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.

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

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

Fork this kernel to get started with this dataset.

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 map shows information produced by the USDA Forest Service Forest Inventory and Analysis (FIA) program related to the latest forest inventory data available online, the latest inventory year used in the most recent State report, publishing year of the latest State report, and the type of inventory (annual or periodic) conducted in each state as of fiscal year 2020.

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 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 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 DownloadsThis 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 CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

This data represents forest area estimates (and percent sampling error) by county for 2015, 2016, and 2017. The data was generated from the Forest Inventory Analysis (FIA) using the EVALIDator web tool (http://apps.fs.fed.us/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.

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 2016. 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

Analysis of ‘FIA Landcover County Estimates - 2015 (Feature Layer)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/f3bb7d74-ce1d-44d0-a567-f025e04afa70 on 11 February 2022.

--- Dataset description provided by original source is as follows ---

This feature class represents forest area estimates (and percent sampling error) by county for the year 2015. The data was generated from the Forest Inventory Analysis (FIA) using the EVALIDator web tool (https://apps.fs.fed.us/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

--- Original source retains full ownership of the source dataset ---

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.

I obtained inventory data from the USDA Forest Inventory and Analysis (FIA) program, covering 1999 to 2022, focusing on eight southeastern states in the U.S. (Alabama, Mississippi, Florida, Georgia, North Carolina, South Carolina, Texas, and Louisiana). For this study, I filtered the data for 2015-2019, during which the FIA captures 20% of plots in each state annually. Using R, I converted aboveground biomass (AGB) from pounds to kg/ha, tree height from feet to meters, and diameter at breast height (DBH) from inches to centimeters to align with the metric system. Structural diversity of tree height and diameter was calculated using the Shannon diversity index based on tree height and diameter classes. Climate data from PRISM and soil carbon to nitrogen data from the World Soil Database were integrated with the FIA data using the nearest neighbors method in the SF package in R. The combined dataset was standardized for Structural Equation Modeling (SEM) analysis.

This feature class represents forest area estimates (and percent sampling error) by county for the year 2015. The data was generated from the Forest Inventory Analysis (FIA) using the EVALIDator web tool (http://apps.fs.fed.us/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

Analysis of ‘FIA Landcover County Estimates - 2017 (Feature Layer)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/93462f57-fe29-444d-ab6b-f90c57c66598 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

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.fed.us/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

--- Original source retains full ownership of the source dataset ---

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.

The U.S. has been providing national-scale estimates of forest carbon stocks and stock change to meet United Nations Framework Convention on Climate Change reporting requirements for years. Through application of a nearest-neighbor imputation approach, mapped estimates of forest biomass 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 would contain the following 7 raster maps: Aboveground Forest Biomass, Belowground Forest Biomass, Forest Tree Bole Biomass, Forest Sapling Biomass, Forest Stump Biomass, Forest Top Biomass, Woodland Specias Biomass. All layers have a 250 meter pixel resolution and values represent biomass pounds per acre. 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-2013-0004This 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 United States (US) Department of Agriculture Forest Service Forest Inventory and Analysis (FIA) program operates the national forest inventory of the US. Traditionally, the FIA program has relied on sample-based approaches—permanent plot networks and associated design-based estimators—to estimate forest variables across large geographic areas and long periods of time. These approaches generally offer unbiased inference on large domains but fail to provide reliable estimates for small domains due to low sample sizes. Rising demand for small domain estimates will thus require the FIA program to adopt non-traditional estimation approaches that are capable of delivering defensible estimates of forest variables at increased spatial and temporal resolution, without the expense of collecting additional field data. In light of this challenge, the development of small area estimation (SAE) methods—estimation techniques that support inference on small domains—for FIA data has become an active and highly productive area of research. Yet, SAE methods remain difficult to apply to FIA data, due in part to the complex data structures and survey design used by the FIA program. Herein, we present the potential of rFIA, an open-source R package designed to increase the accessibility of FIA data, to simplify the application of a broad suite of SAE methods to FIA data. We demonstrate this potential via two case studies: (1) estimation of contemporary county-level forest carbon stocks across the conterminous US using a spatial Fay-Herriot model; and (2) temporally-explicit estimation of multi-decadal trends in merchantable wood volume in Washington County, Maine using a Bayesian multi-level model. In both cases, we show the application of SAE techniques offers considerable improvements in precision over FIA's traditional, post-stratified estimators. Finally, we offer a discussion of the potential role that rFIA and other open-source tools might play in accelerating the adoption of SAE techniques among users of FIA data.

The U.S. has been providing national-scale estimates of forest carbon stocks and stock change to meet United Nations Framework Convention on Climate Change reporting requirements for years. Through application of a nearest-neighbor imputation approach, mapped estimates of forest biomass 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 would contain the following 7 raster maps: Aboveground Forest Biomass, Belowground Forest Biomass, Forest Tree Bole Biomass, Forest Sapling Biomass, Forest Stump Biomass, Forest Top Biomass, Woodland Specias Biomass. All layers have a 250 meter pixel resolution and values represent biomass pounds per acre. 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

The USDA Forest Service, Northern Research Station Forest Inventory and Analysis (FIA) program has prepared a collaborative Forest Inventory and Analysis data summary for the Black Hills National Forest (BHNF) in western South Dakota and northeastern Wyoming. The summary is based on double-intensity sampling (approximately one plot representing each 3,000 acres) from 2017-2018 and accelerated base-plot (approximately one plot representing each 6,000 acres) sampling for 2019. The 2019 data includes measurements of base plots that would typically be remeasured during the period 2020-2023. Included in this package is a summary spreadsheet containing 15 tables with graphs covering the timber resources, volume, growth, mortality, and harvest removals on the forest. Also included is a spreadsheet providing the associated sampling errors for these attributes. A customized version of the FIA EVALIDator data access tool is also provided, that allows additional queries for these Black Hills National Forest data.Data were collected as part of the ongoing forest inventory. Intensifications were made both temporally and spatially at the request of the Black Hills National Forest. By spanning a state boundary, these data require custom estimation units created for the Black Hills National Forest; this precludes loading into the current version of FIADB. FIA is sharing these data with the public through this mechanism until an updated tool is available online.

This study explores factors affecting aboveground carbon (AGC) storage in mixed oak-pine forests across the Southeastern United States. Utilizing USDA Forest Inventory and Analysis (FIA) data from 2009 to 2019, the research spans nine states: Alabama, Mississippi, Florida, Georgia, North Carolina, South Carolina, Texas, Louisiana, and Virginia. Data processing in R included converting units to the metric system and calculating structural diversity using Shannon diversity indices. Climate data from the PRISM Climate Group were integrated with FIA data using longitude and latitude. The research aims to uncover how various factors influence AGC storage and contribute to informed forest management practices.