Estimate land area change from the FIADB
areaChange.RdProduces estimates of annual net and component change in land 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 land type 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).
Usage
areaChange(db, grpBy = NULL, polys = NULL, returnSpatial = FALSE,
byLandType = FALSE, landType = "forest", method = "TI",
lambda = 0.5, treeDomain = NULL, areaDomain = NULL,
variance = FALSE, byPlot = FALSE,
condList = FALSE, chngType = 'net', nCores = 1)Arguments
- db
FIA.DatabaseorRemote.FIA.Databaseobject produced fromreadFIA()orgetFIA(). 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 hierarchically. For example, to produce separate 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 produces 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.- 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", "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
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 useplotFIA()withgrpset tolambdato produce moving average ribbon plots. See Stanke et al 2020 for examples.- 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:
DIA > 20, Dominant/Co-dominant crowns only:CCLCD %in% 2:3. Multiple conditions are combined with&(and) or|(or). Only trees where the condition evaluates to TRUE are used in producing estimates. Should NOT be quoted.- 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% 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.- 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.
- condList
logical; if TRUE, returns condition-level summaries intended for subsequent use with
customPSE().- chngType
character, one of "net" or "component"; if "net", produce estimates of net change in land area, and if "component", produce estimates of component change in land area (i.e., showing all shifts across classified attributes).
- 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.
Estimates are returned in terms of net annual change in land area by default, however users may choose to estimate components of land area change by setting chngType = 'component'. For example, imagine we are interested in estimating the change in forestland area across a region. During our sampling period, 4000 acres of forestland was diverted to non-forest and an additional 6000 acres of non-forest reverted to forestland. rFIA considers these shifts in land classifications change components, and hence these point estimates would be returned when chngType = 'component'. However, we are often interested in net change in land area, rather than individual components. Here net change in forestland area is +2000 acres (6000-4000) and represents the net result of diversion and reversion processes in the region over our study period.
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 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 (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
PERC_CHNG: estimate of annual percent change in land area within domain of interest (% of previous)
AREA_CHNG: estimate of annual change in land area within domain of interest (acres)
PREV_AREA: estimate of total land area within domain of interest at first measurement (acres)
nPlots: number of non-zero plots used to compute area change estimates
Importantly, when chngType = 'component', individual change components will be returned. If no grouping variables are specified in grpBy, results will be grouped by variables named STATUS1 and STATUS2, indicating the land classification at first and second measurements, respectively. Otherwise, if grpBy is specified, change components will be estimated for all shifts in land area across classified attributes represented by the variables (first and second measurements again denoted by the suffix 1 and 2). This is also the case for when additional criteria are specified to the tree, condition, or area domains using treeDomain and/or areaDomain.
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.
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)
# Most recent estimates of change in forested area in RI
areaChange(db = fiaRI_mr)
#> # A tibble: 1 × 9
#> YEAR PERC_CHNG AREA_CHNG PREV_AREA PERC_CHNG_SE AREA_CHNG_SE PREV_AREA_SE
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2018 -0.0656 -238. 363831. 533. 535. 3.85
#> # ℹ 2 more variables: nPlots_AREA <int>, N <int>
# \donttest{
# Same as above grouped by land class
areaChange(db = fiaRI_mr, byLandType = TRUE)
#> # A tibble: 4 × 10
#> YEAR landType PERC_CHNG AREA_CHNG PREV_AREA PERC_CHNG_SE AREA_CHNG_SE
#> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2018 Non-Forest 0.274 849. 309804. 137. 136.
#> 2 2018 Non-Timber Fore… 0 0 18404. NaN NaN
#> 3 2018 Timber 0.177 602. 340739. 196. 194.
#> 4 2018 Water -1.28 -1451. 113025. 61.1 65.6
#> # ℹ 3 more variables: PREV_AREA_SE <dbl>, nPlots_AREA <int>, N <int>
# Estimates for change in forest area where stems greater than 20 in DBH
# occur for all available inventories (time-series)
areaChange(db = fiaRI,
landType = 'forest',
treeDomain = DIA > 20)
#> # A tibble: 6 × 9
#> YEAR PERC_CHNG AREA_CHNG PREV_AREA PERC_CHNG_SE AREA_CHNG_SE PREV_AREA_SE
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2013 6.39 7571. 118470. 37.6 31.5 15.0
#> 2 2014 6.24 7940. 127280. 37.6 31.6 14.5
#> 3 2015 4.20 5928. 141209. 42.2 37.2 13.2
#> 4 2016 3.46 5102. 147485. 44.1 39.6 12.6
#> 5 2017 3.82 5725. 149807. 39.7 34.8 12.3
#> 6 2018 4.42 6535. 147855. 36.0 30.9 12.4
#> # ℹ 2 more variables: nPlots_AREA <int>, N <int>
# Return estimates at the plot-level
areaChange(db = fiaRI,
byPlot = TRUE)
#> # A tibble: 352 × 6
#> YEAR pltID PLT_CN REMPER PROP_CHNG PREV_PROP_FOREST
#> <int> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 2009 1_44_1_155 1.45e14 4 0 0
#> 2 2009 1_44_1_326 1.45e14 1.8 0 0
#> 3 2009 1_44_3_135 1.45e14 4.2 0 1
#> 4 2009 1_44_3_152 1.45e14 4.7 0 0
#> 5 2009 1_44_3_19 1.45e14 3.9 0 0
#> 6 2009 1_44_3_323 1.45e14 1.8 0 0
#> 7 2009 1_44_3_55 1.45e14 4.7 0 0
#> 8 2009 1_44_5_130 1.45e14 4.6 0 0
#> 9 2009 1_44_5_257 1.45e14 3.8 0 0
#> 10 2009 1_44_5_302 1.45e14 4.9 0 0
#> # ℹ 342 more rows
# }