Description:Chlorophyll-a concentration (a proxy for phytoplankton biomass) was retrieved from the MODIS instrument on the Aqua satellite, with data distributed by the NASA Ocean Biology Processing Group, and averaged into monthly climatological composites. The data span the years 2003-2020 and this record includes data at 4 km pixel resolution.Methods:MODIS-Aqua Chlorophyll-a (Chl-a) was acquired from the NASA Ocean Biology Processing Group at processing Level-3 (version 2018), 4-km resolution, where Chl-a concentration was calculated using the OC3/OCI method. The months of January and December were excluded from this dataset, as data in the winter months at higher latitudes are missing due to low sun angle preventing acquisition. The monthly geometric mean value at all pixels was calculated for individual years, then the geometric mean and geometric standard deviation factor of chlorophyll-a were calculated by month from these images. These methods of calculating mean and standard deviation were used due to the log-normal distribution of chlorophyll-a. The geometric standard deviation is a unitless factor, where the lower bound is the ratio of the geometric mean and geometric standard deviation, and the upper bound is the multiplication of the two. In addition to the geometric mean and geometric standard deviation factor the number of occurrences of valid data at each pixel over the period of observation were calculated. Pixels with fewer than two occurrences over the entire period of observation were removed from these maps, and set to a NaN value in the tif files. All resulting rasters were cropped to the Canadian Exclusive Economic Zone and assigned to the NAD83 geographic coordinate reference system (EPSG:4269), and have a final pixel resolution of approximately 0.0417 degrees. The monthly geometric mean, monthly geometric standard deviation factor, and number of occurrences for all pixels are provided.Data Sources:NASA Ocean Biology Processing Group. (2017). MODIS-Aqua Level 2 Ocean Color Data Version R2018.0. NASA Ocean Biology Distributed Active Archive Center. https://doi.org/10.5067/AQUA/MODIS/L2/OC/2018Uncertainties:Satellite values have been evaluated against global datasets, and datasets of samples in the Pacific region (see references). However, uncertainties are introduced when averaging together images over time as each pixel has a differing number of observations. Short-lived or spatially limited events may be missed.

Description:Chlorophyll-a concentration (a proxy for phytoplankton biomass) was retrieved from the MODIS instrument on the Aqua satellite, with data distributed by the NASA Ocean Biology Processing Group, and averaged into monthly climatological composites. The data span the years 2003-2020 and this record includes data at 1 km pixel resolution.Methods:MODIS-Aqua Chlorophyll-a (Chl-a) was acquired from the NASA Ocean Biology Processing Group at processing Level-2 (version 2018), 1-km resolution, where Chl-a concentration was calculated using the OC3/OCI method. The months of January and December were excluded from this dataset, as data in the winter months at higher latitudes are missing due to low sun angle preventing acquisition. The pixels were aligned on a regular grid using the SeaDAS program, after which the monthly geometric mean value at all pixels was calculated for individual years. Finally, the geometric mean and geometric standard deviation factor of chlorophyll-a were calculated by month from these images. These methods of calculating mean and standard deviation were used due to the log-normal distribution of chlorophyll-a. The geometric standard deviation is a unitless factor, where the lower bound is the ratio of the geometric mean and geometric standard deviation, and the upper bound is the multiplication of the two. In addition to these variables, the number of occurrences of valid data at each pixel over the period of observation were calculated. Pixels with fewer than two occurrences over the entire period of observation were removed from these maps, and set to a NaN value in the tif files. A few small gaps between pixels (near the edges of individual images) were filled using the median value of surrounding pixels, provided there were greater than 4 values. Finally, all rasters were cropped to the Canadian Exclusive Economic Zone and assigned to the NAD83 geographic coordinate reference system (EPSG:4269), and have a final pixel resolution of approximately 0.01 degrees. The monthly geometric mean, monthly geometric standard deviation factor, and number of occurrences for all pixels are provided.Data Sources:NASA Ocean Biology Processing Group. (2017). MODIS-Aqua Level 2 Ocean Color Data Version R2018.0. NASA Ocean Biology Distributed Active Archive Center. https://doi.org/10.5067/AQUA/MODIS/L2/OC/2018Uncertainties:Satellite values have been evaluated against global datasets, and datasets of samples in the Pacific region (see references). However, uncertainties are introduced when averaging together images over time as each pixel has a differing number of observations. Short-lived or spatially limited events may be missed.

