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 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)

Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org) Collection: - **[Canada's National Forest Inventory (NFI) 2006](https://open.canada.ca/data/en/dataset/e2fadaeb-3106-4111-9d1c-f9791d83fbf4)**

The number of days in the forecast period with a minimum temperature below the frost temperature, -5°C for herbaceous crops over the non-growing season (ifd_herb_nogrow). Week 1 and week 2 forecasted index is available daily from November 1 to March 31. Week 3 and week 4 forecasted index 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. 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 basis.

The Forest Change Type data described here is an update to previously posted open data. The date range for this data is 2012 to 2015. The Forest Change Type data for the prior period from 1985 to 2011 can be found here: https://opendata.nfis.org/mapserver/nfis-change_eng.html or https://gcgeo.gc.ca/geonetwork/search/eng search for “Forest Change” but you must be logged in to see the data. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The forest change data included in this product is national in scope (entire forested ecosystem) and represents the first wall-to-wall characterization of wildfire and harvest in Canada at a spatial resolution commensurate with human impacts. The information outcomes represent 25 years of stand replacing change in Canada’s forests, derived from a single, consistent spatially-explicit data source, derived in a fully automated manner. This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 1985–2010 for Canada's 650 million hectare forested ecosystems (https://authors.elsevier.com/sd/article/S0034425717301360). Landsat data has a 30m spatial resolution, so the change information is highly detailed and is commensurate with that of human impacts. These data represent annual stand replacing forest changes. The stand replacing disturbances types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2016; http://www.tandfonline.com/doi/full/10.1080/17538947.2016.1187673). The data available is, 1. a binary change/no-change; 2. Change year; and, 3. Change type. When using this data, please cite as: Hermosilla, T.,Wulder, M. A.,White, J. C.,Coops, N. C.,Hobart, G. W., (2017). Updating Landsat time series of surface-reflectance composites and forest change products with new observations. International Journal of Applied Earth Observation and Geoinformation. 63: 104-111. DOI: 10.1016/j.jag.2017.07.013 White, J.C., M.A. Wulder, T. Hermosilla, N.C. Coops, and G. Hobart. (2017). A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment. 192: 303-321. DOI: 10.1016/j.rse.2017.03.035.

The Probability (likelihood) of cool wave days for warm season crops occurring. 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_warm_prob). This temperature is 10°C for warm season crops. 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. Warm season crops require a relatively warm temperature condition. Typical examples include bean, soybean, corn and sweet potato. They normally grow during the summer season and early fall, then ripen in late fall in southern Canada only. Other agricultural regions in Canada do not always experience sufficiently long growing seasons for these plants to achieve maturity. The optimum temperature for such crops is 30°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.

Post-disturbance forest recovery data for Canada's forested ecosystems, representing a total area of ~650 million ha, captures the return of forests following wildfire and harvest that occurred between 1986 and 2012. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). These spatially-explicit outputs represent the rate of spectral recovery: the rate at which a pixel returns to 80% of its pre-disturbance value (White et al. 2017) within the observation period (1985-2017) using the Y2R or Years-to-Recovery metric derived from Landsat times series data. Baseline rates of spectral recovery (Y2R) were defined for each of Canada's 12 forested ecozones. These baselines were then used to identify spatial clusters of recovering pixels on the landscape where Y2R were either significantly faster or slower than their ecozonal baseline. Finally, areas that were disturbed by wildfire and harvest (1986-2012), but which had not recovered by the end of the observation period (2017) are also provided. Note that these areas are still recovering, but they had not yet recovered according to our metric of spectral recovery, by the end of the time series in 2017. For an overview of the methods, the validation of the Y2R metric, and interpretation of the derived trends, see White et al. (2022) and White et al. (2017). White, J.C., Hermosilla, T., Wulder, M.A., Coops, N.C., 2022. Mapping, validating, and interpreting spatio-temporal trends in post-disturbance forest recovery. Remote Sensing of Environment, 271, 112904. https://doi.org/10.1016/j.rse.2022.112904 ( White et al. 2022) White, J.C., Wulder, M.A., Hermosilla, T., Coops, N.C., Hobart, G.W. 2017. A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment, 194, pp. 303-321. DOI: https://doi.org/10.1016/j.rse.2017.03.035 .( White et al. 2017)

The probability (likelihood) of ice freeze days, the number of days in the forecast period with a minimum temperature below the frost temperature, -30°C for woody crops over the dormant period (ifd_wood_dorm_prob). 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. 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 basis.

Map of burned area in Canada's forested ecosystems for the 2023 fire session at 30-m spatial resolution mapped from time-series data from Sentinel-2A and -2B, and Landsat-8 and -9 using the Tracking Intra- and Inter-year Change (TIIC) algorithm (Pelletier et al. 2024). It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Fires are grouped into two classes based on detection period: summer fires and fall fires. Summer burned pixels were detected between May 30 and September 17, and fall burned pixels were detected between September 17 and October 25. For summer fires, burned pixels were identified by TIIC as changed and typed as fire. For the fall period, TIIC only detected changes within a 4-km buffer of the NRCan fire perimeters (https://cwfis.cfs.nrcan.gc.ca/datamart). This approach was used to limit commission errors that can occur due to known limitations of mapping with optical data in the fall due to phenology, snow cover, or low sun angles. For the 2023 fire season, the TIIC algorithm detected 12.74 Mha of burned area in Canada's forested ecozones, representing 1.8% of the total forest-dominated ecozone area. Of the 12.74 Mha, 11.57 Mha (90.9%) was burned by summer fires and 1.16 Mha (9.1%) by fall fires (Pelletier et al, 2024). When using this data, please cite as: Pelletier, F., Cardille, J.A., Wulder, M.A., White, J.C., Hermosilla, T., 2024. Revisiting the 2023 wildfire season in Canada. Science of Remote Sensing. 10, 100145. (Pelletier et al. 2024).