Forest ownership in the conterminous United States circa 2022: distribution of seven ownership types - geospatial dataset

Metadata:
Identification_Information:
Citation:
Citation_Information:
Originator: Harris, Vance
Originator: Caputo, Jesse
Originator: Butler, Brett J.
Publication_Date: 2025
Title:
Forest ownership in the conterminous United States circa 2022: distribution of seven ownership types - geospatial dataset
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2025-0045
Description:
Abstract:
This geospatial dataset depicts ownership patterns of forest land across the conterminous United States circa 2022. Seven forest ownership categories are included, including three public ownerships: federal, state, and local; two private categories: family and corporate (including not-for-profit organizations and institutions); Native American tribal land; and unknown forest ownership. Two additional categories are also included, non-forest and water. Data are derived from the National Land Cover Database (NLCD), third-party ownership data, and current up-to-date publicly available boundaries of public and tribal lands.
Purpose:
These data are intended to support national- and regional-scale planning and analyses – including small area estimation – involving spatially explicit distribution and patterns of forest ownership. Map accuracy varies between ownership categories and regions. This raster dataset combines harmonized land ownership classifications with landcover data to support national- and regional-scale planning and analyses, spatial modeling applications, and small area estimation. Ownership boundaries were derived from third party parcel data and federal spatial products, while landcover was informed by classified raster inputs such as the NLCD. The result is a continuous surface designed to enhance the spatial resolution and accuracy.
Supplemental_Information:
Four previous data publications also model forest ownership types across the conterminous United States. Nelson et al. (2010) depicts public and private forest ownership and differentiates corporate from other private ownership. Hewes et al. (2014) differentiates three public ownership categories (federal, state, and local) and three private ownership categories (family, corporate, and other private). Hewes et al. (2017) depicts these six categories as well as tribal lands. Sass et al. (2020) includes additional data through 2017 and differentiates a new private ownership category: Timber Investment Management Organizations (TIMOs) and Real Estate Investment Trusts (REITs), which are presented as a combined category.

This data publication was made available on 08/20/2025. On 09/10/2025, data were updated to correct codes for two of the public land cover classes (local government and federal government) which were erroneously switched for a small portion of the state of Michigan. A few more details are provided in the process steps section.

For more details regarding these data, see Harris et al. (2021).
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 2020
Ending_Date: 2022
Currentness_Reference:
Ground condition
Status:
Progress: Complete
Maintenance_and_Update_Frequency: Irregular
Spatial_Domain:
Description_of_Geographic_Extent:
conterminous United States
Bounding_Coordinates:
West_Bounding_Coordinate: -124.763060
East_Bounding_Coordinate: -66.949890
North_Bounding_Coordinate: 49.383129
South_Bounding_Coordinate: 24.523087
Keywords:
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: boundaries
Theme_Keyword: environment
Theme:
Theme_Keyword_Thesaurus: National Research & Development Taxonomy
Theme_Keyword: Inventory, Monitoring, & Analysis
Theme_Keyword: Resource inventory
Theme_Keyword: Environment and People
Theme_Keyword: Impact of people on environment
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: forest ownership
Theme_Keyword: forest land
Theme_Keyword: non-forest
Theme_Keyword: owner types
Theme_Keyword: public
Theme_Keyword: private
Theme_Keyword: corporate
Theme_Keyword: tribal
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: conterminous United States
Place_Keyword: CONUS
Place_Keyword: contiguous United States
Place_Keyword: lower 48
Access_Constraints: None
Use_Constraints:
These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation:

Harris, Vance; Caputo, Jesse; Butler, Brett J. 2025. Forest ownership in the conterminous United States circa 2022: distribution of seven ownership types - geospatial dataset. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2025-0045
Point_of_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Jesse Caputo
Contact_Organization: USDA Forest Service, Northern Research Station
Contact_Position: Research Forester
Contact_Address:
Address_Type: mailing and physical
Address: 160 Holdsworth Way
City: Amherst
State_or_Province: MA
Postal_Code: 01003
Country: USA
Contact_Voice_Telephone: 413-545-1655
Contact_Electronic_Mail_Address: jesse.caputo@usda.gov
Data_Set_Credit:
Funding for this project was provided by USDA Forest Service, Northern Research Station (NRS), Forest Inventory & Analysis; the USDA Forest Service, State Private & Tribal Forestry; and the National Council for Air & Stream Improvement (NCASI).


