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

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

The Spatial Database of Planted Trees (SDPT) was compiled by Global Forest Watch using data obtained from national governments, non-governmental organizations and independent researchers. Data were compiled for 82 countries around the world, with most country maps originating from supervised classification or manual polygon delineation of Landsat, SPOT or RapidEye satellite imagery. The category of “planted trees” in the SDPT includes forest plantations of native or introduced species, established through deliberate human planting or seeding. Sometimes called “tree farms,” these forests infuse the global economy with a constant stream of lumber for construction, pulp for paper and fuelwood for energy. The data set also includes agricultural tree crops like oil palm plantations, avocado farms, apple orchards and even Christmas tree farms. The SDPT makes it possible to identify planted forests and tree crops as being separate from natural forests and enables changes in these planted areas to be monitored independently from changes in global natural forest cover.The SDPT contains 173 million hectares of planted forest and 50 million hectares of agricultural trees, or approximately 82% of the world’s total planted forest area in 2015 (FAO 2015). The SDPT was compiled through a procedure that included cleaning and processing each individual data set before creating a harmonized attribute table. Data is available for download in all countries except China and Papua New Guinea. If you are aware of any additional plantations data, please let us know by filling out this form.

This extensive dataset offers a multi-faceted view of global tree cover changes, including loss, gain, and associated carbon emissions and removals, spanning the period from 2001 to 2024. Leveraging advanced satellite data and modeling techniques, it provides critical insights into forest dynamics at various scales.

Key Data Components:

Tree Cover Definition: Defines "tree cover" as all vegetation greater than 5 meters in height, encompassing natural forests and plantations across diverse canopy densities.

Tree Cover Loss & Drivers:

Quantifies "loss" as the removal or mortality of tree cover due to factors such as mechanical harvesting, fire, disease, and storm damage. It emphasizes that "loss" does not equate to deforestation.

Identifies the dominant drivers of tree cover loss at a 1-km resolution, providing granular insight into the causes of change.

Tree Cover Gain: Includes data on areas where tree cover has increased, offering a complete picture of landscape evolution.

Carbon Emissions & Removals (Net Flux):

Provides modeled gross estimates of carbon emissions from stand-replacing disturbances and carbon removals, reflecting annual averages over the 2001-2024 period.

Highlights that emissions and removals reflect gross estimates without accounting for regrowth, emphasizing the need for cautious interpretation.

Tree Cover Extent: Offers snapshots of tree cover extent for various years, allowing for baseline comparisons and trend analysis.

Ancillary Data: Includes metadata, fire alerts, and ISO country codes to enhance data usability and contextualization.

Methodology & Considerations:

The data is a product of sophisticated modeling, acknowledging an inherent degree of error and uncertainty. Users are strongly advised to consult the accompanying metadata and documentation for a thorough understanding of the methodologies, limitations, and appropriate use of the data. Notably, improvements in detection between 2011 and 2015 may influence recent loss estimates. Driver data for individual years should be interpreted with caution due to potential over- or under-estimation for small-scale loss events.

Potential Applications:

This dataset is invaluable for researchers, policymakers, conservationists, and anyone interested in:

Monitoring global deforestation and reforestation efforts.

Assessing the carbon budget of forest ecosystems.

Understanding the drivers of land-use change.

Developing and evaluating climate change mitigation strategies.

Supporting sustainable forest management and land planning.

A single point of access for data collected and managed by the United States Forest Service. Users can use the Geospatial Data Discovery Tool to access data about individual forests or grasslands or about an area of interest that they specify on the national map. Users can find and download datasets by topic area or theme or find and use map services published by the Agency.

This archive publishes and preserves short and long-term research data collected from studies funded by:Forest Service Research and Development (FS R&D)Joint Fire Science Program (JFSP)Aldo Leopold Wilderness Research Institute (ALWRI)Of special interest, our collection includes data from a number of Forest Service Experimental Forests and Ranges.Each archived data set (i.e., "data publication") contains at least one data set, complete metadata for the data set(s), and any other documentation the researcher deemed important to understanding the data set(s). The data catalog entries present the metadata and a link to the data. In some cases the data link is to a different archive.

As part of an effort of the World Resources Institute Global Restoration Initiative to map forest and landscape restoration opportunities, the map of potential forests represents an estimate of where forests would grow under current climate conditions and without human influence. The main source of data for defining potential forest extent is the terrestrial ecoregions of the world (Olson et al. 2001). Each ecoregion was classified as belonging to one of four categories: dense forests, open forests, woodlands, or non-forest, depending on its description (including current and potential vegetation) and its proportion of different forest types, with additional input from the following datasets: current forest extent; bioclimatic zoning and original forest cover extent; and a forest distribution map produced by modeling based on global climate variables and elevation (Hansen et al. 2013, Zomer et al. 2007). The dataset is based on significant simplifications due to limited availability of globally-consistent data. The maps are at a relatively coarse scale and should only be used to estimate potential forest coverage at regional or global scale. Estimates of potential forest coverage are based on current climate conditions in the absence of human disturbance.

