Fire Lab tree list: A tree-level model of the conterminous United States landscape circa 2014
Metadata:
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Identification_Information:
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Citation:
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Citation_Information:
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Originator: Riley, Karin L.
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Originator: Grenfell, Isaac C.
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Originator: Finney, Mark A.
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Originator: Wiener, Jason M.
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Originator: Houtman, Rachel M.
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Publication_Date: 2019
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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
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Description:
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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.
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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.
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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.
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Time_Period_of_Content:
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Time_Period_Information:
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Single_Date/Time:
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Calendar_Date: 2014
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Currentness_Reference:
- Ground condition
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Status:
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Progress: Complete
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Maintenance_and_Update_Frequency: None planned
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Spatial_Domain:
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Description_of_Geographic_Extent:
- Forested areas in the conterminous United States.
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Bounding_Coordinates:
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West_Bounding_Coordinate: -128.97722
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East_Bounding_Coordinate: -65.25445
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North_Bounding_Coordinate: 51.64968
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South_Bounding_Coordinate: 22.76862
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Keywords:
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Theme:
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Theme_Keyword_Thesaurus: ISO 19115 Topic Category
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Theme_Keyword: biota
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Theme_Keyword: environment
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Theme:
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Theme_Keyword_Thesaurus: National Research & Development Taxonomy
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Theme_Keyword: Ecology, Ecosystems, & Environment
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Theme_Keyword: Forest & Plant Health
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Theme_Keyword: Inventory, Monitoring, & Analysis
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Theme_Keyword: Natural Resource Management & Use
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Theme_Keyword: Conservation
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Theme_Keyword: Ecosystem services
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Theme_Keyword: Forest management
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Theme_Keyword: Restoration
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Theme_Keyword: Timber
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Theme_Keyword: Wilderness
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Theme:
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Theme_Keyword_Thesaurus: None
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Theme_Keyword: Forest Inventory Analysis
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Theme_Keyword: imputation
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Theme_Keyword: LANDFIRE
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Theme_Keyword: random forests
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Theme_Keyword: tree list
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Place:
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Place_Keyword_Thesaurus: None
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Place_Keyword: conterminous United States
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Place_Keyword: CONUS
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Access_Constraints: None
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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
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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
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Cross_Reference:
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Citation_Information:
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Originator: Riley, Karin L.
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Originator: Grenfell, Isaac C.
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Originator: Finney, Mark A.
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Publication_Date: 2016
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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
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Cross_Reference:
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Citation_Information:
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Originator: Riley, Karin L.
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Originator: Grenfell, Isaac C.
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Originator: Finney, Mark A.
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Originator: Wiener, Jason M.
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Publication_Date: 2021
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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
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Cross_Reference:
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Citation_Information:
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Originator: Riley, Karin L.
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Originator: Grenfell, Isaac C.
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Originator: Finney, Mark A.
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Originator: Shaw, John D.
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Publication_Date: 2021
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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
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Cross_Reference:
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Citation_Information:
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Originator: Riley, Karin L.
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Originator: Grenfell, Isaac C.
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Originator: Shaw, John D.
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Originator: Finney, Mark A.
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Publication_Date: 2022
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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:
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Attribute_Accuracy:
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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.
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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.
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Lineage:
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Source_Information:
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Source_Citation:
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Citation_Information:
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Originator: USDA Forest Service, Forest Inventory and Analysis
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Publication_Date: Unknown
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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
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Type_of_Source_Media: Online
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Source_Time_Period_of_Content:
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Time_Period_Information:
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Range_of_Dates/Times:
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Beginning_Date: 2004
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Ending_Date: 2017
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Source_Currentness_Reference:
- Ground Condition
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Source_Citation_Abbreviation:
- FIA DataMart
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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.
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Source_Information:
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Source_Citation:
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Citation_Information:
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Originator: U.S. Department of Interior, Geological Survey
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Publication_Date: Unknown
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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/
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Type_of_Source_Media: Online
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Source_Time_Period_of_Content:
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Time_Period_Information:
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Single_Date/Time:
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Calendar_Date: 2014
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Source_Currentness_Reference:
- Ground Condition
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Source_Citation_Abbreviation:
- LANDFIRE
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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.
