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.
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.
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).
Data by state
The forested package provides a few data sets, each corresponding to forest data in one state:
forestedcorresponds to Washington state and is aliased asforested_wa.forested_gacorresponds to Georgia.
Examples
# Washington data:
str(forested)
#> tibble [7,107 × 20] (S3: tbl_df/tbl/data.frame)
#> $ forested : Factor w/ 2 levels "Yes","No": 1 1 2 1 1 1 1 1 1 1 ...
#> $ year : num [1:7107] 2005 2005 2005 2005 2005 ...
#> $ elevation : num [1:7107] 881 113 164 299 806 736 636 224 52 2240 ...
#> $ eastness : num [1:7107] 90 -25 -84 93 47 -27 -48 -65 -62 -67 ...
#> $ northness : num [1:7107] 43 96 53 34 -88 -96 87 -75 78 -74 ...
#> $ roughness : num [1:7107] 63 30 13 6 35 53 3 9 42 99 ...
#> $ tree_no_tree : Factor w/ 2 levels "Tree","No tree": 1 1 1 2 1 1 2 1 1 2 ...
#> $ dew_temp : num [1:7107] 0.04 6.4 6.06 4.43 1.06 1.35 1.42 6.39 6.5 -5.63 ...
#> $ precip_annual : num [1:7107] 466 1710 1297 2545 609 ...
#> $ temp_annual_mean: num [1:7107] 6.42 10.64 10.07 9.86 7.72 ...
#> $ temp_annual_min : num [1:7107] -8.32 1.4 0.19 -1.2 -5.98 ...
#> $ temp_annual_max : num [1:7107] 12.9 15.8 14.4 15.8 13.8 ...
#> $ temp_january_min: num [1:7107] -0.08 5.44 5.72 3.95 1.6 1.12 0.99 5.54 6.2 -4.54 ...
#> $ vapor_min : num [1:7107] 78 34 49 67 114 67 67 31 60 79 ...
#> $ vapor_max : num [1:7107] 1194 938 754 1164 1254 ...
#> $ canopy_cover : num [1:7107] 50 79 47 42 59 36 14 27 82 12 ...
#> $ lon : num [1:7107] -119 -123 -122 -122 -118 ...
#> $ lat : num [1:7107] 48.7 47.1 48.8 45.8 48.1 ...
#> $ land_type : Factor w/ 3 levels "Barren","Non-tree vegetation",..: 3 3 3 3 3 3 2 2 3 2 ...
#> $ county : Factor w/ 39 levels "Adams","Asotin",..: 10 34 37 30 33 33 26 27 27 24 ...
head(forested)
#> # A tibble: 6 × 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
#> # 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>
all.equal(forested, forested_wa)
#> [1] TRUE
# Georgia data:
str(forested_ga)
#> tibble [10,937 × 19] (S3: tbl_df/tbl/data.frame)
#> $ forested : Factor w/ 2 levels "Yes","No": 1 1 1 1 1 1 1 1 1 1 ...
#> $ year : num [1:10937] 2007 2007 2006 2007 2006 ...
#> $ elevation : num [1:10937] 14 66 59 116 283 250 58 140 118 217 ...
#> $ eastness : num [1:10937] 0 -53 -82 -78 63 63 31 56 72 -46 ...
#> $ roughness : num [1:10937] 0 10 6 20 13 14 1 11 17 13 ...
#> $ tree_no_tree : Factor w/ 2 levels "Tree","No tree": 2 1 2 1 1 1 2 1 1 1 ...
#> $ dew_temp : num [1:10937] 13.9 13.8 13.5 12.3 10 ...
#> $ precip_annual : num [1:10937] 1255 1227 1211 1304 1354 ...
#> $ temp_annual_mean: num [1:10937] 19.2 19.1 18.8 18.3 16 ...
#> $ temp_annual_min : num [1:10937] 3.4 3.23 2.71 1.98 -0.43 0.19 3.41 2 1.98 2.68 ...
#> $ temp_annual_max : num [1:10937] 25.4 25.4 25.1 24.7 21.9 ...
#> $ temp_january_min: num [1:10937] 13.1 12.8 12.6 11.8 10 ...
#> $ vapor_min : num [1:10937] 61 66 57 92 90 61 59 100 98 100 ...
#> $ vapor_max : num [1:10937] 1749 1849 1785 1844 1545 ...
#> $ canopy_cover : num [1:10937] 22 82 9 66 27 79 30 58 75 90 ...
#> $ lon : num [1:10937] -81.4 -82.6 -81.7 -84.9 -84.4 ...
#> $ lat : num [1:10937] 32.3 31.7 32.4 32.4 34.1 ...
#> $ land_type : Factor w/ 3 levels "Barren","Non-tree vegetation",..: 3 3 2 3 3 3 3 2 3 3 ...
#> $ county : Factor w/ 159 levels "Appling","Atkinson",..: 51 80 16 26 28 31 34 26 26 96 ...
head(forested_ga)
#> # A tibble: 6 × 19
#> forested year elevation eastness roughness tree_no_tree dew_temp
#> <fct> <dbl> <dbl> <dbl> <dbl> <fct> <dbl>
#> 1 Yes 2007 14 0 0 No tree 13.9
#> 2 Yes 2007 66 -53 10 Tree 13.8
#> 3 Yes 2006 59 -82 6 No tree 13.5
#> 4 Yes 2007 116 -78 20 Tree 12.3
#> 5 Yes 2006 283 63 13 Tree 10.0
#> 6 Yes 2007 250 63 14 Tree 10.8
#> # 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>