This dataset includes the extent of the boreal forest as well as the extent of managed boreal forest worldwide. The extent of boreal forest was produced from Brandt et al. (2013) and a modified version of Goudilin (1987). Managed forest was defined as suggested by IPCC (2003) using data from FAFS (2009), Gauthier et al. (2014), See et al. (2015) and AICC maps. The extent of managed forest mostly includes areas managed for wood production, areas protected from large-scale disturbances as well as formal protected areas.Both boreal forest extent and managed boreal forest extent are available in raster and vector data.Please cite this data product as:Boucher, D., D.G. Schepaschenko, S. Gauthier, P. Bernier, T. Kuuluvainen, A. Z. Shvidenko. 2024. World boreal forest and managed boreal forest extent. DOI: 10.23687/88d70716-2600-4995-8d5f-86f96e383abfThese data were presented in the following article:Gauthier, S., P. Bernier, T. Kuuluvainen, A. Z. Shvidenko, D. G. Schepaschenko. 2015. Boreal forest health and global change. Science 349:819-822. DOI: 10.1126/science.aaa9092References:J. P. Brandt, M. D. Flannigan, D. G. Maynard, I. D. Thompson, W. J. A. Volney, Environ. Rev. 21, 207–226 (2013)I. S. Goudilin, Landscape map of the USSR. Legend to the landscape map of the USSR. Scale 1:2 500 000. Moscow, Ministry of Geology of the USSR (1987) [in Russian].Inter-governmental panel on climate change (IPCC). J. Penman, M. Gytarsky, T. Hiraishi, T. Krug, D. Kruger, et al., Eds., Good practice guidance for land use, land-use change and forestry (IPCC/NGGIP/IGES, Kanawaga, 2003)Federal Agency of Forest Service (FAFS), Forest Fund of the Russian Federation (state by 1 January 2009) (Federal Agency of Forest Service, Moscow, 2009) [in Russian]S. Gauthier et al., Environ. Rev. 22, 256–285 (2014).See et al., Harnessing the power of volunteers, the internet and Google Earth to collect and validate global spatial information using Geo-Wiki. Technological Forecasting and Social Change. (2015). doi:10.1016/j.techfore.2015.03.002 Alaska Interagency Coordination Center (AICC). Fire Information. https://fire.ak.blm.gov/content/maps/aicc/Large%20Maps/Alaska_Fire_Management_Options.pdf (the version of 2014 was used)

Predictive ecosite map of Saskatchewan's provincial forests and adjacent parks within the boreal plain, boreal shield and taiga shield ecozones: version 01.Download: Here The Predictive Ecosite Map of Saskatchewan is based on the classifications of McLaughlan et al. (2010). This version (v01) is an interim proof-of-concept product and is not considered highly accurate or finalized. Ecosite prediction accuracy is anticipated to between 40% and 65%, depending on geographic location. For the boreal plain ecozone, ecosites were derived using a random forest imputation with the yaImpute package in R and mapped by L. Gelhorn (2014-07-24). Pixel values presented here are prefixed with a 2. Non-forest / non-water pixels are often assigned a BP28 ecosite code (228). For the boreal shield and taiga shield ecozones, ecosites were derived using a random forest imputation with the yaImpute package in R and mapped by S. Oldford (2019-11-20). Pixel values presented here are prefixed with a 3 and a 4, respectively. In the case of the taiga shield ecozone, there were limited sample plot data. To increase the sample size for modelling, the majority of taiga shield ecosites data are combined and modeled with boreal sheild ecosites using the ecozonal synonyms of McLaughlan et al. (2010). Ecosites TS01 (401), TS04 (404) and TS17 (417) were modeled as such because no ecozonal synonyms exist. This map is clipped to the Saskatchewan provincial forest and adjacent park boundaries. Water bodies are masked according to the 2015 Landcover Map of Canada of White et al. (2017) and have a pixel value of zero (0). References: McLaughlan, M.S., Wright, R.A. and Jiricka, R.D. (2010). Field guide to the ecosites of Saskatchewan’s provincial forests. Saskatchewan Ministry of Environment, Forest Service. Prince Albert, Saskatchewan. 343 pp. White, J.C., Wulder, M.A., Hermosilla, T., Coops, N.C. and 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. 192: 303-321.

