Forest ownership in the conterminous United States circa 2014: distribution of seven ownership types - geospatial dataset

Metadata:

Identification_Information:
Citation:
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
Description:
Abstract:
This data publication contains 250 meter raster data depicting the spatial distribution of forest ownership types in the conterminous United States. The data are a modeled representation of forest land by ownership type, and include three types of public ownership: federal, state, and local; three types of private: family (includes individuals and families), corporate, and other private (includes conservation and natural resource organizations, and unincorporated partnerships and associations); as well as Native American tribal lands. The most up-to-date data available were used in creating this data publication. A plurality of the ownership data were from 2014, but some data were as old as 2004.
Purpose:
These data are designed for strategic analyses at a national or regional scale which require spatially explicit information regarding the extent, distribution, and prevalence of the ownership types represented. These data are not recommended for tactical analyses on a sub-regional scale, or for informing local management decisions. Furthermore, map accuracies vary considerably and thus the utility of these data can vary geographically under different ownership patterns.
Supplemental_Information:
Two associated data publications; Nelson et al. (2010), and Hewes et al. (2014), also portray ownership types of the conterminous United States. The former primarily depicts National Forest and other public on the public side, and percent in corporate ownership on the private side, while the latter depicts public (federal, state, and local) and private (family, corporate, and other private) ownership categories. This new data publication breaks out a seventh ownership type (tribal).

On 07/23/2020 a newer version of these data became available (Sass et al. 2020) that contains recently available data 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.
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 2004
Ending_Date: 2014
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: -124.732770
East_Bounding_Coordinate: -66.969271
North_Bounding_Coordinate: 49.371730
South_Bounding_Coordinate: 25.130501
Keywords:
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
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: ISO 19115 Topic Category
Theme_Keyword: boundaries
Theme_Keyword: environment
Theme_Keyword: planningCadastre
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: United States of America
Place_Keyword: lower 48
Place_Keyword: contiguous
Place_Keyword: conterminous
Place_Keyword: CONUS
Access_Constraints: None
Use_Constraints:
Appropriate use includes coarse-scale assessment of forest ownership patterns.

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:

Hewes, Jaketon H.; Butler, Brett J.; Liknes, Greg C. 2017. Forest ownership in the conterminous United States circa 2014: distribution of seven ownership types - geospatial dataset. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2017-0007
Point_of_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Butler, Brett J.
Contact_Organization: USDA Forest Service, Northern Research Station
Contact_Position: Research Forester
Contact_Address:
Address_Type: mailing
Address: 160 Holdsworth Way
City: Amherst
State_or_Province: MA
Postal_Code: 01003
Country: USA
Contact_Voice_Telephone: 413-545-1387
Contact_Electronic_Mail_Address: brett.butler2@usda.gov
Data_Set_Credit:
Funding for this project provided by USDA Forest Service, Northern Research Station (NRS), Forest Inventory & Analysis as well as USDA Forest Service, State and Private Forestry Cooperative Forestry.
Native_Data_Set_Environment:
Microsoft Windows 7 Professional 2009 Service Pack 1; ESRI ArcMap 10.1
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: 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
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Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
Two methods were utilized to assess the accuracy of the Thiessen polygon approach to mapping ownership type. Point validation was conducted for a subset of ownership points withheld prior to the creation of preliminary* ownership polygons. Twenty percent of ownership points were held back from each of the 48 states and across all seven ownership types (24,643 points total). These points were then overlaid with the mapped product and a determination was made whether the points fell within an area predicted to have the same ownership type.

* These validation points were added back into the dataset for the final model and creation of Thiessen polygons.