Author Information:

Vance Harris
University of Massachusetts, Amherst (Research Fellow)

Jesse Caputo
USDA Forest Service, Northern Research Station
https://orcid.org/0000-0001-6891-1052

Brett J. Butler
USDA Forest Service, Northern Research Station
https://orcid.org/0000-0002-2465-7993
Cross_Reference:
Citation_Information:
Originator: Harris, Vance
Originator: Caputo, Jesse
Originator: Finley, Andrew
Originator: Butler, Brett J.
Originator: Bowlick, Forrest
Originator: Catanzaro, Paul
Publication_Date: 2021
Title:
Small-area estimation for the USDA Forest Service, National Woodland Owner Survey: Creating a fine-scale land cover and ownership layer to support county-level population estimates
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Frontiers in Forests and Global Change
Issue_Identification: 4: 745840
Other_Citation_Details:
11 p.
Online_Linkage: https://doi.org/10.3389/ffgc.2021.745840
Online_Linkage: https://research.fs.usda.gov/treesearch/65570
Cross_Reference:
Citation_Information:
Originator: Nelson, Mark D.
Originator: Liknes, Greg C.
Originator: Butler, Brett J.
Publication_Date: 2010
Title:
Forest ownership in the conterminous United States: ForestOwn_v1 geospatial dataset
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Newtown Square, PA
Publisher: USDA Forest Service, Northern Research Station
Online_Linkage: https://doi.org/10.2737/RDS-2010-0002
Cross_Reference:
Citation_Information:
Originator: Hewes, Jaketon H.
Originator: Butler, Brett J.
Originator: Liknes, Greg C.
Originator: Nelson, Mark D.
Originator: Snyder, Stephanie A.
Publication_Date: 2014
Title:
Public and private forest ownership in the conterminous United States: distribution of six ownership types - geospatial dataset
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2014-0002
Cross_Reference:
Citation_Information:
Originator: Hewes, Jaketon H.
Originator: Butler, Brett J.
Originator: Liknes, Greg C.
Publication_Date: 2017
Title:
Forest ownership in the conterminous United States circa 2014: distribution of seven ownership types - geospatial dataset
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2017-0007
Cross_Reference:
Citation_Information:
Originator: Sass, Emma M.
Originator: Butler, Brett J.
Originator: Markowski-Lindsay, Marla A.
Publication_Date: 2020
Title:
Forest ownership in the conterminous United States circa 2017: distribution of eight ownership types - geospatial dataset
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2020-0044
Cross_Reference:
Citation_Information:
Originator: Sass, Emma M.
Originator: Butler, Brett J.
Originator: Markowski-Lindsay, Marla A.
Publication_Date: unknown
Title:
Estimated distribution of forest ownerships across the conterminous United States
Geospatial_Data_Presentation_Form: map
Series_Information:
Series_Name: Research Map
Issue_Identification: NRS-11
Publication_Information:
Publication_Place: Newtown Square, PA
Publisher: U.S. Department of Agriculture, Forest Service, Northern Research Station
Online_Linkage: https://doi.org/10.2737/NRS-RMAP-11
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Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
Recent updates to the Ownership Small Area Estimation (SAE) project reflect our continued efforts to refine model performance through iterative evaluation, now applied to a newly completed geospatial data layer integrating land ownership and land cover. Rather than relying on aggregate or global accuracy metrics, we have transitioned to a more localized, state-level accuracy assessment framework. This approach allows us to identify regional variation in model performance and prioritize improvements where they are most impactful.

To assess final classification accuracy, we are using a cross-validation approach in which 100 random samples are selected for each class from each state. While this method knowingly over-samples low-frequency classes, it is intentionally designed to better evaluate the classes most prone to error. Previous work has shown that these rare classes tend to have the highest misclassification rates due to data sparsity, regional inconsistency, or methodological gaps.

