Fire Lab tree list: A tree-level model of the conterminous United States landscape circa 2014

Metadata:

Identification_Information:
Citation:
Citation_Information:
Originator: Riley, Karin L.
Originator: Grenfell, Isaac C.
Originator: Finney, Mark A.
Originator: Wiener, Jason M.
Originator: Houtman, Rachel M.
Publication_Date: 2019
Title:
Fire Lab tree list: A tree-level model of the conterminous United States landscape circa 2014
Geospatial_Data_Presentation_Form: raster and tabular digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2019-0026
Description:
Abstract:
Observations of the forests of the conterminous United States at the level of individual trees would be of utility for any number of applications, ranging from modelling the effect of wildland fire on terrestrial carbon resources to estimation of timber volume. While such observations do exist at selected spots such as established forest plots, most forests have not been mapped with this level of specificity. To fill the gap in tree-level mapping, we used a modelling approach that employed a random forests machine-learning technique. This technique was nearly identical to that employed by Riley et al. (2016), except that it used disturbance variables in addition to topographic and biophysical variables. This method imputes the plot with the best statistical match, according to a “forest” of decision trees, to each pixel of gridded landscape data. A set of predictor variables was used to train the random forests algorithm, which was then leveraged to extrapolate measurements across forested areas of the conterminous United States. Specifically, 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 2014. These variables were present or were derived for both 1) the detailed reference data, which consisted of forest plot data from the U.S. Forest Service’s Forest and Inventory Analysis program (FIA) version 1.7.1 and 2) the landscape target data, which consisted of raster data at 30x30 meter (m) resolution provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE; https://landfire.gov/) FIA plots were imputed to the raster data by the random forests algorithm, providing a tree-level model of all forested areas in the conterminous U.S. Of 67,141 single-condition FIA plots available to random forests, 62,758 of these (93.5%) were utilized in imputation to 2,841,601,981 forested pixels.

The main output of this project (the GeoTIFF available in this data publication) is a map of imputed plot identifiers at 30×30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2014. This map is commonly known as "TreeMap 2014". The map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://apps.fs.usda.gov/fia/datamart/datamart_access.html) or to the Microsoft Access Database and ASCII files included in this data publication to produce tree-level maps or to map other plot attributes. These files also contain attributes regarding the FIA PLOT CN (a unique identifier for each time a plot is measured), the inventory year, the state code and abbreviation, the unit code, the county code, the plot number, 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 unique identifier for each tree and tree visit. Application of the dataset to research questions other than those related to aboveground biomass and carbon 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.
Purpose:
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. Such detailed forest structure data are not available for large areas of public and private lands in the United States, which rely on forest inventory at fixed plot locations at sparse densities. While direct sampling technologies such as light detection and ranging (LiDAR) may eventually make broad coverage of detailed forest inventory feasible, no such data sets at the scale of the conterminous United States (CONUS) are currently available.
Supplemental_Information:
See the Entity and Attributes section for details regarding the relationship between the data files included in this publication and the FIA DataMart.

These data were published on 07/02/2019. On 03/26/2021, the metadata was updated to include reference to a new publication. On 02/01/2024, some additional minor metadata updates were made and trees_CONUS_5_15_2019.mdb was removed from the package because it is an older format and the same content is included via text files.
Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2014
Currentness_Reference:
Ground condition
Status:
Progress: Complete
Maintenance_and_Update_Frequency: None planned
Spatial_Domain:
Description_of_Geographic_Extent:
Forested areas in the conterminous United States.
Bounding_Coordinates:
West_Bounding_Coordinate: -128.97722
East_Bounding_Coordinate: -65.25445
North_Bounding_Coordinate: 51.64968
South_Bounding_Coordinate: 22.76862
Keywords:
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: biota
Theme_Keyword: environment
Theme:
Theme_Keyword_Thesaurus: National Research & Development Taxonomy
Theme_Keyword: Ecology, Ecosystems, & Environment
Theme_Keyword: Forest & Plant Health
Theme_Keyword: Inventory, Monitoring, & Analysis
Theme_Keyword: Natural Resource Management & Use
Theme_Keyword: Conservation
Theme_Keyword: Ecosystem services
Theme_Keyword: Forest management
Theme_Keyword: Restoration
Theme_Keyword: Timber
Theme_Keyword: Wilderness
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: Forest Inventory Analysis
Theme_Keyword: imputation
Theme_Keyword: LANDFIRE
Theme_Keyword: random forests
Theme_Keyword: tree list
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: conterminous United States
Place_Keyword: CONUS
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:

Riley, Karin L.; Grenfell, Isaac C.; Finney, Mark A.; Wiener, Jason M.; Houtman, Rachel M. 2019. Fire Lab tree list: A tree-level model of the conterminous United States landscape circa 2014. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2019-0026
Data_Set_Credit:
This project was funded by the USDA Forest Service, Rocky Mountain Research Station (RMRS).