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.

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).

This project systematically reviewed the literature for measurements of aboveground carbon stocks in monoculture plantation forests. The data compiled here are for monoculture (single-species) plantation forests, which are a subset of a broader review to identify empirical measurements of carbon stocks across all forest types. The database is structured similarly to that of the ForC (https://forc-db.github.io/) and GROA databases (https://github.com/forc-db/GROA).

When using these data, please cite:

Bukoski, J.J., Cook-Patton, S.C., Melikov, C., Ban, H., Liu, J.C., Harris, N., Goldman, E., and Potts, M.D. 2022. Rates and drivers of aboveground carbon accumulation in global monoculture plantation forests. Nature Communications 13(4206). doi: 10.1038/s41467-022-31380-7

This dataset contains key characteristics about the data described in the Data Descriptor European primary forest database (EPFD) v2.0. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

Forest Ownership (2016) created for the Forests to Faucets 2.0 Project derived from the 2016 National Land Cover Database (Value = 41,42,43, 90) , PAD-US v2.1 and the NCED. This layer was generated by combining the raster datasets from the NLCD 2016 and the ownership derived from the Protected Areas Database and NCED (only permanent easements were considered.) Field NLCD_2016_LAND_C are the NLCD values found here: https://www.mrlc.gov/data/legends/national-land-cover-database-2016-nlcd2016-legendValue

Ownership

FOROWN

0

Non Forest

Non Forest

1

NCED Permanent

Protected Forest

2

Federal Land

Federal Forest

3

USDA Forest Service

Forest Service Forest

4

Native American Land

Protected Forest

5

Joint Ownership

Protected Forest

6

Local Land

Protected Forest

7

Private Conservation Land

Protected Forest

8

State Land

Protected Forest

9

Unknown

Protected Forest

10

Private

Private Forest

Sources: Conservation Biology Institute, 2016. PAD-US (CBI Edition) Version 2.1 Shapefile (updated September 1, 2016)U.S. Endowment for Forestry and Communities 2016. National Conservation Easement Database October 5 2016U.S. Geological Survey, 2019. NLCD 2016 Land Cover Conterminous United States. Sioux Falls, SD. (Value = 41,42,43, 90) Yang, L., et al. (2018). "A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies." ISPRS Journal of Photogrammetry and Remote Sensing 146: 108-123.

Global Forest Resources Assessment has been monitoring the world forests at 5 to 10 year intervals since 1946. The Global Forest Resources Assessments (FRA) are now produced every five years in an attempt to provide a consistent approach to describing the world\u2019s forests and how they are changing. The Assessment is based on two primary sources of data: Country Reports prepared by National Correspondents and remote sensing that is conducted by FAO together with national focal points and regional partners. The scope of the FRA has changed regularly since the first assessment published in 1948. These assessments make an interesting history of global forest interests, both in terms of their substantive content, but also in their changing scope. Land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. It does not include land that is predominantly under agricultural or urban land use.

The data included in Data360 is a subset of the data available from the source. Please refer to the source for complete data and methodology details.

This collection includes only a subset of indicators from the source dataset.

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

A comprehensive global database has been assembled to quantify CO2 fluxes and pathways across different levels of integration (from photosynthesis up to net ecosystem production) in forest ecosystems. The database fills an important gap for model calibration, model validation, and hypothesis testing at global and regional scales.

The database archive includes: a Microsoft Office Access Database; data files for all tables in the database; query outputs from the database; and SQL script file for re-creating the database from the tables. The database is structured by site (i.e., a forest or stand of known geographical location, biome, species composition, and management regime). It contains carbon budget variables (fluxes and stocks), ecosystem traits (standing biomass, leaf area index, age), and ancillary information (management regime, climate, soil characteristics) for 529 sites from eight forest biomes. Data entries originated from peer-reviewed literature and personal communications with researchers involved in Fluxnet. Flux estimates were included in the database when they were based on direct measurements (e.g., tower-based eddy covariance system measurements), derived from single or multiple direct measurements, or modeled. Stand description was based on observed values, and climatic description was based on the CRU data set and ORCHIDEE model output. Uncertainty for each carbon balance component in the database was estimated in a uniformed way by expert judgment. Robustness of CO2 balances was tested, and closure terms were introduced as a numerical way to approach data quality and flux uncertainty at the biome level.

