The Forest Inventory and Analysis (FIA) research program has been in existence since mandated by Congress in 1928. FIA's primary objective is to determine the extent, condition, volume, growth, and depletion of timber on the Nation's forest land. Before 1999, all inventories were conducted on a periodic basis. The passage of the 1998 Farm Bill requires FIA to collect data annually on plots within each State. This kind of up-to-date information is essential to frame realistic forest policies and programs. Summary reports for individual States are published but the Forest Service also provides data collected in each inventory to those interested in further analysis. Data is distributed via the FIA DataMart in a standard format. This standard format, referred to as the Forest Inventory and Analysis Database (FIADB) structure, was developed to provide users with as much data as possible in a consistent manner among States. A number of inventories conducted prior to the implementation of the annual inventory are available in the FIADB. However, various data attributes may be empty or the items may have been collected or computed differently. Annual inventories use a common plot design and common data collection procedures nationwide, resulting in greater consistency among FIA work units than earlier inventories. Links to field collection manuals and the FIADB user's manual are provided in the FIA DataMart.

Open the Data Resource: https://research.fs.usda.gov/products/dataandtools/tools/fia-datamart The Forest Inventory and Analysis program of the USDA Forest Service Research and Development Branch collects, processes, analyzes and reports on data necessary for assessing the extent and condition of forest resources in the United States. The FIA DataMart allows visitors to download raw FIA data in comma delimited tables, SQLite databases and customizable batch estimate workbooks. The DataMart map also provides a quick visual reference for the most recent data available for each state or inventory area.

This data describe the abundance of individual lichen species across the U.S. as recorded in the Forest Health and Monitoring dataset of the Forest Inventory and Analysis program (i.e. Phase 3 plots). This dataset is not publicly accessible because: These data are already housed on the USFS Forest Inventory and Analysis site (see below). It can be accessed through the following means: The lichen data for this product are from the USDA Forest Services (USFS) Forest Inventory and Analysis (FIA) Phase 3 (P3) dataset - Forest Health and Monitoring. The metadata and database description for the FIA-P3 is here (https://www.fia.fs.fed.us/library/database-documentation/). The data itself is located at the USFS Data Mart here (https://apps.fs.usda.gov/fia/datamart/CSV/datamart_csv.html) in two files: “LICHEN_PLOT_SUMMARY.zip,” and “LICHEN_VISIT.zip.” Point of contact: Linda Geiser, lgeiser@fs.fed.us. Format: The data are in .csv format.

These files provide code used to attribute old growth status to forest inventory and analysis (FIA) samples ('conditions') in National Forest System regions. To generate estimates of acres of old growth and standard errors, the designation of old growth was appended to the public FIA database and we ran a standard area estimate. See "FIA area estimate example" in this data archive.The manuscript provides supplemental information used by the code, such as criteria and crosswalks from FIA data to regional old growth forest types/vegetation groups, to calculate old growth status in each region. These files provide code used to attribute old growth status to forest inventory and analysis (FIA) samples ('conditions') in National Forest System regions. The code uses public FIA data, available at https://apps.fs.usda.gov/fia/datamart/datamart.html, and supplementary confidential information in some regions (such as geographic information extracted using real coordinates, and modeled tree ages). See "FIA evaluations list" for the complete list the state and years of data used for this work. See "FIA evaluations list" for the complete list the state and years of data used for this work.

This dataset is for examples in the Ecography Software Note, FIESTA: A Forest Inventory Estimation and Analysis R package, by Frescino, Tracey S.; Moisen, Gretchen G.; Patterson, Paul, L.; Toney, Chris; White, Grayson W. The examples demonstrate how to generate estimates of forest attributes using three different FIESTA modules: Green Book (GB), Model-Assisted (MA), and Small Area (SA). Included in the dataset are: a geospatial vector shapefile (.shp) of the Middle Bear-Logan Watershed area of interest (AOI); an R sf object (.rds) defining an ecological extent encompassing the AOI, Ecomap Section M331D (Cleland et al. 2007) ; a SQLite database (.db) including FIA plot data downloaded from FIA's publicly available DataMart (https://apps.fs.usda.gov/fia/datamart/datamart.html) and subset to the M331D boundary; and five auxiliary spatially-explicit raster layers (.img) clipped to the M331D boundary.

