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.

The probability of effective growing season degree days above 175 for cool season crops. This condition must be maintained for at least 5 consecutive days in order for EGDD to be accumulated (egdd_cool_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.

Fish Habitat Assessment Output: 16 of 16 Average Water Level (75.0m ASL) - Juvenile/Adult Habitat - Low Vegetation Association Species (Coldwater Guild) 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.

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.

Harvest changes occurred from 1985 to 2022 displaying the year of greatest harvest disturbance. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The information outcomes represent 38 years of harvest activity in Canada's forests, derived from a single, consistent, spatially explicit data source in a fully automated manner. Time series of Landsat data with 30 m spatial resolution were used to characterize national trends in stand replacing forest disturbances caused by harvest for the period 1985-2022 for Canada's 650-million-hectare forested ecosystems. When using this data, please cite as: Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, 2016. Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth 9(11), 1035-1054. https://doi.org/10.1080/17538947.2016.1187673 ( Hermosilla et al. 2016). See references below for an overview on the data processing, metric calculation, change attribution, and time series change detection methods applied, as well as information on independent accuracy assessment of the data. Hermosilla, T., Wulder, M. A., White, J. C., Coops, N.C., Hobart, G.W., (2015). An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment 158, 220-234. ( Hermosilla et al. 2015a). Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., (2015). Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sensing of Environment 170, 121-132. ( Hermosilla et al. 2015b). Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G. W. Hobart, (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. https://doi.org/10.1016/j.jag.2017.07.013 (Hermosilla et al. 2017)

**1. Overview** Pre-CanLaD v2 updates Canada's 1965-1984 forest disturbance history dataset initially published by Correia et al. (2024) with enhanced detection methods and improved temporal accuracy and coverage. **2. Key Updates in pre-CanLaD v2** **2.1. Enhanced Detection** - Fractional land-cover change analysis identifies post-disturbance forest regeneration using 1985-2020 vegetation cover differences - Dual detection approach combines automated polygon-based classification with manual photo-interpretation - Incorporates National Burn Area Composite (NBAC; Skakun et al., 2022) 1972-1984 fire perimeters for improved fire year attribution **2.2. Detection Results** See **Table 1 in the update report**, available in the download section below. This table presents the total disturbed areas, by disturbance type, for the periods 1950–1964 and 1965–1984. The “New detection” column represents disturbed areas for which no reliable previous data were available. The “Previously recorded” column represents disturbed areas already documented in the datasets described in Table 3 of Correia et al. (2024) and NBAC, for which the predicted disturbance year was subsequently adjusted. **3. Dataset description** Four raster layers available: - preCanLaD_disturbanceType_v2_20251126.tif - Pixel values are disturbance type: - 2 = Fire - 3 = Harvest - 4 = Insect - preCanLaD_disturbanceYear_v2_20251126.tif - Pixel values are disturbance year: - 1950-1984 = years - 999 = unknown (user should convert this value to 1955) - preCanLaD_correctionMask_v2_20251126.tif - Pixel values are data provenance codes: - 0 = Pre-CanLaD-only disturbance - 1 = Confirmed fire (manual or confirmed by pre-CanLaD v1 in the original publication) - 11 = Canadian National Fire Database (CNFDB) corrected fire - 12 = NBAC-corrected fire - 2 = Confirmed harvest - preCanLaD_updateMask_v2_20251126.tif - Pixel values are version change indicator: - 1 = v1 retained - 2 = v2 new/updated **4. Key Improvements** - Improved fire-harvest discrimination through provincial harvest polygon integration - Hierarchical dating system prioritizes NBAC > CNFDB polygons > CNFDB points > model-based dates > manual interpretation and unknown dates - Better harmonization with current historic disturbance records - Quality controls: 11-pixel minimum patch size, 1985-2020 disturbance masking **5. Known Limitations** - Some temporal uncertainty remains; grouping disturbance years into age classes can help mitigate its effects.High-severity burns preferentially detected; low-severity fires may be missed, along with fires in non-forested and open forest areas with low forest recovery rates - Some fire-harvest confusion in transition zones - Harvest detections may be biased toward provinces with publicly available forest inventories because the pre-CanLaD v2 method relies on these inventories to identify additional harvested areas - No pixel-level dates for insect outbreaks **6. Data download** The data can be downloaded from the FTP server (ftp.maps.canada.ca/pub/nrcan_rncan/Forests_Foret/canada_disturbances_1965to1984/v2/), referenced in the “Data and Resources” section, using a browser download manager, such as DownThemAll, or an external client such as FileZilla. **7. Dataset citation** - Guindon L., Correia D.L.P. and S. Brisson. 2026. Pre-CanLaD v2: Canada Landsat Disturbance (CanLaD) 30-m resolution disturbance detection prior to 1984. https://doi.org/10.23687/8d49698f-40f9-40da-b097-a3f4c90adf5a **8. Reference** - Correia, David L., Luc Guindon, and Marc-André Parisien. 2024. “Extending Canadian Forest Disturbance History Maps Prior to 1985.” Ecosphere 15 (8): e4956. - Natural Resources Canada. 2023. Canadian National Fire Database Natural Resources Canada. Edmonton: Canadian Forest Service, Northern Forestry Centre. https://cwfis.cfs.nrcan.gc.ca. - Skakun, R., G. Castilla, J. Metsaranta, E. Whitman, S. Rodrigue, J. Little, K. Groenewegen, and M. Coyle. 2022. “Extending the National Burned Area Composite Time Series of Wildfires in Canada.” Remote Sensing 14: 3050. ___ For detailed methodology, see download links, file “Pre-CanLaD_v2_Update_report_EN.pdf”

Wall-to-wall map of water bodies across Canada's forested ecosystems for the year 2022, derived from the "water" class of the annual Virtual Land Cover of Engine (VLCE) product. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The VLCE maps are based on Landsat image time-series composites and represent annual land cover classifications from 1984 to 2022 at a spatial resolution of 30 m. The classification process integrates forest change information and ancillary topographic and hydrologic variables, applying a regional modeling framework based on a 150x150 km tiling system ( Hermosilla et al., 2022). Training data are drawn from multiple land cover sources and selected proportionally to land cover distributions using a distance-weighted approach. Classifications are refined over time using a Hidden Markov Model to ensure consistency and reduce classification noise between years. Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C. 2022. Land cover classification in an era of big and open data: Optimizing localized implementation and training data selection to improve mapping outcomes. Remote Sensing of Environment. 268, 112780. https://doi.org/10.1016/j.rse.2021.112780. ( Hermosilla et al., 2022) Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W. 2018. Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series. Canadian Journal of Remote Sensing. 44(1) 67-87. https://doi.org/10.1080/07038992.2018.1437719.( Hermosilla et al., 2018)