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 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.

Maps of the ranges of tree species in North America compiled by Elbert Little, of the U.S. Department of Agriculture, Forest Service, and others were digitized for use in USGS vegetation-climate modeling studies. These digital map files are available here for download.

The maps are available in ArcView® shapefile format. Geographic ranges are represented as polygons. There is one shapefile (with associated data files) for each tree species. At present, not all of the maps have been digitized, quality checked, and made available here, but the investigators plan to make most or all of the digitized maps available over time.

These maps are a digital representation of the individual tree species range maps of the "Atlas of the United States Trees" by Elbert L. Little, Jr. The atlas shows the natural distribution or range of the native tree species of North America. These coverages represent 3 volumes of the atlas.

This database contains distribution maps of 135 eastern U.S. tree species based on Importance Values (IV) derived from Forest Inventory Analysis (FIA) data and a geographical information system (GIS) database of Elbert L. Jr. Little's published ranges. Between 1971 and 1977, Elbert L. Jr. Little, Chief Dendrologist with the U.S. Forest Service, published a series of maps of tree species ranges based on botanical lists, forest surveys, field notes ad herbarium specimens. These published maps have become the standard reference for most U.S. and Canadian tree species ranges.

The USDA Forest Service's FIA units are charged with periodically assessing the extent, timber potential, and health of the trees in the United States. The investigators have created a spatial database of individual species IV based on the number of stems and basal area of understory and overstory trees using FIA data from more than 100,000 plots in the eastern United States. The IV ranges from 0 to 100 and gives a measure of the abundance of the species. (See the investigator's Climate Change Atlas for 80 Forest Tree Species of the Eastern United States at [http://www.fs.fed.us/ne/delaware/atlas/web_atlas.html] for details). The investigators have aggregated the plot-level IV to 20km cells.

Both sets of maps (Little's ranges and IV based on FIA data) are available for download. The Little's range maps (little.shp) are vector based and are provided as shape files. These maps can span United States or United States and Canada in extent depending on the species. The Importance Value (IV) are raster maps (asciigrid) in asciigrid format. This is an ascii file with header information that can be used to import data into ArcInfo GRID or ArcView's Spatial Analyst GIS software. The spatial resolution is 20km. These raster maps span the eastern U.S. (east of the 100th meridian) in extent.

Urban Tree Canopy Assessment. This was created using the Urban Tree Canopy Syracuse 2010 (All Layers) file HERE.The data for this map was created using LIDAR and other spatial analysis tools to identify and measure tree canopy in the landscape. This was a collaboration between the US Forest Service Northern Research Station (USFS), the University of Vermont Spatial Laboratory, and SUNY ESF. Because the full map is too large to be viewed in ArcGIS Online, this has been reduced to a vector tile layer to allow it to be viewed online. To download and view the shapefiles and all of the layers, you can download the data HERE and view this in either ArcGIS Pro or QGIS.Data DictionaryDescription source  USDA Forest ServiceList of values  Value 1 Description Tree CanopyValue 2 Description Grass/ShrubValue 3 Description Bare SoilValue 4 Description WaterValue 5 Description BuildingsValue 6 Description Roads/RailroadsValue 7 Description Other PavedField Class Alias Class Data type String Width 20Geometric objects  Feature class name landcover_2010_syracusecity Object type  complex Object count 7ArcGIS Feature Class Properties Feature class name landcover_2010_syracusecity Feature type  Simple Geometry type Polygon Has topology FALSE Feature count 7 Spatial index TRUE Linear referencing  FALSEDistributionAvailable format  Name ShapefileTransfer options  Transfer size 163.805Description Downloadable DataFieldsDetails for object landcover_2010_syracusecityType Feature Class Row count  7 Definition  UTCField FIDAlias FID Data type OID Width  4 Precision 0 Scale 0Field descriptionInternal feature number.Description source ESRIDescription of valueSequential unique whole numbers that are automatically generated.Field ShapeAlias Shape Data type Geometry Width 0 Precision 0 Scale 0Field description Feature geometry.Description source  ESRIDescription of values Coordinates defining the features.Field CodeAlias Code Data type Number Width 4Overview Description  Metadata DetailsMetadata language  English Metadata character set utf8 - 8 bit UCS Transfer FormatScope of the data described by the metadata  dataset Scope name  datasetLast update 2011-06-02ArcGIS metadata properties Metadata format ArcGIS 1.0 Metadata style North American Profile of ISO19115 2003Created in ArcGIS for the item 2011-06-02 16:48:35 Last modified in ArcGIS for the item 2011-06-02 16:44:43Automatic updates Have been performed Yes Last update 2011-06-02 16:44:43Item location history  Item copied or moved 2011-06-02 16:48:35 From T:\TestSites\NY\Syracuse\Temp\landcover_2010_syracusecity To \T7500\F$\Export\LandCover_2010_SyracuseCity\landcover_2010_syracusecity

