Estimate vegation cover by canopy layer from FIADB

Produces estimates of vegetation cover by canopy layer and species growth form from the Forest Inventory and Analysis Database. Estimates can be produced for regions defined within the FIA Database (e.g. counties), at the plot level, or within user-defined areal units. If multiple reporting years (EVALIDs) are included in the data, estimates will be output as a time series. If multiple states are represented by the data, estimates will be output for the full region (all area combined), unless specified otherwise (e.g. grpBy = STATECD). Easy options to implement parallel processing.

Usage

vegStruct(db, grpBy = NULL, polys = NULL,
          returnSpatial = FALSE, landType = "forest",
          method = "TI", lambda = 0.5,
          areaDomain = NULL, totals = FALSE,
          variance = FALSE, byPlot = FALSE,
          nCores = 1)

Arguments

db: FIA.Database or Remote.FIA.Database object produced from readFIA or getFIA. If a Remote.FIA.Database, data will be read in and processed state-by-state to conserve RAM (see details for an example).
grpBy: variables from PLOT or COND tables to group estimates by (NOT quoted). Multiple grouping variables should be combined with c(), and grouping will occur heirarchically. For example, to produce seperate estimates for each ownership group within methods of stand regeneration, specify c(STDORGCD, OWNGRPCD).
polys: sp or sf Polygon/MultiPolgyon object; Areal units to bin data for estimation. Separate estimates will be produced for regions encompassed by each areal unit. FIA plot locations will be reprojected to match projection of polys object.
returnSpatial: logical; if TRUE, merge population estimates with polys and return as sf multipolygon object. When byPlot = TRUE, return plot-level estimates as sf spatial points.
landType: character ("forest" or "timber"); Type of land which estimates will be produced for. Timberland is a subset of forestland (default) which has high site potential and non-reserve status (see details).
method: character; design-based estimator to use. One of: "TI" (temporally indifferent, default), "annual" (annual), "SMA" (simple moving average), "LMA" (linear moving average), or "EMA" (exponential moving average). See Stanke et al 2020 for a complete description of these estimators.
lambda: numeric (0,1); if method = 'EMA', the decay parameter used to define weighting scheme for annual panels. Low values place higher weight on more recent panels, and vice versa. Specify a vector of values to compute estimates using mulitple weighting schemes, and use plotFIA with grp set to lambda to produce moving average ribbon plots. See Stanke et al 2020 for examples.
areaDomain: logical predicates defined in terms of the variables in PLOT and/or COND tables. Used to define the area for which estimates will be produced (e.g. within 1 mile of improved road: RDDISTCD %in% c(1:6), Hard maple/basswood forest type: FORTYPCD == 805. Multiple conditions are combined with & (and) or | (or). Only plots within areas where the condition evaluates to TRUE are used in producing estimates. Should NOT be quoted.
totals: logical; if TRUE, return total population estimates (e.g. total area) along with ratio estimates (e.g. mean trees per acre).
variance: logical; if TRUE, return estimated variance (VAR) and sample size (N). If FALSE, return 'sampling error' (SE) as returned by EVALIDator. Note: sampling error cannot be used to construct confidence intervals.
byPlot: logical; if TRUE, returns estimates for individual plot locations instead of population estimates.
nCores: numeric; number of cores to use for parallel implementation. Check available cores using detectCores. Default = 1, serial processing.

Details

Estimation Details

Estimation of forest variables follows the procedures documented in Bechtold and Patterson (2005) and Stanke et al 2020.

Specifically, percent areal coverage is computed using a sample-based ratio-of-means estimator of total coverage area / total land area within the domain of interest. Percent coverage estimates are returned separately by canopy layer and growth habit (the general appearance of the plant, including size, shape, growth form, and orientation). Canopy layers

Canopy layers

0 - 2.0 feet
2.1 - 6.0 feet
6.1 - 16.0 feet
Greater than 16 feet
Aerial: Canopy cover for all layers

Growth habit

Forbs: herbaceous, broad-leaved plants; includes non-woody-vines and ferns (does not include mosses and cryptobiotic crusts).
Graminoids: grasses and grass-like plants (includes rushes and sedges).
Non-tally tree: tree species not on a particular FIA work unit's tree tally list that are woody plants with a single well-defined, dominant main stem, not supported by other vegetation or structures (not vines), and which are, or expected to become, greater than 13 feet in height. Seedlings, saplings, and mature plants are included.
Shrubs/vines: woody, multiple-stemmed plants of any size, subshrubs (low-growing shrubs under 1.5 feet tall at maturity), and woody vines. Most cacti are included in this category.
Tally tree: all core tree species and any core optional tree species selected by a particular FIA work unit. Only tree species on the FIA Master Tree Species List (or those listed as hybrid, variety, or subspecies) are included. Any plant of that species is included, regardless of its shape and regardless of whether it was tallied on the subplot or microplot during the tree tally. Seedlings, saplings, and mature plants are included.

