Moored instrument time series data include current velocity, temperature, salinity, oxygen, fluorescence, transmissivity, turbidity, and particle capture of carbon, nitrogen, and silicon as well as sediment trap, ice drift and ice draft data.These data were collected by researchers from the Institute of Ocean Sciences, Sidney, BC, from locations ranging from the North Pacific, the Beaufort Sea, and across the Canadian Arctic Archipelago to Baffin Bay.

Moored instrument time series data include current velocity, temperature, salinity, oxygen, fluorescence, transmissivity, turbidity, sediment trap data and particle capture of carbon, nitrogen, and silicon.These data were collected by researchers from the Institute of Ocean Sciences, Sidney, BC, from locations in the North Pacific.The data links below are only a representative sample of the entire collection. If you require more data, please send your request to the data contact.

Moored instrument time series data include current velocity, temperature, salinity, oxygen, fluorescence, transmissivity, turbidity, and particle capture of carbon, nitrogen, and silicon. Also included are sediment trap, ice drift and ice draft data.These data were collected by researchers from the Institute of Ocean Sciences, Sidney, BC, from locations ranging from the Beaufort Sea, and across the Canadian Arctic Archipelago to Baffin Bay.The data links below are only a representative sample of the entire collection. If you require more data, please send your request to the data contact.

A chlorophyll fluorescence time series was collected at various locations around the coast of Vancouver Island, British Columbia, Canada for monitoring phytoplankton concentrations. A Wetlabs ECO fluorometer was deployed every few months on a schedule depending on season and sensor availability. The instrument hung by a chain attached to the side of the buoy, or dock, depending on location, and measured chlorophyll using the fluorescence emission at 695nm. The instrument also measured turbidity by detecting the scattered light at 700nm. The units had internal batteries and data storage and were programmed to make a group of 5 measurements every 30 minutes. A copper wiper covered the sampling window between groups of measurements to reduce fouling. Times are in UTC unless otherwise stated.

The Ocean Data Inventory database is an inventory of all of the oceanographic time series data held by the Ocean Science Division at the Bedford Institute of Oceanography. The data archive includes about 5800 current meter and acoustic doppler time series, 4500 coastal temperature time series from thermographs, as well as a small number (200) of tide gauges. Many of the current meters also have temperature and salinity sensors. The area for which there are data is roughly defined as the North Atlantic and Arctic from 30° - 82° N, although there are some minor amounts of data from other parts of the world. The time period is from 1960 to present. The database is updated on a regular basis.

The Adjusted Precipitation data consist of monthly, seasonal and annual totals of daily adjusted rain, snow and total precipitation (millimetres) for 464 locations in Canada. Adjusted precipitation data incorporate adjustments (derived from comparison of instruments) to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The time periods of the data vary by location, with the oldest data available from the early 1880s at some stations to the most recent update in 2017. Observations at co-located sites were sometimes joined in order to create longer time series. Data availability over most of the Canadian Arctic is restricted to the mid-1940s to present.

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.

Wildfire Year/dNBR/Mask 1985-2015Wildfire change magnitude 85-15. Spectral change magnitude for wildfires that occurred from 1985 and 2015. The wildfire change magnitude included in this product is expressed via differenced Normalized Burn Ratio (dNBR), computed as the variation between the spectral values before and after the change event. This dataset is composed of three layers: (1) binary wildfire mask, (2) year of greatest wildfire disturbance, and (3) differenced Normalized Burn Ratio (dNBR) transformed for data storage efficiency to the range 0-200. The actual dNBR value is derived as follows: dNBR = value / 100. Higher dNBR values are related to higher burn severity. The information outcomes represent 30 years of wildfires in Canada's forests, derived from a single, consistent spatially-explicit data source in a fully automated manner. Time series of Landsat data with 30-m spatial resolution were used to characterize national trends in stand replacing forest disturbances caused by wildfire for the period 1985-2015 for Canada's 650 million hectare forested ecosystems.When using this data, please cite as: Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, 2016. Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth 9(11), 1035-1054. (Hermosilla et al. 2016).See references below for an overview on the data processing, metric calculation, change attribution and time series change detection methods applied, as well as information on independent accuracy assessment of the data.Hermosilla, T., Wulder, M. A., White, J. C., Coops, N.C., Hobart, G.W., 2015. An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment 158, 220-234. (Hermosilla et al. 2015a).Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., 2015. Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sensing of Environment 170, 121-132. (Hermosilla et al. 2015b).Geographic extent: Canada's forested ecosystems (~ 650 Mha)Time period: 1985–2011

The ASTER instrument that was launched onboard NASA’s Terra spacecraft in December 1999 has an along-track stereoscopic capability using two telescopes in its near infrared spectral band to acquire data from nadir and backward views. Over 1.2 million scenes (level-1A products) acquired between March 2000 and August 2008 were used to generate the ASTER Global DEM (ASTGTM) collection.  For more information on the ASTER Global DEM, please see the metadata link.

The ASTER instrument that was launched onboard NASA’s Terra spacecraft in December 1999 has an along-track stereoscopic capability using two telescopes in its near infrared spectral band to acquire data from nadir and backward views. Over 1.2 million scenes (level-1A products) acquired between March 2000 and August 2008 were used to generate the ASTER Global DEM (ASTGTM) collection.  For more information, please see the metadata link above.