Predict plot-level canopy cover from individual tree measurements

calc_tcc_metrics() computes predicted plot-level tree canopy cover (TCC) from standard field inventory measurements. By default, the output includes a full set of stand structure variables used to model the plot-level TCC value (see Details).

Usage

calc_tcc_metrics(tree_list, stem_map = TRUE, full_output = TRUE, digits = 1)

Arguments

tree_list: A data frame with tree records for one FIA plot. In general, the input data frame will have the columns specified in DEFAULT_TREE_COLUMNS (see ?DEFAULT_TREE_COLUMNS). Potentially, only a subset of those columns will be needed depending on values given for the arguments stem_map and full_output described below. If the input data frame has a column named "CRWIDTH" it will be used for tree crown width values, otherwise, crown widths will be calculated with a call to calc_crwidth().
stem_map: A logical value indicating whether to map individual tree stems explicitly, using coordinates specified in terms of distance and azimuth from subplot/microplot centers. The default is TRUE, in which case the input tree_list must contain columns "DIST" and "AZIMUTH". This argument may be set to FALSE if individual tree locations are not available, in which case TCC will be predicted assuming a random arrangement of tree locations (see Details).
full_output: A logical value indicating whether to include the full set of components used to derive the plot-level prediction. By default, the output list includes subplot-level TCC estimates, live tree and sapling counts, stand height metrics, and point pattern statistics, depending on the value given for stem_map (see Details).
digits: Optional integer indicating the number of digits to keep in the return values (defaults to 1). May be passed to calc_crwidth() and calc_ht_metrics().

Value

If full_output = TRUE, a named list with element model_tcc containing the plot-level predicted tree canopy cover as percent (0:100), and additional named elements containing stand structure metrics as described in Details. If full_output = FALSE, a single numeric value of plot-level predicted TCC is returned instead.

Details

This function provides two methods for predicting plot-level TCC.

The default "stem-map" method requires individual tree coordinates to be given in the input as distance and azimuth from subplot centers for trees with diameter >= 5 in. (12.7 cm), and from microplot centers for "saplings" having diameter >= 1 in. (2.54 cm) but < 5 in. (12.7 cm). This method involves mapping trees spatially within the plot boundary to account for crown overlap explicitly, along with empirical modeling of the understory sapling contribution to total canopy cover (Toney et al. 2009). The empirical model for the sapling component also uses the spatial point pattern of overstory trees as a predictor variable (using a square root transformation of Ripley's edge-corrected K-function, Ripley 1977, Stoyan and Penttinen 2000).

Alternatively, TCC can be predicted using a simplified approach that does not include exact stem placement within the plot boundary (stem_map = FALSE). A random arrangement of stems is assumed in that case. This is the method used to estimate tree canopy cover in the Forest Vegetation Simulator (Crookston and Stage 1999).

Both methods require estimates of individual tree crown widths, which are computed with calc_crwidth() if not provided in the input tree list.

The stem-map method also requires computation of several stand structure metrics which are used in various components of the overall model used to derive a plot-level TCC estimate. These additional variables include:

individual subplot and microplot crown overlays via calc_crown_overlay()
a stand height metric (meanTreeHtBAW), and plot-level counts of mature trees and saplings, via calc_ht_metrics()
descriptive spatial statistics for the overstory tree point pattern via create_fia_ppp() |> spatstat.explore::Lest()

By default, calc_tcc_metrics() returns a named list containing the plot-level modeled TCC value, along with those additional component variables. Specific elements of the returned list include some or all of the following, conditionally:

$model_tcc: plot-level predicted canopy cover of trees >= 1 inch (2.54 cm) diameter, derived by one of the two methods described above depending on the value given for argument stem_map = TRUE|FALSE

If the stem-map method is used, then TCC values derived by crown overlay on the individual subplot and microplot boundaries are included, along with means of the four subplot/microplot values:

