| Title: | Automatic Processing of Terrestrial-Based Technologies Point Cloud Data for Forestry Purposes |
|---|---|
| Description: | Process automation of point cloud data derived from terrestrial-based technologies such as Terrestrial Laser Scanner (TLS) or Mobile Laser Scanner. 'FORTLS' enables (i) detection of trees and estimation of tree-level attributes (e.g. diameters and heights), (ii) estimation of stand-level variables (e.g. density, basal area, mean and dominant height), (iii) computation of metrics related to important forest attributes estimated in Forest Inventories at stand-level, and (iv) optimization of plot design for combining TLS data and field measured data. Documentation about 'FORTLS' is described in Molina-Valero et al. (2022, <doi:10.1016/j.envsoft.2022.105337>). |
| Authors: | Juan Alberto Molina-Valero [aut, cph, cre], Adela Martínez-Calvo [aut, com], Arunima Singh [aut, com], Gokul Kottilapurath Surendran [aut, com], Juan Gabriel Álvarez-González [aut, ths], Fernando Montes [aut], César Pérez-Cruzado [aut, ths] |
| Maintainer: | Juan Alberto Molina-Valero <[email protected]> |
| License: | GPL-3 |
| Version: | 1.4.0 |
| Built: | 2025-02-02 06:17:44 UTC |
| Source: | https://github.com/molina-valero/fortls |
Process automation of point cloud data derived from terrestrial-based technologies such as Terrestrial Laser Scanner (TLS) or Mobile Laser Scanner. 'FORTLS' enables (i) detection of trees and estimation of tree-level attributes (e.g. diameters and heights), (ii) estimation of stand-level variables (e.g. density, basal area, mean and dominant height), (iii) computation of metrics related to important forest attributes estimated in Forest Inventories at stand-level, and (iv) optimization of plot design for combining TLS data and field measured data. Documentation about 'FORTLS' is described in Molina-Valero et al. (2022, <doi:10.1016/j.envsoft.2022.105337>).
Usage of FORTLS includes the following functionalities:
Tree detection: this is the first and necessary step for the other functionalities of FORTLS. This can be achieved using the following functions:
normalize: mandatory first step for obtaining the relative coordinates of a TLS point cloud.
tree.detection.single.scan: detects as many trees as possible from a normalized TLS single-scan point clouds.
tree.detection.multi.scan: detects as many trees as possible from a normalized TLS multi-scan, SLAM, or similar terrestrial-based technologies point clouds.
tree.detection.several.plots: includes the two previous functions for a better workflow when there are several plots to be sequentially analyzed.
Estimation of variables when no field data are available: this is the main functionality of FORTLS and can be achieved using the following functions:
distance.sampling: optional function which can be used for considering methodologies for correcting occlusion effects in estimating variables.
estimation.plot.size: enables the best plot design to be determined on the basis of TLS data only.
metrics.variables: is used for estimating metrics and variables potentially related to forest attributes at stand level.
Estimation of variables when field data are available: this is the main and most desirable functionality of FORTLS and can be achieved using the following functions:
distance.sampling: as before.
simulations: computes simulations of TLS and field data for different plot designs. This is a prior step to the next functions.
relative.bias: uses simulations output to assess the accuracy of direct estimations of variables according to homologous TLS and field data.
correlations: uses simulations output to assess correlations among metrics and variables obtained from TLS data, and variables of interest estimated from field data.
optimize.plot.design: using correlations output, represents the best correlations for variables of interest according to the plot design. It is thus possible to select the best plot design for estimating forest attributes from TLS data.
metrics.variables: as before, but in this case plot parameters will be choosen on the basis of field data and better estimates will therefore be obtained.
Maintainer: Juan Alberto Molina-Valero [email protected] [copyright holder]
Authors:
María José Ginzo Villamayor [contributor]
Manuel Antonio Novo Pérez [contributor]
Adela Martínez-Calvo [contributor]
Juan Gabriel Álvarez-González [contributor]
Fernando Montes [contributor]
César Pérez-Cruzado [contributor]
Molina-Valero J. A., Ginzo-Villamayor M. J., Novo Pérez M. A., Martínez-Calvo A., Álvarez-González J. G., Montes F., & Pérez-Cruzado C. (2019). FORTLS: an R package for processing TLS data and estimating stand variables in forest inventories. The 1st International Electronic Conference on Forests — Forests for a Better Future: Sustainability, Innovation, Interdisciplinarity. doi:10.3390/IECF2020-08066
Computes correlations between variables estimates from field data and metrics derived from TLS data. Field estimates and TLS metrics for a common set of plots are required in order to compute correlations. These data must be obtained from any of the three different plot designs currently available (fixed area, k-tree and angle-count), and correspond to plots with incremental values for the plot design parameter (radius, k and BAF, respectively). Two correlation measures are implemented: Pearson's correlation coefficient and Spearman's rho. In addition to estimating these measures, tests for association are also executed, and interactive line charts graphically representing correlations are generated.
correlations(simulations, variables = c("N", "G", "V", "d", "dg", "d.0", "h", "h.0"), method = c("pearson", "spearman"), save.result = TRUE, dir.result = NULL)correlations(simulations, variables = c("N", "G", "V", "d", "dg", "d.0", "h", "h.0"), method = c("pearson", "spearman"), save.result = TRUE, dir.result = NULL)
simulations |
List including estimated variables based on field data and metrics
derived from TLS data. The structure and format must be analogous to output
returned by the
|
variables |
Optional character vector naming field estimates for which correlations
between these and all the available TLS metrics will be computed. If
this argument is specified by the user, it must include at least one of the
following character strings: “
|
method |
Optional character vector naming which correlation measurements will be used.
