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We have found 667 datasets for the keyword "forest". You can continue exploring the search results in the list below.
Datasets: 103,466
Contributors: 42
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667 Datasets, Page 1 of 67
FRI: Forest stands
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.
FADM - Provincial Forest Addition
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
Wooded areas
This dataset is often used by users without access to the Provincial Forest Resource Inventory data, which contains information like municipalities and non-profit groups not affiliated with monitoring or maintaining the Forest Resource Inventory.
Forest Management Units
An administrative unit of forest land designated by the Minister, as authorized under Section 14(1) of the Forests Act.
VRI - 2024 - Forest Vegetation Composite Rank 1 Layer (R1)
Geospatial forest inventory dataset updated for depletions, such as harvesting, and projected annually for growth. Sample attributes in this dataset include: age, species, volume, height. The Vegetation Resources Inventory (VRI) spatial datasets describe both where a vegetation resource (ie timber volume, tree species) is located and how much of a given resource is within an inventory unit. Suggested citation: Forest Analysis and Inventory Branch (2024). VRI - 2024 - Forest Vegetation Composite Rank 1 Layer (R1). British Columbia Data Catalogue. https://catalogue.data.gov.bc.ca/dataset/2ebb35d8-c82f-4a17-9c96-612ac3532d55
Forest Basal Area 2015
Forest Basal Area 2015Cross-sectional area of tree stems at breast height. The sum of the cross-sectional area (i.e. basal area) of each tree in square metres in a plot, divided by the area of the plot (ha) (units = m2ha). 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
Vegetation Inventory - 40k
This feature delineates forest and vegetation stands in the Yukon at a scale of 1: 40 ,000. It is a management level forest inventory (as opposed to a n operational level) - meaning that analysis and mapping are most effective close to the 1:40,000 scale and not larger . This inventory has been completed in various stages : delineation from hardcopy black and white photographs took place from 1987 to 2002; while recent data collection has proceeded through a digital (aka 'softcopy') methodology of scanned photographs and digital elevation models.Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)
SCANFI: the Spatialized CAnadian National Forest Inventory data product
This data publication contains a set of 30m resolution raster files representing 2020 Canadian wall-to-wall maps of broad land cover type, forest canopy height, degree of crown closure and aboveground tree biomass, along with species composition of several major tree species. The Spatialized CAnadian National Forest Inventory data product (SCANFI) was developed using the newly updated National Forest Inventory photo-plot dataset, which consists of a regular sample grid of photo-interpreted high-resolution imagery covering all of Canada’s non-arctic landmass. SCANFI was produced using temporally harmonized summer and winter Landsat spectral imagery along with hundreds of tile-level regional models based on a novel k-nearest neighbours and random forest imputation method. A full description of all methods and validation analyses can be found in Guindon et al. (2024). As the Arctic ecozones are outside NFI’s covered areas, the vegetation attributes in these regions were predicted using a single random forest model. The vegetation attributes in these arctic areas could not be rigorously validated. The raster file « SCANFI_aux_arcticExtrapolationArea.tif » identifies these zones.SCANFI is not meant to replace nor ignore provincial inventories which could include better and more regularly updated inputs, training data and local knowledge. Instead, SCANFI was developed to provide a current, spatially-explicit estimate of forest attributes, using a consistent data source and methodology across all provincial boundaries and territories. SCANFI is the first coherent 30m Canadian wall-to-wall map of tree structure and species composition and opens novel opportunities for a plethora of studies in a number of areas, such as forest economics, fire science and ecology.