relative.bias: Relative Bias Between Field Estimations and TLS metrics

In FORTLS: Automatic Processing of Terrestrial-Based Technologies Point Cloud Data for Forestry Purposes

Relative Bias Between Field Estimations and TLS metrics

Description

Computes relative bias between variables estimated from field data and their TLS counterparts derived from TLS data. Field estimates and TLS metrics for a common set of plots are required in order to compute relative bias. These data must come from any of the three different plot designs currently available (circular fixed area, k-tree and angle-count) and correspond to plots with incremental values for the plot design parameter (radius, k and BAF, respectively). In addition to computing relative bias, interactive line charts graphically representing the values obtained between each field estimate and its related TLS metrics are also generated.

Usage

relative.bias(simulations,
              variables = c("N", "G", "V", "d", "dg", "d.0", "h", "h.0"),
              save.result = TRUE, dir.result = NULL)

Arguments

simulations	List containing variables estimated from field data and metrics derived from TLS data. The structure and format must be analogous to output returned by the simulations function. In particular, teh list must include at least one of the following named elements: fixed.area.plot: same description and format as indicated for same named element in simulations argument of correlations function. The only difference is the columns required when it is included in the argument: in adittion to id, radius and at least one of the field estimate columns, it must include at least one TLS counterpart for each field estimate condidered. k.tree.plot: same description and format as indicated for same named element in simulations argument of correlations function. The only difference is the columns required when it is included in the argument: in adittion to id, k and at least one of the field estimate columns, it must include at least one TLS counterpart for each field estimate considered. angle.count.plot: same description and format as indicated for the same named element in the simulations argument of correlations function. The only difference is the columns that are required when this element is included in the argument: in adittion to id, BAF and at least one of the field estimate columns, it must contain at least one TLS counterpart for each field estimate considered.
variables	Optional character vector naming field estimates for which the relative bias between them and all their available TLS counterparts will be computed. If this argument is specified by the user, it must contain at least one of the following character strings: “N”, “G”, “V”, “d”, “dg”, “dgeom”, “dharm”, “d.0”, “dg.0”, “dgeom.0”, “dharm.0”, “h”, “hg”, “hgeom”, “hharm”, “h.0”, “hg.0”, “hgeom.0”, or “hharm.0”. If this argument is not specified by the user, it will be set to c("N", "G", "V", "d", "dg", "d.0", "h", "h.0") by default. In both cases, all the elements in simulations argument must include at least the columns corresponding to the field estimations specified in the variables argument.
save.result	Optional logical which indicates whether or not the output files described in ‘Output Files’ section must be saved in dir.result. If this argument is not specified by the user, it will be set to TRUE by default and, as a consequence, the output files will be saved.
dir.result	Optional character string naming the absolute path of an existing directory where files described in ‘Output Files’ section will be saved. .Platform$file.sep must be used as the path separator in dir.result. If this argument is not specified by the user, and save.result is TRUE, it will be set to NULL by default and, as a consequence, the current working directory of the R process will be assigned to dir.result during the execution.

Details

For each radius, k or BAF value (according to the currently available plot designs: circular fixed area, k-tree and angle-count), this function computes the relative bias between each variable estimated from field data, and specified in the variables argument, and their counterparts derived from TLS data, and existing in the data frames included in the simulations argument. TLS metrics considered counterparts for each field estimate are detailed below (see simulations ‘Value’ function for details about used notation):

• TLS counterparts for N are N.tls, N.hn, N.hr, N.hn.cov, N.hr.cov and N.sh in the fixed area and k-tree plot designs; and N.tls and N.pam in the angle-count plot design.

• TLS counterparts for G are G.tls, G.hn, G.hr, G.hn.cov, G.hr.cov and G.sh in the fixed area and k-tree plot designs; and G.tls and G.pam in the angle-count plot design.

• TLS counterparts for V are V.tls, V.hn, V.hr, V.hn.cov, V.hr.cov and V.sh in the fixed area and k-tree plot designs; and V.tls and V.pam in the angle-count plot design.

• TLS counterparts for d, dg, dgeom, dharm, d.0, dg.0, dgeom.0, and dharm.0 are, respectively: d.tls, dg.tls, dgeom.tls, dharm.tls, d.0.tls, dg.0.tls, dgeom.0.tls,
  and dharm.0.tls in any of the three available plot designs.

• TLS counterparts for h, hg, hgeom, hharm, h.0, hg.0, hgeom.0, and hharm.0 are, respectively h.tls, hg.tls, hgeom.tls, hharm.tls, h.0.tls, hg.0.tls, hgeom.0.tls, and
  hharm.0.tls in any of the three available plot designs. In adittion, P99 is also taken into account as a counterpart for all these field estimates.