Model error in this product reflects three primary sources: (1) positional and thematic error introduced by overlaying vector (e.g., parcel, tribal) and raster (NLCD) source data, (2) algorithmic error introduced during the ownership classification process, and (3) spatial inconsistency in the availability and quality of ownership data across states. Given that this product was modeled at the state level and later mosaicked into a national raster, accuracy is being reported at the state level. As of August 2025, this effort is ongoing and definitive error metrics are not yet available for most classes. Preliminary error explanations are provided below based on known data and classification logic.
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: Unknown
Attribute_Accuracy_Explanation:
Accuracy assessment for the Family Forest class is currently under development. Final metrics will be based on state-level cross-validation using 100 random samples per state.
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: Unknown
Attribute_Accuracy_Explanation:
Accuracy assessment for the Corporate and Other Private Forest class is currently under development. Final metrics will be based on state-level cross-validation using 100 random samples per state.
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: Unknown
Attribute_Accuracy_Explanation:
Accuracy assessment for the Federal Forest class is currently under development. Final metrics will be based on state-level cross-validation using 100 random samples per state.
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: Unknown
Attribute_Accuracy_Explanation:
Accuracy assessment for the State Forest class is currently under development. Final metrics will be based on state-level cross-validation using 100 random samples per state.
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: Unknown
Attribute_Accuracy_Explanation:
Accuracy assessment for the Local Forest class is currently under development. Final metrics will be based on state-level cross-validation using 100 random samples per state.
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: Unknown
Attribute_Accuracy_Explanation:
The Tribal Forest classification is derived directly from the BIA Tribal boundaries layer intersected with the NLCD land cover raster. No classification algorithm was applied to this class. Its accuracy is a direct reflection of the spatial and thematic accuracy of these two input datasets.
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: High confidence
Attribute_Accuracy_Explanation:
The Unknown Forest class represents pixels where spatial parcel boundaries and/or ownership attribution were missing. These areas are explicitly flagged as unknown due to a lack of input data. Thus, classification confidence is high because the label explicitly indicates missing information. The only source of error is the NLCD’s ability to correctly delineate forest versus non-forest land cover.
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: NLCD-dependent
Attribute_Accuracy_Explanation:
The Non Forest class aggregates all non-forested land cover types identified in NLCD, without regard to ownership. No classification algorithm was applied to these areas, so error is limited to the NLCD’s accuracy in identifying forest versus non-forest pixels.
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: NLCD-dependent
Attribute_Accuracy_Explanation:
The Water class consists of pixels classified as water in the NLCD. Ownership attribution was not applied to this class. Therefore, classification error is entirely dependent on NLCD's ability to correctly identify water features.
Logical_Consistency_Report:
see Attribute Accuracy
Completeness_Report:
This ownership and land cover dataset is complete for the contiguous 48 United States.

Parcel-level ownership data were compiled using the best available spatial and tabular sources for each state. Coverage varies across the country, particularly for private lands, due to inconsistent availability of parcel data and ownership records. Where ownership could not be reliably determined, areas were explicitly labeled as "Unknown Forest." These areas still retain valid land cover attributes.

Land cover was derived from the NLCD 2016 10-meter Percent Tree Canopy Layer (Analytical Version), which provided the basis for a forest/non-forest classification. This layer, used in conjunction with ecoregion boundaries, supported a consistent delineation of forested areas across the entire study area. The land cover inputs are considered complete and consistent at the national scale. Tribal ownership was supplemented with the BIA AIAN National Land Area Representation dataset, and protected public lands were cross-validated against PAD-US 4.0. County boundaries from the U.S. Census TIGER/Line shapefiles were used for reference in assigning local spatial context.
Positional_Accuracy:
Horizontal_Positional_Accuracy:
Horizontal_Positional_Accuracy_Report:
This product was developed by overlaying multiple spatial data sources including vector parcel boundaries, tribal land delineations, and raster land cover data. The forest/non-forest mask was derived from the NLCD 2016 Percent Tree Canopy Layer at 10-meter resolution. This resolution marks a significant improvement over earlier 30-meter products and allows for finer-grained identification of forested areas.