Area estimates were also compared for ownership types by state and ecoregion section between mapped product and estimates of USDA Forest Service Forest Inventory and Analysis (FIA) summary data for the 436 state-ecoregion combinations where recent FIA data was available. Insufficient FIA plot sample sizes for 34 state-ecoregion combinations in Arizona, California, Colorado, Georgia, Idaho, Kansas, Minnesota, Montana, Nebraska, New Jersey, New York, North Dakota, Oklahoma, Oregon, South Carolina, South Dakota, Utah, Vermont, Wisconsin and Wyoming precluded development of reliable area estimates and therefore were omitted from the area comparisons.

Map accuracies varied considerably, with highs of 95 and 93% overall accuracy for federal and tribal ownership types, respectively, and lows of 12 and 28% for other private and local ownership types, respectively. These low accuracies values are likely due to the comparatively small number of modeling points used in the model, in combination with the lack of a supplemental data source. Thus, utility of these data vary geographically under different ownership patterns. See assessment results below for more detailed information.
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: 95% (Federal Ownership type)
Attribute_Accuracy_Explanation:
The percentage of validation points that were accurately predicted as belonging in federal ownership (n=8,095 points).
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: 76% (State Ownership type)
Attribute_Accuracy_Explanation:
The percentage of validation points that were accurately predicted as belonging in state ownership (n=1,691 points).
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: 28% (Local Ownership type)
Attribute_Accuracy_Explanation:
The percentage of validation points that were accurately predicted as belonging in local ownership (n=608 points). This comparatively low accuracy rate is likely due to the small number of modeling points (2,014, or 3.4% of the total modeling points) and lack of external data source, unlike federal and state public ownership types which were supplemented via PAD.
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: 63% (Family Ownership type)
Attribute_Accuracy_Explanation:
The percentage of validation points that were accurately predicted as belonging in family ownership (n=9,214 points).
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: 45% (Corporate Ownership type)
Attribute_Accuracy_Explanation:
The percentage of validation points that were accurately predicted as belonging in corporate ownership (n=4,062 points).
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: 93% (Tribal Ownership type)
Attribute_Accuracy_Explanation:
The percentage of validation points that were accurately predicted as belonging in tribal ownership (n=569 points).
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: 12% (Other Private Ownership type)
Attribute_Accuracy_Explanation:
The percentage of validation points that were accurately predicted as belonging in other private ownership (n=404 points). As with local ownership, the low accuracy rate for this ownership type is likely due to a small number of modeling points (1,496 or 2.6% of the total modeling points) and lack of external data source.
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: 92% (Public Ownership category)
Attribute_Accuracy_Explanation:
The percentage of validation points that were accurately predicted as belonging in public ownership (n=10,394 points).
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: 82% (Private Ownership category)
Attribute_Accuracy_Explanation:
The percentage of validation points that were accurately predicted as belonging in private ownership (n=13,680 points).
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: 71% (Overall)
Attribute_Accuracy_Explanation:
The overall percentage of validation points that were accurately predicted (n=24,643 points).
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: 0.2% (percent difference: total forest area)
Attribute_Accuracy_Explanation:
Based on FIA data, forest land area encompasses a total of 686.3 million (+ or - 3.7 million) acres within the 48 conterminous states. The corresponding map-based estimate of total forest area was 684.9 million acres, which falls within the 95% confidence interval surrounding the FIA-based estimate. Map based estimates of statewide forest land area fell within 95% confidence intervals surrounding FIA-based estimates for all states.