"Vegetation Zones of Canada: a Biogeoclimatic Perspective" maps Canadian geography in relation to gradients of regional climate, as expressed by potential vegetation on zonal sites. Compared to previous similar national-scale products, "Vegetation Zones of Canada" benefits from the work of provincial and territorial ecological classification programs over the last 30+ years, incorporating this regional knowledge of ecologically significant climatic gradients into a harmonized national map. This new map, reflecting vegetation and soils adapted to climates prior to approximately 1960, can serve as a broad-scale (approximately 1:5 M to 1:10 M) geospatial reference for monitoring and modeling effects of climate changes on Canadian ecosystems. "Vegetation Zones of Canada: a Biogeoclimatic Perspective" employs a two-level hierarchical legend. Level 1 vegetation zones reflect the global-scale latitudinal gradient of annual net radiation, as well as the effects of high elevation and west to east climatic and biogeographic variation across Canada. Within the level 1 vegetation zones, level 2 zones distinguish finer scale variation in zonal vegetation, especially in response to elevational and arctic climatic gradients, climate-related floristics and physiognomic diversity in the Great Plains, and maritime climatic influences on the east and west coasts. Thirty-three level 2 vegetation zones are recognized: High Arctic Sparse Tundra Mid-Arctic Dwarf Shrub Tundra Low Arctic Shrub Tundra Subarctic Alpine Tundra Western Boreal Alpine Tundra Cordilleran Alpine Tundra Pacific Alpine Tundra Eastern Alpine Tundra Subarctic Woodland-Tundra Northern Boreal Woodland Northwestern Boreal Forest West-Central Boreal Forest Eastern Boreal Forest Atlantic Maritime Heathland Pacific Maritime Rainforest Pacific Dry Forest Pacific Montane Forest Cordilleran Subboreal Forest Cordilleran Montane Forest Cordilleran Rainforest Cordilleran Dry Forest Eastern Temperate Mixed Forest Eastern Temperate Deciduous Forest Acadian Temperate Forest Rocky Mountains Foothills Parkland Great Plains Parkland Intermontane Shrub-Steppe Rocky Mountains Foothills Fescue Grassland Great Plains Fescue Grassland Great Plains Mixedgrass Grassland Central Tallgrass Grassland Cypress Hills GlaciersPlease cite this dataset as: Baldwin, K.; Allen, L.; Basquill, S.; Chapman, K.; Downing, D.; Flynn, N.; MacKenzie, W.; Major, M.; Meades, W.; Meidinger, D.; Morneau, C.; Saucier, J-P.; Thorpe, J.; Uhlig, P. 2019. Vegetation Zones of Canada: a Biogeoclimatic Perspective. [Map] Scale 1:5,000,000. Natural Resources Canada, Canadian Forest Service. Great Lake Forestry Center, Sault Ste. Marie, ON, Canada.

The spatial representation for a Forest Addition, which is any Forest land that is to be designated by the Lieutenant Governor, into an established forest, to be managed and used for the social and economic benefit of the Province

Forest stands (FSTAND) is a vector delineation of relatively homogeneous forest stands or naturally non-forested areas as polygons with a 0.5 ha minimum area and a 2.0 ha median area.Download: Here The Saskatchewan Ministry of Environment, Forest Service Branch, has developed a forest resource inventory (FRI) which meets a variety of strategic and operational planning information needs for the boreal plains. Such needs include information on the general land cover, terrain, and growing stock (height, diameter, basal area, timber volume and stem density) within the provincial forest and adjacent forest fringe. This inventory provides spatially explicit information as 10 m or 20 m raster grids and as vectors polygons for relatively homogeneous forest stands or naturally non-forested areas with a 0.5 ha minimum area and a 2.0 ha median area. Forest stands (FSTAND) is a vector delineation of relatively homogeneous forest stands or naturally non-forested areas as polygon with a 0.5 ha minimum area and a 2.0 ha median area. For more information, see the Forest Inventory Standard of the Saskatchewan Environmental Code, Forest Inventory Chapter.

This dataset shows the boundaries of the province's six fire management zones that existed prior to 2014 in which most forest fires received the same type of response. These management zones were based on: * common forest and forest fire management objectives * land use * density of values at risk * fire load * forest ecology The 2014 Wildland Fire Management Strategy moved from a zone-based approach to one where each wildland fire is assessed and receives an appropriate response according to the circumstances and condition of the fire.