This resource contains the data and scripts used for: Goeking, S. A. and D. G. Tarboton, (2022). Spatially distributed overstory and understory leaf area index estimated from forest inventory data. Water. https://doi.org/10.3390/w1415241.

Abstract from the paper: Abstract: Forest change affects the relative magnitudes of hydrologic fluxes such as evapotranspiration (ET) and streamflow. However, much is unknown about the sensitivity of streamflow response to forest disturbance and recovery. Several physically based models recognize the different influences that overstory versus understory canopies exert on hydrologic processes, yet most input datasets consist of total leaf area index (LAI) rather than individual canopy strata. Here, we developed stratum-specific LAI datasets with the intent of improving the representation of vegetation for ecohydrologic modeling. We applied three pre-existing methods for estimating overstory LAI, and one new method for estimating both overstory and understory LAI, to measurements collected from a probability-based plot network established by the US Forest Service’s Forest Inventory and Analysis (FIA) program, for a modeling domain in Montana, MT, USA. We then combined plot-level LAI estimates with spatial datasets (i.e., biophysical and re-mote sensing predictors) in a machine learning algorithm (random forests) to produce annual gridded LAI datasets. Methods that estimate only overstory LAI tended to underestimate LAI relative to Landsat-based LAI (mean bias error ≥ 0.83), while the method that estimated both overstory and understory layers was most strongly correlated with Landsat-based LAI (r2 = 0.80 for total LAI, with mean bias error of -0.99). During 1984-2019, interannual variability of under-story LAI exceeded that for overstory LAI; this variability may affect partitioning of precipitation to ET vs. runoff at annual timescales. We anticipate that distinguishing overstory and understory components of LAI will improve the ability of LAI-based models to simulate how for-est change influences hydrologic processes.

This resource contains one CSV file, two shapefiles (each within a zip file), two R scripts, and multiple raster datasets. The two shapefiles represent the boundaries of the Middle Fork Flathead river and South Fork Flathead River watersheds. The raster datasets represent annual leaf area index (LAI) at 30 m resolution for the entire modeling domain used in this study. LAI was estimated using method LAI4, which produced separate overstory and understory LAI datasets. Filenames contain years, e.g., "LAI4_2019" is overstory LAI for 2019; "LAI4under_2019" is understory LAI for 2019.

The CSV files in this Resource contain annual time series of LAI and ET ratio (annual evapotranspiration divided by annual precipitation) for the South Fork Flathead River and Middle Fork Flathead River watersheds, 1984-2019. LAI methods represented in this time series are LAI1 and LAI4 from the paper. LAI1 consists of only overstory LAI, and LAI4 consists of overstory (LAI4), understory (LAI4_under), and total (LAI4_total) LAI. For each LAI estimation method, summary statistics of the entire watershed are included (min, first quartile, median, third quartile, and max).

The two R scripts (R language and environment for statistical computing) summarize Forest Inventory & Analysis (FIA) data from the FIA database (FIADB) to estimate LAI at FIA plots. 1) FIADB_queries_public.r: Script for compiling FIA plot measurements prior to estimating LAI 2) LAI_estimation_public: Script for estimating LAI at FIA plots using the four methods described in this paper

Before running the R scripts, users must obtain several FIADB tables (PLOT, COND, TREE, and P2VEG_SUBP_STRUCTURE; all four tables must be renamed with lower-case names, e.g., "plot"). These tables can be obtained using one of two methods: 1) By downloading CSV files for the appropriate U.S. state(s) from the FIA DataMart (https://apps.fs.usda.gov/fia/datamart/datamart.html). If this method is used, the CSV files must be imported (read) into R before proceeding. 2) By using r package 'rFIA' to download the tables from FIADB for the U.S. state(s) of interest.