Users may specify alternatives to the 'Temporally Indifferent' estimator using the method argument. Alternative design-based estimators include the annual estimator ("ANNUAL"; annual panels, or estimates from plots measured in the same year), simple moving average ("SMA"; combines annual panels with equal weight), linear moving average ("LMA"; combine annual panels with weights that decay linearly with time since measurement), and exponential moving average ("EMA"; combine annual panels with weights that decay exponentially with time since measurement). The "best" estimator depends entirely on user-objectives, see Stanke et al 2020 for a complete description of these estimators and tradeoffs between precision and temporal specificity.

When byPlot = FALSE (i.e., population estimates are returned), the "YEAR" column in the resulting dataframe indicates the final year of the inventory cycle that estimates are produced for. For example, an estimate of current forest area (e.g., 2018) may draw on data collected from 2008-2018, and "YEAR" will be listed as 2018 (consistent with EVALIDator). However, when byPlot = TRUE (i.e., plot-level estimates returned), the "YEAR" column denotes the year that each plot was measured (MEASYEAR), which may differ slightly from its associated inventory year (INVYR).

Stratified random sampling techniques are most often employed to compute estimates in recent inventories, although double sampling and simple random sampling may be employed for early inventories. Estimates are adjusted for non-response bias by assuming attributes of non-response plot locations to be equal to the mean of other plots included within thier respective stratum or population.

Working with "Big Data"

If FIA data are too large to hold in memory (e.g., R throws the "cannot allocate vector of size ..." errors), use larger-than-RAM options. See documentation of link{readFIA} for examples of how to set up a Remote.FIA.Database. As a reference, we have used rFIA's larger-than-RAM methods to estimate forest variables using the entire FIA Database (~50GB) on a standard desktop computer with 16GB of RAM. Check out our website for more details and examples.

Easy, efficient parallelization is implemented with the parallel package. Users must only specify the nCores argument with a value greater than 1 in order to implement parallel processing on their machines. Parallel implementation is achieved using a snow type cluster on any Windows OS, and with multicore forking on any Unix OS (Linux, Mac). Implementing parallel processing may substantially decrease free memory during processing, particularly on Windows OS. Thus, users should be cautious when running in parallel, and consider implementing serial processing for this task if computational resources are limited (nCores = 1).

Forest land must have at least 10-percent canopy cover by live tally trees of any size, including land that formerly had such tree cover and that will be naturally or artificially regenerated. Forest land includes transition zones, such as areas between heavily forest and non-forested lands that meet the mimium tree canopy cover and forest areas adjacent to urban and built-up lands. The minimum area for classification of forest land is 1 acre in size and 120 feet wide measured stem-to-stem from the outer-most edge. Roadside, streamside, and shelterbelt strips of trees must have a width of at least 120 feet and continuous length of at least 363 feet to qualify as forest land. Tree-covered areas in agricultural production settings, such as fruit orchards, or tree-covered areas in urban settings, such as city parks, are not considered forest land.

Timber land is a subset of forest land that is producing or is capable of producing crops of industrial wood and not withdrawn from timber utilization by statute or administrative regulation. (Note: Areas qualifying as timberland are capable of producing at least 20 cubic feet per acre per year of industrial wood in natural stands. Currently inaccessible and inoperable areas are NOT included).

Value

Dataframe or sf object (if returnSpatial = TRUE). If byPlot = TRUE, values are returned for each plot (proportion of plot in domain of interest; PLOT_STATUS_CD = 1 when forest exists at the plot location). All variables with names ending in SE, represent the estimate of sampling error (%) of the variable. When variance = TRUE, variables ending in VAR denote the variance of the variable and N is the total sample size (i.e., including non-zero plots).