Despite the improved resolution, some uncertainty remains due to spatial misalignment between vector ownership data and raster land cover classifications. Positional accuracy is further influenced by the inherent registration error in source datasets such as the BIA tribal boundaries and PAD-US public lands. Overall horizontal positional accuracy is expected to be within approximately 100 meters (i.e., ten 10-meter pixels), but may vary in areas where source datasets are incomplete, outdated, or inconsistently formatted.
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: Dewitz, J.
Publication_Date: 2019
Title:
National Land Cover Database (NLCD) 2016 Products
Edition: ver. 3.0, November 2023
Geospatial_Data_Presentation_Form: raster digital data
Series_Information:
Series_Name: U.S. Geological Survey data release
Publication_Information:
Publisher: U.S. Geological Survey
Online_Linkage: https://doi.org/10.5066/P96HHBIE
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 2001
Ending_Date: 2016
Source_Currentness_Reference:
Ground Condition
Source_Citation_Abbreviation:
Dewitz (2019)
Source_Contribution:
The 10 meter landcover descriptions were used as the basis for our final raster layer. They were used to estimated land cover distributions on the parcel layer, and were conjoined with ownership information to allow for global estimations of distributions.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Bureau of Indian Affairs, Office of Trust Services, Division of Land Titles and Records
Publication_Date: 2024
Title:
BIA AIAN National Land Area Representation (AIAN-LAR) GIS
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publisher: U.S. Department of the Interior, Bureau of Indian Affairs
Online_Linkage: https://www.arcgis.com/home/item.html?id=e21128c26386412ca682accf7a57361a
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 201906
Source_Currentness_Reference:
Ground Condition
Source_Citation_Abbreviation:
BIA AIAN-LAR
Source_Contribution:
This dataset depicts tribal land boundaries.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Department of Commerce, Census Bureau
Publication_Date: 2020
Title:
TIGER/Line Shapefile, 2020, Nation, U.S., Counties and Equivalent Entities
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publisher: U.S. Department of Commerce, Census Bureau
Online_Linkage: https://catalog.data.gov/dataset/tiger-line-shapefile-2020-nation-u-s-counties-and-equivalent-entities/resource/41767289-92ae-4e5e-bfa3-f4d8c25ed702
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2020
Source_Currentness_Reference:
Ground Condition
Source_Citation_Abbreviation:
Census Bureau (2020)
Source_Contribution:
Used to associate county information for the parcel data.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Geological Survey (USGS) Gap Analysis Project (GAP)
Publication_Date: 2024
Title:
Protected Areas Database of the United States (PAD-US) 4
Geospatial_Data_Presentation_Form: raster digital data
Series_Information:
Series_Name: U.S. Geological Survey data release
Publication_Information:
Publisher: U.S. Geological Survey
Online_Linkage: https://doi.org/10.5066/P96WBCHS
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2024
Source_Currentness_Reference:
Publication Date
Source_Citation_Abbreviation:
PAD-US 4.0
Source_Contribution:
Used to confirm and reinforce public ownership areas across the study area, including filling in “unknown” ownerships.
Process_Step:
Process_Description:
Ownership classification and mapping were conducted using a multi-stage geospatial and statistical workflow. Parcel boundary data were obtained from county and state sources and converted into raster format. These were spatially joined with 10-meter resolution NLCD land cover data to calculate proportional land cover composition for each parcel. Ownership names were parsed and classified using a hierarchical method: structured keyword detection (e.g., for federal, state, corporate, trust, and religious entities), followed by a machine learning classification using a Random Forest model trained on manually labeled ownership data. Ownership records were further consolidated using phonetic matching (Double Metaphone) to group similar names and assign unique owner IDs. The resulting parcel-level data were encoded into raster format using ownership and land cover codes. Finally, a post-processing step in ArcGIS Pro clipped each state’s raster to its administrative boundary and mosaicked them into a seamless national raster surface.

A full description of the processing steps as well as the python scripts used can be found on GitHub:

Harris, Vance; Caputo, Jesse. 2025. FIA_OWN_Map. https://github.com/familyforestresearchcenter/FIA_OWN_MAP


Below is a summary of the steps:

Main Script (Main.py)

Main.py orchestrates the full pipeline. It sequentially runs all modules, cleans up intermediate files, and packages final outputs into a {STATE}.zip file containing:
- {STATE}_Full_Data_Table.csv
- {STATE}_Final_Encoded.tif
- {STATE}_Parcel_IDs.json


Step-by-Step Modules

1. Preprocessing (Preprocessing_opt.py)
- Reads parcel data from a .gdb file.
- Converts parcels to raster.
- Clips NLCD 10m raster to parcel extent.
- Calculates land cover proportions for each parcel.
- Joins county boundaries and Indigenous Lands (ILs).
- Computes parcel descriptors: centroid coordinates and area.
- Outputs: i) temp.csv – Parcel data with land cover stats and county info; ii) {STATE}_Parcel_IDs.json – GeoJSON of parcel geometries and IDs.

2. Ownership Classification (Classify_Unknowns_opt.py)
- Normalizes and cleans ownership names (OWN1, OWN2).
- Keyword classification: Detects Federal, State, Local government, corporate, religious, trust, and family ownerships.
- Machine learning classification: Remaining unknowns are classified using a Random Forest model (classify_unknown_ownership_model.pkl) trained on manually labeled NWOS data.
- Outputs: new_classified_state_temp.csv – Ownership classifications.