Note that publicly available plot coordinates are accurate within 1 km and are not true plot locations, which are legally confidential to protect the integrity of the sample locations and the privacy of landowners. Access to true plot location data requires review by FIA's Spatial Data Services unit, who can be contacted at SM.FS.RMRSFIA_Help@usda.gov.

The quantity and condition of downed dead wood (DDW) is emerging as a major factor governing forest ecosystem processes such as carbon cycling, fire behavior, and tree regeneration. Despite this, systematic inventories of DDW are sparse if not absent across major forest biomes. The Forest Inventory and Analysis program of the United States (US) Forest Service has conducted an annual DDW inventory on all coterminous US forest land since 2002 (~1 plot per 38,850 ha), with a sample intensification occurring since 2012 (~1 plot per 19,425 ha). The data are organized according to DDW components and by sampling information which can all be linked to a multitude of auxiliary information in the national database. As the sampling of DDW is conducted using field efficient line-intersect approaches, several assumptions are adopted during population estimation that serve to identify critical knowledge gaps. The plot- and population-level DDW datasets and estimates provide the first insights into an understudied but critical ecosystem component of temperate forests of North America with global application. Resources in this dataset:Resource Title: Data files. File Name: Web Page, url: https://www.nature.com/articles/sdata2018303#Sec9 Data Citations: USDA Forest Inventory and Analysis DataMart https://apps.fs.usda.gov/fia/datamart/datamart.html (2018); Woodall, C. W. et al. Dryad Digital Repository https://doi.org/10.5061/dryad.9sv4765 (2018) (data links appear at the bottom of the References section)

description: Source data for forest stand age were obtained from the USDA Forest Inventory and Analysis (FIA) DataMart and were projected for future scenarios based on selected IPCC emission scenarios. The spatial extent is the conterminous United States and the temporal extent is from 2006 through 2050. The data of this variable are spatially gridded data in GeoTiff format and have been re-projected to Albers Equal Area in the NAD83 datum at a resolution of 2000 meters.; abstract: Source data for forest stand age were obtained from the USDA Forest Inventory and Analysis (FIA) DataMart and were projected for future scenarios based on selected IPCC emission scenarios. The spatial extent is the conterminous United States and the temporal extent is from 2006 through 2050. The data of this variable are spatially gridded data in GeoTiff format and have been re-projected to Albers Equal Area in the NAD83 datum at a resolution of 2000 meters.

Observations of the forests of the conterminous United States at the level of individual trees would be of utility for any number of applications, ranging from modelling the effect of wildland fire on terrestrial carbon resources to estimation of timber volume. While such observations do exist at selected spots such as established forest plots, most forests have not been mapped with this level of specificity. To fill the gap in tree-level mapping, we used a modelling approach that employed a random forests machine-learning technique. This technique was nearly identical to that employed by Riley et al. (2016), except that it used disturbance variables in addition to topographic and biophysical variables. This method imputes the plot with the best statistical match, according to a “forest” of decision trees, to each pixel of gridded landscape data. A set of predictor variables was used to train the random forests algorithm, which was then leveraged to extrapolate measurements across forested areas of the conterminous United States. Specifically, 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 2014. These variables were present or were derived for both 1) the detailed reference data, which consisted of forest plot data from the U.S. Forest Service’s Forest and Inventory Analysis program (FIA) version 1.7.1 and 2) the landscape target data, which consisted of raster data at 30x30 meter (m) resolution provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE; https://landfire.gov/) FIA plots were imputed to the raster data by the random forests algorithm, providing a tree-level model of all forested areas in the conterminous U.S. Of 67,141 single-condition FIA plots available to random forests, 62,758 of these (93.5%) were utilized in imputation to 2,841,601,981 forested pixels.

