Description Usage Arguments Details Value Note Author(s) References See Also Examples
Produces estimates of total area (acreage) from FIA data. Estimates can be produced for regions defined within the FIA Database (e.g. counties), at the plot level, or within user-defined areal units. Options to group estimates by species, size class, and other variables defined in the FIADB. 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).
1 2 3 4 5 |
db |
|
grpBy |
variables from PLOT, COND, or TREE tables to group estimates by (NOT quoted). Multiple grouping variables should be combined with |
polys |
|
returnSpatial |
logical; if TRUE, merge population estimates with |
byLandType |
logical; if TRUE, return estimates grouped by individual land type classes ("timberland", "non-timberland forest", "non-forest", and "water"). |
landType |
character, one of: "forest", "non-forest", "census water", "non-census water", "water", or "all"; Type of land that 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 |
treeDomain |
logical predicates defined in terms of the variables in PLOT, TREE, and/or COND tables. Used to define the type of trees for which estimates will be produced (e.g. DBH greater than 20 inches: |
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: |
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 ( |
byPlot |
logical; if TRUE, returns estimates for individual plot locations instead of population estimates. |
condList |
logical; if TRUE, returns condition-level summaries intended for subsequent use with |
nCores |
numeric; number of cores to use for parallel implementation. Check available cores using |
Estimation Details
Estimation of forest variables follows the procedures documented in Bechtold and Patterson (2005) and Stanke et al 2020.
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
).
Definition of forestland
Forest land must be at least 10-percent stocked by 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 forested and nonforested lands that are at least 10-percent stocked with trees and forest areas adjacent to urban and builtup lands. The minimum area for classification of forest land is 1 acre and 120 feet wide measured stem-to-stem from the outer-most edge. Unimproved roads and trails, streams, and clearings in forest areas are classified as forest if less than 120 feet wide. 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).
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
AREA: estimate of total area wihtin domain of interest (acres)
nPlots: number of non-zero plots used to compute area estimates
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).
Hunter Stanke and Andrew Finley
rFIA website: https://rfia.netlify.app/
FIA Database User Guide: https://www.fia.fs.fed.us/library/database-documentation/
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | ## Load data from the rFIA package
data(fiaRI)
data(countiesRI)
## Most recents subset
fiaRI_mr <- clipFIA(fiaRI)
## Most recent estimates of forested area in RI
area(db = fiaRI_mr)
## Same as above grouped by land class
area(db = fiaRI_mr, byLandType = TRUE)
## Estimates for area where stems greater than 20 in DBH occur for
## available inventories (time-series)
area(db = fiaRI,
landType = 'forest',
treeDomain = DIA > 20)
## Same as above, but implemented in parallel (much quicker)
parallel::detectCores(logical = FALSE) # 4 cores available, we will take 2
area(db = fiaRI,
landType = 'forest',
treeDomain = DIA > 20,
nCores =2)
## Return estimates at the plot-level
area(db = fiaRI,
byPlot = TRUE)
|
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