The Forest Responses to Anthropogenic Stress (FORAST) project was designed to determine whether evidence of alterations of long-term growth patterns of several species of eastern forest trees was apparent in tree-ring chronologies from within the region and to identify environmental variables that were temporally or spatially correlated with any observed changes. The project was supported principally by the U.S. Environmental Protection Agency (EPA) with additional support from the National Park Service. The FORAST project was initiated in 1982 as exploratory research to document patterns of radial growth of forest trees during the previous 50 or more years within 15 states in the northeastern United States. Radial growth measurements from more than 7,000 trees are provided along with data on a variety of measured and calculated indices of stand characteristics (basal area, density, and competitive indices); climate (temperature, precipitation, and drought); and anthropogenic pollutants (state and regional emissions of SO2 and NOX, ozone monitoring data, and frequency of atmospheric-stagnation episodes and atmospheric haze). These data were compiled into a single database to facilitate exploratory analysis of tree growth patterns and responses to local and regional environmental conditions. The project objectives, experimental design, and documentation of procedures for assessing data collected inmore » the 3-year research project are reported in McLaughlin et al. (1986).For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ndps/db1005.html« less

Discerning whether certain timber species were harvested from natural forests versus less restricted planted forests can help ascertain the legality of wood products that enter the global market. However, readily available global planted forest data to the species level have been scarce. We confronted the need for such data by developing a two-pronged dataset, consisting of ‘polygon’ and ‘non-polygon’ location-based data, collectively, Planted Forest Timber Data. We obtained the polygon data from the World Resources Institute’s Spatial Database of Planted Trees v2.0, extracting data specific to traded timber species. We derived the non-polygon data from peer-reviewed literature and government documents. The polygon dataset encompasses 27 countries and 253 species and the non-polygon dataset spans 91 countries and 447 species. The polygon data are stored among 27 geopackages, one for each country. Each summarized row of polygon data contains up to 13 possible fields. The non-polygon data..., The Planted Forest Timber Data is composed of two types of information, polygon and non-polygon data, divided into two distinct living datasets. The polygon dataset includes visual delineations of the planted forest boundaries. These data are organized into GeoPackages with an accompanying summary table that links the collective data together. The planted forest plots in the non-polygon dataset do not have delineated boundaries, but still have species information at least at the country level. The polygon dataset is composed of a subset of the Spatial Database of Planted Trees v2.0, specifically the portion of data that pertained to tree species commonly associated with the timber trade. We used government Lacey Act data and the Botanic Gardens Conservation International’s Working List of Commercial Timber Species to identify and isolate the species that qualify as timber. We also calculated the area of each planted forest plot. We assembled the non-polygon dataset by querying scientifi..., , # Global planted forest data for timber species

Access the polygon dataset on Zenodo (https://zenodo.org/records/14010483) Access the non-polygon dataset on Dryad (https://doi.org/10.5061/dryad.2280gb626) Access the article associated with the polygon and non-polygon datasets on Nature (https://doi.org/10.1038/s41597-024-04125-y)

The Planted Forest Timber Data (PFTD) consists of two living datasets, polygon and non-polygon. These data include planted forest plots for timber to the species level, with locations at least to the country level. Between the two datasets, the planted forests range from small experimental plots to large commercial operations. The polygon data includes visual delineations of the planted forest boundaries. The non-polygon data lack delineated boundaries but have species information at least at the country level. The data were obtained...,

October 2009

This polygon feature class contains the boundaries of 86 of 87 experimental forests, ranges and watersheds, including cooperating experimental areas. Experimental Forest and Range Areas Metadata

The purpose of the SNF study was to improve our understanding of the relationship between remotely sensed observations and important biophysical parameters in the boreal forest. A key element of the experiment was the development of methodologies to measure forest stand characteristics to determine values of importance to both remote sensing and ecology. Parameters studied were biomass, leaf area index, above ground net primary productivity, bark area index and ground coverage by vegetation. Thirty two quaking aspen and thirty one black spruce sites were studied. Sites were chosen in uniform stands of aspen or spruce. The dominant species in the site constituted over 80 percent, and usually over 95 percent, of the total tree density and basal area. Aspen stands were chosen to represent the full range of age and stem density of essentially pure aspen, of nearly complete canopy closure, and greater than two meters in height. Spruce stands ranged from very sparse stands on bog sites, to dense, closed stands on more productive peatlands. Use of multiple plots within each site allowed estimation of the importance of spatial variation in stand parameters. Within each plot, all woody stems greater than two meters in height were recorded by species and the following dimensions were measured: diameter breast height, height of the tree, height of the first live branch, and depth of crown. For each plot, a two meter diameter subplot was defined at the center of each plot. Within this subplot, the percent of ground coverage by plants under one meter in height was determined by species. These data, averaged for the five plots in each site, are presented in this data set (i.e., SNF Forest Understory Cover Data (Table)) in tabular format, e.g. plant species with a count for that species at each site. The same data are presented in the SNF Forest Understory Cover Data data set but are arranged with a row for each species and site and a percent ground coverage for each combination.