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 conterminous U.S. for landscape conditions circa 2014. This map is commonly known as "TreeMap 2014". The map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://apps.fs.usda.gov/fia/datamart/datamart_access.html) or to the Microsoft Access Database and ASCII files included in this data publication to produce tree-level maps or to map other plot attributes. These 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 conterminous United States (CONUS) are currently available.See the Entity and Attributes section for details regarding the relationship between the data files included in this publication and the FIA DataMart.

These data were published on 07/02/2019. On 03/26/2021, the metadata was updated to include reference to a new publication. On 02/01/2024, some additional minor metadata updates were made and trees_CONUS_5_15_2019.mdb was removed from the package because it is an older format and the same content is included via text files.

Initializing forest landscape models (FLMs) to simulate changes in tree species composition requires accurate fine-scale forest attribute information mapped contiguously over large areas. Nearest-neighbor imputation maps have high potential for use as the initial condition within FLMs, but the tendency for field plots to be imputed over large geographical distances results in species frequently mapped outside of their home ranges, which is problematic. We developed an approach for evaluating and selecting field plots for imputation based on their similarity in feature-space, their species composition, and their geographical distance between source and imputation to produce a map that is appropriate for initializing an FLM. We applied this approach to map 13m ha of forest throughout the six New England states (Rhode Island, Connecticut, Massachusetts, New Hampshire, Vermont, and Maine). The map itself is a .img raster file of FIA plot CN numbers. To access FIA data from this map, one has to link the mapcodes in this map to FIA data supplied by USDA FIA database (https://apps.fs.usda.gov/fia/datamart/datamart.html). Due to plot confidentiality and integrity concerns, pixels containing FIA plots were always assigned to some other plot than the actual one found there.

This collection contains the subsets of FIA (https://apps.fs.usda.gov/fia/datamart/datamart.html) and PRISM (http://prism.oregonstate.edu/) data used in "Climate-driven, but dynamic and complex? A reconciliation of competing hypotheses for species' distributions".The two datasets contain demographic data (survival and growth in SurvivalData.csv and recruitment in RecruitData.csv) and climate data used to fit vital rate models estimating the effects of climate and competition on Pinus edulis in the southwestern US.

Modern tree distributions modeled under current climate and predicted to past climate. Values of '2' represent presence. The column mark is current presence, while _20000 is 20 ka, _14000 is 14 ka, _13000 is 13 ka, etc. For quick download, the .dbf for each species can be joined to the shapefile (us_can_ecosub). Alternatively, download and use the zipped folder of shapefiles (glac_shapes).. {"references": ["Canada National Forest Inventory, https://doi.org/10.23687/ec9e2659-1c29-4ddb-87a2-6aced147a990.", "FIA DataMart, https://apps.fs.usda.gov/fia/datamart/datamart.html", "Dryad Dataset, https://doi.org/10.5061/dryad.1597g"]} Manuscript under review

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.

Fragmentation transforms the environment along forest edges. The prevailing narrative, driven by tropical research, suggests that edge environments increase tree mortality and structural degradation resulting in net decreases in ecosystem productivity. We show that temperate forest edges exhibit increased forest growth (basal area increment; BAI) and biomass (basal area; BA) with no change in total mortality relative to the forest interior. To assess forest edges, we analyzed more than 48,000 forest inventory plots (USDA FIA) across the north-eastern US using a quasi-experimental matching design. At forest edges adjacent to anthropogenic land covers, we report increases of 36.3% and 24.1% in forest growth and biomass, respectively. We then scale the edge impacts on growth (along anthropogenic edges only) across our study area using maps of land-cover and forest type. We find large variability in the effect of including edges on estimates of total forest growth, largely driven by differences in the prevalence of fragmentation. Estimated increases in forest growth range from a 23% increase in the agricultural-dominated western areas, a 2% increase in the least-fragmented northern regions, and a 15% increase within the metropolitan east coast. Finally, we also quantify forest fragmentation globally, at 30-m resolution, showing that temperate forests contain 52% more edge forest area than tropical forests. We provide two tables containing the post-matched dataset of FIA subplots, including subplot BA, BAI, and edge status. We include the associated environmental covariates, extracted from gridded raster data, and used in our matching and statistical analyses. Due to plot confidentiality restrictions we do not provide spatial locations of the FIA subplots, but we do provide unique plot identifiers that allow users to link each record to the publically-available data provided by the USDA FIA database (https://apps.fs.usda.gov/fia/datamart/). This dataset can be used to recreate our regression modelling results and regional estimates of forest growth. We also provide two polygon shapefile layers: EPA Level IV Ecoregion boundaries with spatially-aggregated estimates of edge and total BAI (m2 yr-1) as well as total forest area and edge forest area (forest less than 30 m from a non-forest pixel)and global terrestrial ecoregions boundaries with spatially-aggregated estimates of total forest area and edge forest area (forest less than 30 m from a non-forest pixel).