Author information:

Karin L. Riley
USDA Forest Service, Rocky Mountain Research Station
http://orcid.org/0000-0001-6593-5657

Isaac C. Grenfell
USDA Forest Service, Rocky Mountain Research Station
https://orcid.org/0000-0002-3779-1681

Mark A. Finney
USDA Forest Service, Rocky Mountain Research Station
https://orcid.org/0000-0002-6584-1754

John D. Shaw
USDA Forest Service, Rocky Mountain Research Station
http://orcid.org/0000-0002-5797-1006

Jason M. Wiener
University of Montana

Rachel M. Houtman
Oregon State University
https://orcid.org/0000-0003-2097-9423
Cross_Reference:
Citation_Information:
Originator: Riley, Karin L.
Originator: Grenfell, Isaac C.
Originator: Finney, Mark A.
Publication_Date: 2016
Title:
Mapping forest vegetation for the western United States using modified random forests imputation of FIA forest plots
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Ecosphere
Issue_Identification: 7(10): e01472
Online_Linkage: https://doi.org/10.1002/ecs2.1472
Online_Linkage: https://www.fs.usda.gov/research/treesearch/53114
Cross_Reference:
Citation_Information:
Originator: Riley, Karin L.
Originator: Grenfell, Isaac C.
Originator: Finney, Mark A.
Originator: Wiener, Jason M.
Publication_Date: 2021
Title:
TreeMap, a tree-level model of conterminous US forests circa 2014 produced by imputation of FIA plot data
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Scientific Data
Issue_Identification: 8: 11
Online_Linkage: https://doi.org/10.1038/s41597-020-00782-x
Online_Linkage: https://www.fs.usda.gov/research/treesearch/61840
Cross_Reference:
Citation_Information:
Originator: Riley, Karin L.
Originator: Grenfell, Isaac C.
Originator: Finney, Mark A.
Originator: Shaw, John D.
Publication_Date: 2021
Title:
TreeMap 2016: A tree-level model of the forests of the conterminous United States circa 2016
Geospatial_Data_Presentation_Form: raster and tabular digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Online_Linkage: https://doi.org/10.2737/RDS-2021-0074
Cross_Reference:
Citation_Information:
Originator: Riley, Karin L.
Originator: Grenfell, Isaac C.
Originator: Shaw, John D.
Originator: Finney, Mark A.
Publication_Date: 2022
Title:
TreeMap 2016 dataset generates CONUS-wide maps of forest characteristics including live basal area, aboveground carbon, and number of trees per acre
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Journal of Forestry
Issue_Identification: 120(6): 607-632
Online_Linkage: https://doi.org/10.1093/jofore/fvac022
Online_Linkage: https://www.fs.usda.gov/research/treesearch/65597
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Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
We used the imputed inventory data to generate maps of forest cover, forest height, vegetation group, and disturbance type at 30×30 meter resolution for all forested pixels in the western United States, as a means of assessing the accuracy of our methodology. The results showed good correspondence between the target LANDFIRE data and the imputed plot data, with an overall within-class agreement of 97% for forest cover, 99% for forest height, 93% for vegetation group, and 90% for disturbance type.
Completeness_Report:
Forest plots available for imputation were those that met the following criteria: 1) physically located in CONUS, 2) single-condition, 100% forested FIA Phase 2 plots. There were 67,141 plots that met these criteria and were available for imputation; of these, 62,758 (93.5%) were chosen for imputation at least once.
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: USDA Forest Service, Forest Inventory and Analysis
Publication_Date: Unknown
Title:
FIA forest plot data
Edition: 1.7.1
Geospatial_Data_Presentation_Form: tabular digital data
Publication_Information:
Publisher: FIA DataMart
Other_Citation_Details:
Retrieved 20 September 2017
Online_Linkage: https://apps.fs.usda.gov/fia/datamart/datamart_access.html
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 2004
Ending_Date: 2017
Source_Currentness_Reference:
Ground Condition
Source_Citation_Abbreviation:
FIA DataMart
Source_Contribution:
We obtained the measurements of tree size, height, species, and status (dead or alive) and plot elevation, aspect, and slope from the USDA Forest Service's Forest Inventory Analysis (FIA). Version 1.7.1 data were downloaded from the FIA DataMart. We restricted the plot data to single-condition forested plots only; conditions are defined by changes in vegetation or land use, and some plots have more than one condition because of harvesting or fire. Because we wanted the plots used in imputation to be more or less homogeneous, we used single-condition plots only. Plot locations in the FIA DataMart are fuzzed to protect plot integrity and true coordinates are not available to the public; for the purposes of this study, we obtained true locations from FIA via a Memorandum of Cooperation.
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Department of Interior, Geological Survey
Publication_Date: Unknown
Title:
LANDFIRE: Target Landscape Data
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publisher: U.S. Department of Interior, Geological Survey
Online_Linkage: https://landfire.cr.usgs.gov/viewer/
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2014
Source_Currentness_Reference:
Ground Condition
Source_Citation_Abbreviation:
LANDFIRE
Source_Contribution:
LANDFIRE provides a suite of topographic, biophysical, and vegetation data at 30-meter grid resolution for the conterminous United States that served as the target data for this project.
Process_Step:
Process_Description:
A suite of rasters mapping vegetation and disturbance for the landscape circa 2014 were obtained from the LANDFIRE project’s website (https://www.landfire.gov/version_comparison.php?mosaic=Y), and rasters mapping biophysical characteristics were obtained via a shared server at the Missoula Fire Sciences Lab. These rasters include: slope, aspect, elevation, Existing Vegetation Cover, Existing Vegetation Height, Existing Vegetation Type, Vegetation Disturbance, average photosynthetically active radiation, average precipitation, average relative humidity, average maximum temperature, average minimum temperature, and average vapor pressure deficit. We used these rasters as target data for the random forests model (see Riley et al. 2016 for more details on methodology). Most of these rasters are publicly available at LANDFIRE’s website; the biophysical predictor rasters may be available on request from the LANDFIRE project.