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Process_Step:
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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
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Source_Used_Citation_Abbreviation:
- FIA DATAMART; LANDFIRE
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Process_Date: 2019
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Cloud_Cover: 0%
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Spatial_Data_Organization_Information:
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Direct_Spatial_Reference_Method: Raster
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Raster_Object_Information:
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Raster_Object_Type: Pixel
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Row_Count: 97279
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Column_Count: 154180
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Spatial_Reference_Information:
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Horizontal_Coordinate_System_Definition:
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Local:
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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)
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Geodetic_Model:
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Horizontal_Datum_Name: North American Datum of 1983
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Ellipsoid_Name: Geodetic Reference System 80
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Semi-major_Axis: 6378137.0000
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Denominator_of_Flattening_Ratio: 298.25722210088
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Entity_and_Attribute_Information:
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Detailed_Description:
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Entity_Type:
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Entity_Type_Label: national_c2014_tree_list
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Entity_Type_Definition:
- raster digital data file
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Entity_Type_Definition_Source:
- USFS Researchers
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Attribute:
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Attribute_Label: OID
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Attribute_Definition:
- Unique primary key field automatically generated.
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Attribute_Definition_Source:
- ESRI
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Attribute_Domain_Values:
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Unrepresentable_Domain:
- Sequential unique whole numbers that are automatically generated.
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Attribute:
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Attribute_Label: Value
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Attribute_Definition:
- Numeric field that holds a unique identifier for each forest plot (identical to the tree list identifier or “tl_id” field)
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Attribute_Definition_Source:
- Riley et al. (2016)
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Attribute:
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Attribute_Label: Count
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Attribute_Definition:
- Number of pixels to which a tree list identifier was assigned.
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Attribute_Definition_Source:
- ESRI
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Attribute:
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Attribute_Label: tl_id
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Attribute_Definition:
- Numeric field that holds a unique identifier for each forest plot (identical to the “Value” field)
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Attribute_Definition_Source:
- FIA: https://apps.fs.usda.gov/fia/datamart/datamart_access.html
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Overview_Description:
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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)
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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:
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Distributor:
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Contact_Information:
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Contact_Organization_Primary:
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Contact_Organization: USDA Forest Service, Research and Development
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Contact_Position: Research Data Archivist
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Contact_Address:
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Address_Type: mailing and physical
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Address: 240 West Prospect Road
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City: Fort Collins
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State_or_Province: CO
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Postal_Code: 80526
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Country: USA
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Contact_Voice_Telephone: see Contact Instructions
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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.
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Resource_Description: RDS-2019-0026
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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.
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Standard_Order_Process:
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Digital_Form:
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Digital_Transfer_Information:
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Format_Name: ASCII
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Format_Version_Number: see Format Specification
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Format_Specification:
- Comma-delimited ASCII text file (TXT)
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Digital_Transfer_Option:
-
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Online_Option:
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Computer_Contact_Information:
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Network_Address:
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Network_Resource_Name:
https://doi.org/10.2737/RDS-2019-0026
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Digital_Form:
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Digital_Transfer_Information:
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Format_Name: TIF
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Format_Version_Number: see Format Specification
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Format_Specification:
- GeoTIFF file
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Digital_Transfer_Option:
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Online_Option:
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Computer_Contact_Information:
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Network_Address:
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Network_Resource_Name:
https://doi.org/10.2737/RDS-2019-0026
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Fees: none
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Metadata_Reference_Information:
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Metadata_Date: 20240201
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Metadata_Contact:
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Contact_Information:
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Contact_Person_Primary:
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Contact_Person: Karin Riley
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Contact_Organization: USDA Forest Service, Rocky Mountain Research Station
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Contact_Position: Research Ecologist
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Contact_Address:
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Address_Type: mailing and physical
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Address: 5775 W. Broadway
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City: Missoula
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State_or_Province: Montana
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Postal_Code: 59808
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Country: USA
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Contact_Voice_Telephone: 406-329-4806
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Contact_Electronic_Mail_Address:
karin.l.riley@usda.gov
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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.
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Metadata_Standard_Name: FGDC Content Standard for Digital Geospatial Metadata
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Metadata_Standard_Version: FGDC-STD-001-1998
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