High-resolution map of leading tree species distribution for Canada’s forested ecosystems (2019). Leading tree species map produced from a 2019 Landsat image composite, geographic and climate data, elevation derivatives, and remote sensing derived phenology following the framework described in Hermosilla et al. (2022). Regional classification models were generated based on Canada’s National Forest Inventory using a 150x150 km tiling system. The leading tree species are defined by representing the most voted tree species from the Random Forests classification models (i.e. the class with the highest class membership probability).The data represents leading tree species of Canada's forested ecosystems in 2019. An image compositing window of August 1 ± 30 days was used to generate the best-available-pixel (BAP) image composites utilized as source data for the classification.The science and methods developed to generate the information outcomes shown here, that track and characterize the history of Canada’s forests, were led by Canadian Forest Service of Natural Resources Canada, developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS), partnered with the University of British Columbia, augmented by processing capacity from Digital Research Alliance of Canada.For an overview on the data, image processing, and methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2022) https://doi.org/10.1016/j.rse.2022.113276When using this data, please cite as: Hermosilla, T., Bastyr, A., Coops, N.C., White, J.C., Wulder, M.A., 2022. Mapping the presence and distribution of tree species in Canada’s forested ecosystems. Remote Sensing of Environment 282, 113276.

BiologicEcologic ISO Feature Dataset symbolization and publication. September 5, 2017.

This dataset is used is used to determine the significance or status of wetland classes and certain other natural heritage features. It is also used to set targets for Wilderness Class Provincial parks, State of the Forest reporting and to study natural disturbance regimes.

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.

This dataset is used for national and coarse-scale provincial reporting such as analyses of climate, demographics and watersheds.

This dataset includes metrics of seagrass productivity and resilience collected from field sites along the Atlantic coast of Nova Scotia, Canada. Field sites were located across a gradient of temperature and light conditions. Sampling was conducted monthly from May 2018 to July 2019. Seagrass density and plants were sampled at 10 haphazardly distributed sampling stations within each seagrass bed at approximately the same depth. Stations were ~10m apart and at least 2m from any seagrass-bare interface. Quadrats were used to determine vegetative and reproductive shoot density, and hand corers to collect seagrass above and belowground biomass. Three plants from each sampling station were also collected and processed in the laboratory for length and width leaf 3, number leaves per shoot, rhizome width, and rhizome water soluble carbohydrates. Also included in this data set are time-series records of bottom temperature at each site measured in 15-mins intervals using HOBO TidbiTv2 loggers.Cite this data as: Wong, Melisa C., and Michael Dowd. 2023. “The Role of Short-Term Temperature Variability and Light in Shaping the Phenology and Characteristics of Seagrass Beds.” Ecosphere 14(11): e4698. https://doi.org/10.1002/ecs2.4698

Ecodomains are areas broad climatic uniformity, defined at the global level

This dataset is used to: * assess biodiversity levels * define seed zones * map ecosystem types * set targets for the identification of natural heritage systems

The National Ecological Framework for Canada's "Soil Texture by Ecoregion” dataset contains tables that provide soil texture information within the ecoregion framework polygon. It provides soil texture codes and their English and French language descriptions as well as the percentage of the polygon that the component occupies.Soil texture indicates the relative proportions of the various soil separates (sand, silt, clay) as described by classes of texture. Soil separates are mineral particles, 2.0 mm in diameter and include: gravel 0.2 -7.5 cm and cobbles 7.5-25.0 cm. There are 12 texture group classes definitions and one class definition for Not Applicable (which indicates, for example, water, ice or urban areas).

Ecodivisions are areas of broad climatic and physiographic uniformity, defined at the continental level.