The probability (likelihood) of ice freeze days for herbaceous crops during in a dormant period (ifd_herb_dorm_prob). The number of days in the forecast period with a minimum temperature below the frost temperature. It is -15°C for herbaceous crops over the dormant period. Week 1 and week 2 forecasted probability is available daily from November 1 to March 31. Week 3 and week 4 forecasted probability is available weekly (Thursday) from November 1 to March 31. Over-wintering crops are biennial and perennial field crops such as herbaceous plants (strawberry, alfalfa, timothy, and many other forage crops) and woody fruit trees (apple, pear, peach, cherry, plum, apricot, chestnut, pecan, grape, etc.). These crops normally grow and develop in the growing season and become dormant in the non-growing season. However, extreme weather and climate events such as cold waves in the growing season and ice freezing events during the winter are a major constraint for their success of production and survival in Canada. The winter survival of these plants depends largely on agrometeorological conditions from late autumn to early spring, especially ice-freezing damage during the winter season. Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind. The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily and weekly basis.

The probability of effective growing season degree days above 100 for cool season crops. This condition must be maintained for at least 5 consecutive days in order for EGDD to be accumulated (egdd_cool_100prob). Week 1 and week 2 forecasted probability is available daily from April 1 to October 31. Week 3 and week 4 forecasted probability is available weekly (Thursday) from April 1 to October 31. Cumulative heat-energy satisfies the essential requirement of field crop growth and development towards a high yield and good quality of agricultural crop products. Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind. The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily and weekly basis.

The probability of effective growing season degree days above 100 for warm season crops. This condition must be maintained for at least 5 consecutive days in order for EGDD to be accumulated (egdd_warm_100prob). Week 1 and week 2 forecasted probability is available daily from April 1 to October 31. Week 3 and week 4 forecasted probability is available weekly (Thursday) from April 1 to October 31. Cumulative heat-energy satisfies the essential requirement of field crop growth and development towards a high yield and good quality of agricultural crop products. Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind. The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily and weekly basis.

This dataset provides a Canada-wide map of vegetation height and the delineation of the northern forest limit. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Vegetation height estimates were derived from ICESat-2 LiDAR observations, integrated with Landsat time series and topographic variables to model spatial patterns. The northern forest limit represents the transition between boreal forest and tundra, an ecologically significant zone for monitoring climate change impacts and biodiversity. Vegetation height was modeled for six time-periods including 1985-1995, 1990-2000, 1995-2005, 2000-2010, 2005-2015 and 2010-2021. Predictions for each time period represent the median conditions for that period. Predictions of height and the probability of canopy presence were generated using Random Forests models trained on spaceborne-lidar data collected by ICESat-2 from 2019-2021 and overlapping Landsat satellite imagery from 2010-2021. These Random Forests models were then applied to the entire archive of Landsat imagery, representing a period of ~35 years. This dataset provides spatially explicit prediction of vegetation height (m) along the Canadian northern forest limit at 30 m spatial resolution. Pixels with a low (< 50 %) probability of containing a vegetation canopy have been assigned a height of 0 m. The science and methods for this dataset were the result of a collaboration between the Canadian Forest Service of Natural Resources Canada, partnered with the Integrated Remote Sensing Studio (IRSS) in the Faculty of Forestry at the University of British Columbia. When using this data, please cite: Travers-Smith, H., Coops, N. C., Mulverhill, C., Wulder, M. A., Ignace, D., Lantz, T. C. (2024). Mapping vegetation height and identifying the northern forest limit across Canada using ICESat-2, Landsat time series and topographic data. Remote Sensing of Environment, 305, 114097. https://doi.org/10.1016/j.rse.2024.114097 (Travers-Smith et al. 2024). Additional details outlining application of the model to the time-series of Landsat data can be found here: Travers-Smith, H., Coops, N., Mulverhill, C., Wulder, M. A., Lantz, T. C., Ignace, D. (2025). Satellite observations reveal stable forest limits and shrub expansion across the Canadian forest-tundra ecotone. Environmental Research Letters, 20(10). https://doi.org/10.1088/1748-9326/adfc7f (Travers-Smith et al. 2025).

Fish Habitat Assessment Output: 2 of 16 High Water Level (75.4m ASL) - Spawning Habitat - Low Vegetation Association Species (All Temperature Windows) Habitat suitability was assessed for the Bay of Quinte Area of Concern, at a 3 m grid resolution, using the Habitat Ecosystem Assessment Tool (HEAT), temperature algorithms, vegetation models, and water level input. Habitat classifications were based on three variables: depth (elevation), vegetation, and substrate; and modified by temperature suitabilities. The final suitability maps were based on documented habitat and temperature associations for the fish in the area. Different life stages (spawning requirements, nursery habitat, adult habitat) were modeled for the years of 1972-2011. Suitability values were scaled from 0 (not suitable) to 1 (highly suitable) and converted to suitability classes of very low, low, medium, and high. The final maps for each guild – life stage combination are maximum suitability values from the 39-year period modelled.