$subpN_crown_overlay: estimated canopy cover of trees >= 5-in. (12.7 cm) diameter in subplot N based on crown overlay (N = 1:4)
$subp_overlay_mean: mean of the four subplot crown overlays
$micrN_crown_overlay: estimated canopy cover of saplings in the microplot of subplot N based on crown overlay (N = 1:4)
$micr_overlay_mean: mean of the four microplot crown overlays

A set of spatial point pattern statistics is also included when the stem-map method is used. A square root transformation of Ripley's K function using isotropic edge correction is computed with spatstat.explore::Lest() for trees >= 5-in. (12.7 cm) diameter within the four-subplot observation window. The mean of the following values is a predictor variable in a linear regression model used to estimate the sapling contribution to total tree canopy cover:

$L_rft: estimates of the L-function at r feet (r = 6, 8, 10, and 12)

If the argument full_output = TRUE (the default), then the output will also include all of the the named elements from the output of calc_ht_metrics().

References

Crookston, N.L. and A.R. Stage. (1999). Percent canopy cover and stand structure statistics from the Forest Vegetation Simulator. Gen. Tech. Rep. RMRS-GTR-24. Ogden, UT: U. S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 11 p. https://research.fs.usda.gov/treesearch/6261.

Ripley, B.D. (1977). Modelling spatial patterns. Journal of the Royal Statistical Society: Series B (Methodological), 39(2): 172–192. https://doi.org/10.1111/j.2517-6161.1977.tb01615.x.

Stoyan, D., and Penttinen, A. (2000). Recent applications of point process methods in forestry statistics. Statistical Science, 15(1), 61–78. http://www.jstor.org/stable/2676677.

Toney, C., J.D. Shaw and M.D. Nelson. 2009. A stem-map model for predicting tree canopy cover of Forest Inventory and Analysis (FIA) plots. In: McWilliams, Will; Moisen, Gretchen; Czaplewski, Ray, comps. Forest Inventory and Analysis (FIA) Symposium 2008; October 21-23, 2008; Park City, UT. Proc. RMRS-P-56CD. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 19 p. https://research.fs.usda.gov/treesearch/33381.

Examples

# using the spatially explicit "stem-map model" by default
calc_tcc_metrics(plantation)
#> $model_tcc
#> [1] 88.4
#> 
#> $subp1_crown_overlay
#> [1] 86.8
#> 
#> $subp2_crown_overlay
#> [1] 91.7
#> 
#> $subp3_crown_overlay
#> [1] 80.2
#> 
#> $subp4_crown_overlay
#> [1] 87.2
#> 
#> $subp_overlay_mean
#> [1] 86.475
#> 
#> $micr1_crown_overlay
#> [1] 0
#> 
#> $micr2_crown_overlay
#> [1] 0
#> 
#> $micr3_crown_overlay
#> [1] 20.2
#> 
#> $micr4_crown_overlay
#> [1] 22.5
#> 
#> $micr_overlay_mean
#> [1] 10.675
#> 
#> $L_6ft
#> [1] 3.868305
#> 
#> $L_8ft
#> [1] 6.627377
#> 
#> $L_10ft
#> [1] 7.300455
#> 
#> $L_12ft
#> [1] 11.35045
#> 
#> $numTrees
#> [1] 89
#> 
#> $meanTreeHt
#> [1] 44.8
#> 
#> $meanTreeHtBAW
#> [1] 45.3
#> 
#> $meanTreeHtDom
#> [1] 44.8
#> 
#> $meanTreeHtDomBAW
#> [1] 45.3
#> 
#> $maxTreeHt
#> [1] 51
#> 
#> $predomTreeHt
#> [1] 50.7
#> 
#> $numSaplings
#> [1] 2
#> 
#> $meanSapHt
#> [1] 34.5
#> 
#> $maxSapHt
#> [1] 43
#> 

# return only the predicted TCC value (`$model_tcc`)
calc_tcc_metrics(plantation, full_output = FALSE)
#> [1] 88.4

# using the "FVS method" which assumes random tree locations
calc_tcc_metrics(plantation, stem_map = FALSE, full_output = FALSE)
#> [1] 81.4