Forest 95th Percentile Elevation(Ht) 2015 95th percentile of first returns height (m). Products relating the structure of Canada's forested ecosystems have been generated and made openly accessible. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The shared products are based upon peer-reviewed science and relate aspects of forest structure including: (i) metrics calculated directly from the lidar point cloud with heights normalized to heights above the ground surface (e.g., canopy cover, height), and (ii) modelled inventory attributes, derived using an area-based approach generated by using co-located ground plot and ALS data (e.g., volume, biomass). Forest structure estimates were generated by combining information from lidar plots (Wulder et al. 2012) with Landsat pixel-based composites (White et al. 2014; Hermosilla et al. 2016) using a nearest neighbour imputation approach with a Random Forests-based distance metric. These products were generated for strategic-level forest monitoring information needs and are not intended to support operational-level forest management. All products have a spatial resolution of 30 m. For a detailed description of the data, methods applied, and accuracy assessment results see Matasci et al. (2018). When using this data, please cite as follows: Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018b. 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. Matasci et al. 2018) Geographic extent: Canada's forested ecosystems (~ 650 Mha) Time period: 1985–2011

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 probability of effective growing season degree days above 175 for warm season crops. This condition must be maintained for at least 5 consecutive days in order for EGDD to be accumulated (egdd_warm_175prob). 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 250 for warm season crops. This condition must be maintained for at least 5 consecutive days in order for EGDD to be accumulated (egdd_warm_250prob). La probabilité prévue pour les semaines 1 et 2 est disponible tous les jours du 1er avril au 31 octobre. La probabilité prévue pour les semaines 3 et 4 est disponible toutes les semaines (jeudi) du 1er avril au 31 octobre. 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 (likelihood) of heat wave days for warm season crops occurring. Heat wave days: The number of days in the forecast period with a maximum temperature above the cardinal maximum temperature, the temperature at which crop growth ceases. This temperature is 35°C for warm season crops (dhw_warm_prob). 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.

Probability of total precipitation above 50mm over the forecast period (pweek50_prob). Week 1 and week 2 forecasted probability is available daily from September 1 to August 31. Week 3 and week 4 forecasted probability is available weekly (Thursday) from September 1 to August 31. Precipitation (moisture availability) establishes the economic yield potential and product quality of field crops. Both dry and wet precipitation extremes have the ability to inhibit proper crop growth. The greatest daily precipitation index covers the risk of excessive precipitation in the short term, while the other indices pertain to longer term moisture availability. 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.

Forest Elevation(Ht) Mean 2015 Mean height of lidar first returns (m). Represents the mean canopy height. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Products relating the structure of Canada's forested ecosystems have been generated and made openly accessible. The shared products are based upon peer-reviewed science and relate aspects of forest structure including: (i) metrics calculated directly from the lidar point cloud with heights normalized to heights above the ground surface (e.g., canopy cover, height), and (ii) modelled inventory attributes, derived using an area-based approach generated by using co-located ground plot and ALS data (e.g., volume, biomass). Forest structure estimates were generated by combining information from lidar plots (Wulder et al. 2012) with Landsat pixel-based composites (White et al. 2014; Hermosilla et al. 2016) using a nearest neighbour imputation approach with a Random Forests-based distance metric. These products were generated for strategic-level forest monitoring information needs and are not intended to support operational-level forest management. All products have a spatial resolution of 30 m. For a detailed description of the data, methods applied, and accuracy assessment results see Matasci et al. (2018). When using this data, please cite as follows: Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018b. 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. Matasci et al. 2018) Wulder et al. 2018) Geographic extent: Canada's forested ecosystems (~ 650 Mha) Time period: 1985–2011

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)

Probability of daily precipitation above 10mm over the forecast period (p1d10_prob). Week 1 and week 2 forecasted probability is available daily from September 1 to August 31. Week 3 and week 4 forecasted probability is available weekly (Thursday) from September 1 to August 31. Units: mm Precipitation (moisture availability) establishes the economic yield potential and product quality of field crops. Both dry and wet precipitation extremes have the ability to inhibit proper crop growth. The greatest daily precipitation index covers the risk of excessive precipitation in the short term, while the other indices pertain to longer term moisture availability. 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.