Basal Area (BA). 30 meter pixel resolution. Data represents forest conditions circa 2002.These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service’s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013–2027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002This 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.

This product is part of the TreeMap data suite. It provides detailed spatial information on forest characteristics including number of live and dead trees, biomass, and carbon across the entire forested extent of the continental United States in 2016. TreeMap v2016 contains one image, a 22-band 30 x 30m resolution …

Modeled distribution is taken from Tree species distribution in the United States Part 1 in the Journal of Maps by Rachel Riemann, Barry T. Wilson, Andrew J. Lister, Oren Cook & Sierra Crane-Murdoch.Rachel Riemann, Barry T. Wilson, Andrew J. Lister, Oren Cook & Sierra Crane-Murdoch (2018) Tree species distribution in the United States Part 1, Journal of Maps, 14:2, 561-566, DOI: https://doi.org/10.1080/17445647.2018.1513383

Contained within the 2nd Edition (1915) of the Atlas of Canada, is a map that shows the northern limits of approximately 40 different tree species in Canada, including an extension into the Northern U.S. Red, green and blue lines delineate the limits of the trees and forests. The map also includes rivers, major bodies of water, and the specific locations of several tree types.

This data publication contains data on tree species functional traits, forest stand age and vegetation phenology used for modeling and analysis of small tree survival across a heterogeneous tropical landscape (Puerto Rico and the U.S. Virgin Islands). Trees are at increased risk of mortality from increased heat, drought, fire, storms and other causes. However, predictions of tree mortality are highly uncertain. Tabular data include: 1) wood specific gravity values collected for this study for 176 trees of 104 tree species (oven dry weight/green volume); 2) citations and taxonomic basis for assigning the categories of tree species mycorrhizal putative status; 3) forest-cover data gathered from orthorectified pairs of aerial photos from the years 1936-1937 for Puerto Rico forest inventory plots that were forested in the years 2001-2008; and 4) tree species functional groups and functional traits used for modeling small tree survival across Puerto Rico and U.S. Virgin Islands (PRVI). Spatial data include: 1) a spatial raster dataset representing forest stand age class circa the year 2001 in GeoTIFF (TIF) format; and 2) a spatial raster dataset (TIF) of four metrics of average annual phenology of vegetation greenness from 2010-2014 for Puerto Rico and the Virgin Islands produced from Landsat satellite imagery.These data were collected to analyze tree demography, diversity, productivity and nutrient cycling, including carbon storage, to better manage and understand Caribbean forests and tropical forests in general.For more information about this study and these data, see Helmer et al. (2023). These data were published on 01/06/2023. On 07/24/2023 minor changes were made to the metadata to update out of date URLs as well as update a reference that is now available.

Contained within the 1st Edition (1906) of the Atlas of Canada is a map that shows the northern limits of approximately 40 different tree species in Canada, including an extension into the Northern U.S. Using green lines the map displays the northern limits of the principal trees found within the Southern Forest. Blue lines indicate the northern, and in a few incidences the southern, limits of the principal trees found within the Northern Forest. Red lines show the limits of the trees within the Cordilleran Forest. For this map, and the Atlas of Canada Forests map, the line of division between the Northern and Southern Forests has been taken as the northern limit of red and white pine. These trees are assigned to the Northern Forest, including those whose limit is south of the pine tree limits within the Southern Forest. The map also includes, rivers, major bodies of water, and a few tree types labeled in specific locations.

