Fish Habitat Assessment Output: 10 of 16 Average Water Level (75.0m 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.

Landsat-derived forest age for Canada 2022 Satellite-based forest age map for 2022 across Canada's forested ecozones at a 30-m spatial resolution. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Remotely sensed data from Landsat (disturbances, surface reflectance composites, forest structure) and MODIS (Gross Primary Production) are utilized to determine age. Age can be determined where disturbance can be identified directly (disturbance approach) or inferred using spectral information (recovery approach) or using inverted allometric equations to model age where there is no evidence of disturbance (allometric approach). The disturbance approach is based upon satellite data and mapped changes and is the most accurate. The recovery approach also avails upon satellite data plus logic regarding forest succession, with an accuracy that is greater than pure modeling. Given the lack of widespread recent disturbance over Canada's forests, the allometric approach is required over the greatest area (86.6%). Using information regarding realized heights and growth and yield modeling, ages are estimated where none are otherwise possible. Trees of all ages are mapped, with trees >150 years old combined in an - old tree - category. See Maltman et al. (2023) for an overview of the methods, data, image processing, as well as information on agreement assessment using Canada's National Inventory (NFI). Maltman, J.C., Hermosilla, T., Wulder, M.A., Coops, N.C., White, J.C., 2023. Estimating and mapping forest age across Canada's forested ecosystems. Remote Sensing of Environment 290, 113529. ( Maltman et al. 2023).

Satellite-based forest area consistent with FAO definitions for Canada. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The forest area is based on the Food and Agricultural Organization of the United Nations (FAO) definition. The FAO definition incorporates land use, whereby trees removed by fire and harvesting for instance, remain forest as the trees will return. The included map displays the current forest cover for year as noted (i.e. 2022), plus the satellite-based temporally informed forest area where tree cover has been temporarily lost due to stand replacing disturbances (i.e., fire, harvest). For an overview of the methods, data, image processing, as well as information on accuracy assessment see Wulder et al. (2020). Open Access: Wulder, M.A., T. Hermosilla, G. Stinson, F.A. Gougeon, J.C. White, D.A. Hill, B.P. Smiley. (2020). Satellite-based time series land cover and change information to map forest area consistent with national and international reporting requirements. Forestry: An International Journal of Forest Research 93(3), 331-34, https://doi.org/10.1093/forestry/cpaa0063 . ( Wulder et al. 2020)

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).

Landsat-derived forest age for Canada 2022 Satellite-based forest age map for 2022 across Canada's forested ecozones at a 30-m spatial resolution. Remotely sensed data from Landsat (disturbances, surface reflectance composites, forest structure) and MODIS (Gross Primary Production) are utilized to determine age. Age can be determined where disturbance can be identified directly (disturbance approach) or inferred using spectral information (recovery approach) or using inverted allometric equations to model age where there is no evidence of disturbance (allometric approach). The disturbance approach is based upon satellite data and mapped changes and is the most accurate. The recovery approach also avails upon satellite data plus logic regarding forest succession, with an accuracy that is greater than pure modeling. Given the lack of widespread recent disturbance over Canada's forests, the allometric approach is required over the greatest area (86.6%). Using information regarding realized heights and growth and yield modeling, ages are estimated where none are otherwise possible. Trees of all ages are mapped, with trees >150 years old combined in an - old tree - category. This product was developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). See Maltman et al. (2023) for an overview of the methods, data, image processing, as well as information on agreement assessment using Canada's National Inventory (NFI). Maltman, J.C., Hermosilla, T., Wulder, M.A., Coops, N.C., White, J.C., 2023. Estimating and mapping forest age across Canada's forested ecosystems. Remote Sensing of Environment 290, 113529. ( Maltman et al. 2023).

Landsat-derived forest age for Canada 2022 Satellite-based forest age map for 2022 across Canada's forested ecozones at a 30-m spatial resolution, developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Remotely sensed data from Landsat (disturbances, surface reflectance composites, forest structure) and MODIS (Gross Primary Production) are utilized to determine age. Age can be determined where disturbance can be identified directly (disturbance approach) or inferred using spectral information (recovery approach) or using inverted allometric equations to model age where there is no evidence of disturbance (allometric approach). The disturbance approach is based upon satellite data and mapped changes and is the most accurate. The recovery approach also avails upon satellite data plus logic regarding forest succession, with an accuracy that is greater than pure modeling. Given the lack of widespread recent disturbance over Canada's forests, the allometric approach is required over the greatest area (86.6%). Using information regarding realized heights and growth and yield modeling, ages are estimated where none are otherwise possible. Trees of all ages are mapped, with trees >150 years old combined in an - old tree - category. See Maltman et al. (2023) for an overview of the methods, data, image processing, as well as information on agreement assessment using Canada's National Inventory (NFI). Maltman, J.C., Hermosilla, T., Wulder, M.A., Coops, N.C., White, J.C., 2023. Estimating and mapping forest age across Canada's forested ecosystems. Remote Sensing of Environment 290, 113529. ( Maltman et al. 2023).

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)