Percent difference was calculated as the absolute value of the difference between the FIA area estimate and the map-based area divided by the FIA estimate.
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: 11.4% (percent difference: federally-owned forest land)
Attribute_Accuracy_Explanation:
Map Acres - 211,288,800
FIA Acres - 189,719,401 (95% C.I.: 1,901,509)
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: 40.1% (percent difference: state-owned forest land)
Attribute_Accuracy_Explanation:
Map Acres - 63,675,582
FIA Acres - 45,465,514 (95% C.I.: 941,964)
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: 15.5% (percent difference: locally-owned forest land)
Attribute_Accuracy_Explanation:
Map Acres - 11,571,343
FIA Acres - 13,698,749 (95% C.I.: 496,275)
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: 11.0% (percent difference: family-owned forest land)
Attribute_Accuracy_Explanation:
Map Acres - 253,247,248
FIA Acres - 284,394,364 (95% C.I.: 2,414,043)
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: 8.8% (percent difference: corporate-owned forest land)
Attribute_Accuracy_Explanation:
Map Acres - 112,709,784
FIA Acres - 123,559,905 (95% C.I.: 1,641,229)
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: 13.9% (percent difference: other privately-owned forest land)
Attribute_Accuracy_Explanation:
Map Acres - 11,504,902
FIA Acres - 13,368,082 (95% C.I.: 583,971)
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: 29.6% (percent difference: tribally-owned forest land)
Attribute_Accuracy_Explanation:
Map Acres - 20,910,984
FIA Acres - 16,137,500 (95% C.I.: 581,997)
Logical_Consistency_Report:
see Attribute Accuracy
Completeness_Report:
FOROWN2016 is complete for the conterminous United States.

PAD-US 2.0 (CBI Edition) data were compiled with the best (most complete, recent) datasets available for each state in the United States. Therefore, completeness may vary. Refer to the metadata for the state of interest for further information about completeness.
Positional_Accuracy:
Horizontal_Positional_Accuracy:
Horizontal_Positional_Accuracy_Report:
Several vector and raster datasets were used in the development of these data, along with FIA field plot data. Uncertainty in the geographic registration in each of these data sources will contribute to the positional accuracy of the final product. Overall horizontal positional accuracy is expected to be within 1 pixel in either dimension (i.e. 250 meters).
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: Conservation Biology Institute
Publication_Date: 20121031
Title:
PAD-US (CBI Edition)
Edition: Version 2
Geospatial_Data_Presentation_Form: vector digital data
Series_Information:
Series_Name: Protected Areas Database
Publication_Information:
Publication_Place: Corvallis, Oregon
Publisher: Conservation Biology Institute
Other_Citation_Details:
Only polygons where own_type was equal to 'Federal Land' or 'State Land' were utilized.
Online_Linkage: https://consbio.org/products/projects/pad-us-cbi-edition
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: Unknown
Ending_Date: Unknown
Source_Currentness_Reference:
publication date
Source_Citation_Abbreviation:
PAD-US 2.0 (CBI Edition)
Source_Contribution:
PAD-US (CBI Edition) was used to supplement modeled public ownership.

