Arctic SDI catalogue
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Forest Total Aboveground Biomass 2015 Total aboveground biomass. Individual tree total aboveground biomass is calculated using species-specific equations. In the measured ground plots, aboveground biomass per hectare is calculated by summing the values of all trees within a plot and dividing by the area of the plot. Aboveground biomass may be separated into various biomass components (e.g. stem, bark, branches, foliage) (units = t/ha). 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) Geographic extent: Canada's forested ecosystems (~ 650 Mha) Time period: 1985–2011
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The raster maps depict a suite of forest attributes in 2001* and 2011 at 250 m by 250 m spatial resolution. The maps were produced using the k nearest neighbours method applied to MODIS imagery and trained from National Forest Inventory photo plot data. For detailed information about map production methods please refer to Beaudoin et al. (2018) "Tracking forest attributes across Canada between 2001 and 2011 using the k nearest neighbours mapping approach applied to MODIS imagery." Canadian Journal of Forest Research 48, 85-93. https://cfs.nrcan.gc.ca/publications?id=38979 The map datasets may be downloaded from https://nfi.nfis.org/downloads/nfi_knn2011.zip or https://open.canada.ca/data/en/dataset/ec9e2659-1c29-4ddb-87a2-6aced147a990 * Note: the forest composition (leading tree genus) map depicts forest attributes in 2001. How can this data be used? The resolution and accuracy of these map products are best suited for strategic-level forest reporting and informing policy and decision making at regional to national scales. As these maps also offer a coherent set of quantitative values for a large suite of forest attributes, they can be used as baseline information for modelling and in calculations such as merchantable forest volume or percentage of tree species. It is also possible to overlay these maps with other maps produced on the same pixel grid to make assessments of disturbance impacts, such as fire and harvests.
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The Marine Geoscience for Marine Spatial Planning (MGMSP) program, implemented by Natural Resources Canada (NRCan), is an initiative with the goal of offering innovative regional geoscience products to support the Department of Fisheries and Oceans (DFO) in their Marine Spatial Planning endeavors. To develop spatial management plans for various expansive bioregions across Canada, the DFO has undertaken the task of creating comprehensive ocean management strategies. Presently, the MGMSP program is concentrating its efforts on two significant bioregions, namely the Scotian Shelf and Newfoundland and Labrador Shelves bioregions. In pursuit of this objective, the work presented in this report has focused on the assimilation and gridding of numerous disparate bathymetry datasets sourced from authoritative and reliable channels. The purpose of this comprehensive data gathering approach is to establish a unified bathymetric grid, with a consistent spatial resolution, which can be utilized in both oceanographic modeling and geological interpretation. By collating information from a diverse range of sources, we aim to create a comprehensive and reliable foundation that will enable accurate and informed decision-making in the field of marine spatial planning, as well as enhance the accuracy and reliability of subsequent analyses and simulations.
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Wildfire Year/dNBR/Mask 1985-2015 Wildfire change magnitude 85-15. Spectral change magnitude for wildfires that occurred from 1985 and 2015. The wildfire change magnitude included in this product is expressed via differenced Normalized Burn Ratio (dNBR), computed as the variation between the spectral values before and after the change event. This dataset is composed of three layers: (1) binary wildfire mask, (2) year of greatest wildfire disturbance, and (3) differenced Normalized Burn Ratio (dNBR) transformed for data storage efficiency to the range 0-200. The actual dNBR value is derived as follows: dNBR = value / 100. Higher dNBR values are related to higher burn severity. The information outcomes represent 30 years of wildfires 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 wildfire for the period 1985-2015 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. (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). Geographic extent: Canada's forested ecosystems (~ 650 Mha) Time period: 1985–2011
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The Probability (likelihood) of frost occurring. The number of days in the forecast period with a minimum temperature below the frost temperature, the temperature at which frost damage occurs. This temperature is -2°C for cool season crops (ffd_cool_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. Cool season crops require a relatively low temperature condition. Typical examples include wheat, barley, canola, oat, rye, pea, and potato. They normally grow in late spring and summer, and mature between the end of summer and early fall in the southern agricultural areas of Canada. 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 and weekly basis.
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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)**
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Fish Habitat Assessment Output: 3 of 16 High Water Level (75.4m ASL) - Nursery Habitat - High 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.
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The raster maps depict a suite of forest attributes in 2001* and 2011 at 250 m by 250 m spatial resolution. The maps were produced using the k nearest neighbours method applied to MODIS imagery and trained from National Forest Inventory photo plot data. For detailed information about map production methods please refer to Beaudoin et al. (2018) "Tracking forest attributes across Canada between 2001 and 2011 using the k nearest neighbours mapping approach applied to MODIS imagery." Canadian Journal of Forest Research 48, 85-93. https://cfs.nrcan.gc.ca/publications?id=38979 The map datasets may be downloaded from https://nfi.nfis.org/downloads/nfi_knn2011.zip or https://open.canada.ca/data/en/dataset/ec9e2659-1c29-4ddb-87a2-6aced147a990 * Note: the forest composition (leading tree genus) map depicts forest attributes in 2001. How can this data be used? The resolution and accuracy of these map products are best suited for strategic-level forest reporting and informing policy and decision making at regional to national scales. As these maps also offer a coherent set of quantitative values for a large suite of forest attributes, they can be used as baseline information for modelling and in calculations such as merchantable forest volume or percentage of tree species. It is also possible to overlay these maps with other maps produced on the same pixel grid to make assessments of disturbance impacts, such as fire and harvests.
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The raster maps depict a suite of forest attributes in 2001* and 2011 at 250 m by 250 m spatial resolution. The maps were produced using the k nearest neighbours method applied to MODIS imagery and trained from National Forest Inventory photo plot data. For detailed information about map production methods please refer to Beaudoin et al. (2018) "Tracking forest attributes across Canada between 2001 and 2011 using the k nearest neighbours mapping approach applied to MODIS imagery." Canadian Journal of Forest Research 48, 85-93. https://cfs.nrcan.gc.ca/publications?id=38979 The map datasets may be downloaded from https://nfi.nfis.org/downloads/nfi_knn2011.zip or https://open.canada.ca/data/en/dataset/ec9e2659-1c29-4ddb-87a2-6aced147a990 * Note: the forest composition (leading tree genus) map depicts forest attributes in 2001. How can this data be used? The resolution and accuracy of these map products are best suited for strategic-level forest reporting and informing policy and decision making at regional to national scales. As these maps also offer a coherent set of quantitative values for a large suite of forest attributes, they can be used as baseline information for modelling and in calculations such as merchantable forest volume or percentage of tree species. It is also possible to overlay these maps with other maps produced on the same pixel grid to make assessments of disturbance impacts, such as fire and harvests.
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Fish Habitat Assessment Output: 8 of 16 High Water Level (75.4m ASL) - Juvenile/Adult Habitat - Low Vegetation Association Species (Coldwater) 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.