canopy spruce woodland with lichen ground cover. Nor are treeless peatlands near the northern treeline captured in the training data, resulting in unknown mapping quality in those regions.