Source data were produced at various map scales, usually at 1:100,000 or finer
Source_Information:
Source_Citation:
Citation_Information:
Originator: Wilson, B. Tyler
Originator: Lister, Andrew J.
Originator: Riemann, Rachel I.
Publication_Date: 2012
Title:
A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Forest Ecology and Management
Issue_Identification: 271:182-198
Online_Linkage: https://doi.org/10.1016/j.foreco.2012.02.002
Type_of_Source_Media: digital file
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 2000
Ending_Date: 2009
Source_Currentness_Reference:
ground condition
Source_Citation_Abbreviation:
Proportion Forest Layer
Source_Contribution:
The Proportion Forest Layer was used in conjunction with the Ecoregion Layer to produce a forest/non-forest mask.
Source_Information:
Source_Citation:
Citation_Information:
Originator: USDA Forest Service
Publication_Date: Unknown
Title:
Forest Inventory and Analysis Forest Ownership Data
Geospatial_Data_Presentation_Form: vector digital data
Series_Information:
Series_Name: FIA Plots
Type_of_Source_Media: digital file
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 2001
Ending_Date: 2014
Source_Currentness_Reference:
ground condition
Source_Citation_Abbreviation:
FIA Ownership Data
Source_Contribution:
FIA forested plots (n=123,111) were used to produce the Thiessen Polygon ownership layer.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Cleland, D.T.
Originator: Freeouf, J.A.
Originator: Keys, J.E., Jr.
Originator: Nowacki, G.J.
Originator: Carpenter, C.
Originator: McNab, W.H.
Publication_Date: 2007
Title:
Ecological subregion sections and subsections of the conterminous United States
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publication_Place: Washington D.C.
Publisher: U.S. Department of Agricultuure Forest Service
Other_Citation_Details:
General Technical Report WO-76, scale 1:3,500,000
Type_of_Source_Media: digital file
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2007
Source_Currentness_Reference:
publication date
Source_Citation_Abbreviation:
Ecoregion Layer
Source_Contribution:
The Ecoregion Layer was used to create the forest/non-forest mask from Wilson et al.'s (2012) Proportion Forest Layer.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Butler, Brett J.
Originator: Hewes, Jaketon H.
Originator: Liknes, Greg C.
Originator: Nelson, Mark D.
Originator: Snyder, Stephanie A.
Publication_Date: 2014
Title:
A comparison of techniques for generating forest ownership spatial products
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Applied Geography
Issue_Identification: 46:21-34
Online_Linkage: https://doi.org/10.1016/j.apgeog.2013.09.020
Online_Linkage: https://www.fs.usda.gov/treesearch/pubs/45192
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 2003
Ending_Date: 2007
Source_Currentness_Reference:
publication date
Source_Citation_Abbreviation:
Butler et al. (2014)
Source_Contribution:
Thiessen polygon, multinomial logit, and classification tree methods were tested for producing a forest ownership spatial dataset across four states from differing geographic regions and with divergent ownership patterns (Alabama, Arizona, Michigan, and Oregon). Over 17,000 sample points with classified forest ownership, collected as part of the USDA Forest Service, Forest Inventory and Analysis (FIA) program, were used in the methods testing exercise; 90% for model building and 10% for validation assessment.

The percentage of validation points across the four states correctly predicted ranged from 76.3 to 78.9 among the methods with corresponding weighted kappa values ranging from 0.73 to 0.76. Different methods performed significantly better in different states when examined comparing the percentage of correctly predicted points and weighted kappa values.

In Alabama where family ownership dominates (63%), followed by corporate ownership (29%) according to FIA estimates, the classification tree method performed significantly better than the Thiessen polygon approach. The multinomial logit approach was not significantly different from the other two approaches.

In Arizona where federal ownership dominates (52%), followed by other private (31%), accuracies were not significantly different among the methods.

In Michigan where family forest ownership dominates (46%), followed by state ownership (21%), accuracy of the classification tree approach was significantly higher than the other approaches, which were not significantly different from each other.

In Oregon, where federal ownership dominates (60%) followed by corporate (20%), accuracy of the Thiessen polygon approach was significantly higher than the other approaches, which were not significantly different from each other.

Overall, the Thiessen polygon method was deemed preferable because: it had a lower bias towards dominant ownership categories; required fewer inputs; and was simpler to implement.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Bureau of the Census (BOC)
Publication_Date: 20140819
Title:
tl_2014_us_tbg.shp
Edition: 2014
Geospatial_Data_Presentation_Form: vector digital data
Series_Information:
Series_Name: Tribal Block Group National Shapefile
Issue_Identification: 2014
Online_Linkage: https://www.census.gov/geo/maps-data/data/tiger-line.html
Type_of_Source_Media: download
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 20140101
Source_Currentness_Reference:
ground condition
Source_Citation_Abbreviation:
Tribal Block Group
Source_Contribution:
The tl_2014_us_tbg.shp was used to supplement modeled tribal ownership.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Bureau of the Census (BOC)
Originator: Research and Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS)
Publication_Date: 2010
Title:
State boundaries
Edition: 2010
Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:
Publication_Place: Suitland, MD
Publisher: U.S. Census Bureau (BOC)
Other_Citation_Details:
Compiled by the University of Tennessee Center for Transportation Research, GIS GROUP from TIGER/Line machine readable files.

