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

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 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

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

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/fiadb-api/evalidator). The areas were calculated within county limits using the US Census Bureau's county spatial data (https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.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

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 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.

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.

Analysis of ‘FIA Landcover County Estimates - 2016 (Feature Layer)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/07da9093-9869-48ef-a532-d467016fe485 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 2016. 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 ---

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

State and local governments are increasingly interested in understanding the role forests and harvested wood products play in regional carbon sinks and storage, their potential contributions to state-level greenhouse gas (GHG) reductions, and the interactions between GHG reduction goals and potential economic opportunities. We used empirically driven process-based forest carbon dynamics and harvested wood product models in a systems-based approach to project the carbon impacts of various forest management and wood utilization activities in Maryland and Pennsylvania from 2007–2100. To quantify state-wide forest carbon dynamics, we integrated forest inventory data, harvest and management activity data, and remotely-sensed metrics of landuse change and natural forest disturbances within a participatory modeling approach. We accounted for net GHG emissions across (1) forest ecosystems (2) harvested wood products, (3) substitution benefits from wood product utilization, and (4) leakage associated with reduced instate harvesting activities. Based on state agency partner input, a total of 15 management scenarios were modeled for Maryland and 13 for Pennsylvania, along with two climate change scenarios and two bioenergy scenarios for each state. Our findings show that both strategic forest management and wood utilization can provide substantial climate mitigation potential relative to business-as-usual practices, increasing the forest C sink by 29% in Maryland and 38% in Pennsylvania by 2030 without disrupting timber supplies. Key climate-smart forest management activities include maintaining and increasing forest extent, fostering forest resiliency and natural regeneration, encouraging sustainable harvest practices, balancing timber supply and wood utilization with tree growth, and preparing for future climate impacts. This study adds to a growing body of work that quantifies the relationships between forest growth, forest disturbance, and harvested wood products. Methods Primary data inputs to CBM-CFS3 included a detailed forest inventory, growth-yield relations to estimate forest productivity, and estimates of harvest yields and intensity, land-use change, and natural disturbances. Inventory data are categorized by a series of forest classifiers defining relevant characteristics such as spatially referenced boundaries, ownership, forest type, site productivity, or reserve status. Allometric equations are used to predict tree volume-to-biomass relationships (Boudewyn et al., 2007). For this study, forest inventory, growth-yield curves, and harvest data were estimated from the USDA Forest Service Forest Inventory and Analysis (FIA) program, which we accessed through the FIA DataMart (USDA Forest Service, 2019) using the rFIA package (Stanke et al., 2020) in the R programming environment (R Core Team, 2020), which enables data exploration and user-defined spatio-temporal queries and estimation of the FIA database (FIADB). Methodologies derived from Bechtold & Patterson, 2005 and Pugh et al., 2018 were used to estimate each state’s forest inventory by a predetermined list of classifiers. Natural disturbance history was estimated from both the FIADB and LANDFIRE (USGS, 2016) datasets to better constrain initial belowground and soil carbon parameters during what the modeling framework refers to as the model spin-up period (Kurz et al., 2009). Estimates of merchantable volume and corresponding biomass from FIADB were used to calibrate the model’s allometric volume-to-biomass assumptions to match forest type groups and growth conditions in Maryland and Pennsylvania. Harvest removals were estimated as average annual removal of merchantable timber in cubic feet between 2007 and 2019, converted to metric tons of carbon using methodologies and specific gravities reported by Smith et al., 2006. To assign a harvest type and intensity to each record of volumetric removal, stand age at the time of removal was calculated by taking the mid-point average between time t1 and t2 (Bechtold & Patterson, 2005) where t1 is the year the unharvested stand was measured and t2 is the repeat interval year measurement post-harvest. In collaboration with state partners, harvest type and intensity were determined heuristically for each forest type based upon state-level management documentation, peer-reviewed literature, and expert input. A complete list of harvest types and intensities prescribed to each forest type group as a product of stand age can be found in Table S3 of the manuscript. Longer-term averages from 2007-2019 were used to estimate annual area targets for all land-use change (LUC) and natural disturbance events including wind, fire, disease, and insects. Annual LUC average rates by ownership and forest type group were derived by overlaying a geospatial forestland ownership dataset (Sass et al., 2020), the Protected Areas Database of the U.S. (PAD-US), a national geodatabase of protected areas (USGS, 2018), and the National Land Cover Database (NLCD), a remotely-sensed data product used to characterize land cover and land cover change (Wickham et al., 2021). Wind disturbance events were calculated using the LANDFIRE Historic Disturbance dataset (USGS, 2016), a remotely-sensed data product provided by the USGS that estimates annual disturbance events. Annual averages for wildfire disturbances were derived from the LANDFIRE Historic Disturbance dataset (USGS, 2016) and validated through annual reports from the National Interagency Fire Center (NIFC). Annual prescribed fire acres were estimated from reports provided by the Maryland DNR Forest Service and Pennsylvania DCNR Bureau of Forestry and scaled to represent treatments on forestlands only. Annual acreages of insect and disease disturbance were derived from the National Insect & Disease Detection Survey (USDA Forest Service, 2020), a spatial data product produced by USDA that collects and reports data on forest insects, diseases, and other disturbances. For more information on all input and activity data see Supplementary Materials 1.2. A complete list of BAU parameters can be found in Table S2. State-specific trade and commodity data from Resource Planning Act (RPA) assessments (USDA Forest Service, 2021), US Commodity Flow Surveys (US Department of Transportation et al., 2020), US International Trade Commission export data (US International Trade Commission, 2021), and published peer-reviewed data (Howard & Liang, 2019) when available, or US averages from the same sources, were used to adapt and parameterize both HWP models. FAOSTAT data (FAO, 2021) were utilized to determine the commodity distributions of exported roundwood. Softwood products were parameterized and modeled separately from hardwood products, as the two wood types differ in exports and commodities produced as well as their associated product half-lives and displacement (Dymond, 2012; Howard et al., 2017). Published data were used to calculate softwood- and hardwood-specific half-lives for Maryland and Pennsylvania sawn wood and veneer products, while we relied on literature estimates for other products (Skog, 2008; J. E. Smith et al., 2006b). To calculate substitution benefits, we coupled region-specific data (USDA Forest Service, 2021), US consumption rates (Howard et al., 2017), product weights (C. Smyth et al., 2017), and LCA data (Bala et al., 2010; Dylewski & Adamczyk, 2013; Hubbard et al., 2020; Meil & Bushi, 2013; Puettmann, 2020a, 2020b; Puettmann et al., 2016; Puettmann & Salazar, 2018, 2019), following methods developed by Smyth et al. (2017). Landfill CO2 and CH4 emissions rely on PICC defaults for methane generation (k) and landfill half-lives for wet, temperate climates (Pingoud et al., 2006). See Supplementary Materials 1.3 for more details on substitution and leakage calculation methods.

