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 a data frame, forested, from which we can develop a model on the more easily-accessible remotely sensed data to predict whether a plot is forested or non-forested.
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")
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>
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