YEAR: reporting year associated with estimates
LAYER: canopy layer
GROWTH_HABIT: species growth habit
COVER_PCT: estimate of percent areal coverage of the growth habit within the canopy layer
COVER_AREA_TOTAL: estimate of areal coverage of the growth habit within the canopy layer (acres)
AREA_TOTAL: estimate of total land area (acres)
nPlots_VEG: number of non-zero plots used to compute areal coverage estimates
nPlots_AREA: number of non-zero plots used to compute land area estimates

References

rFIA website: https://rfia.netlify.app/

FIA Database User Guide: https://research.fs.usda.gov/understory/forest-inventory-and-analysis-database-user-guide-nfi

Bechtold, W.A.; Patterson, P.L., eds. 2005. The Enhanced Forest Inventory and Analysis Program - National Sampling Design and Estimation Procedures. Gen. Tech. Rep. SRS - 80. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 85 p. https://www.srs.fs.usda.gov/pubs/gtr/gtr_srs080/gtr_srs080.pdf

Stanke, H., Finley, A. O., Weed, A. S., Walters, B. F., & Domke, G. M. (2020). rFIA: An R package for estimation of forest attributes with the US Forest Inventory and Analysis database. Environmental Modelling & Software, 127, 104664.

Author

Hunter Stanke and Andrew Finley

Note

All sampling error estimates (SE) are returned as the "percent coefficient of variation" (standard deviation / mean * 100) for consistency with EVALIDator. IMPORTANT: sampling error cannot be used to construct confidence intervals. Please use variance = TRUE for that (i.e., return variance and sample size instead of sampling error).

Examples

# Load data from the rFIA package
data(fiaRI)
data(countiesRI)

# Most recents subset
fiaRI_mr <- clipFIA(fiaRI)

# \donttest{
# Estimates across RI for the most recent inventory year
vegStruct(db = fiaRI_mr)
#> # A tibble: 25 × 8
#>     YEAR LAYER  GROWTH_HABIT COVER_PCT COVER_PCT_SE nPlots_VEG nPlots_AREA     N
#>    <dbl> <chr>  <chr>            <dbl>        <dbl>      <int>       <int> <int>
#>  1  2018 0 to … Forbs          27.3            43.6          6           6   199
#>  2  2018 0 to … Graminoids      7.61           47.3          6           6   199
#>  3  2018 0 to … Non-tally t…    0.432          43.1          6           6   199
#>  4  2018 0 to … Shrubs/vines   21.3            18.1          6           6   199
#>  5  2018 0 to … Tally tree      7.01           26.8          6           6   199
#>  6  2018 2.1 t… Forbs           4.28           53.1          6           6   199
#>  7  2018 2.1 t… Graminoids      0.0454         80.6          6           6   199
#>  8  2018 2.1 t… Non-tally t…    1.13           49.4          6           6   199
#>  9  2018 2.1 t… Shrubs/vines   12.1            25.6          6           6   199
#> 10  2018 2.1 t… Tally tree     10.4            32.2          6           6   199
#> # ℹ 15 more rows


# Return estimates at the plot-level
vegStruct(db = fiaRI,
          byPlot = TRUE)
#> # A tibble: 150 × 7
#>     PLT_CN  YEAR pltID      LAYER           GROWTH_HABIT  PROP_COVER PROP_FOREST
#>      <dbl> <int> <chr>      <chr>           <chr>              <dbl>       <dbl>
#>  1 1.45e13  2013 1_44_7_169 0 to 2.0 feet   Forbs             0.138            1
#>  2 1.45e13  2013 1_44_7_169 0 to 2.0 feet   Graminoids        0.115            1
#>  3 1.45e13  2013 1_44_7_169 0 to 2.0 feet   Non-tally tr…     0.005            1
#>  4 1.45e13  2013 1_44_7_169 0 to 2.0 feet   Shrubs/vines      0.227            1
#>  5 1.45e13  2013 1_44_7_169 0 to 2.0 feet   Tally tree        0.11             1
#>  6 1.45e13  2013 1_44_7_169 2.1 to 6.0 feet Forbs             0.0025           1
#>  7 1.45e13  2013 1_44_7_169 2.1 to 6.0 feet Graminoids        0                1
#>  8 1.45e13  2013 1_44_7_169 2.1 to 6.0 feet Non-tally tr…     0.01             1
#>  9 1.45e13  2013 1_44_7_169 2.1 to 6.0 feet Shrubs/vines      0.03             1
#> 10 1.45e13  2013 1_44_7_169 2.1 to 6.0 feet Tally tree        0.045            1
#> # ℹ 140 more rows
  # }