This dataset was generated by the TU Wien Department of Geodesy and Geoinformation.European Sentinel-1 forest type and tree cover density maps represent first continental-scale forest layers based on Sentinel-1 C-Band Synthetic Aperture Radar (SAR) backscatter data. For the year 2017 they cover the majority of European continent with 10 m and 100 m sampling for forest type and tree cover density, respectively. The maps were derived using the method described in https://www.tandfonline.com/doi/full/10.1080/01431161.2018.1479788.The forest type map shows the dominant forest type class (coniferous, broadleaf). Tree cover density map shows the percentage of forest canopy cover within the 100 m pixel.Please be referred to our peer-reviewed article at https://doi.org/10.3390/rs13030337 for details and accuracy assessment accross Europe.Dataset RecordThe forest type and tree cover density maps are sampled at 10 m and 100 m pixel spacing respectively, georeferenced to the Equi7Grid and divided into square tiles of 100km extent ("T1"-tiles). With this setup, the forest maps consist of 728 tiles over the European continent, with data volumes of 3.12 GB and 378.3 MB.The tiles' file-format is a LZW-compressed GeoTIFF holding 16-bit integer values, with tagged metadata on encoding and georeference. Compatibility with common geographic information systems as QGIS or ArcGIS, and geodata libraries as GDAL is given.In this repository, we provide each forest map as tiles, whereas two zipped dataset-collections are available for download below.Code AvailabilityFor the usage of the Equi7Grid we provide data and tools via the python package available on GitHub at https://github.com/TUW-GEO/Equi7Grid. More details on the grid reference can be found in https://www.sciencedirect.com/science/article/pii/S0098300414001629.AcknowledgementsThe computational results presented have been achieved using the Vienna Scientific Cluster (VSC).

This data publication contains raster maps of live tree basal area for each tree species along with corresponding assessment data. An efficient approach for mapping multiple individual tree species over large spatial domains was used to develop these raster datasets. The method integrates vegetation phenology derived from MODIS imagery and raster data describing relevant environmental parameters with extensive field plot data of tree species basal area to create maps of tree species abundance and distribution at a 250-meter (m) pixel size for the contiguous United States. The approach uses the modeling techniques of k-nearest neighbors and canonical correspondence analysis, where model predictions are calculated using a weighting of nearest neighbors based on proximity in a feature space derived from the model. The approach also utilizes a stratification derived from the 2001 National Land-Cover Database tree canopy cover layer.The mapping methodology and resultant datasets were intended to address three major issues. 1) Land use policy decisions are often made at the landscape scale because landscape processes, like risk of forest pests or fire, occur over large areas. 2) Distribution and abundance information is often needed for individual species as opposed to forest types because individual species can play significant roles in natural systems, may have high economic impact, or may be indicators for ecosystem health. 3) The maintenance of a realistic species covariance structure across a set of maps of individual species is important because species assemblage information is used in coarse scale modeling of ecosystem processes like response to disturbance, urbanization, and climate change.Original metadata date was 09/09/2013. Minor metadata updates on 12/15/2016.

This data set, a collaboration between the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland, Google, USGS, and NASA, displays tree cover over all global land (except for Antarctica and a number of Arctic islands) for the year 2000 at 30 × 30 meter resolution. “Percent tree cover” is defined as the density of tree canopy coverage of the land surface and is color-coded by density bracket (see legend).Data in this layer were generated using multispectral satellite imagery from the Landsat 7 thematic mapper plus (ETM+) sensor. The clear surface observations from over 600,000 images were analyzed using Google Earth Engine, a cloud platform for earth observation and data analysis, to determine per pixel tree cover using a supervised learning algorithm.The tree cover canopy density of the displayed data varies according to the selection - use the legend on the map to change the minimum tree cover canopy density threshold.

