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.

Context

US Forest Service Forest Inventory and Analysis National Program.

The Forest Inventory and Analysis (FIA) Program of the U.S. Forest Service provides the information needed to assess America's forests.

https://www.fia.fs.fed.us/

Content

As the Nation's continuous forest census, our program projects how forests are likely to appear 10 to 50 years from now. This enables us to evaluate whether current forest management practices are sustainable in the long run and to assess whether current policies will allow the next generation to enjoy America's forests as we do today.

FIA reports on status and trends in forest area and location; in the species, size, and health of trees; in total tree growth, mortality, and removals by harvest; in wood production and utilization rates by various products; and in forest land ownership.

The Forest Service has significantly enhanced the FIA program by changing from a periodic survey to an annual survey, by increasing our capacity to analyze and publish data, and by expanding the scope of our data collection to include soil, under story vegetation, tree crown conditions, coarse woody debris, and lichen community composition on a subsample of our plots. The FIA program has also expanded to include the sampling of urban trees on all land use types in select cities.

For more details, see: https://www.fia.fs.fed.us/library/database-documentation/current/ver70/FIADB%20User%20Guide%20P2_7-0_ntc.final.pdf

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Acknowledgements

https://www.fia.fs.fed.us/

https://cloud.google.com/blog/big-data/2017/10/get-to-know-your-trees-us-forest-service-fia-dataset-now-available-in-bigquery

FIA is managed by the Research and Development organization within the USDA Forest Service in cooperation with State and Private Forestry and National Forest Systems. FIA traces it's origin back to the McSweeney - McNary Forest Research Act of 1928 (P.L. 70-466). This law initiated the first inventories starting in 1930.

Banner Photo by @rmorton3 from Unplash.

Inspiration

Estimating timberland and forest land acres by state.

https://cloud.google.com/blog/big-data/2017/10/images/4728824346443776/forest-data-4.png" alt="enter image description here"> https://cloud.google.com/blog/big-data/2017/10/images/4728824346443776/forest-data-4.png

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.

This feature class represents forest area estimates (and percent sampling error) by county for the year 2019. The data was generated from the Forest Inventory Analysis (FIA) using the EVALIDator web tool (https://apps.fs.usda.gov/Evalidator/evalidator.jsp). The areas were calculated within county limits using the US Census Bureau's county spatial data (https://www.census.gov/geo/maps-data/data/cbf/cbf_counties.html). Features and attributes of the county layer were adapted to match attributes within the FIA database (FIADB) and features have been generalized by removing vertices to enhance performance. Future iterations of this dataset will be produced using refined methods and higher resolution spatial data. Metadata and Downloads

Data on Forest Inventory and Analysis (FIA) includes information on Palau's forests 2013-2014. The Pacific Northwest Forest Inventory and Analysis (PNW-FIA) program measures and compiles data on plots in coastal Alaska, California, Hawaii, Oregon, Washington, and U.S.- affiliated Pacific Islands. Most data are available in Access databases and can be downloaded by clicking one of the links below. PNW data are combined with data from all states in the U.S. and stored in the national FIADB. Data for any state can be accessed on the national website (see links to national tools below). Please be aware that some documents may be very large. The PNW-FIA Program shifted from a periodic to an annual inventory system in 2001. Periodic inventories sampled primarily timberland plots outside of national forests and most reserved areas, in a single state within a 2- or 3-year window. Typically, re-assessments occurred every ten years in the West. For the annual inventory in the Pacific Northwest all forested plots are now sampled, with one-tenth of the plots in any given state being visited annually. A full annual inventory cycle is complete in ten years. To download and use the FIA Database, follow this link https://www.fs.fed.us/pnw/rma/fia-topics/inventory-data