This record contains two-weekly minimum sea ice concentration images of the Canadian Beaufort Sea at 1.1 km resolution. The dataset originated from the Canadian Ice Service (CIS) Digital Archive weekly regional charts for the western Arctic (See “additional credit” for a link to these data), created by synthesis of remotely-sensed, ship and airborne observations (Fequet, 2005). These vector ice charts were gridded at 1.1 km resolution and aggregated into two-week composites by calculating the minimum sea-ice concentration at each grid cell over each two-week interval in each year. Week numbers were defined using the ISO 8601 convention, and sea-ice concentration isrepresented in tenths (with 0/10 corresponding to an ice-free pixel, ranging to 10/10 corresponding to 100% pixel coverage with sea-ice). The result is 12 composite images per year in 1998 through 2020 (23 years), corresponding to https://open.canada.ca/data/en/dataset/ee27e86f-7b18-4e3f-8444-0c5efb6110a4. For further details, see Galley et al., 2022.

The data shared are spatially explicit projections of wildfire burn probability across Canada’s forested ecozones under multiple future climate scenarios at a 30-m spatial resolution. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Four future climate scenarios were used to examine the spatiotemporal distribution of burn probability in the 21st century based on climate, vegetation, and topographic conditions ( Mulverhill et al. 2024). Projected burn probability is provided for four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) and four future time periods, including 2021-2040, 2041-2060, 2061-2080, and 2081-2100, along with a baseline period representing average climate conditions and burn probability between 1991 and 2020. Outputs represent the probability that the conditions (climate, vegetation, topography) of a given pixel resemble those of historically burned areas. All non-climate variables were held static; therefore, projections represent burn probability under future climate scenarios given contemporary (2020) forest conditions. When using this dataset, please cite Mulverhill et al. (2025), as below. Mulverhill, C., Coops, N. C., Wulder, M. A., Hermosilla, T., White, J. C., & Bater, C. W. (2025). Projected Future Changes in Burn Probability in Canada’s Forests and Communities Under Different Climate Change Scenarios. Canadian Journal of Remote Sensing, 51(1). https://doi.org/10.1080/07038992.2025.2560347(Mulverhill et al. 2025). For a detailed description of the source data and methods applied to the baseline period to enable the Mulverhill et al. (2025) projections, see: Mulverhill, C., Coops, N.C., Wulder, M.A., White, J.C., Hermosilla, T., and Bater, C.W. 2024. “Multidecadal mapping of status and trends in annual burn probability over Canada’s forested ecosystems.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 209 pp. 279–295. https://doi.org/10.1016/j.isprsjprs.2024.02.006(Mulverhill et al. 2024).

Cool Wave Days are the number of days in the forecast period with a minimum temperature below the cardinal minimum temperature, the lowest temperature at which crop growth will begin (dcw_cool). This temperature is 5°C for cool season crops. Week 1 and week 2 forecasted index is available daily from April 1 to October 31. Week 3 and week 4 forecasted index is available weekly (Thursday) from April 1 to October 31. Cool season crops require a relatively low temperature condition. Typical examples include wheat, barley, canola, oat, rye, pea, and potato. They normally grow in late spring and summer, and mature between the end of summer and early fall in the southern agricultural areas of Canada. The optimum temperature for such crops is 25°C. Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind. The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily and weekly basis.

This dataset provides wall-to-wall maps of forest structure across Canada's 650 million hectare forested ecosystems for the year 2022, generated at a spatial resolution of 30 m. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Structure estimates include key attributes such as canopy height, canopy cover, and aboveground biomass, derived using a combination of airborne lidar and Landsat-based spectral composites. Structure models were trained using the - lidar-plot framework - (Wulder et al. 2012), which integrates co-located airborne lidar data and ground plot measurements with Landsat time-series composites (Hermosilla et al. 2016). A Nearest Neighbour imputation approach was applied to estimate structural attributes across the full extent of Canada's forested area. These nationally consistent products are intended to support strategic-level forest monitoring and assessment and are not designed for operational forest management. For further details on the methods, accuracy assessment, and source data, see Matasci et al. (2018). Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment, 216, 697-714. https://doi.org/10.1016/j.rse.2018.07.024 (Matasci et al. 2018)