Fish Habitat Assessment Output: 6 of 16 High Water Level (75.4m ASL) - Juvenile/Adult Habitat - Low Vegetation Association Species (Warmwater 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.

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 during the forecast period with an average wind speed greater than 30 km/h and a maximum temperature above 30°C (drying). Week 1 and week 2 forecasted index is available daily from September 1 to August 31. Week 3 and week 4 forecasted index is available weekly (Thursday) from September 1 to August 31. Winds can significantly influence crop growth and yield mainly due to mechanical damage of plant vegetative and reproductive organs, an imbalance of plant-soil-atmosphere water relationships, and pest and disease distributions in agricultural fields. The maximum wind speed and the number of strong wind days over the forecast period represent short term and extended strong wind events respectively. 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 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.

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)**

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) Related Products (16): - **[Poplars, Aspens and Cottonwoods (Genus Populus) in Canada 2006](https://ouvert.canada.ca/data/en/dataset/08620b3f-0bda-46f2-968d-e47d5a6032de)** - **[Birches (Genus Betula) in Canada 2006](https://ouvert.canada.ca/data/en/dataset/1410d784-ffde-43c8-a816-14cdfa0aa9c4)** - **[Treed land in Canada 2006](https://ouvert.canada.ca/data/en/dataset/1f1806b9-3927-496c-8c91-8789809f4472)** - **[Merchantable forest volume in Canada 2006](https://ouvert.canada.ca/data/en/dataset/2b3569c6-ff95-40a5-a958-dc68e3aa558b)** - **[Needle-leaved species in Canada 2006](https://ouvert.canada.ca/data/en/dataset/39ffee48-f89b-4b65-af03-58a706bac7a1)** - **[Forest height in Canada 2006](https://ouvert.canada.ca/data/en/dataset/3b860e37-32e6-4f47-a423-a7519ffa4429)** - **[Total live above-ground biomass in Canada 2006](https://ouvert.canada.ca/data/en/dataset/53af4b0e-015b-405e-8de8-e7eb4498eda1)** - **[Total forest volume in Canada 2006](https://ouvert.canada.ca/data/en/dataset/5b6b60d5-8299-45d4-8bd2-c274e75bc115)** - **[Spruces (Genus Picea) in Canada 2006](https://ouvert.canada.ca/data/en/dataset/72af5640-bb51-4c7a-8f41-6b71227a598f)** - **[Tree Crown Closure in Canada 2006](https://ouvert.canada.ca/data/en/dataset/a1510fe3-8ef0-4130-9ee4-8a7ea1f9a22d)** - **[Forest Composition across Canada 2006](https://ouvert.canada.ca/data/en/dataset/a42bd5d6-83a7-4fb7-a257-389dcf7ea48d)** - **[True Firs (Genus Abies) in Canada 2006](https://ouvert.canada.ca/data/en/dataset/d845f357-e2b5-494c-821f-064dba664427)** - **[Hemlocks (Genus Tsuga) in Canada 2006](https://ouvert.canada.ca/data/en/dataset/e676e5ab-b709-46ba-b471-4e982dce0c07)** - **[Broad-leaved species in Canada 2006](https://ouvert.canada.ca/data/en/dataset/e7b9b34a-70f8-47c6-9498-94361b9febbf)** - **[Maples (Genus Acer) in Canada 2006](https://ouvert.canada.ca/data/en/dataset/ed296d14-b222-45e7-9dcb-0ca8015207ad)** - **[Cedars (Genus Thuja) in Canada 2006](https://ouvert.canada.ca/data/en/dataset/fc8bb212-9ffe-447f-9152-e26baff7a735)**

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)**

**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”