Directory of suggested edits to information in NYC Street Tree Map. Users can suggest a different species, diameter, or other notes about the tree. Edits are reviewed by a NYC Street Tree Map administrator before they are incorporated into the Map. This directory tracks the content and status of each suggested edit.

Forest type group layer was developed using data from over 213,000 national forest inventory plots measured during the period 2014-2018 from the USDA Forest Service Forest Inventory and Analysis (FIA) program, in conjunction with other auxiliary information. Roughly 4,900 Landsat 8 Operational Land Imager scenes, collected during the same time period, were processed to extract information about vegetation phenology. This information, along with climatic and topographic raster data, were used in an ecological ordination model of tree species. The model produced a feature space of ecological gradients that was then used to impute FIA plots to pixels. The plots imputed to each pixel were then used to assign values of forest type groups. Data source: FIA BIGMAP General Layer Catalog, Forest Type Groups of the Continental United States at https://usfs.maps.arcgis.com/apps/LayerShowcase/index.html?appid=22cc1bd57eb84b46a89a2d9935325e0fFor more information about the methods used to produce this dataset please see the following references:Wilson, Barry T.; Knight, Joseph F.; McRoberts, Ronald E. 2018. Harmonic regression of Landsat time series for modeling attributes from national forest inventory data. ISPRS Journal of Photogrammetry and Remote Sensing. 137: 29-46.Wilson, Barry Tyler; Woodall, Christopher W.; Griffith, Douglas M. 2013. Imputing forest carbon stock estimates from inventory plots to a nationally continuous coverage. Carbon Balance and Management. 8:1. doi:10.1186/1750-0680-8-1Wilson, B. Tyler; Lister, Andrew J.; Riemann, Rachel I. 2012. A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data. Forest Ecology and Management. 271: 182-198.Ohmann, Janet L.; Gregory, Matthew J. 2002. Predictive mapping of forest composition and structure with direct gradient analysis and nearest neighbor imputation in coastal Oregon, U.S.A. Canadian Journal of Forest Research. 32: 725-741A detailed description of FIA Forest Types can be found in the Forest Atlas of the United States HERE. Code Forest type group100 White / red / jack pine group120 Spruce / fir group140 Longleaf / slash pine group150 Tropical softwoods group160 Loblolly / shortleaf pine group170 Other eastern softwoods group180 Pinyon / juniper group200 Douglas-fir group220 Ponderosa pine group240 Western white pine group260 Fir / spruce / mountain hemlock group280 Lodgepole pine group300 Hemlock / Sitka spruce group320 Western larch group340 Redwood group360 Other western softwoods group370 California mixed conifer group380 Exotic softwoods group390 Other softwoods group400 Oak / pine group500 Oak / hickory group600 Oak / gum / cypress group700 Elm / ash / cottonwood group800 Maple / beech / birch group900 Aspen / birch group910 Alder / maple group920 Western oak group940 Tanoak / laurel group960 Other hardwoods group970 Woodland hardwoods group980 Tropical hardwoods group988 Cloud forest990 Exotic hardwoods group999 Nonstocked

European colonization radically transformed the landscapes of eastern North America. Understanding this legacy is vital to managing current ecological communities. To do so requires spatially explicit information about presettlement vegetation. Here we test the ability of species distribution models, which have rarely been used with historical data like land survey records, to generate useful predictions of presettlement tree distributions. These models also allow us to assess pre-disturbance vegetation-environment relationships. Location: Cuyahoga County, Ohio, USA Methods: Generalized linear models, generalized boosting models, random forests, and maximum entropy models related the distributions of 17 tree taxa to elevation, slope, aspect, and soil type, based on 4234 tree observations from circa-1800 surveys. Cluster analysis defined forest types and created a prediction map of forest types using random forests. Results: This study generated high-resolution predictions of presettleme..., We analyzed historical survey data on 17 individual tree species and 10 forest types using species distribution models to create prediction maps of pre-settlement distributions. , , # Presettlement tree distributions and forest types of northeast Ohio, USA, mapped with species distribution models

https://doi.org/10.5061/dryad.wwpzgmsv8

Description of the data and file structure

The accompanying code and data was used to generate distribution models and prediction maps for analyses described in Flinn et al. Raster versions of the maps included in the paper are included here.Â