At the time of download the data were available:
//www.census.gov
//www.bts.gov/programs/geographic_information_services/
Larger_Work_Citation:
Citation_Information:
Originator: Research and Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS)
Publication_Date: 2011
Title:
National Transportation Atlas Databases (NTAD) 2011
Publication_Information:
Publication_Place: Washington DC
Publisher: Research and Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS)
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2010
Source_Currentness_Reference:
ground condition
Source_Citation_Abbreviation:
State layer
Source_Contribution:
State layer used in data processing.
Process_Step:
Process_Description:
Using ArcToolbox\Analysis\Proximity\Create Thiessen Polygons tool was utilized to generate ownership polygons from 72,986* points at which ownership type is known.

2,677 Federal-owned Points
2,294 State-owned Points
2,519 Locally-owned Points
44,085 Family-owned Points
19,321 Corporate-owned Points
1,869 Other Private-owned Points
221 Tribal-owned Points

Polygons were assigned the ownership type of the parent FIA point from which they were derived.

*Original dataset of FIA points with which to model ownership consisted of 123,111 points. 50,125 of these original points were held back from Thiessen Polygon creation as they intersected PAD federal or state or Tribal Block Group tribal ownership.
Source_Used_Citation_Abbreviation:
FIA Ownership Data
Source_Used_Citation_Abbreviation:
PAD-US 2.0 (CBI Edition)
Source_Used_Citation_Abbreviation:
Tribal Block Group
Process_Date: 2015
Process_Step:
Process_Description:
Using ArcToolbox\Analysis\Overlay Union tool, Thiessen Polygon layer was combined with PAD-US (CBI) Version 2.0 where PAD "Category" = Fee and Owner Type = Federal Land or State Land; and also combined with Tribal Block Group Layer to demarcate tribal ownership.
Source_Used_Citation_Abbreviation:
FIA Ownership Data
Source_Used_Citation_Abbreviation:
PAD-US 2.0 (CBI Edition)
Source_Used_Citation_Abbreviation:
Tribal Block Group
Process_Date: 2015
Process_Step:
Process_Description:
Within the ownership polygon attribute table using the "Field Calculator", ownership type of unioned layers was set equal to ownership type of Thiessen Polygon layer, except where PAD was equal to federal or state, or where Tribal Block Group indicated ownership by a tribal entity. In those instances PAD and Tribal Block Group determined the ownership type.
Source_Used_Citation_Abbreviation:
FIA Ownership Data
Source_Used_Citation_Abbreviation:
PAD-US 2.0 (CBI Edition)
Source_Used_Citation_Abbreviation:
Tribal Block Group
Process_Date: 2015
Process_Step:
Process_Description:
Using ArcToolbox\Conversion\To Raster Polygon to Raster tool, ownership polygons were converted to raster format, with the extent and cell size equal to that in the Forest Layer.
Source_Used_Citation_Abbreviation:
Proportion Forest Layer
Process_Date: 2015
Process_Step:
Process_Description:
Using ArcToolbox\Spatial Analyst Tools\Local Combine tool, Ecoregion, State and Proportion Forest layers were combined to produce one raster dataset with integrated data.

Forest/Non-forest mask was derived by applying a threshold value to the Proportion Forest layer for each ecoregion/state combination such that total forest area in each ecoregion/state unit closely matched FIA statistics.

