Title: Forest Attributes in U.S. States
Version: 0.2.0
Description: A small subset of plots throughout the U.S. are sampled and assessed "on-the-ground" as forested or non-forested by the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program, but the FIA also has access to remotely sensed data for all land in the country. The 'forested' package contains data frames intended for use in predictive modeling applications where the more easily-accessible remotely sensed data can be used to predict whether a plot is forested or non-forested. Currently, the package provides data for Washington and Georgia.
License: MIT + file LICENSE
URL: https://github.com/simonpcouch/forested, https://simonpcouch.github.io/forested/
BugReports: https://github.com/simonpcouch/forested/issues
Depends: R (≥ 4.1)
Suggests: knitr
Config/Needs/website: tidyverse/tidytemplate
Config/testthat/edition: 3
Config/usethis/last-upkeep: 2025-05-05
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-08-19 17:58:40 UTC; simoncouch
Author: Grayson White ORCID iD [aut], Hannah Frick ORCID iD [aut], Simon Couch ORCID iD [aut, cre], Posit Software, PBC ROR ID [cph, fnd]
Maintainer: Simon Couch <simon.couch@posit.co>
Repository: CRAN
Date/Publication: 2025-08-19 18:10:02 UTC

forested: Forest Attributes in U.S. States

Description

logo

A small subset of plots throughout the U.S. are sampled and assessed "on-the-ground" as forested or non-forested by the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program, but the FIA also has access to remotely sensed data for all land in the country. The 'forested' package contains data frames intended for use in predictive modeling applications where the more easily-accessible remotely sensed data can be used to predict whether a plot is forested or non-forested. Currently, the package provides data for Washington and Georgia.

Author(s)

Maintainer: Simon Couch simon.couch@posit.co (ORCID)

Authors:

Other contributors:

See Also

Useful links:

Forest Attributes in the U.S.

Description

The U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program provides all sorts of estimates of forest attributes for uses in research, legislation, and land management. The FIA uses a set of criteria to classify a plot of land as "forested" or "non-forested," and that classification is a central data point in many decision-making contexts. A small subset of plots in the U.S. are sampled and assessed "on-the-ground" as forested or non-forested, but the FIA has access to remotely sensed data for all land in the country. Practitioners can develop a model on the more easily-accessible remotely sensed data to predict whether a plot is forested or non-forested.

Usage

forested

forested_wa

forested_ga

Format

A data frame with:

forested

Whether the plot is classified as "forested" or not, as a factor with levels "Yes" and "No".

year

Year when the plot was classified "on-the-ground" as forested or not. The remaining, remotely-sensed variables are measured at different times or averaged over multiple years.

elevation

Elevation, in meters.

eastness

Transformed aspect degrees to eastness (-100 to 100).

northness

Transformed aspect degrees to northness (-100 to 100).

roughness

Degree of irregularity of the plot.

tree_no_tree

LANDFIRE tree/non-tree lifeform mask, as a factor with levels "Tree" and "No tree".

dew_temp

Mean annual dewpoint temperature (1991-2020), in degrees Celsius.

precip_annual

Mean annual precipitation (1991-2020), in mm × 100.

temp_annual_mean

Mean annual temperature (1991-2020), in degrees Celsius.

temp_annual_min

Mean annual minimum temperature (1991-2020), in degrees Celsius.

temp_annual_max

Mean annual maximum temperature (1991-2020), in degrees Celsius.

temp_january_min

Mean minimum temperature in January (1991-2020), in degrees Celsius.

vapor_min, vapor_max

Minimum and maximum annual vapor pressure deficit (1991-2020), in Pa x 100.

canopy_cover

Analytical Tree Canopy Cover, as a percent.

lon, lat

The longitude and latitude of the center of the plot with a slight perturbation.

land_type

Land cover type from European Space Agency (ESA) 2020 WorldCover global land cover product, as a factor with levels "Tree", "Non-tree vegetation", and "Barren".

county

The county in the state, as a factor.

The number of rows varies by state. Washington has 7107 rows, Georgia has 10937.

The Georgia data has one less column than the Washington data as its northness column has been omitted due to issues with the source raster.

An object of class tbl_df (inherits from tbl, data.frame) with 7107 rows and 20 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 10937 rows and 19 columns.

Data by state

The forested package provides a few data sets, each corresponding to forest data in one state:

Source

For more information on the source data, see Table 1 in:

White, Grayson W.; Yamamoto, Josh K.; Elsyad, Dinan H.; Schmitt, Julian F.; Korsgaard, Niels H.; Hu, Jie Kate; Gaines III, George C.; Frescino Tracey S.; McConville, Kelly S. (2024). Small area estimation of forest biomass via a two-stage model for continuous zero-inflated data. Forthcoming: arXiv 2402.03263 (ver. 2.0).

For more on data definitions:

Wieczorek, Jerzy A.; White, Grayson W.; Cody, Zachariah W.; Tan, Emily X.; Chistolini, Jacqueline O.; McConville, Kelly S.; Frescino, Tracey S.; Moisen, Gretchen G. (2024). Assessing small area estimates via artificial populations from KBAABB: a kNN-based approximation to ABB. Forthcoming: arXiv 2306.15607 (ver. 2.0.

Source data pre-preprocessed using the FIESTA R Package (GPL-3):

Frescino, Tracey S.; Moisen, Gretchen G.; Patterson, Paul L.; Toney, Chris; White, Grayson W. (2023). FIESTA: A forest inventory estimation and analysis R package. Ecography 2023: e06428 (ver. 1.0).

Examples

# Washington data:
str(forested)
head(forested)
all.equal(forested, forested_wa)

# Georgia data:
str(forested_ga)
head(forested_ga)