This data set is a condensed forest cover type digital map of Saskatchewan and is a product of the Saskatchewan Environment and Resource Management, Forestry Branch - Inventory Unit (SERM-FBIU). This map was generalized from SERM township maps of vegetation cover at an approximate scale of 1:63,000 (1 in. = 1 mile). The cover information was iteratively generalized until it was compiled on a 1:1,000,000 scale map base. This data set was prepared by SERM-FBIU. The data is a condensed forest cover type map of Saskatchewan at a scale of 1:1,000,000.

At this scale 1cm on the map represents 1km on the ground. Each map covers a minimum area of 0.5 degrees longitude by 0.5 degrees latitude or about 54 kilometres by 54 kilometres. The contour …Show full descriptionAt this scale 1cm on the map represents 1km on the ground. Each map covers a minimum area of 0.5 degrees longitude by 0.5 degrees latitude or about 54 kilometres by 54 kilometres. The contour interval is 20 metres. Many maps are supplemented by hill shading. These maps contain natural and constructed features including road and rail infrastructure, vegetation, hydrography, contours, localities and some administrative boundaries. Product Specifications Coverage: Australia is covered by more than 3000 x 1:100 000 scale maps, of which 1600 have been published as printed maps. Unpublished maps are available as compilations. Currency: Ranges from 1961 to 2009. Average 1997. Coordinates: Geographical and either AMG or MGA coordinates. Datum: AGD66, GDA94; AHD Projection: Universal Transverse Mercator UTM. Medium: Printed maps: Paper, flat and folded copies. Compilations: Paper or film, flat copies only.

This data set provides high-resolution (1-m) tree canopy cover for states in the Northeast USA. State-level canopy cover data are currently available for Pennsylvania (data for nominal year 2008), Delaware (2014), and Maryland (2013). The data were derived with a rules-based expert system which facilitated integration of leaf-on LiDAR and imagery data into a single classification workflow, exploiting the spectral, height, and spatial information contained in the datasets. Additional states will be added as data processing is completed.

The LULC HRL Map is produced from a combination of multi-sources data: the French national topographic database; the Land Parcel Identification System (LPIS) database; and Corine Land Cover. The LULC HRL classification contains 11 land cover categories: 11 Industrial or Commercial buildings and other Facilities 12 Agricultural buildings 13 Low-rise Residential or Mixed buildings 14 High-rise Residential or Mixed buildings 2 Fields 3 Meadows/Grassy plots 4 Bushes/Shrubs 5 Trees/Forest 6 Vineyards 7 Water bodies 8 Others artificial surfaces The LULC HRL Map is produced from a combination of multi-sources data: the French national topographic database (Institut national de l’information géographique et forestière, the French national geographic institute); the Land Parcel Identification System (LPIS) database (Agency for Services and Payment, French public institution responsible for the implementation of national and European public policies; Integrated Administration and Control System, European Union); and Corine Land Cover (European Environment Agency, Joint Research Center, European Union). The topographic database contains a land cover description employed for topographic map production at a scale of 1:25 000, with a minimum unit of collection of approximately 8 ha. The information is relatively precise on the contours of urban areas (buildings), road and rail infrastructures, hydrography, and trees and shrubs; however, it does not make it possible to distinguish the land uses within the agricultural, forested, or natural areas. The LPIS database, which draws on the digital cadastral database (1:500–1:5000), allows us to identify those agricultural areas for which subsidies are sought under the European Common Agricultural Policy (CAP). It was used to determine the agricultural land-use (grass-like vegetation and arable land) on the scale of cadastral parcels. Corine Land Cover (CLC) is thematically much richer, in particular in agri- cultural areas, but its spatial resolution, which is rather coarse (approximately 1:100 000), means it cannot identify the nature of a polygon of less than 25 ha. Despite its rather coarse resolution, CLC has a thematically richer land-use nomenclature than can be used to refine plant cover. The land-cover information layer was constructed in two steps. The first was to generate a simplified geometry of land use in vector form (polygons and lines). The operation begins by detecting the “polygonal skeleton” that integrates roads, railways, and the hydrographic network attributing to them a footprint proportional to their width. Next are added (1) agricultural surface features from the LPIS (field, meadow, orchard, other agriculture use); (2) plant-covered areas, mostly forest and orchard; and (3) artificialized surfaces (buildings, quarries, parking areas, etc.). Each addition is made by masking and expansion so as to approximate the “polygonal skeleton”. The features not described in the topographic database and the LPIS are categorized as “unidentified polygons”. Some of this class is marked down as grassland-lawn using CLC classes “321” (Natural grasslands) and “231” (Pastures). Processing is done with the PostGIS functionalities: intersection, union, dilation, erosion, etc. of polygons or lines (PostGIS, 2018). This stage enables eight land-use categories to be defined: (1) urban footprints, (2) fields, (3) meadows, (4) forests, (5) orchards, (6) rivers and water bodies, (7) road and rail infrastructure footprints, (8) unidentified polygons. This first vectorial geometric model is changed into a 5m resolution raster layer and then supplemented to produce a land-use layer com- patible with the landscape analysis contemplated. Categories (4), (6) and (7) describing relatively homogeneous and straightforward landscape features were kept unchanged. The improvement described below was primarily for heterogeneous and complex landscape features (categories (1), (2), (3) and (5)) that are replaced by simple landscape objects (buildings, mineral surfaces, copses, fields, grass-covered areas, etc.). The improvement also covers pixels in category (8). Pixels of the urban footprint (1) are differentiated into three types of landscape items: the built area, parking areas, and urban plant cover. The built area is incrusted by distinguishing its height and function: (11, LRM) Low-rise Residential or Mixed buildings (< 12m∼1–2 storeys); (12, HRM) High-rise Residential or Mixed buildings (≥12m∼3 storeys and more); (13, ICF) Industrial or Commercial buildings and other Facilities; (14) agricultural buildings. Parking areas were also created around some buildings and classified as category (7): a 5m (1 pixel) buffer around HRM polygons and ICF polygons between 50 and 999m2; a 25m buffer for ICF polygons of 1000m2 (5 pixels) and more. The buffer sizes were established from existing planning and building codes. Non-built and non-parking areas in the urban footprint are converted into plant cover in the following proportions: grass 50% of pixels; Trees 25%; shrubs and bushes 25%. These proportions are based on the visual identification and quantification of green areas/ expanses in built the environment using orthophoto images. This is done by first converting non-built and non-parking areas into grass pixels and then drawing tree pixels and shrub and bush pixels at random. For the field (2) and meadow (3) categories identified with tree cover (presence of trees in CLC), 10% of randomly drawn pixels are converted into trees. The pixels classified as orchards (5) and that are within a polygon classified as vineyard (221) in CLC are reclassified as vineyard. The remaining pixels are first converted into grass and then into shrubs and bushes by randomly drawing 70% of the pixels. Pixels in category (8), “unidentified polygons”, are reclassified by comparison with the CLC polygons.

