The following examples demonstrates how to use the
artificial_stand function of rTLS package to
simulate potential stands based on tree point clouds.
In general, the scanning of a plot using TLS is a time-consuming work
that usually requires several people and steps to achieve a quality
product. In studies where plots are the sample units, the scanning of
several plots is a considerable challenge to answer specific questions.
The idea behind artificial_stand is to provide a general
overview of artificial stands that can be created and modified by users;
allowing us to recreate potential scenarios of the spatial variation of
the forest structure. To archive this, users can select several
arguments related to the number of tree point clouds to use, the future
coordinates of the trees in the stands, or the plot size, to mention
some.
Since the artificial_stand function is focused on tree
point clouds, users should first define the location of point clouds
files to use. This needs to be a vector defining the their names and
where the files are located if these are not in the working directory.
These files may have any ASCII format and these will be read it in
artificial_stand using the fread function of
data.table. You could define the specific delimited format
of the files using the ... argument passed to
fread.
As an example, here we will use a tree point cloud already embedded
in rTLS, pc_tree.txt. This file is located in
the extdata of rTLS, following:
library(rTLS)
path <- system.file("extdata", "pc_tree.txt", package = "rTLS")The structure of the location vector should be similar than
path depending on the number and identify of point clouds
for the future artificial stand. For example, if the user wants a stand
with four different tree, the path vector should have paths
for four different files. On the other hand, if the user wants a stand
with four trees regardless their identity, the user can set the argument
sample = TRUE in order to use the same path four different
times. Users can also select the argument replace = TRUE in
order to do a sample selection with replacement in scenarios when you
have more paths than the number of trees required.
In order to select the plot size of the future artificial stands
users can use the argument dimension. This need to be a
vector of length two defining the width and length of the plot. It is
expected that the unit scale of the point could files match with the
unit scale for plot. An example for an hectare plot could be:
plot_size <- c(100, 100)Then the number of trees for the future stand may depends on the
arguments previously described in the selection of files. For this
example, let’s assume that we want to create a stand of 10 trees using
the same point cloud. This can be done following
n.trees = 10.
The location of these trees in the future artificial stand could be
random or could be previously defined. In order to provide random
coordinates to the trees in the future stands we most use
coordinates = NULL. On the other hand, if we want to
provide specific coordinates we should add a data.table of
two columns and with equal to describing the basal XYZ coordinates of
the point clouds in the future stand.
#Number of trees
trees <- 10
#Random coordinates
coor_1 <- NULL
#Predefined coordinates
coor_2 <- data.table(X = c(10, 10, 30, 30, 50, 50, 70, 70, 90, 90), Y = c(25, 75, 25, 75, 25, 75, 25, 75, 25, 75))Users should define other arguments related with the potential
overlap of crowns if they use random coordinates using the
overlap argument. In addition, user should also define if
they want a random or specific rotation angles in the yaw axis of the
point cloud. If the users wants a specific angle of rotation in their
point clouds these need to be defined in the degrees argument using
vector describing the degrees of rotation. An example of all of this
could be:
# 5% of overlaping between crowns
overl <- 5
# Trees without rotation
rota_1 <- FALSE
# Trees with random rotation
rota_2 <- FALSE
degreess_1 <- NULL
# Trees with rotation and specific angles
rota_3 <- FALSE
degreess_2 <- seq(0, 360, length.out = 10)Once users have selected their arguments related to the point cloud
paths, number of trees, plot size, and tree coordinates it is possible
to run artifical_stands. Following our example of 10 trees
using the same point cloud in an hectare plot we should have the
following:
# A total random artifical stand
stand_1 <- artificial_stand(path, n.trees = trees, dimension = plot_size, coordinates = coor_1, overlap = overl, sample = TRUE, replace = TRUE)
# An artifical stand with defined tree coordinates and random tree rotation
stand_2 <- artificial_stand(path, n.trees = trees, dimension = plot_size, coordinates = coor_2, sample = TRUE, replace = TRUE, rotation = rota_2, degrees = degreess_1)
# An artifical stand with defined tree coordinates and defined tree rotation
stand_3 <- artificial_stand(path, n.trees = trees, dimension = plot_size, coordinates = coor_2, sample = TRUE, replace = TRUE, rotation = rota_3, degrees = degreess_2)
# An artifical stand with defined tree coordinates and without tree rotation
stand_4 <- artificial_stand(path, n.trees = trees, dimension = plot_size, coordinates = coor_2, sample = TRUE, replace = TRUE, rotation = FALSE)The resulting output of this function is a list that contains: