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.

This collection is a legacy product that is no longer maintained.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)

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.

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.

Index Grid for NTS 1:250,000 scale maps

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”.

Our Imagery Base Maps and Mosaics of a number of Raster Datasets.  This includes the ASTER DEM, CDED and Shaded Relief Datasets.  As well as a number of mosaics, including SPOT, RapidEye, Landsat, and MVI Landcover data.

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

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.

BTM ( Baseline Thematic Mapping) Landsat Image Catalogue Acquisition Dates. This polygon coverage contains the date of capture of the Landsat images making up the seamless BC Landsat image catalogue. This is not a multipart feature