At this scale 1cm on the map represents 1km on the ground. Each map covers a minimum area of 0.5 degrees longitude by 0.5 degrees latitude or about 54 kilometres by 54 kilometres. The contour …Show full descriptionAt this scale 1cm on the map represents 1km on the ground. Each map covers a minimum area of 0.5 degrees longitude by 0.5 degrees latitude or about 54 kilometres by 54 kilometres. The contour interval is 20 metres. Many maps are supplemented by hill shading. These maps contain natural and constructed features including road and rail infrastructure, vegetation, hydrography, contours, localities and some administrative boundaries. Product Specifications Coverage: Australia is covered by more than 3000 x 1:100 000 scale maps, of which 1600 have been published as printed maps. Unpublished maps are available as compilations. Currency: Ranges from 1961 to 2009. Average 1997. Coordinates: Geographical and either AMG or MGA coordinates. Datum: AGD66, GDA94; AHD Projection: Universal Transverse Mercator UTM. Medium: Printed maps: Paper, flat and folded copies. Compilations: Paper or film, flat copies only.

We matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding.

The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) is an ongoing multiinstitutional, international effort addressing the response of biogeography and biogeochemistry to environmental variability in climate and other drivers in both space and time domains. The objectives of VEMAP are the intercomparison of biogeochemistry models and vegetationtype distribution models (biogeography models) and determination of their sensitivity to changing climate, elevated atmospheric carbon dioxide concentrations, and other sources of altered forcing. The vegetation data set includes one variable: vegetation type. Vegetation types are defined physiognomically in terms of dominant lifeform and leaf characteristics (including leaf seasonal duration, shape, and size) and, in the case of grasslands, physiologically with respect to dominance of species with the C3 versus C4 photosynthetic pathway. The physiognomic classification criteria are based on our understanding of vegetation characteristics that influence biogeochemical dynamics (Running et al. 1994). The U.S. distribution of these types is based on a 0.5 degree latitude/longitude gridded map of Kuchler's (1964, 1975) potential natural vegetation provided by the TEM group (D. Kicklighter and A.D. McGuire, personal communication). Kuchler's map is based on current vegetation and historical information and, for purposes of VEMAP Phase I model experiments, is presumed to represent potential vegetation under current climate and atmospheric CO2 concentrations (355 ppm). A complete users guide to the VEMAP Phase I database which includes more information about this data set can be found at ftp://daac.ornl.gov/data/vemap-1/comp/Phase_1_User_Guide.pdf. ORNL DAAC maintains additional information associated with the VEMAP Project. Data Citation: This data set should be cited as follows: Kittel, T. G. F., N. A. Rosenbloom, T. H. Painter, D. S. Schimel, H. H. Fisher, A. Grimsdell, VEMAP Participants, C. Daly, and E. R. Hunt, Jr. 1998. VEMAP Phase I Database, revised. Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.