This metadata refers to the HRL Forest 2018 primary status layer Tree Cover Density (TCD). The TCD raster product provides information on the proportional crown coverage per pixel at 10m spatial resolution and ranges from 0% (all non-tree covered areas) to 100%, whereby Tree Cover Density is defined as the "vertical projection of tree crowns to a horizontal earth’s surface“. The production of the High Resolution Forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. The HRL Forest product consists of 3 types of (status) products and additional change products. The status products are available for 2012, 2015, and 2018 reference years: 1. Tree cover density (TCD) (level of tree cover density in a range from 0-100%) 2. Dominant leaf type (DLT) (broadleaved or coniferous majority) 3. Forest type product (FTY). The forest type product allows to get as close as possible to the FAO forest definition. In its original (10m (2018) / 20m (2012, 2015)) resolution it consists of two products: a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps (now only available on demand), based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and imperviousness 2009 data). For the final 100 m product trees under agricultural use and urban context from the support layer are removed. NEW for 2018: the 10m 2018 reference year FTY product now also has the agricultural/urban trees removed. In the past this was done only for the 100m product, now it is consistently applied for both the 10m and the 100m FTY products. This dataset is provided as 10 meter rasters (fully conformant with the EEA reference grid) in 100 x 100 km tiles grouped according to the EEA38 countries and the United Kingdom.

Here we present three datasets describing three large European landscapes in France (Bauges Geopark - 89 000 ha), Poland (Milicz forest district - 21 000 ha) and Slovenia (Snežnik forest - 4700 ha) down to the tree level. Individual trees were generated combining inventory plot data, vegetation maps and Airborne Laser Scanning (ALS) data. Together, these landscapes (hereafter virtual landscapes) cover more than 100 000 ha including about 64 000 ha of forest and consist of more than 42 million trees of 51 different species.

For each virtual landscape we provide a table (in .csv format) with the following columns:
- cellID25: the unique ID of each 25x25 m² cell
- sp: species latin names
- n: number of trees
- dbh: tree diameter at breast height (cm)
- h: tree height (m)

We also provide, for each virtual landscape, a raster (in .asc format) with the cell IDs (cellID25) which makes data spatialisation possible.

Finally, we provide a proof of how multiplying the trees dbh by the α correction coefficient makes it possible to reach the cells BA value derived from the ALS mapping (see algorithm presented in the associated Open Research Europe article).

Below is an example of R code that opens the datasets and creates a tree density map.

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# load package
library(raster)
library(dplyr)

# set work directory
setwd() # define path to the I-MAESTRO_data folder

# load tree data
tree <- read.csv2('./milicz/trees.csv', sep = ',')

# load spatial data
cellID <- raster('./milicz/cellID25.asc')

# convert raster into dataframe
cellIDdf <- as.data.frame(cellID)

# calculate tree density from tree dataframe
dens <- tree %>% group_by(cellID25) %>% summarise(n = sum(n))

# merge the two dataframes
dens <- left_join(cellIDdf, dens)

# add density to raster
cellID$dens <- dens$n

# plot density map
plot(cellID$dens)

This data set, a collaboration between the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland, Google, USGS, and NASA, measures areas of tree cover loss across all global land (except Antarctica and other Arctic islands) at approximately 30 × 30 meter resolution. The data were generated using multispectral satellite imagery from the Landsat 5 thematic mapper (TM), the Landsat 7 thematic mapper plus (ETM+), and the Landsat 8 Operational Land Imager (OLI) sensors. Over 1 million satellite images were processed and analyzed, including over 600,000 Landsat 7 images for the 2000-2012 interval, and more than 400,000 Landsat 5, 7, and 8 images for updates for the 2011-2022 interval. The clear land surface observations in the satellite images were assembled and a supervised learning algorithm was applied to identify per pixel tree cover loss.In this data set, “tree cover” is defined as all vegetation greater than 5 meters in height, and may take the form of natural forests or plantations across a range of canopy densities. Tree cover loss is defined as “stand replacement disturbance,” or the complete removal of tree cover canopy at the Landsat pixel scale. Tree cover loss may be the result of human activities, including forestry practices such as timber harvesting or deforestation (the conversion of natural forest to other land uses), as well as natural causes such as disease or storm damage. Fire is another widespread cause of tree cover loss, and can be either natural or human-induced.This data set has been updated five times since its creation, and now includes loss up to 2022 (Version 1.10). The analysis method has been modified in numerous ways, including new data for the target year, re-processed data for previous years (2011 and 2012 for the Version 1.1 update, 2012 and 2013 for the Version 1.2 update, and 2014 for the Version 1.3 update), and improved modelling and calibration. These modifications improve change detection for 2011-2022, including better detection of boreal loss due to fire, smallholder rotation agriculture in tropical forests, selective losing, and short cycle plantations. Eventually, a future “Version 2.0” will include reprocessing for 2000-2010 data, but in the meantime integrated use of the original data and Version 1.7 should be performed with caution. Read more about the Version 1.7 update here.When zoomed out (< zoom level 13), pixels of loss are shaded according to the density of loss at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover loss, whereas pixels with lighter shading indicate a lower concentration of tree cover loss. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13).The tree cover canopy density of the displayed data varies according to the selection - use the legend on the map to change the minimum tree cover canopy density threshold.