The relative bias between a field estimation and any of its TLS counterparts is estimated as follows

\frac{\frac{1}{n}\sum_{i = 1}^{n}{y_{i}} - \frac{1}{n}\sum_{i = 1}^{n}{x_{i}}}{\frac{1}{n}\sum_{i = 1}^{n}{x_{i}}} * 100

where x_{i} and y_{i} are the values of the field estimate and its TLS counterpart, respectively, corresponding to plot i for i = 1, \ldots, n.

Value

fixed.area.plot	If no “fixed.area.plot” element exists in simulations argument, missing; otherwise, the matrix will include estimates of the the relative bias under circular fixed area plot design between each field estimation specified in variables argument and its TLS counterpart(s) existing in the “fixed.area.plot” element in the simulations argument. Each row will correspond to a radius value, and the following columns will be included: radius: radius (m) of the simulated plots used for computing the estimated relative bias. Column(s) ‘<x>.<y>’: numeric column(s) containing estimated relative bias between ‘<x>’, a field estimation, and ‘<y>’, a TLS counterpart.
k.tree.plot	If no “k.tree.plot” element exists in the simulations argument, missing; otherwise, the matrix will include the relative bias estimated in the k-tree plot design between each field estimation specified in variables argument and its TLS counterpart(s) existing in “k.tree.plot” element in simulations argument. Each row will correspond to a k value, and the following columns will be included: k: number of trees (trees) of the simulated plots used for computing the estimated relative bias. Column(s) ‘<x>.<y>’: numeric column(s) containing estimated relative bias between ‘<x>’, a field estimation, and ‘<y>’, a TLS counterpart.
angle.count.plot	If no “angle.count” element exists in simulations argument, missing; otherwise, the matrix will contain estimated relative bias under angle-count plot design between each field estimation specified in the variables argument and its TLS counterpart(s) existing in the “angle.count.plot” element in the simulations argument. Each row will correspond to a BAF value, and the following columns will be included: BAF: BAF ({m}^{2}/ha) of the simulated plots used for computing the estimated relative bias. Column(s) ‘<x>.<y>’: numeric column(s) containing estimated relative bias between ‘<x>’, a field estimation, and ‘<y>’, a TLS counterpart.

Output Files

During the execution, if the save.result argument is TRUE, the function will print the matrices described in the ‘Value’ section to files. These are written without row names in dir.result directory using write.csv function from the utils package. The pattern used for naming these files is ‘RB.<plot design>.csv’, where ‘<plot design>’ is equal to “fixed.area.plot”, “k.tree.plot” or “angle.count.plot” is according to the plot design.

Furthermore, if the save.result argument is TRUE, interactive line charts graphically representing relative bias values will also be created and saved in the dir.result directory by means of the saveWidget function in the htmlwidgets package. Generated widgets allow users to consult relative bias data directly on the plots, select/deselect different sets of traces, to zoom and scroll, and so on. The pattern used for naming these files is ‘RB.<x>.<plot design>.html’, where ‘<plot design>’ is indicated for the previously described files, and ‘<x>’ equals N, G, V, d and/or h according to the variables argument. All relative biases related to diameters are plotted in the same chart (files named as ‘RB.d.<plot design>.html’), and the same applies to those related to heights (files named as ‘RB.h.<plot design>.html’).

Note

The results obtained using this function are merely descriptive, and they do not guarantee any type of statistical accuracy in using TLS metrics instead of field estimations in order to estimate forest attributes of interest.

Author(s)

Juan Alberto Molina-Valero and Adela Martínez-Calvo.

See Also

simulations, correlations.

Examples

# Load variables estimated from field data, and TLS metrics
# corresponding to Rioja data set

data("Rioja.simulations")


# Establish directory where relative bias results corresponding to Rioja example
# will be saved. For instance, current working directory

dir.result <- getwd()


# Compute relative bias between field-based estimates of TLS metrics
# corresponding to Rioja example
# Relative bias for variables by default

rb <- relative.bias(simulations = Rioja.simulations, dir.result = dir.result)


# Relative bias for variable 'N'

rb <- relative.bias(simulations = Rioja.simulations, variables = "N",
                    dir.result = dir.result)


# Relative bias corresponding to angle-count design for all available variables

rb <- relative.bias(simulations = Rioja.simulations["angle.count"],
                    variables <- c("N", "G", "V", "d", "dg", "dgeom", "dharm",
                                   "d.0", "dg.0", "dgeom.0", "dharm.0", "h",
                                   "hg", "hgeom", "hharm", "h.0", "hg.0",
                                   "hgeom.0", "hharm.0"),
                    dir.result = dir.result)
  

FORTLS documentation built on Sept. 11, 2023, 5:09 p.m.