Tabular information regarding FIA plots was obtained from the FIA DataMart. True plot locations are not available in the DataMart and were obtained directly from FIA via a Memorandum of Cooperation. We obtained information directly from the FIA DataMart regarding plot elevation, slope, and aspect, as well as tree-level information for each plot. We calculated plot-level tree cover and height using the StrClass keyword in the Forest Vegetation Simulator (Crookston and Stage 1999). Existing Vegetation Type was assigned to each plot using a series of scripts we obtained from LANDFIRE; this information is not publicly available and was obtained via a Memorandum of Cooperation. We assigned biophysical predictors to each plot via spatial overlay of the plot locations with the LANDFIRE rasters in ArcGIS. This plot-level information was used as reference data in the random forests model.

We then used a modified random forests approach with the LANDFIRE vegetation, disturbance, and biophysical predictors as the target data, to which we imputed the FIA plot data at 30-meter (m) grid resolution (Riley et al. 2016). This method imputed the plot with the best statistical match, according to a “forest” of decision trees, to each pixel of gridded landscape data.

For complete details see Riley et al. (2016).

Crookston, Nicholas L.; Stage, Albert R. 1999. Percent canopy cover and stand structure statistics from the Forest Vegetation Simulator. Gen. Tech. Rep. RMRS-GTR-24. Ogden, UT: U. S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 11 p. https://doi.org/10.2737/RMRS-GTR-24