This dataset shows the forest cover on the 3rd edition of the topographic maps at 1:20,000 (1910 - 1940). Very suitable as a supplement to the forest age map or for specific research such as locating deforestations. Use for detailed studies is not recommended, except for site exploration. The 3rd edition of the topographic maps show geographical deviations. This map was selected for vectorization of historic forests because automation was possible. As a source of information, the map has the disadvantage that it was created over a long period of time as a result of WWI. East and West Flanders (including 1:50,000 numbers 15, 22, 30 and 38) were mapped around 1910. The eastern half of Flanders was mapped in the 1920s and 1930s. Some map sheets (19/7, 19/8, 20/5, 28/8 and 30/3) were not even re-mapped in the early 20th century and therefore reflect the situation around 1885.

The Prince of Wales Existing Vegetation mapping project encompasses over 4.2 million acres of Southeastern Alaska—2.3 million of which are terrestrial. This map was designed to be consistent with the standards established in the Existing Vegetation Classification and Technical Guide (Nelson et al. 2015), and to provide baseline information to support project planning and inform land management of the Prince of Wales and surrounding islands. The final map comprises seven distinct, integrated feature layers: 1) vegetation type; 2) tree canopy cover; 3) trees per acre (TPA) for trees ≥ 1’ tall; 4) trees per acre for trees ≥ 6” diameter at breast height (dbh); 5) quadratic mean diameter (QMD) for trees ≥ 2” dbh; 6) quadratic mean diameter for trees ≥ 9” dbh; and 7) thematic tree size. The dominance type map consists of 18 classes, including 15 vegetation classes and 3 other land cover types. Continuous tree canopy cover, TPA, QMD, and thematic tree size was developed for areas classified as forest on the final vegetation type map layer. Geospatial data, including remotely sensed imagery, topographic data, and climate information, were assembled to classify vegetation and produce the maps. A semi-automated image segmentation process was used to develop the modeling units (mapping polygons), which delineate homogeneous areas of land cover. Field plots containing thematic vegetation type and tree size information were used as reference for random forest prediction models. Important model drivers included 30 cm orthoimagery collected during the height of the 2019 growing season, in addition to Sentinel 2 and Landsat 8 satellite imagery, for vegetation type prediction. Additionally, detailed tree inventory data were collected at precise field locations to develop forest metrics for Quality Level 1 (QL1) Light Detection and Ranging (LiDAR) data. LiDAR information was acquired across approximately 80% of the project’s land area. Continuous tree canopy cover and 2^nd order forest metrics (TPA and QMD) were modeled across the LiDAR coverage area, and subsequently, extrapolated to the full project extent using Interferometric Synthetic Aperture Radar (IfSAR) as the primary topographic data source.

Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

References:

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.

The map of the protected natural forests in the Grand-Duchy of Luxembourg is the result of a cartography of private and public forests in the framework of which data on forest formations protected under article 17 of the amended law of the 19th January 2004 concerning the protection of nature and natural resources (Nature Protection Law) were collected. The cartography was compiled in 2014 and is based on phytosociological field inventories of all the forest formations in the Grand-Duchy, realised during the years 1992-2002. None of the data collected and mapped between 1992 and 2002 have been updated by further inventories or by verification in the field. The “2014” map of protected natural forests thus reflects the situation as recorded during the years 1992-2002. Due to natural developments and changes induced through forest management since the original inventories (around 20 years ago), the actual situation encountered in the field today can differ from that shown in the “2014” map. The map can therefore only serve as a support tool for forest owners in the framework of article 17 conform forest management of natural forests protected by Nature Protection Law. In any case, the information it contains must be confirmed and, if necessary, updated in the field. Some protected forest formations have not been mapped in the original cartography and are therefore not represented in the simplified map: i.e. forest borders, copses and conversion or transformation states of coppice to high forest. Neither are small-area biotopes such as sources, natural ponds, rock formations, and so on represented on the map. They are nevertheless subject to protection under article 17 of the Nature Protection Law. All forest formations that are not protected under article 17 figure on the map as seen on the topographic maps of the Administration du Cadastre et de la Topographie. The guidance and best practice note ("Leitfaden für forstliche Bewirtschaftungs- und Pflegemaßnahmen von geschützten Waldbiotopen"), available online on the site of the Ministère du Développement Durable et des Infrastructures (http://www.environnement.public.lu/forets/dossiers/pfn/documents/Leitfaden_7_11_2014.pdf ), contains forest management recommendations helping to avoid the destruction, deterioration or degradation of the protected forests.