Terrain contours (TRNCNT) is a vector delineation of areas of equivalent elevation, in 5 m classes, as contour lines.Download: Here The Saskatchewan Ministry of Environment, Forest Service Branch, has developed a forest resource inventory (FRI) which meets a variety of strategic and operational planning information needs for the boreal plains. Such needs include information on the general land cover, terrain, and growing stock (height, diameter, basal area, timber volume and stem density) within the provincial forest and adjacent forest fringe. This inventory provides spatially explicit information as 10 m or 20 m raster grids and as vectors polygons for relatively homogeneous forest stands or naturally non-forested areas with a 0.5 ha minimum area and a 2.0 ha median area.  Terrain contours (TRNCNT) is a vector delineation of areas of equivalent elevation, in 5 m classes, as contour lines.  For more information, see the Forest Inventory Standard of the Saskatchewan Environmental Code, Forest Inventory Chapter.

This feature class represents Manitoba's Forest Management Unit (FMU) boundaries.Forest Management Units (FMU's) define a forested area with common forest conditions that are managed in a similar manner. Forest Sections are comprised of FMU's. Forest inventories within Forest Management Units are analysed to determine allowable harvest limits of softwood and hardwood tree species within each Forest Management Unit. Version 3: The southern portion of FMU 67 within the Highrock Forest Section has been adjusted to align with base features captured in 2009. Additionally, an 11 hectare portion of the Saskatchewan River Forest Section (FMU 59) has been added to the Highrock Forest Section. Version 4: The northern portion of FMU 68 along the Rail Haul within the Highrock Forest Section has been adjust so that the boundary falls within water only. Additionally, version 4 splits the 'White Zone' forest section (FMU 76) by ecozones, creating FMU 76 (Taiga Shield), FMU 77 (Southern Arctic), FMU 78 (Hudson Plain) and FMU 79 (Boreal Shield). Version 4 is dated February 8, 2013. Fields Included: OBJECTID: Sequential unique whole numbers that are automatically generated . MANAGEMENT_UNIT_NUMBER : Management Unit (MU) number . S ECTION : Forest section number . SECTION_NAME : Forest section name .

This spatial data represents the boundaries of Manitoba's forest sections. Forest sections are administrative areas comprised of Forest Management Units (FMU's). There are 14 uniquely named forest sections in Manitoba, 9 of which are capable of growing commercial forests.Manitoba's f orest sections are administrative areas comprised of Forest Management Units (FMU's). There are 14 uniquely named forest sections in Manitoba, 9 of which are capable of growing commercial forests. The Aspen Parkland forest section in the south along with the northern forest sections of Boreal Shield, Taiga Shield, Hudson Plains and Southern Arctic are incapable of growing commercial forests. The four northern forest sections were previously called the 'white zone' and all have retained the previous white zone forest section number of 10. The northern forest section boundaries are based on the following ecozones:Hudson Plains: A subarctic area encompassing the coastal areas of Hudson Bay. The area is formed into a wide, level plain, characterised by poor drainage that has resulted in large and numerous peatlands, lakes, coastal marshes, and tidal flats. Alder, willow, black spruce, and tamarack are the most common tree species.Taiga Shield: Terrain is typically flat or with rolling hills caused by glacial retreat; long eskers and uplands are common. Shallow soils remain damp year-round and regularly freeze and thaw; this leads to tilted growing trees, sometimes called ‘drunken forests’. The northern edge of the forest section is delineated by the tree line. Black spruce, jack pine, birch, tamarack, white spruce, balsam fir, trembling aspen, and balsam poplar are common tree species.Southern Arctic: The southern boundary designated the tree line. Moraines, eskers, kettle lakes, and ponds are common. Permafrost occurs in a continuous sheet throughout the section; polygonal hummocks often result from the freeze and thaw of the soils.Boreal Shield : This forest section represents the upper boundary of the boreal shield ecozone, characterised by long, cold winters and warm summers. Permafrost is widespread. Uplands and lowland tree species are common. Soil varies from poorly drained muskeg to glacially-deposited sand. Coniferous trees include white and black spruce, balsam fir, jack pine, and tamarack; hardwood tree species include birch, trembling aspen, and balsam poplar. Forest fires and insect outbreaks are the natural drivers of forest succession. The ten forest sections south of forest section 10 are sometimes referred to as the 'green zone' and include the following: Pineland, Aspen Parkland, Mountain, Interlake, Lake Winnipeg East, Churchill, Nelson River, Hayes River, Saskatchewan River and Highrock. Fields Included: S ECTION : Forest section number . SECTION_NAME : Forest section name .