The Canadian Index of Multiple Deprivation (CIMD) is an area-based index which used 2021 Census of Population microdata to measure four key dimensions of deprivation at the dissemination area (DA)-level: residential instability, economic dependency, situational vulnerability and ethno-cultural composition.Using factor analysis, DA-level factor scores were calculated for each dimension. Within a dimension, ordered scores were assigned a quintile value, 1 through 5, where 1 represents the least deprived and 5 represents the most deprived.The CIMD allows for an understanding of inequalities in various measures of health and social well-being. While it is a geographically-based index of deprivation and marginalization, it can also be used as a proxy for an individual. The CIMD has the potential to be widely used by researchers on a variety of topics related to socio-economic research. Other uses for the index may include: policy planning and evaluation, or resource allocation.*** Correction October 22, 2024 ***A correction has been made to the variables in the following downloadable 2021 CIMD index datasets : Canada, Atlantic, Quebec, Ontario, Prairies, and British-Columbia. This correction impacts all the data in these datasets.

The Canadian long term satellite data record (LTDR) derived from 1-km resolution Advanced Very High Resolution Radiometer (AVHRR) data was produced by the Canada Center for Remote Sensing (CCRS). Processing included: geolocation, calibration, and compositing using Earth Observation Data Manager (Latifovic et al. 2005), cloud screening (Khlopenkov and Trishchenko, 2006), BRDF correction (Latifovic et. al., 2003), atmosphere and other corrections as described in Cihlar et. al. (2004). For temporal analysis of vegetation cross-sensor correction of Latifovic et al. (2012) is advised. Data collected by the AVHRR instrument on board the National Oceanic and Atmospheric Administration (NOAA) 9,11,14,16,17,18 and 19 satellites were used to generate Canada-wide 1-km 10-day AVHRR composites. Data are available starting in 1985. It is important to note that there are three types of AVHRR sensors: (i) AVHRR-1 flown onboard TIROS-N, NOAA-6, NOAA-8, and NOAA-10; (ii) AVHRR-2 flown onboard NOAA-7, NOAA-9, NOAA-11, NOAA-12, and NOAA-14; and (iii) AVHRR-3 currently operational onboard NOAA-15, NOAA-16, NOAA-17, NOAA-18 and NOAA-19. The AVHRR-1 has four channels, AVHRR-2 has five channels and the AVHRR-3 has six channels, although only five channels of AVHRR-3 can be operational at any one time. As such, channels 3A (1.6 m) and 3B (3.7 m) work interchangeably. The processing procedure was designed to minimize artefacts in AVHRR composite images. There are thirty six 10-day image composites per year. The following three processing levels are provided: P1) top of atmosphere reflectance and brightness temperature, P2) reflectance at surface and surface temperature and P3) reflectance at surface normalized to a common viewing geometry (BRDF normalization). The processing level P1 and P2 are provided for all 36 composites while level P3 is provided for 21 composites from April – October.

This collection is a legacy product that is no longer supported. It may not meet current government standards.This inventory presents chronologically the satellite images acquired, orthorectified and published over time by Natural Resources Canada. It is composed of imagery from the Landsat7 (1999-2003) and RADARSAT-1 (2001-2002) satellites, as well as the CanImage by-product and the control points used to process the images.Landsat7 Orthorectified Imagery: The orthoimage dataset is a complete set of cloud-free (less than 10%) orthoimages covering the Canadian landmass and created with the most accurate control data available at the time of creation.RADARSAT-1 Orthorectified Imagery: The 5 RADARSAT-1 images (processed and distributed by RADARSAT International (RSI) complete the landsat 7 orthoimagery coverage. They are stored as raster data produced from SAR Standard 7 (S7) beam mode with a pixel size of 15 m. They have been produced in accordance with NAD83 (North American Datum of 1983) using the Universal Transverse Mercator (UTM) projection. RADARSAT-1 orthoimagery were produced with the 1:250 000 Canadian Digital Elevation Data (CDED) and photogrammetric control points generated from the Aerial Survey Data Base (ASDB).CanImage -Landsat7 Orthoimages of Canada,1:50 000: CanImage is a raster image containing information from Landsat7 orthoimages that have been resampled and based on the National Topographic System (NTS) at the 1:50 000 scale in the UTM projection. The product is distributed in datasets in GeoTIFF format. The resolution of this product is 15 metres.Landsat7 Imagery Control Points: the control points were used for the geometric correction of Landsat7 satellite imagery. They can also be used to correct vector data and for simultaneously displaying data from several sources prepared at different scales or resolutions.