This historical map series consists of Forest Cover printed monochrome maps named using the National Topographic System (NTS) map sheet identifier. This series is not updated and contains a range of publication dates. The Forest Cover maps were created at a scale of 1:100 000.

At this scale 1cm on the map represents 1km on the ground. Each map covers a minimum area of 0.5 degrees longitude by 0.5 degrees latitude or about 54 kilometres by 54 kilometres. The contour interval is 20 metres. Many maps are supplemented by hill shading.

A woody perennial plant, having a self-supporting main stem or trunk.

Data Dictionary for tree_pnt: https://docs.topo.linz.govt.nz/data-dictionary/tdd-class-tree_pnt.html

This layer is a component of the Topo50 map series. The Topo50 map series provides topographic mapping for New Zealand and it's offshore dependancies, at 1:50,000. For some small islands the printed map scale is 1:25,000. Although presented at 1:25,000 this layer, for all intents and purposes, forms part of the Topo50 map series.

Further information on Topo50: http://www.linz.govt.nz/topography/topo-maps/topo50

This data publication contains two collections of raster maps of forest attributes across Canada, the first collection for year 2001, and the second for year 2011. The 2001 collection is actually an improved version of an earlier set of maps produced also for year 2001 (Beaudoin et al 2014, DOI: https://doi.org/10.1139/cjfr-2013-0401) that is itself available through the web site “http://nfi-nfis.org”. Each collection contains 93 maps of forest attributes: four land cover classes, 11 continuous stand-level structure variables such as age, volume, biomass and height, and 78 continuous values of percent composition for tree species or genus. The mapping was done at a spatial resolution of 250m along the MODIS grid. Briefly the method uses forest polygon information from the first version of photoplots database from Canada’s National Forest Inventory as reference data, and the non-parametric k-nearest neighbors procedure (kNN) to create the raster maps of forest attributes. The approach uses a set of 20 predictive variables that include MODIS spectral reflectance data, as well as topographic and climate data. Estimates are carried out on target pixels across all Canada treed landmass that are stratified as either forest or non-forest with 25% forest cover used as a threshold. Forest cover information was extracted from the global forest cover product of Hansen et al (2013) (DOI: https://doi.org/10.1126/science.1244693). The mapping methodology and resultant datasets were intended to address the discontinuities across provincial borders created by their large differences in forest inventory standards. Analysis of residuals has failed to reveal residual discontinuities across provincial boundaries in the current raster dataset, meaning that our goal of providing discontinuity-free maps has been reached. The dataset was developed specifically to address strategic issues related to phenomena that span multiple provinces such as fire risk, insect spread and drought. In addition, the use of the kNN approach results in the maintenance of a realistic covariance structure among the different variable maps, an important property when the data are extracted to be used in models of ecosystem processes. For example, within each pixel, the composition values of all tree species add to 100%. Details on the product development and validation can be found in the following publication: Beaudoin, A., Bernier, P.Y., Villemaire, P., Guindon, L., Guo, X.-J. 2017. Tracking forest attributes across Canada between 2001 and 2011 using a kNN mapping approach applied to MODIS imagery, Canadian Journal of Forest Research 48: 85–93. DOI: https://doi.org/10.1139/cjfr-2017-0184 Please cite this dataset as: 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 This dataset contains these NFI forest attributes: ## LAND COVER : landbase vegetated, landbase non-vegetated, landcover treed, landcover non-treed ## TREE STRUCTURE : total above ground biomass, tree branches biomass, tree foliage biomass, stem bark biomass, stem wood biomass, total dead trees biomass, stand age, crown closure, tree stand heigth, merchantable volume, total volume ## TREE SPECIES : abies amabilis (amabilis fir), abies balsamea (balsam fir), abies lasiocarpa (subalpine fir), abies spp. (unidentified fir), acer macrophyllum (bigleaf maple), acer negundo (manitoba maple, box-elder), acer pensylvanicum (striped maple), acer rubrum (red maple),

We matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding.