The Copernicus High Resolution Layer (HRL) Forest defines Tree Cover Density as the "vertical projection of tree crowns to a horizontal earth’s surface“ and provides information on the proportional crown coverage per pixel. This information is derived from multispectral High Resolution (HR) satellite data using Very High Resolution (VHR) satellite data and/or aerial ortho-imagery as reference data. Tree Cover Density is assessed on VHR sources by visual interpretation following a point grid approach and subsequently transferred to the HR data by a linear function.Semi-automatic classification of pre-processed multitemporal High Resolution (HR) satellite image data (Sentinel-2, Landsat 8) with reference year 2015 (+/- 1 year), using supervised and unsupervised elements, leading to scene-based initial land cover classifications. This HRL maps the degree (0-100% per pixel) of tree cover density without a minimum mapping unit (MMU), but with a minimum mapping width (MMW) of 20m.Find out more and download the data from land.copernicus.eu.

The high resolution forest product consists of three types of (status) products and additional change products. The status products are available for the 2012, 2015 and 2018 reference years: 1. Tree cover density providing level of tree cover density in a range from 0-100%; 2. Dominant leaf type providing information on the dominant leaf type: broadleaved or coniferous; 3. A Forest type product. The forest type product allows to get as close as possible to the FAO forest definition. In its original (20m) resolution it consists of two products: 1) a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps, based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and high resolution imperviousness 2009 data). For the final 100m product trees under agricultural use and urban context from the support layer are removed. The high resolution forest change products comprise a simple tree cover density change product for 2012-2015 (% increase or decrease of real tree cover density changes). The production of the high resolution forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme.

DATA OVERVIEW

Mapped attributes are

• above ground biomass, AGB;
• downed wood biomass, i.e., the sum of coarse and fine woody debris, DWB;
• canopy bulk density, CBD;
• canopy height,CH;
• canopy base height, CBH;
• and canopy fuel load, CFL.

Models were fit using auxiliary information that included lidar data from 20 acquisitions in Oregon and climate data. Measurements in plots of the Forest Inventory and Analysis program (FIA) were used to obtain plot-level ground observations for predictive modeling. Tree and transect measurements in FIA plots were respectively used to obtain plot-level values of AGB and DWB. To obtain plot-level values of CBD, CH, CBH and CFL, tree measurements in FIA plots were processed with FuelCalc. Plot level auxiliary variables were obtained intersecting the axiliary information layers with the FIA plots. Predictive models were random forest models in which a parametric component was added to model the error variance. The error variance was modeled as a power function of the predictive value and was used to produce uncertainty maps. A different model was fit for each variable and the resulting models were used to obtain maps of synthetic predictions for all areas covered by the 20 lidar acquisitions. The modeled error variance was used to generate uncertainty maps for the predictions of each response variable. Model accuracy was assessed globally (for the entire dataset) and separately for each one of the 20 lidar acquisitions included in the dataset.

Results from the accuracy assessment can be found in Appendix A and Appendix B of Mauro et al. (2021).