Riley, Karin L.; Grenfell, Isaac C.; Finney, Mark A. 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 and https://www.fs.usda.gov/research/treesearch/53114
Source_Used_Citation_Abbreviation:
FIA DATAMART; LANDFIRE
Process_Date: 2019
Cloud_Cover: 0%
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Spatial_Data_Organization_Information:
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:
Raster_Object_Type: Pixel
Row_Count: 97279
Column_Count: 154180
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Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Local:
Local_Description:NAD_1983_Albers
Authority: Custom
Projection: Albers
false_easting: 0.0
false_northing: 0.0
central_meridian: -96.0
standard_parallel_1: 29.5
standard_parallel_2: 45.5
latitude_of_origin: 23.0
Linear Unit: Meter (1.0)
Geodetic_Model:
Horizontal_Datum_Name: North American Datum of 1983
Ellipsoid_Name: Geodetic Reference System 80
Semi-major_Axis: 6378137.0000
Denominator_of_Flattening_Ratio: 298.25722210088
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Entity_and_Attribute_Information:
Detailed_Description:
Entity_Type:
Entity_Type_Label: national_c2014_tree_list
Entity_Type_Definition:
raster digital data file
Entity_Type_Definition_Source:
USFS Researchers
Attribute:
Attribute_Label: OID
Attribute_Definition:
Unique primary key field automatically generated.
Attribute_Definition_Source:
ESRI
Attribute_Domain_Values:
Unrepresentable_Domain:
Sequential unique whole numbers that are automatically generated.
Attribute:
Attribute_Label: Value
Attribute_Definition:
Numeric field that holds a unique identifier for each forest plot (identical to the tree list identifier or “tl_id” field)
Attribute_Definition_Source:
Riley et al. (2016)
Attribute:
Attribute_Label: Count
Attribute_Definition:
Number of pixels to which a tree list identifier was assigned.
Attribute_Definition_Source:
ESRI
Attribute:
Attribute_Label: tl_id
Attribute_Definition:
Numeric field that holds a unique identifier for each forest plot (identical to the “Value” field)
Attribute_Definition_Source:
FIA: https://apps.fs.usda.gov/fia/datamart/datamart_access.html
Overview_Description:
Entity_and_Attribute_Overview:
Below is a description of the data files included in this publication and their relationship to each other and the FIA DataMart.

IMPORTANT INFORMATION

The tree list raster (\Data\national_c2014_tree_list.tif) can be linked to tree data in the tree table (\Data\Tree_table_CONUS.txt) via the the tree list identifier values (“tl_id”) or sequence numbers (“CN”); the latter corresponds to a unique identifier used by FIA. (Note that FIA uses the attribute name “CN” in the “PLOT” table and “PLT_CN” in other tables but these are equivalent.) The “CN” field signifies a single visit to a plot. All plot CNs utilized in this analysis were single condition, 100% forested, physically located within the boundaries of CONUS and were obtained from FIA in December of 2012.


DATA FILE DESCRIPTIONS

\Data\national_c2014_tree_list.tif:
Raster dataset (GeoTIFF file), and associated files, representing model output generated by random forests imputation of forest inventory plot data measured by Forest Inventory Analysis (FIA) to unsampled (and sampled) spatial locations on the landscape for circa 2014 conditions. The primary attributes of the raster are a plot identifier (“tl_id”) and sequence number (“CN”) which can be used to link the raster to information available in the FIA Datamart via the sequence number (CN) field in the PLOT table to produce tree-level maps or to map other plot attributes. Predictor variables in the random forests imputation were chosen to optimize the prediction of aboveground forest carbon. These include topographic variables (slope, aspect, and elevation from the FIA PLOT and COND tables), true plot location, vegetation (forest cover, height and vegetation group assigned to each plot via FVS and LANDFIRE methods), disturbance (years since disturbance and disturbance type as derived from LANDFIRE disturbance rasters), and biophysical variables (maximum and minimum temperature, relative humidity, precipitation, photosynthetically active radiation, and vapor pressure deficit derived by overlay of the plot coordinates with LANDFIRE biophysical rasters). Variables and methods are defined in the detailed section above.


\Data\TL_CN_Lookup.txt:
Comma-delimited ASCII text file containing a list of the unique forest plot identifiers (“tl_id”) and associated survey record numbers (“CN”) for the imputed Forest Inventory and Analysis (FIA) plots.

Variables include:

OID = Unique identifier for each row

Value = Unique identifier for each forest plot (identical in content to “tl_id” field)

Count = Number of pixels to which this forest plot was imputed

CN = Sequence number (or “PLT_CN”) field in FIA DataMart TREE table (equivalent to CN field in PLOT table); a unique sequence number that identifies a survey record. Note that CN signifies a unique visit to a plot. This field is a text field due to its length and should not be converted to a numeric field or most software programs will round or truncate it.

tl_id = Unique identifier for each forest plot (stands for “tree list ID”; identical in content to “Value” field in this table and equivalent to “tl_id” field in “national_c2014_tree_list.tif”)


\Data\Tree_table_CONUS.txt:
Comma-delimited ASCII text file containing a list of tree attributes for all imputed Forest Inventory and Analysis (FIA) plots.

