“Wood Buffalo National Park - Total GHG Emissions” datasets consist of estimates of GHG emissions (carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O)) in carbon dioxide equivalents (CO2e) from forested ecosystems in Wood Buffalo National Park from 1990 to 2020 (tonnes carbon dioxide equivalent per hectare). Total GHG emissions for 31 national parks were estimated using the Generic Carbon Budget Model (GCBM), a spatially explicit carbon budget model developed by Canadian Forest Service which uses forest inventory, disturbance, and mean annual temperature data along with yield data to estimate growth and merchantable volume for dominant tree species. Species- and Ecozone-specific equations are then used to convert merchantable volume to aboveground and belowground biomass carbon. The GCBM simulates carbon dynamics to produce spatially explicit estimations of carbon stocks and fluxes. The model simulates and tracks carbon stocks, transfers between Intergovernmental Panel on Climate Change (IPCC)-defined pools, and other metrics including net ecosystem production, net biome production, and emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) in annual time steps. The stocks and fluxes are also tracked by disturbance event (e.g., forest fires). Total GHG emissions include those from natural processes like respiration and decomposition and those due to natural and anthropogenic disturbances, including wildfires, prescribed burns, and insect outbreaks. These were calculated as the sum of CO2, CH4, and N2O emission estimates in tonnes carbon (tonnes C) generated by the GCBM. Emissions estimates were then converted to carbon dioxide equivalents (CO2e) using the 100-year Global Warming Potential (IPCC Fourth Assessment Report) factors for CH4 (25) and N2O (298). These products have a spatial resolution of 30m. This information is part of the Parks Canada Carbon Atlas Series. To obtain a copy of this report, please contact changementclimatique-climatechange@pc.gc.ca. When using this data, please cite as follows: Sharma, T., Kurz, W.A., Fellows, M., MacDonald, A.L., Richards, J., Chisholm, C., Seutin, G., Richardson, K., Keenleyside, K. (2023). Parks Canada Carbon Atlas Series: Carbon Dynamics in the Forests of Canada’s National Parks. Scientific Report. Parks Canada Agency, Gatineau, QC, Canada, 104 p.

“Wood Buffalo National Park - Total Ecosystem Forest Carbon Density” is the annual carbon density (tonnes carbon per hectare) within Wood Buffalo’s forested ecosystems over a 31-year period from 1990 to 2020. Total Ecosystem Forest Carbon Density includes aboveground and belowground biomass, soil carbon, and dead organic matter. Total Ecosystem Forest Carbon Density was estimated for 31 national parks using the Generic Carbon Budget Model (GCBM), a spatially explicit carbon budget model developed by Canadian Forest Service which uses forest inventory, disturbance, and mean annual temperature data along with yield data to estimate growth and merchantable volume for dominant tree species. Species- and Ecozone-specific equations are then used to convert merchantable volume to aboveground and belowground biomass carbon. Ecozones were classified according to Canada Ecological Land Classification Level 1. The GCBM simulates carbon dynamics to produce spatially explicit estimations of carbon stocks and fluxes. The model simulates and tracks carbon stocks, transfers between Intergovernmental Panel on Climate Change-defined pools, and other metrics including net ecosystem production, net biome production, and emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) in annual time steps. The stocks and fluxes are also tracked by disturbance event (e.g., forest fires, insect outbreaks). Total Ecosystem Forest Carbon Density accounts for the effects of natural and anthropogenic disturbances, including wildfires, prescribed burns, and insect outbreaks. These products have a spatial resolution of 30m. This information is part of the Parks Canada Carbon Atlas Series. To obtain a copy of this report, please contact changementclimatique-climatechange@pc.gc.ca. When using this data, please cite as follows: Sharma, T., Kurz, W.A., Fellows, M., MacDonald, A.L., Richards, J., Chisholm, C., Seutin, G., Richardson, K., Keenleyside, K. (2023). Parks Canada Carbon Atlas Series: Carbon Dynamics in the Forests of Canada’s National Parks. Scientific Report. Parks Canada Agency, Gatineau, QC, Canada, 104 p.

PURPOSE: This Archer fiord data is associated with a larger program ArcticCORE, which was created to fulfill knowledge gaps and develop long term protection in the extremely remote Tuvaijuittuq region. The main objectives of this expedition were to improve our comprehension of the key drivers for productive capacity, diversity and ecosystem structure in areas connected to Baffin Bay and Tuvaijuittuq, including Archer fiord. DESCRIPTION: ArcticCORE is a 5-year broader program aiming to characterize Tuvaijuittuq’s unique ecosystem and its influence and connectivity with the adjacent ecosystems to inform sustainable management and conservation initiatives in Tuvaijuittuq and the eastern Arctic. In an Arctic Ocean with rapidly declining sea ice, Tuvaijuittuq area retains the oldest and thickest sea ice, and can act as a refuge for ice-dependent species. This program aims to characterize the Arctic marine ecosystem and establish baseline measurements for future comparisons in the region. From 2023, water collection was carried out at four stations throughout Archer Fiord and analyzed for primary productivity, chlorophyll a, phytoplankton flow cytometry and phytoplankton taxonomy down to the lowest identifiable level. These data will contribute to a better understanding of the key drivers for productive capacity, diversity and ecosystem structure in Archer fiord. Characterization of these upstream areas are relevant for an ecosystem-based approach to fisheries management in Baffin Bay, a priority for DFO and an intrinsic part of mandated activities, as they influence the ecosystem and fisheries resources downstream.