This data set is a 1:2.5 million scale forest cover map for the land area of the Former Soviet Union that was completed in 1990 (Garsia 1990). There are forty-five classes distinguished in this data set, of which 38 are forest cover classes. The purpose of this map was to create a generalized and up-to-date map of forest cover for the USSR. This map should not be viewed as a detailed forest cover map but more like an economic forestry map. The most important tree species of a region are highlighted rather than the dominant trees species or tree cover. Very few tree species are defined. In many cases, of course, the dominant and the most important trees species are the same.

This data layer references data from a high-resolution tree canopy change-detection layer for Seattle, Washington. Tree canopy change was mapped by using remotely sensed data from two time periods (2016 and 2021). Tree canopy was assigned to three classes: 1) no change, 2) gain, and 3) loss. No change represents tree canopy that remained the same from one time period to the next. Gain represents tree canopy that increased or was newly added, from one time period to the next. Loss represents the tree canopy that was removed from one time period to the next. Mapping was carried out using an approach that integrated automated feature extraction with manual edits. Care was taken to ensure that changes to the tree canopy were due to actual change in the land cover as opposed to differences in the remotely sensed data stemming from lighting conditions or image parallax. Direct comparison was possible because land-cover maps from both time periods were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to manual review and correction.

University of Vermont Spatial Analysis Laboratory in collaboration with City of Seattle.

This dataset consists of City of Seattle Topo Basins areas which cover the following tree canopy categories:

• Existing tree canopy percent
• Possible tree canopy - vegetation percent
• Relative percent change
• Absolute percent change

For more information, please see the 2021 Tree Canopy Assessment.

The link: * Access the data directory* is available in the section*Dataset description sheets; Additional information*. Ecoforest maps in PDF format are available at a scale of 1/20,000 and cover Quebec territory approximately up to the 52nd parallel. Each map covers an average area of approximately 250 km2 and presents forest information for the territory concerned. Its accuracy is approximately 10 meters. These maps are an image of the current ecoforest map for the corresponding year. Maps 2021, 2022, 2023 and 2024 The maps 2021, 2022, 2023, as well as those of 2024 are produced from the up-to-date ecoforest map of the corresponding year. It represents the result of the photo-interpretation of aerial photographs taken during the 4th and 5th ecoforestry inventories of southern Quebec to which were added natural disturbances (fires, epidemics, windfalls, etc.) and forest interventions (harvesting and reforestation) carried out in the public forest following the year in which the picture was taken. According to the maps, data using the forest stand inventory approach (AIPF) is included when available for a complete sheet. Main components: type of vegetation (forest species group, density class, class of density, class of height, age class, etc.) or type of AIPF vegetation (detailed forest species, density (%), height (m), age class, etc.); slope class; class of slope; nature of the terrain (peatlands, height class, age class, etc.); topography (level curves); fragmentation. 2019 and 2020 maps The 2019 maps, as well as those of 2020, are produced from up-to-date ecoforest maps of the corresponding year. They represent the result of the photo-interpretation of aerial photographs taken during the 4th and 5th ecoforestry inventories of southern Quebec to which were added natural disturbances (fires, epidemics, windfalls, etc.) and forest interventions (harvesting and reforestation) carried out in the public forest following the year in which the photo-interpretation of aerial photographs from the 4th and 5th ecoforestry inventories of southern Quebec were taken. An update is then carried out taking into account natural disturbances (fires, epidemics, windfalls, etc.) and forest interventions (harvesting and reforestation) carried out in public forests. The information presented corresponds to the current ecoforest map of 2019 or 2020 as the case may be. On each of the maps, the name of the stands is expressed by the group of species. Main components: type of vegetation (forest species, density, height, height, age class, etc.); slope class; nature of the terrain (peatlands, gravel, etc.); hydrography (lakes, rivers, streams, streams, swamps, etc.); transport network and swamps, etc.); transport network and bridges; topography (level curves); fragmentation. Maps 2015 The 2015 maps are produced from the photo-interpretation of aerial photographs from the 3rd and 4th ecoforest inventories of southern Quebec. An update is then carried out taking into account natural disturbances (fires, epidemics, windfalls, etc.) and forest interventions (cutting and planting) carried out in public forests. The information presented corresponds to the 2015 updated ecoforest map. MAP FOR PRINTING (GEOREFERENCED)**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.