Each variable has two associated maps. These maps are named using the following convention where VARIABLE is the acronym for each variable (AGB, DWB, CBD, CH, CBH or CFL):

Predictions of forest attributes:

VARIABLE.tif

Standard deviation of modeled errors:

SD_VARIABLE.tif

### There are two additional rasters. The first one, year.tif is necessary to obtain the reference year for each lidar acquisition. The second one, forest_mask.tif provides a forest vs non-forest mask. Forested areas are coded as 1s and non-forested areas with no-datas. This mask is a resampled subset of the PALSAR JAXA 2014 ‘New global 25m-resolution PALSAR mosaic and forest/non-forest map (2007-2010) - version 1’ from the Japan Aerospace Exploration Agency Earth Observation Research Center (www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm). Its reference year is 2009. Models to predict forest attributes were created using ground observations in forested areas. For many applications it is advisable to use the provided mask to excluded non-forested areas from analyses. This can be done, for example, multiplying the desired raster by the forest mask. Exceptions to this may occur in relatively open forested lands where the mask eliminates areas that actually sustain forest. In those areas, the use of an add-hoc forest mask might be more appropriate. ### Reference year: year.tif ### Forest mask: forest_mask.tif ###

UNITS:

For a given variable, both predictions and standard deviation of model errors have the same units. These units are:

• Variable (Abreviation): Units

• Above ground biomass (AGB): Mg/ha

• Downed wood biomass (DWB):Mg/ha

• Canopy bulk density (CBD): Kg/m3 (Kilogram per cubic meter)

• Canopy height (CH): m

• Canopy base height (CBH): m

  Canopy fuel load (CFL):Mg/ha

COORDINATE REFERENCE SYSTEM:

The reference system for all maps is EPSG 5070

USAGE

These data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights.

Please include the following citation in any publication that uses these data:

Mauro, F., Hudak, A.T., Fekety, P.A., Frank, B., Temesgen, H., Bell, D.M., Gregory, M.J., McCarley, T.R., 2021. Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon. Remote Sensing 13. https://doi.org/10.3390/rs13020261

‘Tree – tree’ interactions are important structuring mechanisms for forest community dynamics. Forest management takes advantage of competition effects on tree growth by removing or retaining trees to achieve management goals. Both competition and silviculture have thus a strong effect on density and distribution of tree related microhabitats (TreMs) which are key features for forest taxa at the stand scale (e.g. Bouget et al. 2013, 2014). In particular, spatially explicit data to understand patterns and mechanisms of TreM formation in forest stands are rare. To train and eventually improve decision making capacities related to the integration of biodiversity aspects into forest management to date more than 100 usually 1 ha (100 m x 100m) permanent plots were established in different forest communities of Europe. Due to their demonstration character the selection of plots was non-systematic. They do, however, cover a broad range of forest types (e.g. beech-oak, beech-fir (-spruce), oak-hornbeam, pine-spruce, etc.), altitudinal gradients (from 25 m – 1850 m) and site conditions (e.g. oligotrophic Luzulo-Fagetum or Vaccinio-Pinetum to mesotrophic Galio-Fagetum or Milio-Fagetum). For each plot the following data is collected: (1) tree location as polar coordinates (stem base map), (2) tree species, (3) forest mensuration data (dbh in [cm], tree height in [m]), (4) tree related microhabitats (TreMs) and (5) tree status (living or standing dead). In addition to the spatial dendrometric data we provide information on plot establishment, forest type, plot location (state, region, country), elevation, means for annual precipitation and temperature, and the natural forest community (Kraus et al., 2018).

Forests are a major component of the global carbon cycle, and accurate estimation of forest carbon stocks and fluxes is important in the context of anthropogenic global change. Airborne laser scanning (ALS) data sets are increasingly recognized as outstanding data sources for high-fidelity mapping of carbon stocks at regional scales. We develop a tree-centric approach to carbon mapping, based on identifying individual tree crowns (ITCs) and species from airborne remote sensing data, from which individual tree carbon stocks are calculated. We identify ITCs from the laser scanning point cloud using a region-growing algorithm and identifying species from airborne hyperspectral data by machine learning. For each detected tree, we predict stem diameter from its height and crown-width estimate. From that point on, we use well-established approaches developed for field-based inventories: above-ground biomasses of trees are estimated using published allometries and summed within plots to estimate carbon density. We show this approach is highly reliable: tests in the Italian Alps demonstrated a close relationship between field- and ALS-based estimates of carbon stocks (r2 = 0·98). Small trees are invisible from the air, and a correction factor is required to accommodate this effect. An advantage of the tree-centric approach over existing area-based methods is that it can produce maps at any scale and is fundamentally based on field-based inventory methods, making it intuitive and transparent. Airborne laser scanning, hyperspectral sensing and computational power are all advancing rapidly, making it increasingly feasible to use ITC approaches for effective mapping of forest carbon density also inside wider carbon mapping programs like REDD++.