Files and variables

File: Flinn_et_al._JVS_forest_type.txt

Description:Â Data file for forest type analysis described in Flinn et al.Â

Variables

• Unique_ID: unique identifier for survey linesÂ
• POINT_X: Longitude coordinate (WGS84)
• POINT_Y: Latitude coordinate (WGS84)
• Group10: forest type code, with values assigned as follows: 1 = Quercus-Castanea, 2 = Fraxinus, 3 = Fraxinus-Acer-Tilia, 7 = Fagus-Acer, 70 = Acer, 75 = Quercus-Carya, 91 = Fraxinus-Ulmus, 92 = Quercus, 384 = Fagus, 390 = Tsuga, Picea, or Pinus

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Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

This map is a digital representation of the Tamarack (Larix laricinia) tree species range map in the "Atlas of the United States Trees" by Elbert L. Little, Jr. The atlas shows the natural distribution or range of the native tree species of North America.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Street tree data from the TreesCount! 2015 Street Tree Census, conducted by volunteers and staff organized by NYC Parks & Recreation and partner organizations. As of June 2016, mapping is still in progress – this is a partial release. Tree data collected includes tree species, diameter and perception of health. Accompanying blockface data is available indicating status of data collection and data release citywide.

Maps of the number, size, and species of trees in forests across the western United States are desirable for many applications such as estimating terrestrial carbon resources, predicting tree mortality following wildfires, and for forest inventory. However, detailed mapping of trees for large areas is not feasible with current technologies, but statistical methods for matching the forest plot data with biophysical characteristics of the landscape offer a practical means to populate landscapes with a limited set of forest plot inventory data. We used a modified random forests approach with Landscape Fire and Resource Management Planning Tools (LANDFIRE) vegetation and biophysical predictors as the target data, to which we imputed plot data collected by the USDA Forest Service’s Forest Inventory Analysis (FIA) to the landscape at 30-meter (m) grid resolution (Riley et al. 2016). This method imputes the plot with the best statistical match, according to a “forest” of decision trees, to each pixel of gridded landscape data. In this work, we used the LANDFIRE data set as the gridded target data because it is publicly available, offers seamless coverage of variables needed for fire models, and is consistent with other data sets, including burn probabilities and flame length probabilities generated for the continental United States. The main output of this project (the GeoTIFF available in this data publication) is a map of imputed plot identifiers at 30×30 m spatial resolution for the western United States for landscape conditions circa 2009. The map of plot identifiers can be linked to the FIA databases available through the FIA DataMart or to the ACCDB/CSV files included in this data publication to produce tree-level maps or to map other plot attributes. These ACCDB/CSV files also contain attributes regarding the FIA PLOT CN (a unique identifier for each time a plot is measured), the inventory year, the state code and abbreviation, the unit code, the county code, the plot number, the subplot number, the tree record number, and for each tree: the status (live or dead), species, diameter, height, actual height (where broken), crown ratio, number of trees per acre, and a unique identifier for each tree and tree visit. Application of the dataset to research questions other than those related to aboveground biomass and carbon 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.Geospatial data describing tree species or forest structure are required for many analyses and models of forest landscape dynamics. Forest data must have resolution and continuity sufficient to reflect site gradients in mountainous terrain and stand boundaries imposed by historical events, such as wildland fire and timber harvest. Such detailed forest structure data are not available for large areas of public and private lands in the United States, which rely on forest inventory at fixed plot locations at sparse densities. While direct sampling technologies such as light detection and ranging (LiDAR) may eventually make broad coverage of detailed forest inventory feasible, no such data sets at the scale of the western United States are currently available.When linking the tree list raster (“CN_text” field) to the FIA data via the plot CN field (“CN” in the “PLOT” table and “PLT_CN” in other tables), note that this field is unique to a single visit to a plot. The raster contains a “Value” field, which also appears in the ACCDB/CSV files in the “tl_id” field in order to facilitate this linkage. All plot CNs utilized in this analysis were single condition, 100% forested, physically located in the Rocky Mountain Research Station (RMRS) and Pacific Northwest Research Station (PNW) obtained from FIA in December of 2012.

Original metadata date was 01/03/2018. Minor metadata updates made on 04/30/2019.