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.

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States. 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 30×30 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) or to the text and SQL files included in this data publication to produce tree-level maps or to map other plot attributes. The accompanying database files included in this publication also contain attributes regarding the FIA plot CN (or control number, a unique identifier for each time a plot is measured), 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 code for cause of death where applicable. 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. Because falling snags cause hazard to firefighting personnel and other forest users, in response to requests from the field, we provide a separate map that provides a rating of the severity of snag hazard based on the density and height of snags. 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.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. The TreeMap 2014 dataset (Riley et al. 2019) was the first of its kind to provide such detailed forest structure data across the forests of the conterminous United States. The TreeMap 2016 dataset updates the TreeMap 2014 dataset to landscape conditions c2016. Prior to this imputed forest data, assessments relied largely on forest inventory at fixed plot locations at sparse densities.See the Entity and Attributes section for details regarding the relationship between the data files included in this publication and the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB).

These data were published on 08/26/2021. On 02/01/2024, the metadata was updated to include reference to a recently published article and update URLs for Forest Service websites.

For more information about these data, see Riley et al. (2022).

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.

The Forest Inventory and Analysis dataset is a nationwide survey of the forest assets of the United States. The Forest Inventory and Analysis (FIA) research program has been in existence since mandated by Congress in 1928. FIA's primary objective is to determine the extent, condition, volume, growth, and use of trees on the Nation's forest land. This dataset includes the most recent data available from the USFS datamart , it does not include historical data. Original field names have been expanded to full names and code values have been expanded to full names in all tables, in addition, each table contains data from all States. A full description of the original tables is available from the USFS . A user's guide with example summary reports is also available from the USFS . This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Dieses Produkt ist Teil der TreeMap-Datensuite. Es enthält detaillierte räumliche Informationen zu Waldmerkmalen wie der Anzahl lebender und toter Bäume, der Biomasse und dem Kohlenstoffgehalt in den gesamten bewaldeten Gebieten der kontinentalen USA im Jahr 2016. TreeMap v2016 enthält ein Bild, eine 22-Band-Karte mit einer Auflösung von 30 × 30 m der Wälder der kontinentalen USA um 2016. Jedes Band stellt ein Attribut dar, das aus ausgewählten FIA-Daten abgeleitet wurde (und ein Band die TreeMap-ID). Beispiele für Attribute sind Waldtyp, Prozentsatz der Baumkronenbedeckung, Bestand an lebenden Bäumen, Biomasse lebender/toter Bäume und Kohlenstoff in lebenden/toten Bäumen. TreeMap-Produkte sind die Ausgabe eines maschinellen Lernens-Algorithmus für den Zufallswald, der