A web map of total forest ecosystem carbon density, which illustrates how much carbon is stored in forest ecosystems and harvested wood products. This data was developed by the United States Forest Service's Forest Inventory and Analysis (FIA) program. This map displays areas within California that store different levels of carbon, categorized by five carbon classes (no estimate, less than 50 Mg/ha, 50 to 100 Mg/ha, 100 to 150 Mg/ha, 150 to 200 Mg/ha, and greater than 200 Mg/ha).For more information on this layer and additional information on the FIA program, please refer to the following website:

https://www.fia.fs.fed.us/forestcarbon/default.asp.This story map is part of the Watershed Improvement Program (WIP) and Watershed Information Network (WIN).The Total Forest Ecosystem Carbon Density web map is a feature service used in the Sierra Nevada Cascade story map; therefore, it should not be altered or deleted under any circumstances while the story map is in use.

The high resolution forest product consists of three types of (status) products and additional change products. The status products are available for the 2012 and 2015 reference years: 1. Tree cover density providing level of tree cover density in a range from 0-100% 2. Dominant leaf type providing information on the dominant leaf type: broadleaved or coniferous 3. A Forest type product. The forest type product allows to get as close as possible to the FAO forest definition. In its original (20m) resolution it consists of two products: 1) a dominant leaf type product that has a MMU of 0.5 ha, as well as a 30% tree cover density threshold applied, and 2) a support layer that maps, based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and high resolution imperviousness 2009 data). For the final 100m product trees under agricultural use and urban context from the support layer are removed. The high resolution forest change products comprise a simple tree cover density change product for 2012-2015 (% increase or decrease of real tree cover density changes).

A verification of the Tree Cover Density layer was performed by the Institute of Nature Research during autumn of 2018 and the data and resulting report are made available on the NLSI websites.

Forest structure is strongly related to forest ecology and it is a key parameter to understand ecosystem processes and services. Airborne laser scanning (ALS) is becoming an important tool in environmental mapping. It is increasingly common to collect ALS data at high enough point density to recognize individual tree crowns (ITCs) allowing analyses to move beyond classical stand level approaches. In this paper an effective and simple method to map ITCs, and their stem diameter and above ground biomass is presented. ALS data were used to delineate ITCs and to extract ITCs' height and crown diameter; then using newly developed allometries the ITCs' DBH and AGB were predicted. Gini coefficient of DBHs was also predicted and mapped aggregating ITCs predictions. Two datasets from spruce dominated temperate forests were considered: one was used to develop the allometric models, while the second was used to validate the methodology. The proposed approach provides accurate predictions of individual DBH and AGB (R2 = 0.85 and 0.78, respectively) and of tree size distributions. The proposed method had a higher generalization ability compared to a standard area based method, in particular for the prediction of the Gini coefficient of DBHs. The delineation method used detected more than 50% of the trees with DBH >10 cm. The detection rate was particularly low for trees with DBH below 10 cm, but they represent a small amount of the total biomass. The Gini coefficient of the DBH distribution was predicted at plot level with R2 = 0.46. The approach described in this work, easy applicable in different forested areas, is an important development of the traditional area based remote sensing tools and can be applied for more detailed analysis of forest ecology and dynamics.

