rFIA (version 0.1.1)

biomass: Estimate volume, biomass, and carbon stocks from the FIADB

Description

Produces estimates of volume, biomass, and carbon on a per acre basis from FIA data, along with population estimates for each variable. 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).

Usage

biomass(db, grpBy = NULL, polys = NULL, returnSpatial = FALSE, bySpecies = FALSE,
        bySizeClass = FALSE, landType = 'forest', treeType = 'live',
        treeDomain = NULL, areaDomain = NULL, totals = FALSE, byPlot = FALSE,
        SE = TRUE, nCores = 1)

Arguments

db

FIA.Database object produced from readFIA; Function requires that PLOT, TREE, COND, POP_PLOT_STRATUM_ASSGN, POP_ESTN_UNIT, POP_EVAL, POP_STRATUM, POP_EVAL_TYP, POP_EVAL_GRP tables exist in FIA.Database object.

grpBy

variables from PLOT, COND, or TREE 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 ecoregion subsections, specify c(ECOSUBCD, OWNGRPCD).

polys

sp or sf Polygon/MultiPolgyon object; Areal units to bin data for estimation. Seperate estimates will be produces for region encompassed by each areal unit.

returnSpatial

logical; if TRUE, return sf spatial object (polys must also be specified).

bySpecies

logical; if TRUE, returns estimates grouped by species.

bySizeClass

logical; if TRUE, returns estimates grouped by size class (2-inch intervals, see makeClasses to compute different size class intervals).

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).

treeType

character ('all', 'live', 'dead', or 'gs'); Type of tree which estimates will be produced for. All (default) includes all stems, live and dead, greater than 1 in. DBH. Live/Dead includes all stems greater than 1 in. DBH which are live or dead (leaning less than 45 degrees), respectively. GS (growing-stock) includes live stems greater than 5 in. DBH which contain at least one 8 ft merchantable log.

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% c(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% 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 population estimates (e.g. total area, total biomass) along with ratio estimates (e.g. mean biomass per acre).

byPlot

logical; if TRUE, returns estimates for individual plot locations (population totals not returned).

SE

logical; if TRUE, returns estimates with samping error (approx. 5x faster without returning samping errors)

nCores

numeric; number of cores to use for parallel implementation. Check available cores using detectCores. Default = 1, serial processing.

Value

Dataframe or SF object (if returnSpatial = TRUE). If byPlot = TRUE, totals are returned for each plot. All variables with names ending in SE represent the estimate of sampling error (%) of the variable. All variables with names ending in TOTAL represent the population total of the variable.

• YEAR: reporting year associated with estimates

• NETVOL_ACRE: estimate of mean net volume per acre (cu.ft./acre)

• SAWVOL_ACRE: estimate of mean merchantable saw volume per acre (cu.ft./acre)

• BIO_AG_ACRE: estimate of mean aboveground biomass per acre (tons/acre)

• BIO_BG_ACRE: estimate of mean belowground biomass per acre (tons/acre)

• BIO_ACRE: estimate of mean total biomass per acre (tons/acre)

• CARB_AG_ACRE: estimate of mean aboveground carbon per acre (tons/acre)

• CARB_BG_ACRE: estimate of mean belowground carbon per acre (tons/acre)

• CARB_ACRE: estimate of mean total carbon per acre (tons/acre)

• nPlots_VOL: number of non-zero plots used to compute volume, biomass, and carbon estimates

• nPlots_AREA: number of non-zero plots used to compute land area estimates

Details

Estimation of attributes follows the procedures documented in Bechtold and Patterson (2005). Specifically, volume, biomass, and carbon mass per acre are computed using a sample-based ratio-of-means estimator of total volume (carbon or biomass) / total land area within the domain of interest.

Net volume estimates (NETVOL) include only the volume of wood in the central stem of a sample tree, from a 1-foot stump to a minimum 4-inch top diameter, or to where the central stem breaks into limbs all of which are greater than 4.0 inches in diameter. Does not include rotten, missing, and form cull portions of the main stem. Saw volume estimates (SAWVOL) incldue the net volume in the sawlog portion of the tree, from a 1-foot stump to a 9 inches (hardwood) or 7 inches (softwood) top. All volume estimates are reported in cubic feet (cu. ft. / acre). For estimates in board feet, multiply output values by 12.

Biomass (BIO) and carbon (CARB) estimates are computed seperately for aboveground (AG) and belowground (BG) stocks, and their totals are the summation of above and belowground stocks. All biomass and carbon estimates are reported in oven-dry mass (short tons). Belowground mass for an individual tree includes modeled estimates for coarse roots (> 0.1"). Above ground mass includes all portions of a tree above the root collar, excluding foliage.

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.

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).

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).

References

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

See Also

tpa, vitalRates, growMort

Examples

# NOT RUN {
## Load data from the rFIA package
data(fiaRI)
data(countiesRI)

## Most recents subset
fiaRI_mr <- clipFIA(fiaRI)


## Most recent estimates for growing-stock on timber land by species
biomass(db = fiaRI_mr,
        landType = 'timber',
        treeType = 'gs')
# }
# NOT RUN {
## Estimates for live white pine ( > 12" DBH) on forested mesic sites (all available inventories)
biomass(fiaRI_mr,
        treeType = 'live',
        treeDomain = SPCD == 129 & DIA > 12, # Species code for white pine
        areaDomain = PHYSCLCD %in% 21:29) # Mesic Physiographic classes

## Most recent estimates grouped by stand age on forest land
# Make a categorical variable which represents stand age (grouped by 10 yr intervals)
fiaRI_mr$COND$STAND_AGE <- makeClasses(fiaRI_mr$COND$STDAGE, interval = 10)
biomass(db = fiaRI_mr,
        grpBy = STAND_AGE)

## Estimates for snags greater than 20 in DBH on forestland for all
##  available inventories (time-series)
biomass(db = fiaRI,
        landType = 'forest',
        treeType = 'dead',
        treeDomain = DIA > 20)

## Most recent estimates for live stems on forest land by species
biomass(db = fiaRI_mr,
        landType = 'forest',
        treeType = 'live',
        bySpecies = TRUE)

## Same as above, but implemented in parallel (much quicker)
parallel::detectCores(logical = FALSE) # 4 cores available, we will take 2
biomass(db = fiaRI_mr,
        landType = 'forest',
        treeType = 'live',
        bySpecies = TRUE,
        nCores = 2)


## Most recent estimates for all stems on forest land grouped by user-defined areal units
ctSF <- biomass(fiaRI_mr,
                polys = countiesRI,
                returnSpatial = TRUE)
plot(ctSF) # Plot multiple variables simultaneously
plotFIA(ctSF, BIO_AG_ACRE) # Plot of aboveground biomass per acre
# }