# Limitations1- The spectral disturbances of some areas disturbed by pests are not comprehensively represented in the training set, thus making it impossible to predict all defoliation cases. One such area, severely impacted by the recent eastern spruce budworm outbreak, is located on the North Shore of the St-Lawrence River. These forests are misrepresented in our training data, there is therefore an imprecision in our estimates.2- Attributes of open stand classes, namely shrub, herbs, rock and bryoid, are more difficult to estimate through the photointerpretation of aerial images. Therefore, these estimates could be less reliable than the forest attribute estimates.3- As reported in the manuscript, the uncertainty of tree species cover predictions is relatively high. This is particularly true for less abundant tree species, such as ponderosa pine and tamarack. The tree species layers are therefore suitable for regional and coarser scale studies. Also, the broadleaf proportion are slightly underestimated in this product version.4- Our validation indicates that the areas in Yukon exhibit a notably lower R2 value. Consequently, estimates within these regions are less dependable. 5- Urban areas and roads are classified as rock, according to the 2020 Agriculture and Agri-Food Canada land-use classification map. Even though those areas contain mostly buildings and infrastructure, they may also contain trees. Forested urban parks are usually classified as forested areas. Vegetation attributes are also predicted for forested areas in agricultural regions.Updates of this dataset will eventually be available on this metadata page.# Details on the product development and validation can be found in the following publication:Guindon, L., Manka, F., Correia, D.L.P., Villemaire, P., Smiley, B., Bernier, P., Gauthier, S., Beaudoin, A., Boucher, J., and Boulanger, Y. 2024. A new approach for Spatializing the Canadian National Forest Inventory (SCANFI) using Landsat dense time series. Can. J. For. Res. https://doi.org/10.1139/cjfr-2023-0118# Please cite this dataset as: Guindon L., Villemaire P., Correia D.L.P., Manka F., Lacarte S., Smiley B. 2023. SCANFI: Spatialized CAnadian National Forest Inventory data product. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/18e6a919-53fd-41ce-b4e2-44a9707c52dc # The following raster layers are available:• NFI land cover class values: Land cover classes include Water, Rock, Bryoid, Herbs, Shrub, Treed broadleaf, Treed mixed and Treed conifer• Aboveground tree biomass (tonnes/ha): biomass was derived from total merchantable volume estimates produced by provincial agencies• Height (meters): vegetation height• Crown closure (%): percentage of pixel covered by the tree canopy• Tree species cover (%): estimated as the proportion of the canopy covered by each tree species: o Balsam fir tree cover in percentage (Abies balsamea) o Black spruce tree cover in percentage (Picea mariana) o Douglas fir tree cover in percentage (Pseudotsuga menziesii) o Jack pine tree cover in percentage (Pinus banksiana) o Lodgepole pine tree cover in percentage (Pinus contorta) o Ponderosa pine tree cover in percentage (Pinus ponderosa) o Tamarack tree cover in percentage (Larix laricina) o White and red pine tree cover in percentage (Pinus strobus and Pinus resinosa) o Broadleaf tree cover in percentage (PrcB) o Other coniferous tree cover in percentage (PrcC)
Vegetation Inventory - 5k - Leading Species
This feature delineates forest and vegetation stands in the Yukon at a scale of 1:5,000. It is an operational level forest inventory (as opposed to a management level). This inventory has been completed in various stages, between 2013 and 2014, and delineation via softcopy from stereo images acquired in the years 2007 and 2012. The aerial images used for the Haines Junction region (Champagne and Aishihik Traditional Territory) had a ground sample distance (GSD) of 40 cm and were collected in both color and infrared. The aerial imagery in the southcentral Yukon were 1:40,000 black and white hard copy images, scanned at 60 microns or approximately 1m GSD.Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)
Woodlands Improvement Act areas
Data from the former Kemptville, Midhurst and Aylmer Districts were compiled in this dataset to show forest areas on private land, managed by the landowner in cooperation with the Ontario Ministry of Natural Resources under the Woodlands Improvement Act (WIA). The WIA allowed for 15-year management agreements between landowners and the MNR to plant and improve woodlands on private land. The WIA was in place from 1966-1999. This data is being provided as-is. No additional data is available, and no updates or maintenance are planned for this dataset.
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