Pixels for each ecoregion/state unit that exceeded the threshold were designated as "forest" using the Field Calculater within the attribute table.
Source_Used_Citation_Abbreviation:
Ecoregion Layer
Source_Used_Citation_Abbreviation:
Proportion Forest Layer
Source_Used_Citation_Abbreviation:
State Layer
Process_Date: 2015
Source_Produced_Citation_Abbreviation:
Forest Mask
Process_Step:
Process_Description:
Using ArcToolbox\Spatial Analyst Tools\Extraction Extract by Mask tool, modeled ownership pixels from raster were identified and retained where spatially coincident with forest.
Source_Used_Citation_Abbreviation:
Forest Mask
Process_Date: 2015
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Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Planar:
Map_Projection:
Map_Projection_Name: Albers Conical Equal Area
Albers_Conical_Equal_Area:
Standard_Parallel: 29.5
Standard_Parallel: 45.5
Longitude_of_Central_Meridian: -96
Latitude_of_Projection_Origin: 23
False_Easting: 0
False_Northing: 0
Planar_Coordinate_Information:
Planar_Coordinate_Encoding_Method: row and column
Coordinate_Representation:
Abscissa_Resolution: 250
Ordinate_Resolution: 250
Planar_Distance_Units: meters
Geodetic_Model:
Horizontal_Datum_Name: North American Datum of 1983
Ellipsoid_Name: World Geodetic System of 1984
Semi-major_Axis: 6378137
Denominator_of_Flattening_Ratio: 298.25722210088
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Entity_and_Attribute_Information:
Detailed_Description:
Entity_Type:
Entity_Type_Label: OWNERSHIP
Entity_Type_Definition:
Entity type predicted to own forestland
Entity_Type_Definition_Source:
Woudenberg, Sharon W.; Conkling, Barbara L.; O'Connell, Barbara M.; LaPoint, Elizabeth B.; Turner, Jeffery A.; Waddell, Karen L. 2010. The Forest Inventory and Analysis database: database description and users manual version 4.0 for Phase 2. Gen. Tech. Rep. RMRS-GTR-245. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 336 p. https://doi.org/10.2737/RMRS-GTR-245
Attribute:
Attribute_Label: OWNERSHIP_TYPE
Attribute_Definition:
Seven values of ownership type:

0 = Non-Forest.
1 = Federal (Public): Owned by the federal government. FIA Codes 11-13, 21-25.
2 = State (Public): Owned by a state government. FIA Code 31.
3 = Local (Public): Owned by a local government. FIA Code 32.
4 = Family (Private): Owned by families, individuals, trusts, estates, family partnerships, and other unincorporated groups of individuals that own forest land. FIA Code 45.
5 = Corporate (Private): Owned by corporations. FIA Code 41.
6 = Other Private (Private): Owned by conservation and natural resource organizations, unincorporated partnerships and associations. FIA Codes 42-43.
7 = Tribal: Owned by Native American tribes. FIA Code 44.
Attribute_Definition_Source:
Woudenberg et al. (2010)
Overview_Description:
Entity_and_Attribute_Overview:
The Public 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 proportion tree 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 250 meter raster.
Entity_and_Attribute_Detail_Citation:
Woudenberg, Sharon W.; Conkling, Barbara L.; O'Connell, Barbara M.; LaPoint, Elizabeth B.; Turner, Jeffery A.; Waddell, Karen L. 2010. The Forest Inventory and Analysis database: database description and users manual version 4.0 for Phase 2. Gen. Tech. Rep. RMRS-GTR-245. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 336 p. https://doi.org/10.2737/RMRS-GTR-245
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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 July 2020. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Resource_Description: RDS-2017-0007
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: GRID
Format_Version_Number: see Format Specification
Format_Specification:
ArcGIS raster grid file
File_Decompression_Technique: Files zipped with 7-Zip 19.0
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2017-0007
Fees: None
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Metadata_Reference_Information:
Metadata_Date: 20200723
Metadata_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Butler, Brett J.
Contact_Organization: USDA Forest Service, Northern Research Station
Contact_Position: Research Forester
Contact_Address:
Address_Type: mailing
Address: 160 Holdsworth Way
City: Amherst
State_or_Province: MA
Postal_Code: 01003
Country: USA
Contact_Voice_Telephone: 413-545-1387
Contact_Electronic_Mail_Address: brett.butler2@usda.gov
Metadata_Standard_Name: FGDC Content Standard for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001-1998
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https://www.fs.usda.gov/rds/archive/products/RDS-2017-0007/_metadata_RDS-2017-0007.html