We present gridded 8 km-resolution data products of the estimated aboveground biomass, stem density, and basal area of tree taxa at the time of Euro-American settlement of the midwestern United States for the states of Minnesota, Wisconsin, Michigan, Illinois, and Indiana. The data come from settlement-era Public Land Survey (PLS) data (ca. 0.8-km resolution) of trees recorded by land surveyors. The surveyor notes have been transcribed, cleaned, and processed to estimate aboveground biomass (megagrams per hectare), stem density (stems greater than or equal 8 inches DBH per hectare), and basal area (square meters per hectare) at individual points on the landscape. The point-level data are then aggregated within grid cells and statistically smoothed using a statistical model that accounts for zero-inflated continuous data with smoothing based on generalized additive modeling techniques and approximate Bayesian uncertainty estimates. We expect this data product to be useful for understanding the state of vegetation in the midwestern United States prior to large-scale Euro-American settlement. In addition to specific regional questions, the data product can serve as a baseline against which to investigate how forests and ecosystems change after intensive settlement. The data products (including both raw [Level 1: averages of point level values within each grid cell] and statistically smoothed estimates [Level 2] at the 8-km scale) are being made available at the LTER network data portal as version 1.0. This material is based upon work supported by the National Science Foundation under grants #DEB-1241874, 1241868, 1241870, 1241851, 1241891, 1241846, 1241856, 1241930.

IV Forest Inventory of the Balearic Islands. Part of the IV Spanish Forest Map published by the Directorate General of Forest Policy and Rural Development Ministry of Agriculture, Food and Environment. Includes forest fuel model (Rothermel), the structure of the vegetation foerestal, the type of training trees, shrubs and type of training. Part of the Spanish forest map 1:25000. Have been contributed to the area of Data Bank of the nature of the Department of Environmental Quality and Assessment and environment.The project carried out between 2007 and 2017.

This dataset series shows the distribution map (raster format: geotiff) of Pinus cembra. The distribution map is provided for Europe (EU28 plus part of other countries within the spatial extent), computed using the FISE harmonised European dataset of taxa presence/absence (based on the integration and harmonisation of the datasets by European National Forestry Inventories; BioSoil; Forest Focus/Monitoring; EUFGIS; GeneticDiversity). The distribution is estimated by means of statistical interpolation (Constrained Spatial Multi-Frequency Analysis, C-SMFA). Available years: 2006. The maps are available in the Forest Information System for Europe (FISE). FISE is run by the European Commission, Joint Research Centre. See the field Lineage for further information.

When using these data, please cite the relevant data sources. A suggested citation is included in the following:

• Various authors, 2016. Pinus cembra in Europe: an outline on distribution, habitat, importance and threats. In: Online European Atlas of Forest Tree Species. FISE Comm. Publications Office of the European Union. pp. e01bd9b+. (Under review: please, check the current status at: https://w3id.org/mtv/FISE-Comm/v01/e01bd9b)

• de Rigo, D., Caudullo, G., Houston Durrant, T., San-Miguel-Ayanz, J., 2016. The European Atlas of Forest Tree Species: modelling, data and information on forest tree species. In: San-Miguel-Ayanz, J., de Rigo, D., Caudullo, G., Houston Durrant, T., Mauri, A. (Eds.), European Atlas of Forest Tree Species. Publ. Off. EU, Luxembourg, pp. e01aa69+. https://w3id.org/mtv/FISE-Comm/v01/e01aa69

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This dataset series shows the distribution map (raster format: geotiff) of Tilia spp. (whole genus Tilia, i.e. all observed species in that genus). The distribution map is provided for Europe (EU28 plus part of other countries within the spatial extent), computed using the FISE harmonised European dataset of taxa presence/absence (based on the integration and harmonisation of the datasets by European National Forestry Inventories; BioSoil; Forest Focus/Monitoring; EUFGIS; GeneticDiversity). The distribution is estimated by means of statistical interpolation (Constrained Spatial Multi-Frequency Analysis, C-SMFA). Available years: 2006. The maps are available in the Forest Information System for Europe (FISE). FISE is run by the European Commission, Joint Research Centre. See the field Lineage for further information.