3. Name Matching (Name_Matching_opt.py)
- Groups parcels by ownership using: i) Double Metaphone phonetic encoding; ii) Address-based grouping to resolve variations.
- Assigns a Unq_ID (unique ownership identifier).
- Outputs: Full_Data_Table.csv – Consolidated ownership records with parcel counts.

4. Summary (Summary_Script_opt.py)
- Calculates: i) Forest area per parcel (acres); ii) Forest parcel counts per owner.
- Aggregates total forested area and parcel statistics by ownership ID.
- Standardizes column names to FIA codes (e.g., OWNCD, NLCD_41_PROP).
- Outputs: Updated Full_Data_Table.csv with forest metrics.

5. Map Data Encoding (Map_Data_opt.py)
- Joins ownership codes (OWNCD) to parcels.
- Combines ownership codes and NLCD data into an encoded raster.
- Applies a reclassification dictionary (New_Raster_Reclass.pickle).
- Outputs: Own_Type_by_Landcover_Encoded.tif.

6. Final Overlay (Last_Overlay_opt.py)
- Applies PADUS and Indigenous Lands overlays to refine or fill gaps in ownership coding.
- Outputs: {STATE}_Final_Encoded.tif.

7. Final Raster Mosaicking (ArcGIS Pro)
The final step in the FIA Ownership Map workflow is performed in ArcGIS Pro and produces a seamless national raster by:
i) Clipping each state raster to its corresponding state boundary using the 2019 Census state boundaries hosted on ArcGIS Online (AGOL): BND - States 500K (Census 2019) (Source: Esri Feature Service)
ii) Mosaicking the clipped rasters using the Mosaic To New Raster tool in ArcGIS Pro to create a contiguous, nationwide raster surface that encodes ownership type by land cover.
This step ensures all outputs are cleanly clipped, standardized, and aligned to authoritative boundaries for downstream visualization and spatial analysis.
Process_Date: 2025
Process_Step:
Process_Description:
In the original version of this product (published 08/20/2025), the codes for two of the public land cover classes (local government and federal government) were erroneously switched for a small portion of the state of Michigan. The error was due to this state (and only this state) having been run using an earlier and incorrect version of one of the scripts. This issue has been corrected in the new version of these data.
Process_Date: 202509
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Spatial_Data_Organization_Information:
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:
Raster_Object_Type: Grid Cell
Row_Count: 102570
Column_Count: 161190
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Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Planar:
Map_Projection:
Map_Projection_Name: USA Contiguous Albers Equal Area Conic
Albers_Conical_Equal_Area:
Standard_Parallel: 29.5
Standard_Parallel: 45.5
Longitude_of_Central_Meridian: -96.0
Latitude_of_Projection_Origin: 37.5
False_Easting: 0.0
False_Northing: 0.0
Planar_Coordinate_Information:
Planar_Coordinate_Encoding_Method: coordinate pair
Coordinate_Representation:
Abscissa_Resolution: 0.0000000037527980722984474
Ordinate_Resolution: 0.0000000037527980722984474
Planar_Distance_Units: meter
Geodetic_Model:
Horizontal_Datum_Name: D North American 1983
Ellipsoid_Name: GRS 1980
Semi-major_Axis: 6378137.0000
Denominator_of_Flattening_Ratio: 298.257222101
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Entity_and_Attribute_Information:
Detailed_Description:
Entity_Type:
Entity_Type_Label: US_forest_ownership.tif
Entity_Type_Definition:
Entity type predicted to own forestland
Entity_Type_Definition_Source:
O’Connell, Barbara M.; Conkling, Barbara L.; Wilson, Andrea M.; Burrill, Elizabeth A.; Turner, Jeffery A.; Pugh, Scott A.; Christiansen, Glenn; Ridley, Ted; Menlove, James. 2017. The Forest Inventory and Analysis Database: Database description and user guide version 7.0 for Phase 2. U.S. Department of Agriculture, Forest Service. 830 p. [Online]. https://www.fia.fs.fed.us/library/database-documentation/
Attribute:
Attribute_Label: OID
Attribute_Definition:
Internal feature number.
Attribute_Definition_Source:
Esri
Attribute_Domain_Values:
Unrepresentable_Domain:
Sequential unique whole numbers that are automatically generated.
Attribute:
Attribute_Label: Value
Attribute_Definition:
Nine values of ownership type:

0 = Unknown Forest

1 = Non-Forest

2 = Water

3 = Family Forest (Private): Owned by families, individuals, trusts, estates, family partnerships, and other unincorporated groups of individuals that own forest land (FIA Code 45).