Organic soils in the boreal forest commonly store as much carbon as the vegetation above ground. While recent efforts through the National Forest Inventory has yielded new spatial datasets of forest structure across the vast area of Canada’s boreal forest, organic soils are poorly mapped. In this geospatial dataset, we produce a map primarily of forested and treed peatlands, those with more than 40 cm of peat accumulation and over 10% tree canopy cover. National Forest Inventory ground plots were used to identify the range of forest structure that corresponds to the presence of over 40 cm of peat soils. Areas containing that range of forest cover were identified using the National Forest Inventory k-NN forest structure maps and assigned a probability (0-100% as integer) of being a forested or treed peatland according to a statistical model. While this mapping product captures the distribution of forested and treed peatlands at a 250 m resolution, open, completely treeless peatlands are not fully captured by this mapping product as forest cover information was used to create the maps. The methodology used in the creation of this product is described in:Thompson DK, Simpson BN, Beaudoin A. 2016. Using forest structure to predict the distribution of treed boreal peatlands in Canada. Forest Ecology and Management, 372, 19-27. https://cfs.nrcan.gc.ca/publications?id=36751 This distribution uses an updated forest attribute layer current to 2011 from:Beaudoin A, Bernier PY, Villemaire P, Guindon L, Guo XJ. 2017. Species composition, forest properties and land cover types across Canada’s forests at 250m resolution for 2001 and 2011. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/ec9e2659-1c29-4ddb-87a2-6aced147a990 Additionally, this distribution varies slightly from the original published in 2016 in that here slope data is derived from the CDEM: https://open.canada.ca/data/en/dataset/7f245e4d-76c2-4caa-951a-45d1d2051333 The above peatland probability map was further processed to delineate bogs vs fens (based on mapped Larix content via the k-NN maps), as well as an approximation of the extent of open peatlands using EOSD data. The result is a 9-type peatland map with a more complete methodology as detailed in: Webster, K. L., Bhatti, J. S., Thompson, D. K., Nelson, S. A., Shaw, C. H., Bona, K. A., Hayne, S. L., & Kurz, W. A. (2018). Spatially-integrated estimates of net ecosystem exchange and methane fluxes from Canadian peatlands. Carbon Balance and Management, 13(1), 16. https://doi.org/10.1186/s13021-018-0105-5 In plain text, the legend for the 9-class map is as follows:value="0" label="not peat" alpha="0"value="1" label="Open Bog" alpha="255" color="#0a4b32"value="2" label="Open Poor Fen" alpha="255" color="#5c5430"value="3" label="Open Rich Fen" alpha="255" color="#792652"value="4" label="Treed Bog" alpha="255" color="#6a917b"value="5" label="Treed Poor Fen" alpha="255" color="#aba476"value="6" label="Treed Rich Fen" alpha="255" color="#af7a8f"value="7" label="Forested Bog" alpha="255" color="#aad7bf"value="8" label="Forested Poor Fen" alpha="255" color="#fbfabc"value="9" label="Forested Rich Fen" alpha="255" color="#ffb6db"This colour scale is given in qml/xml format in the resources below. The 9-type peatland map from Webster et al 2018 was further refined slightly following two simple conditions: (1) any 250-m raster cell with greater than 40% pine content is classified as upland (non-peat); (2) all 250-m raster cells classified as water or agriculture via the NRCan North American Land Cover Monitoring System (https://doi.org/10.3390/rs9111098) is also classified as non-peatland (value of zero in the 9-class map. This mapping scheme was used at a regional scale in the following paper: Thompson, D. K., Simpson, B. N., Whitman, E., Barber, Q. E., & Parisien, M.-A. (2019). Peatland Hydrological Dynamics as A Driver of Landscape Connectivity and Fire Activity in the Boreal Plain of Canada. Forests, 10(7), 534. https://doi.org/10.3390/f10070534 And is reproduced here at a national scale. Note that this mapping product does not fully capture all permafrost peatland features covered by open canopy spruce woodland with lichen ground cover. Nor are treeless peatlands near the northern treeline captured in the training data, resulting in unknown mapping quality in those regions.