When using these data, please cite the relevant data sources. A suggested citation is included in the following:

• Various authors, 2016. Tilia cordata, Tilia platyphyllos and other limes in Europe: an outline on distribution, habitat, importance and threats. In: Online European Atlas of Forest Tree Species. FISE Comm. Publications Office of the European Union. pp. e010ec5+. (Under review: please, check the current status at: https://w3id.org/mtv/FISE-Comm/v01/e010ec5)

• de Rigo, D., Caudullo, G., Houston Durrant, T., San-Miguel-Ayanz, J., 2016. The European Atlas of Forest Tree Species: modelling, data and information on forest tree species. In: San-Miguel-Ayanz, J., de Rigo, D., Caudullo, G., Houston Durrant, T., Mauri, A. (Eds.), European Atlas of Forest Tree Species. Publ. Off. EU, Luxembourg, pp. e01aa69+. https://w3id.org/mtv/FISE-Comm/v01/e01aa69

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FIA Modeled Abundance:�This dataset portrays the live tree mean basal area (square feet per acre) of the species across the contiguous United States. The underlying data publication contains raster maps of live tree basal area for each tree species along with corresponding assessment data. An efficient approach for mapping multiple individual tree species over large spatial domains was used to develop these raster datasets. The method integrates vegetation phenology derived from MODIS imagery and raster data describing relevant environmental parameters with extensive field plot data of tree species basal area to create maps of tree species abundance and distribution at a 250-meter (m) pixel size for the contiguous United States. The approach uses the modeling techniques of k-nearest neighbors and canonical correspondence analysis, where model predictions are calculated using a weighting of nearest neighbors based on proximity in a feature space derived from the model. The approach also utilizes a stratification derived from the 2001 National Land-Cover Database tree canopy cover layer.�This data depicts current species abundance and distribution across the contiguous United States, modeled by using FIA field plot data. Although the absolute values associated with the maps differ from species to species, the highest values within each map are always associated with darker colors. The Little's Range Boundaries show the historical tree species ranges across North America. This is a digital representation of maps by Elbert L. Little, Jr., published between 1971 and 1977. These maps were based on botanical lists, forest surveys, field notes and herbarium specimens.Forest-type Groups:This dataset portrays the forest type group. Each group is a subset of the National Forest Type dataset which portrays 28 forest type groups across the contiguous United States. These data were derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, ecoregions, and PRISM climate data.Harvest Growth:This data shows the percentage of timber that is harvested when compared to the total live volume, at a county-by-county level. Timber volume in forests is constantly in flux, and harvest plays an important role in shaping forests. While most counties have some timber harvest, harvest volumes represent low percentages of standing timber volume.Carbon Harvest:The Carbon Harvest raster dataset represents Mg of annual pulpwood harvested (carbon) by county, derived from the Forest Inventory Analysis in 2016.

This dataset series shows the distribution map (raster format: geotiff) of Castanea sativa. The distribution map is provided for Europe (EU28 plus part of other countries within the spatial extent), computed using the FISE harmonised European dataset of taxa presence/absence (based on the integration and harmonisation of the datasets by European National Forestry Inventories; BioSoil; Forest Focus/Monitoring; EUFGIS; GeneticDiversity). The distribution is estimated by means of statistical interpolation (Constrained Spatial Multi-Frequency Analysis, C-SMFA). Available years: 2006. The maps are available in the Forest Information System for Europe (FISE). FISE is run by the European Commission, Joint Research Centre. See the field Lineage for further information.

When using these data, please cite the relevant data sources. A suggested citation is included in the following:

• Various authors, 2016. Castanea sativa in Europe: an outline on distribution, habitat, importance and threats. In: Online European Atlas of Forest Tree Species. FISE Comm. Publications Office of the European Union. pp. e0125e0+. (Under review: please, check the current status at: https://w3id.org/mtv/FISE-Comm/v01/e0125e0)

• de Rigo, D., Caudullo, G., Houston Durrant, T., San-Miguel-Ayanz, J., 2016. The European Atlas of Forest Tree Species: modelling, data and information on forest tree species. In: San-Miguel-Ayanz, J., de Rigo, D., Caudullo, G., Houston Durrant, T., Mauri, A. (Eds.), European Atlas of Forest Tree Species. Publ. Off. EU, Luxembourg, pp. e01aa69+. https://w3id.org/mtv/FISE-Comm/v01/e01aa69

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