This dataset corresponds to daily snow cover percentage at 1km resolution grid over land areas of Canada from 2006-2010. The data are subsampled by 4km to reduce data volumes and considering the geolocation uncertainty of the input satellite imagery. The daily maps are generated by assimilation of daily cloud screened NOAA AVHRR satellite imagery and Canadian Meteorological Centre (CMC) snow depth analysis snow depth and density fields within an off-line version of the CMC daily snow depth model. The snow depth model is modified to include snowpack reflectance model and a surface radiative transfer scheme that relates vegetation and snowpack reflectance to top-of-canopy bi-directional reflectance. A logistic vegetation phenology model is used to parameterize temporal dynamics of canopy leaf area index. A per-pixel particle filter with a 30 day moving window is applied to assimilation observations corresponding to 1km resolution visible band directional reflectance and normalized difference vegetation index and 24km CMC daily snow depth and monthly snow density fields. The assimilation is forced using daily air temperature and precipitation fields. Validation of the datasets has been performed by comparison to MODIS snow cover maps and in-situ snow depth stations across Canada. Validation suggests similar accuracy to MODIS snow cover products over relatively flat terrain. Validation over mountainous regions is ongoing.

Great Slave Lake in the Northwest Territories is one of the most important areas for inland breeding gulls and terns (larids) in northern Canada. Numerous rocky islands on the North Arm provide breeding habitat for a number of colonially-nesting larid species. Great Slave Lake is the only known breeding site for Caspian Terns in the Northwest Territories and the North Arm is the northernmost known breeding range for the species in North America. Given its importance to a variety of migrating, nesting and staging birds, various portions of the area have been recognized as; a Key Migratory Bird Terrestrial Habitat site, an Important Bird Area, and an International Biological Program site. This dataset contains results from surveys of active larid breeding colonies from between 1986 and 2010 on the portion of the North Arm west of Yellowknife. Most data were collected through ground surveys, with travel by boat, between mid June to early July. Information recorded at each colony included species, numbers of nests, clutch size, presence of chicks, and habitat characteristics (vegetation cover). The majority of larid colonies were located on islands along the northeastern shoreline. The southwest shoreline saw limited use by nesting larids due to the reduced number and different characteristics of the islands in that area. The most regularly occurring species were Herring Gull, Mew Gull, Ring-billed Gull, California Gull, Common Tern, Arctic Tern, and Caspian Tern. Results of these surveys have been published in the report “Gull and Tern Breeding Colonies on the North Arm of Great Slave Lake, Northwest Territories: 1986-2010 (Woodard, Fournier, and Robertson, 2013). Woodard, P.F., M.A. Fournier, and M.O. Wiebe Robertson. 2013. Gull and Tern Breeding Colonies on the North Arm of Great Slave Lake, Northwest Territories: 1986-2010. Technical Report Series No. 526, Canadian Wildlife Service, Yellowknife, NT. More details are available in the metadata document for download. CWS-North DatasetID: 071_0

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.

Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) sensors were used to generate the circa 2010 Mosaic of Canada at 30 m spatial resolution. All scenes were processed to Standard Terrain Correction Level 1T by the United States Geological Survey (USGS). Further processing performed by the Canada Centre for Remote Sensing included conversion of sensor measurements to top of atmosphere reflectance, cloud and cloud shadow detection, re-projection, selection of best measurements, mosaic generation ,noise removal and quality control. To provide a clear sky measurement for each location in Canada, data from the years 2009, 2010, and 2011 were used, but 2010 was preferentially selected. Bands 3 (0.63-0.69 µm), 4 (0.76-0.90 µm), 5 (1.55-1.75 µm), and 7 (2.08-2.35 µm) are provided in this version as significant atmosphere effects strongly limit the quality of the blue (0.45-0.52 µm) and green (0.52-0.60 µm) bands. Multi-criteria compositing was used for the selection of the most representative pixel. For ETM+ onboard Landsat 7 a scan line malfunction caused missing lines of data in all scenes collected after May 2003. Atmosphere and target variability between scenes cause these lines to have significant radiometric differences in some cases. A Fourier transformation approach was applied to correct this occurrence. This mosaic was developed for land cover and biophysical mapping applications across Canada. Other applications of these data are also possible, but should consider the temporal and spectral limitations of the product. Research to enhance the spatial, spectral and temporal aspects are in development for future versions of moderate resolution products from historical Landsat sensors, Landsat 8, and Sentinel 2 data.

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.