Estimate forest structural stage distribution from FIADB
standStruct.RdEstimates the stand structural stage distribution of an area of forest/ timberland 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. If multiple reporting years (EVALIDs) are included in the data, estimates will be output as a time series. Easy options to implement parallel processing. Stand structural stage is classified for each stand (condition) using a method similar to that of Frelich and Lorimer (1991) but substitute basal area for exposed crown area (see Details, References). 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).
Usage
standStruct(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.DatabaseorRemote.FIA.Databaseobject produced fromreadFIAorgetFIA. If aRemote.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, specifyc(STDORGCD, OWNGRPCD).- polys
sporsfPolygon/MultiPolgyon object; Areal units to bin data for estimation. Separate estimates will be produced for region encompassed by each areal unit. FIA plot locations will be reprojected to match projection ofpolysobject.- returnSpatial
logical; if TRUE, merge population estimates with
polysand return assfmultipolygon object. WhenbyPlot = TRUE, return plot-level estimates assfspatial 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 wieghting schemes, and useplotFIAwithgrpset tolambdato 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, the percent land area occupied by forest in each stand structural stage are computed using a sample-based ratio-of-means estimator of total area in structural stage / total land area within the domain of interest. Stand structural stage is classified based on the relative basal area of canopy stems in various size classes (defined below). Only stems which are identified on-site as dominant, subdominant, or intermdediate crown-classes are used to classify stand structural stage.
Diameter Classes
Pole: 12.7 - 25.9 cm
Mature: 26 - 45.9 cm
Large: 46+ cm
Structural Stage Classification
Pole Stage: > 67% BA in pole and mature classes, with more BA in pole than mature.
Mature Stage: > 67% BA in pole and mature classes, with more BA in mature than pole OR > 67% BA in mature and large classes, with more BA in mature.
Late-Successional Stage: > 67% BA in mature and large classes, with more in large
Mosiac: Any plot not meeting above criteria.
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 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 (structural stage of dominant stand type; 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).
STAGE: Stand structural stage.
PERC: % land area in each structural stage.
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.
Frelich, L. E., and Lorimer, C. G. (1991). Natural Disturbance Regimes in Hemlock-Hardwood Forests of the Upper Great Lakes Region. Ecological Monographs, 61(2), 145-164. doi:10.2307/1943005
Goodell, L., and Faber-Langendoen, D. (2007). Development of stand structural stage indices to characterize forest condition in Upstate New York. Forest Ecology and Management, 249(3), 158-170. doi:10.1016/j.foreco.2007.04.052
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 rFIA package
data(fiaRI)
data(countiesRI)
## Most recents subset
fiaRI_mr <- clipFIA(fiaRI)
## Calculate structural stage distribution of all forestland
standStruct(fiaRI_mr)
#> # A tibble: 4 × 6
#> YEAR STAGE COVER_PCT COVER_PCT_SE nPlots_AREA N
#> <dbl> <chr> <dbl> <dbl> <int> <int>
#> 1 2018 LATE 18.8 19.1 127 199
#> 2 2018 MATURE 58.8 7.61 127 199
#> 3 2018 MOSAIC 0.912 64.0 127 199
#> 4 2018 POLE 21.5 16.6 127 199
# \donttest{
## Same as above at plot-level (classify stands)
standStruct(fiaRI_mr,
byPlot = TRUE)
#> # A tibble: 136 × 6
#> YEAR pltID PLT_CN STAGE PROP_STAGE PROP_FOREST
#> <int> <chr> <dbl> <chr> <dbl> <dbl>
#> 1 2013 1_44_1_228 1.45e13 POLE 0.667 0.667
#> 2 2013 1_44_3_144 1.45e13 MATURE 0.170 0.170
#> 3 2013 1_44_7_126 2.47e14 MATURE 0.5 0.5
#> 4 2013 1_44_7_169 1.45e13 MATURE 1 1
#> 5 2013 1_44_7_177 2.47e14 POLE 0.25 0.25
#> 6 2013 1_44_7_229 1.45e13 MATURE 1 1
#> 7 2013 1_44_7_245 1.45e13 MATURE 0.25 0.25
#> 8 2013 1_44_7_306 1.45e13 MATURE 0.217 0.217
#> 9 2013 1_44_7_341 2.47e14 MATURE 1 1
#> 10 2013 1_44_7_61 1.45e13 POLE 1 1
#> # ℹ 126 more rows
## Calculate structural stage distribution of all forestland by owner group, over time
standStruct(fiaRI_mr,
grpBy = OWNGRPCD)
#> # A tibble: 8 × 7
#> YEAR OWNGRPCD STAGE COVER_PCT COVER_PCT_SE nPlots_AREA N
#> <dbl> <int> <chr> <dbl> <dbl> <int> <int>
#> 1 2018 30 LATE 17.4 35.9 40 199
#> 2 2018 30 MATURE 58.8 13.6 40 199
#> 3 2018 30 MOSAIC 0.729 102. 40 32
#> 4 2018 30 POLE 23.0 28.6 40 199
#> 5 2018 40 LATE 19.4 22.5 90 167
#> 6 2018 40 MATURE 58.8 9.06 90 199
#> 7 2018 40 MOSAIC 0.993 77.8 90 167
#> 8 2018 40 POLE 20.8 20.6 90 167
## Calculate structural stage distribution of all forestland on xeric sites, over time
standStruct(fiaRI_mr,
areaDomain = PHYSCLCD %in% c(11:19))
#> # A tibble: 2 × 6
#> YEAR STAGE COVER_PCT COVER_PCT_SE nPlots_AREA N
#> <dbl> <chr> <dbl> <dbl> <int> <int>
#> 1 2018 MOSAIC 0 NaN 127 199
#> 2 2018 POLE 100 0 127 32
## Calculate structural stage distribution of all forestland, over time
standStruct(fiaRI)
#> # A tibble: 24 × 6
#> YEAR STAGE COVER_PCT COVER_PCT_SE nPlots_AREA N
#> <dbl> <chr> <dbl> <dbl> <int> <int>
#> 1 2013 LATE 13.5 23.6 123 197
#> 2 2013 MATURE 62.1 7.00 123 197
#> 3 2013 MOSAIC 1.48 66.0 123 197
#> 4 2013 POLE 22.9 16.6 123 197
#> 5 2014 LATE 13.5 23.5 123 196
#> 6 2014 MATURE 62.6 7.05 123 196
#> 7 2014 MOSAIC 1.20 77.6 123 196
#> 8 2014 POLE 22.7 16.7 123 196
#> 9 2015 LATE 14.1 22.6 124 194
#> 10 2015 MATURE 60.7 7.30 124 194
#> # ℹ 14 more rows
# }