jedem Pixel der gerasterten LANDFIRE-Eingabedaten den am besten passenden FIA-Plot (Forest Inventory Analysis) zuweist. Ziel ist es, die sich ergänzenden Stärken der detaillierten, aber räumlich spärlichen FIA-Daten mit den weniger detaillierten, aber räumlich umfassenden LANDFIRE-Daten zu kombinieren, um bessere Schätzungen der Waldeigenschaften in verschiedenen Maßstäben zu erhalten. TreeMap wird sowohl im privaten als auch im öffentlichen Sektor für Projekte wie die Planung von Brennstoffbehandlungen, die Kartierung von Gefahren durch umgestürzte Bäume und die Schätzung von terrestrischen Kohlenstoffressourcen eingesetzt. TreeMap unterscheidet sich von anderen imputierten Waldvegetationsprodukten dadurch, dass jedem Pixel eine FIA-Parzellen-ID zugewiesen wird, während andere Datensätze Waldmerkmale wie die lebende Stammfläche (z.B. Ohmann und Gregory 2002; Pierce Jr et al. 2009; Wilson, Lister und Riemann 2012). Die FIA-Parzellen-ID kann mit den Hunderten von Variablen und Attributen verknüpft werden, die für jeden Baum und jede Parzelle im FIA DataMart, dem öffentlichen Repository der FIA mit Parzelleninformationen, erfasst wurden (Forest Inventory Analysis 2022a). Die Methodik von 2016 enthält Störungen als Antwortvariable, was zu einer höheren Genauigkeit bei der Kartierung von gestörten Gebieten führt. Die Genauigkeit innerhalb der Klasse betrug über 90% für Waldbedeckung, Höhe, Vegetationsgruppe und Störungscode im Vergleich zu LANDFIRE-Karten. In 57,5% der Fälle entsprach mindestens ein Pixel innerhalb des Radius der Validierungsfelder der Klasse der vorhergesagten Werte für die Waldbedeckung, in 80,0% der Fälle für die Höhe, in 80,0% der Fälle für die Baumart mit der größten Grundfläche und in 87,4 % der Fälle für die Störung. Zusätzliche Ressourcen Weitere Informationen zu den Methoden und zur Genauigkeitsbewertung finden Sie in der TreeMap-Veröffentlichung von 2016. Der TreeMap 2016 Data Explorer ist eine webbasierte Anwendung, mit der Nutzer TreeMap-Attributdaten aufrufen und herunterladen können. Das TreeMap Research Data Archive für den Download des vollständigen Datensatzes, Metadaten und Supportdokumente. TreeMap Raster Data Gateway für den Download von TreeMap-Attributdaten, Metadaten und Supportdokumenten FIA Database Manual Version 8 (FIA-Datenbankhandbuch Version 8) mit ausführlicheren Informationen zu den in TreeMap 2016 enthaltenen Attributen Bei Fragen oder spezifischen Datenanfragen wenden Sie sich bitte an sm.fs.treemaphelp@usda.gov. Forstinventaranalyse. 2022a. DataMart für die Analyse von Waldinventaren. Forest Inventory Analysis DataMart FIADB_1.9.0. 2022. https://apps.fs.usda.gov/fia/datamart/datamart.html. Ohmann, Janet L. und Matthew J. Gregory. 2002. Predictive Mapping of Forest Composition and Structure with Direct Gradient Analysis and Nearest- Neighbor Imputation in Coastal Oregon, USA. Ja. J. Für. Res. 32:725–741. doi: 10.1139/X02-011. Pierce, Kenneth B Jr, Janet L Ohmann, Michael C Wimberly, Matthew J Gregory und Jeremy S Fried. 2009. Mapping Wildland Fuels and Forest Structure for Land Management: A Comparison of Nearest Neighbor Imputation and Other Methods. Ja. J. Für. Res. 39: 1901–1916. doi:10.1139/X09-102. Wilson, B Tyler, Andrew J Lister und Rachel I Riemann. 2012. A Nearest-Neighbor Imputation Approach to Mapping Tree Species over Large Areas Using Forest Inventory Plots and Moderate Resolution Raster Data. Forest Ecol. Verwalten 271:182–198. doi: 10.1016/j. foreco.2012.02.002.

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.