Forest Attributes in U.S. States

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.

forested

Data on Forest Attributes in U.S. States

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 country 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. forested is an R data package containing state-by-state data frames with which we can develop models on the more easily-accessible remotely sensed data to predict whether a plot is forested or non-forested.

Installation

Install the most recent release of forested from CRAN with:

install.packages("forested")

Install the development version of forested from GitHub with:

# install.packages("pak")
pak::pak("simonpcouch/forested")

Example

library(tibble)
library(forested)

forested
#> # A tibble: 7,107 × 20
#>    forested  year elevation eastness northness roughness tree_no_tree dew_temp
#>    <fct>    <dbl>     <dbl>    <dbl>     <dbl>     <dbl> <fct>           <dbl>
#>  1 Yes       2005       881       90        43        63 Tree             0.04
#>  2 Yes       2005       113      -25        96        30 Tree             6.4 
#>  3 No        2005       164      -84        53        13 Tree             6.06
#>  4 Yes       2005       299       93        34         6 No tree          4.43
#>  5 Yes       2005       806       47       -88        35 Tree             1.06
#>  6 Yes       2005       736      -27       -96        53 Tree             1.35
#>  7 Yes       2005       636      -48        87         3 No tree          1.42
#>  8 Yes       2005       224      -65       -75         9 Tree             6.39
#>  9 Yes       2005        52      -62        78        42 Tree             6.5 
#> 10 Yes       2005      2240      -67       -74        99 No tree         -5.63
#> # ℹ 7,097 more rows
#> # ℹ 12 more variables: precip_annual <dbl>, temp_annual_mean <dbl>,
#> #   temp_annual_min <dbl>, temp_annual_max <dbl>, temp_january_min <dbl>,
#> #   vapor_min <dbl>, vapor_max <dbl>, canopy_cover <dbl>, lon <dbl>, lat <dbl>,
#> #   land_type <fct>, county <fct>