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).

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 (http://apps.fs.fed.us/fiadb-downloads/datamart.html).

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 NBCD 2000 (National Biomass and Carbon Dataset for the Year 2000) data set provides a high-resolution (30 m) map of year-2000 baseline estimates of basal area-weighted canopy height, aboveground live dry biomass, and standing carbon stock for the conterminous United States. This data set distributes, for each of 66 map zones, a set of six raster files in GeoTIFF format. There is a detailed README companion file for each map zone. There is also an ArcGIS shapefile (mapping_zone_shapefile.shp) with the boundaries of all the map zones. A mosaic image of biomass at 240 m resolution for the whole conterminous U.S. is also included.

Please read this important note regarding the differences of Version 2 from Version 1 of the NBCD 2000 data.

With Version 1, in some mapping zones, certain land cover types (in particular Shrubs, NLCD Type 52) were missing from and unaccounted for in modeled estimates because of a lack of reference data.

In Version 1, when landcover types were missing in the models, the model for the deciduous tree cover type was applied. While more woody vegetation was mapped, the authors think this had little effect on model performance as in most cases NLCD version 1 cover type was not a strong predictor of modeled estimates (See companion Mapping Zone Readme files).

In Version 2, after renewed modeling efforts and user feedback, these previously unaccounted for cover types are now included in modeled estimates.

All 66 mapping zones were updated with the previously unmapped land cover types now mapped. The authors recommend use of the new version for all analyses and will only support the updated version.

Development of the data set used an empirical modeling approach that combined USDA Forest Service Forest Inventory and Analysis (FIA) data with high-resolution InSAR data acquired from the 2000 Shuttle Radar Topography Mission (SRTM) and optical remote sensing data acquired from the Landsat ETM+ sensor. Three-season Landsat ETM+ data were systematically compiled by the Multi-Resolution Land Characteristics Consortium (MRLC) between 1999 and 2002 for the entire U.S. and were the foundation for development of both the USGS National Land Cover Dataset 2001 (NLCD 2001) and the Landscape Fire and Resource Management Planning Tools Project (LANDFIRE). Products from both the NLCD 2001 (landcover and canopy density) and LANDFIRE (existing vegetation type) projects as well as topographic information from the USGS National Elevation Dataset (NED) were used within the NBCD 2000 project as spatial predictor layers for canopy height and biomass estimation. Forest survey data provided by the USDA Forest Service FIA program were made available to the project under a national Memorandum of Understanding. The response variables (canopy height and biomass) used in model development and validation were derived from the FIA database (FIADB). Production of the NLCD 2001 and LANDFIRE projects was based on a mapping zone approach in which the conterminous U.S. was split into 66 ecoregionally distinct mapping zones. This mapping zone approach was also adopted by the NBCD 2000 project.

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.

The NBCD 2000 (National Biomass and Carbon data set for the Year 2000) data set provides a high-resolution (30 m) map of year-2000 baseline estimates of basal area-weighted canopy height, above-ground, live, dry biomass, and standing carbon stock for the conterminous United States. This data set distributes, for each of 66 map zones, a set of six raster files in GeoTIFF format. There is a detailed README companion file for each map zone. There is also an ArcGIS shapefile (mapping_zone_shapefile.shp) with the boundaries of all the map zones. A mosaic image of biomass at 240-m resolution for the whole conterminous U.S. is also included.

Development of the data set used an empirical modeling approach that combined USDA Forest Service Forest Inventory and Analysis (FIA) data with high-resolution InSAR data acquired from the 2000 Shuttle Radar Topography Mission (SRTM) and optical remote sensing data acquired from the Landsat ETM+ sensor. Three-season Landsat ETM+ data were systematically compiled by the Multi-Resolution Land Characteristics Consortium (MRLC) between 1999 and 2002 for the entire U.S. and were the foundation for development of both the USGS National Land Cover data set 2001 (NLCD 2001) and the Landscape Fire and Resource Management Planning Tools Project (LANDFIRE). Products from both the NLCD 2001 (landcover and canopy density) and LANDFIRE (existing vegetation type) projects as well as topographic information from the USGS National Elevation data set (NED) were used within the NBCD 2000 project as spatial predictor layers for canopy height and biomass estimation. Forest survey data provided by the USDA Forest Service FIA program were made available to the project under a national Memorandum of Understanding. The response variables (canopy height and biomass) used in model development and validation were derived from the FIA database (FIADB). Production of the NLCD 2001 and LANDFIRE projects was based on a mapping zone approach in which the conterminous U.S. was split into 66 ecoregionally distinct mapping zones. This mapping zone approach was also adopted by the NBCD 2000 project.

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.

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