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 Washington State 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 state. 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 7,107 rows and 19 columns:
- 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".
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
str(forested)
#> tibble [7,107 × 19] (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 ...
head(forested)
#> # A tibble: 6 × 19
#> 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 11 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>