The National Rail Network (NRWN) is a geometric and attributive description of the Canadian rail network.The NRWN product consists of the features classes: Track Segment, Railway Crossing, Railway Station, Marker Post, Junction and Railway Structure. Descriptive attributes include amongst others: Track Classification, Track Name, Track Operator, Track User, Track Owner, Subdivision Name, Junction Type, Crossing Type, Level of Crossing, Warning System, Transport Canada Identifier, Station Name, Station Type, Station User, Structure Type.

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 National Hydro Network (NHN) focuses on providing a quality geometric description and a set of basic attributes describing Canada's inland surface waters. It provides geospatial digital data compliant with the NHN Standard such as lakes, reservoirs, watercourses (rivers and streams), canals, islands, drainage linear network, toponyms or geographical names, constructions and obstacles related to surface waters, etc. The best available federal and provincial data are used for its production, which is done jointly by the federal and interested provincial and territorial partners. The NHN is created from existing data at the 1:50 000 scale or better. The NHN data have a great potential for analysis, cartographic representation and display and will serve as base data in many applications. The NHN Work Unit Limits were created based on Water Survey of Canada Sub-Sub-Drainage Area.

Description:Chlorophyll-a concentration (a proxy for phytoplankton biomass) was retrieved from the MODIS instrument on the Aqua satellite, with data distributed by the NASA Ocean Biology Processing Group, and averaged into monthly climatological composites. The data span the years 2003-2020; records were created for both 1 km and 4 km pixel resolutions to be consistent with other satellite products.Methods:MODIS-Aqua Chlorophyll-a (Chl-a) was acquired from the NASA Ocean Biology Processing Group where Chl-a concentration was calculated using the OC3/OCI method. The months of January and December were excluded from these datasets, as data in the winter months at higher latitudes are missing due to low sun angle preventing acquisition. The monthly geometric mean value at all pixels was calculated for individual years, then the geometric mean and geometric standard deviation factor of chlorophyll-a were calculated by month from these images. These methods of calculating mean and standard deviation were used due to the log-normal distribution of chlorophyll-a. The geometric standard deviation is a unitless factor, where the lower bound is the ratio of the geometric mean and geometric standard deviation, and the upper bound is the multiplication of the two. In addition to the geometric mean and geometric standard deviation factor the number of occurrences of valid data at each pixel over the period of observation were calculated. Pixels with fewer than two occurrences over the entire period of observation were removed from these maps and set to a NaN value in the tif files. All resulting rasters were cropped to the Canadian Exclusive Economic Zone, assigned to the NAD83 geographic coordinate reference system (EPSG:4269), and have final pixel resolutions of approximately 0.01 degrees and 0.0417 degrees. The monthly geometric mean, monthly geometric standard deviation factor, and number of occurrences for all pixels are provided.Data Sources:NASA Ocean Biology Processing Group. (2017). MODIS-Aqua Level 2 Ocean Color Data Version R2018.0. NASA Ocean Biology Distributed Active Archive Center. https://doi.org/10.5067/AQUA/MODIS/L2/OC/2018Uncertainties:Satellite values have been evaluated against global datasets, and datasets of samples in the Pacific region (see references). However, uncertainties are introduced when averaging together images over time as each pixel has a differing number of observations. Short-lived or spatially limited events may be missed.

The hydrographic features of the CanVec series include watercourses, water linear flow segments, hydrographic obstacles (falls, rapids, etc.), waterbodies (lakes, watercourses, etc.), permanent snow and ice features, water wells and springs.The Hydrographic features theme provides quality vector geospatial data (current, accurate, and consistent) of Canadian hydrographic phenomena. It aims to offer a geometric description and a set of basic attributes on hydrographic features that comply with international geomatics standards, seamlessly across Canada.The CanVec multiscale series is available as prepackaged downloadable files and by user-defined extent via a Geospatial data extraction tool.Related Products (Open Maps Links):[Topographic Data of Canada - CanVec Series](https://open.canada.ca/data/en/dataset/8ba2aa2a-7bb9-4448-b4d7-f164409fe056)