4 = Corporate/Other Private Forest (Private): Owned by corporations (FIA Code 41) or owned by conservation and natural resource organizations, unincorporated partnerships and associations (FIA Codes 42-43).

5 = Tribal Forest: Owned by Native American tribes (FIA Code 44).

6 = Federal Forest (Public): Owned by the federal government (FIA Codes 11-13, 21-25).

7 = State Forest (Public): Owned by a state government (FIA Code 31).

8 = Local Forest (Public): Owned by a local government (FIA Code 32).

Attribute_Definition_Source:
O’Connell et al. (2017)
Attribute:
Attribute_Label: Count
Attribute_Definition:
The count of pixels of each ownership type in the dataset.
Overview_Description:
Entity_and_Attribute_Overview:
Below you will find the data available in this data publication and a short description of its contents.

\Data\US_forest_ownership.tif: Georeferenced (GeoTIFF) raster file (and associated files) depicting the spatial distribution of seven categories of forest ownership, in the conterminous United States circa 2020, including three public ownerships: (1) federal, (2) state, and (3) local; two private categories: (4) family and (5) corporate (including not-for-profit organizations and institutions); (6) Native American tribal land; and (7) unknown forest ownership. Two additional categories are also included, unknown non-forest and water.

Legend:
0 = Unknown Forest
1 = Non-Forest
2 = Water
3 = Family Forest
4 = Corporate/Other Private Forest
5 = Tribal Forest
6 = Federal Forest
7 = State Forest
8 = Local Forest


The Protected Areas Database was used to identify Federal and State Lands, and the Tribal Block Group National shapefile was used to identify Tribal lands. Remaining land area was interpolated into owner classes from values at known points using Thiessen polygons. This interpolated land area included some remaining points/plots with Federal, State, or Tribal owner codes that fell outside the areas in the previous two datasets. Finally, a forest proportion dataset was used to remove all non-forest lands. The percent canopy cover used as the forest/non-forest threshold was chosen individually for each ecoregion-state combination such that the total forest area in each ecoregion-state unit most closely matched FIA statistics. The final dataset is a 30-meter raster.
Entity_and_Attribute_Detail_Citation:
O’Connell, Barbara M.; Conkling, Barbara L.; Wilson, Andrea M.; Burrill, Elizabeth A.; Turner, Jeffery A.; Pugh, Scott A.; Christiansen, Glenn; Ridley, Ted; Menlove, James. 2017. The Forest Inventory and Analysis Database: Database description and user guide version 7.0 for Phase 2. U.S. Department of Agriculture, Forest Service. 830 p. [Online]. https://www.fia.fs.fed.us/library/database-documentation/
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Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Research and Development
Contact_Position: Research Data Archivist
Contact_Address:
Address_Type: mailing and physical
Address: 240 West Prospect Road
City: Fort Collins
State_or_Province: CO
Postal_Code: 80526
Country: USA
Contact_Voice_Telephone: see Contact Instructions
Contact Instructions: This contact information was current as of September 2025. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Resource_Description: RDS-2025-0045
Distribution_Liability:
Metadata documents have been reviewed for accuracy and completeness. Unless otherwise stated, all data and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. However, neither the author, the Archive, nor any part of the federal government can assure the reliability or suitability of these data for a particular purpose. The act of distribution shall not constitute any such warranty, and no responsibility is assumed for a user's application of these data or related materials.

The metadata, data, or related materials may be updated without notification. If a user believes errors are present in the metadata, data or related materials, please use the information in (1) Identification Information: Point of Contact, (2) Metadata Reference: Metadata Contact, or (3) Distribution Information: Distributor to notify the author or the Archive of the issues.
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Format_Name: TIF
Format_Version_Number: see Format Specification
Format_Specification:
Georeferenced (GeoTIFF) raster file
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2025-0045
Fees: None
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Metadata_Reference_Information:
Metadata_Date: 20250910
Metadata_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Jesse Caputo
Contact_Organization: USDA Forest Service, Northern Research Station
Contact_Position: Research Forester
Contact_Address:
Address_Type: mailing and physical
Address: 160 Holdsworth Way
City: Amherst
State_or_Province: MA
Postal_Code: 01003
Country: USA
Contact_Voice_Telephone: 413-545-1655
Contact_Electronic_Mail_Address: jesse.caputo@usda.gov
Metadata_Standard_Name: FGDC Content Standard for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001-1998
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