Variables include:

tl_id = Unique identifier for each forest plot (stands for “tree list ID”; equivalent to “tl_id” field in “national_c2014_tree_list.tif”)

CN = Field in FIA DataMart PLOT table (equivalent to PLT_CN field in TREE table); a unique sequence number that identifies a survey record

INVYR = FIA inventory year (from FIA PLOT table)

STATECD = State code. Bureau of the Census Federal Information Processing Standards (FIPS) two-digit code for each State. (from FIA PLOT table)

State_Abbreviation = Two-letter state abbreviation code

UNITCD = FIA survey unit code (from FIA PLOT table)

COUNTYCD = County code, equivalent to FIPS codes from the Bureau of the Census (from FIA PLOT table)

PLOT = Phase 2 plot number. Does not uniquely identify a plot unless used in combination with other variables (from FIA PLOT table), hence we use the unique sequence number (“PLT_CN”).

SUBP = Subplot number (from FIA TREE table)

TREE = Tree record number. A number used to uniquely identify a tree on a subplot. (from FIA TREE table)

STATUSCD = Status code. Indicates whether the sample tree is live, cut, or dead at the time of measurement. As per Burrill et al. (2017): “0=No status - Tree is not presently in the sample (remeasurement plots only). Tree was incorrectly tallied at the previous inventory, currently not tallied due to definition or procedural change, or is not tallied due to natural causes. 1=Live tree. 2=Dead tree. 3=Removed - Cut and removed by direct human activity related to harvesting, silviculture or land clearing. This tree is assumed to be utilized.” (from FIA TREE table)

SPCD = Tree species code (from FIA TREE table, see FIADB User Guide for a description of the codes: https://www.fia.fs.usda.gov/library/database-documentation/)

DIA = Tree diameter in inches at breast height (4.5 feet above ground line on uphill side of tree) or root crown for woodland species (at the ground line or stem root collar, whichever is higher). (from FIA TREE table)

HT = Total height of a tree in feet from the ground to tip of apical meristem. If main stem is broken, the total height is estimated. (from FIA TREE table)

ACTUALHT = Actual height of a tree in feet from the ground to the highest remaining portion of the tree still attached to the bole. (from FIA TREE table)

CR = Compacted crown ratio (percent of tree bole supporting live healthy foliage when compared to height (ACTUALHT)). (from FIA TREE table)

TREEID = unique TreeID: State_Abbreviation & Format([UNITCD],"0") & Format([COUNTYCD],"000") & Format([PLOT],"000000") & Format([subp],"00") & Format([tree],"0000000")

TreeVisitID = unique tree visit ID: State_Abbreviation & Format([UNITCD],"0") & Format([COUNTYCD],"000") & Format([PLT_CN],"00000000000000") & Format([subp],"00") & Format([tree],"0000000")

TPA_UNADJ = The number of trees per acre that the sample tree theoretically represents based on the sample design. (from FIA TREE table)
Entity_and_Attribute_Detail_Citation:
Burrill, Elizabeth A.; Wilson, Andrea M.; Turner, Jeffrey A.; Pugh, Scott A.; Menlove, James; Christiansen, Glenn. 2017. The Forest Inventory and Analysis Database: database description and user guide for Phase 2 (version 7.2). U.S. Department of Agriculture Forest Service. https://www.fia.fs.usda.gov/library/database-documentation/

Riley, Karin L.; Grenfell, Isaac C.; Finney, Mark A. 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 and https://www.fs.usda.gov/research/treesearch/53114
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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 February 2024. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Resource_Description: RDS-2019-0026
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: ASCII
Format_Version_Number: see Format Specification
Format_Specification:
Comma-delimited ASCII text file (TXT)
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2019-0026
Digital_Form:
Digital_Transfer_Information:
Format_Name: TIF
Format_Version_Number: see Format Specification
Format_Specification:
GeoTIFF file
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2019-0026
Fees: none
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Metadata_Reference_Information:
Metadata_Date: 20240201
Metadata_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Karin Riley
Contact_Organization: USDA Forest Service, Rocky Mountain Research Station
Contact_Position: Research Ecologist
Contact_Address:
Address_Type: mailing and physical
Address: 5775 W. Broadway
City: Missoula
State_or_Province: Montana
Postal_Code: 59808
Country: USA
Contact_Voice_Telephone: 406-329-4806
Contact_Electronic_Mail_Address: karin.l.riley@usda.gov
Contact Instructions: This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Metadata_Standard_Name: FGDC Content Standard for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001-1998
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