Data on Forest Inventory and Analysis (FIA) includes information on Palau's forests 2013-2014. The Pacific Northwest Forest Inventory and Analysis (PNW-FIA) program measures and compiles data on plots in coastal Alaska, California, Hawaii, Oregon, Washington, and U.S.- affiliated Pacific Islands. Most data are available in Access databases and can be downloaded by clicking one of the links below. PNW data are combined with data from all states in the U.S. and stored in the national FIADB. Data for any state can be accessed on the national website (see links to national tools below). Please be aware that some documents may be very large. The PNW-FIA Program shifted from a periodic to an annual inventory system in 2001. Periodic inventories sampled primarily timberland plots outside of national forests and most reserved areas, in a single state within a 2- or 3-year window. Typically, re-assessments occurred every ten years in the West. For the annual inventory in the Pacific Northwest all forested plots are now sampled, with one-tenth of the plots in any given state being visited annually. A full annual inventory cycle is complete in ten years. To download and use the FIA Database, follow this link https://www.fs.fed.us/pnw/rma/fia-topics/inventory-data

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 feature class represents forest area estimates (and percent sampling error) by county for the year 2018. The data was generated from the Forest Inventory Analysis (FIA) using the EVALIDator web tool (https://apps.fs.usda.gov/Evalidator/evalidator.jsp). The areas were calculated within county limits using the US Census Bureau's county spatial data (https://www.census.gov/geo/maps-data/data/cbf/cbf_counties.html). Features and attributes of the county layer were adapted to match attributes within the FIA database (FIADB) and features have been generalized by removing vertices to enhance performance. Future iterations of this dataset will be produced using refined methods and higher resolution spatial data. Metadata and Downloads

The Forest and Inventory database (FIADB) can be used to produce information on a wide variety of forest statistics including forest area, numbers of trees, tree biomass, tree volume, volume of growth, volume of mortality, and volume harvested. These statistics can be categorized by different data elements. The area estimates, for example, can be categorized by county, forest type, ownership, and stand-size, while estimates for numbers of trees, biomass, and volume can additionally be categorized by tree species, and tree diameter. The FIADB structure was developed to provide users with as much data as possible in a consistent manner among States. Frequency of data collection for individual states has varied over time. Annual inventories use a common plot design and common data collection procedures nationwide, resulting in greater consistency among FIA work units than earlier inventories. Many inventories conducted prior to the implementation of the annual inventory are available in the FIADB - the earliest dating back to 1968. However, various data attributes may be nulled or the items may have been collected or computed differently. Data are distributed via the FIA DataMart in the FIADB structure as well as a number of derived structures, reflecting pre-defined subsets of FIADB. Links to field collection manuals and the FIADB user's manual are provided in this metadata document.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 1998 Farm Bill required 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. In addition to the data, summary reports for individual States are published.To protect the integrity of the FIA sample, the exact coordinates of our sample plot locations are kept confidential. This protects the privacy of landowners who allow FIA field crews on their land, as well as protects the plots from any tampering. This policy of location confidentiality is incorporated into law through the Fiscal Year 2000 Consolidated Appropriations Bill (PL 106-113) which amended the Food Security Act of 1985 (7 U.S.C. 2276(d)) to include FIA data to the list of items that require confidential treatment.

The original date for this metadata document is 07/18/2019. On 07/29/2022 minor metadata updates were made, which included the correction of old URLs where possible.

The tree age dataset was derived for tally trees in the Forest Inventory and Analysis program (FIA) of the US Forest Service using an age-size relationship modeling framework that incorporates species-specific and environmental variables.

Associated paper: Lu, J., Huang, C., Schleeweis, K., Zou, Z., & Gong, W. (2025). Tree age estimation across the US using forest inventory and analysis database. Forest Ecology and Management, 584, 122603.

This feature class represents forest area estimates (and percent sampling error) by county for the year 2017. The data was generated from the Forest Inventory Analysis (FIA) using the EVALIDator web tool (https://apps.fs.usda.gov/Evalidator/evalidator.jsp). The areas were calculated within county limits using the US Census Bureau's county spatial data (https://www.census.gov/geo/maps-data/data/cbf/cbf_counties.html). Features and attributes of the county layer were adapted to match attributes within the FIA database (FIADB) and features have been generalized by removing vertices to enhance performance. Future iterations of this dataset will be produced using refined methods and higher resolution spatial data. Metadata and Downloads

Area of sample population randomly sampled, mean stand age currently, and in 1930, and Chi-square test results.

This feature class represents forest area estimates (and percent sampling error) by county for the year 2017. The data was generated from the Forest Inventory Analysis (FIA) using the EVALIDator web tool (https://apps.fs.usda.gov/Evalidator/evalidator.jsp). The areas were calculated within county limits using the US Census Bureau's county spatial data (https://www.census.gov/geo/maps-data/data/cbf/cbf_counties.html). Features and attributes of the county layer were adapted to match attributes within the FIA database (FIADB) and features have been generalized by removing vertices to enhance performance. Future iterations of this dataset will be produced using refined methods and higher resolution spatial data. Metadata and Downloads

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.

Through application of a nearest-neighbor imputation approach, mapped estimates of forest carbon density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product contains the following 8 raster maps: total forest carbon in all stocks, live tree aboveground forest carbon, live tree belowground forest carbon, forest down dead carbon, forest litter carbon, forest standing dead carbon, forest soil organic carbon, and forest understory carbon. The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004. Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004.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.

Although many species display relationships – both positive and negative – with various forest successional stages, researchers often rely on datasets that describe forest presence rather than forest age (i.e., National Landcover Database). In 2023, the USDA Forest Service introduced standardized region-specific mature and old-growth (MOG) forest definitions for the United States, but these definitions have not been readily integrated to address questions in ecology and conservation. Here, we introduce ‘mapMOG’—an open-access R function that applies the recently adopted federal MOG definitions to Forest Inventory and Analysis (FIA) plots across the contiguous United States (US). Additionally, our novel function interpolates MOG status across US forested lands between FIA plots. To demonstrate the utility of these data for forest landscape ecological modeling, we compare the predictive power of the MOG covariate against binary forest/non-forest and percent canopy cover covariates to examine forest habitat associations across three taxa, geographies, and modelling frameworks: avian richness in Mid-Atlantic national parks; Seminole bat (Lasiurus seminolus) occupancy in the Southeastern Coastal Plain; and Cascade torrent salamander (Rhyacotriton cascadae) distribution associated with US National Forests in the Pacific Northwest. In all three cases, our MOG covariate produced by our function explained variation in the wildlife occurrence data better than the alternative forest metrics. Finally, we compared imputed results across multiple spatial scales and found notable but statistically insignificant differences in interpolated MOG scores.

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.

Phylogenetic supertree encompassing all species in the USDA Forest Service Forest Inventory and Analysis (FIA) tree species database, in Newick format.

Nonnative invasive plant species cause long-term detrimental effects on forest ecosystems, including declines in biological diversity, alterations to forest succession, and changes in nutrient, carbon, and water cycles. The damage caused by these exotic species, and the efforts to control them, are costly, even before accounting for the impacts to nonmarket economic services such as recreation and landscape aesthetics. The Forest Inventory and Analysis (FIA) program collects invasive plant data based on expert-derived lists of problematic invasive plant species defined as those of any growth form likely to cause economic or environmental harm. Using each state's most recent evaluation period between 2005 and 2018, we determined the number and percent of FIA plots invaded by non-native plant species for each U.S. county, as well as the mean number of invasive species and percent cover of invasive species on the plots inventoried for invasive species in each county. These county-level data are provided as both a shapefile and Geopackage.These data were developed to assess the degree of invasion of U.S. forests by non-native plants for the 2020 Resources Planning Act (RPA) Assessment (https://www.fs.usda.gov/research/inventory/rpaa) chapter on Disturbances to Forests and Rangelands.These data were published on 03/10/2023. Metadata updated on 10/17/2023 to include reference to published RPA Assessment.

This dataset provides forest biomass estimates for the conterminous United States based on data from the USDA Forest Inventory and Analysis (FIA) program. FIA maintains uniformly measured field plots across the conterminous U.S. This dataset, derived from field survey data from 2009-2019, includes statistical estimates of biomass at the finest scale (64,000-hectare hexagons) allowed by FIA's sample density. Estimates include the mean (and standard error of the mean) biomass for both live and dead trees, calculated using three sets of allometric equations. There is also an estimate of the area of forestland in each hexagon. These data can be useful for assessing the accuracy of remotely sensed biomass estimates.