If this argument is specified by the user, it must include at least one of
the following character strings: “ |
save.result |
Optional logical indicating wheter or not the output files described in
‘Output Files’ section must be saved in |
dir.result |
Optional character string naming the absolute path of an existing directory
where files described in the ‘Output Files’ section will be saved.
|
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
correlations between each variable estimated from field data
specified in the variables argument and all the metrics derived from TLS
data existing in the data frames included in the simulations argument.
Two correlation measures are implemented at present: Pearson's correlation
coefficient and Spearman's rho. For each method, in addition to the
estimated measure, the p-value of a test for association is also returned.
The cor.test function from the utils package is used to compute
both the estimated correlations and the p-values of the associated tests; more
details about these measures and their tests for association can be found in
the corresponding documentation. There cannot be
missing data for three or more plots, and there cannot be zero standard deviation,
in order to prevent missing correlation values for each (field estimation, TLS
metric) pair and plot design parameter (radius, k or BAF).
Apart from estimated correlations and their corresponding p-values, for ecah method, the function also returns the plot design parameter and field estimates, the value of the optimal correlation (i.e. the maximum of the absolute value of available correlations) and the TLS metric to which it corresponds.
correlations |
A list including the estimated correlations for each measure specified in the
|
correlations.pval |
List containing the p-value of the test for association corresponding to each
measure specified in |
opt.correlations |
List containing the optimal correlations, and the names of the TLS metrics to
which they correspond, for each measure specified in
|
During the execution, if the save.result argument is TRUE, the
function prints to files the matrices and data frames included in
correlations and opt.correlations elements described in
‘Value’. Both are written without row names in
dir.result directory by using the write.csv function in the
utils package. The patterns used for naming these files are
‘correlations.<plot design>.<method>.csv’ and
‘opt.correlations.<plot design>.plot.<method>.csv’ for correlation
matrices and optimal correlation data frames, respectively, where
‘<plot design>’ is equal to “fixed.area.plot”,
“k.tree.plot” or “angle.count.plot” according to
plot design, and ‘<method>’ equals “pearson” or
“spearman” according to the correlation measure.
Furthermore, if the save.result argument is TRUE, interactive line
charts graphically representing correlations will also be created and saved
in the dir.result directory by means of saveWidget function
in the htmlwidgets package. Generated widgets enable users to
consult correlation data directly on the plots, select/deselect different
sets of traces, to zoom and scroll, etc. The pattern used for naming
these files is ‘correlations.<x>.<plot design>.<method>.html’, where both
‘<plot design>’ and ‘<method>’ are as indicated for the previous
described files, and ‘<x>’ is equal to any of elements specified in the
variables argument.
This function is particularly useful for further steps related to model-based and model-assisted approaches, as correlations measure the strength of a relationship between two variables (linear for Pearson's correlation, monotonic for Spearman's correlation).
Juan Alberto Molina-Valero and Adela Martínez-Calvo.
simulations, optimize.plot.design.
cor.test in utils package.
# Load field estimates and TLS metrics corresponding to Rioja data set data("Rioja.simulations") # Establish directory where correlation results corresponding to the Rioja example # will be saved. For instance, current working directory # dir.result <- getwd() # Compute correlations between field estimates and TLS metrics corresponding # to Rioja example # Pearson's and Spearman's correlations for variables by default # corr <- correlations(simulations = Rioja.simulations, dir.result = dir.result) # Pearson's and Spearman's correlations for variable 'N' # corr <- correlations(simulations = Rioja.simulations, variables = "N", # dir.result = dir.result) # Only Pearson's correlations for variables by default # corr <- correlations(simulations = Rioja.simulations, method = "pearson", # dir.result = dir.result) # Pearson's and Spearman's correlations corresponding to angle-count design for # all available variables # corr <- correlations(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)# Load field estimates and TLS metrics corresponding to Rioja data set data("Rioja.simulations") # Establish directory where correlation results corresponding to the Rioja example # will be saved. For instance, current working directory # dir.result <- getwd() # Compute correlations between field estimates and TLS metrics corresponding # to Rioja example # Pearson's and Spearman's correlations for variables by default # corr <- correlations(simulations = Rioja.simulations, dir.result = dir.result) # Pearson's and Spearman's correlations for variable 'N' # corr <- correlations(simulations = Rioja.simulations, variables = "N", # dir.result = dir.result) # Only Pearson's correlations for variables by default # corr <- correlations(simulations = Rioja.simulations, method = "pearson", # dir.result = dir.result) # Pearson's and Spearman's correlations corresponding to angle-count design for # all available variables # corr <- correlations(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)
Calculation of the probability of detection of every tree by using distance sampling methodologies (more specifically point transects methods), by means of fitting detection functions to the histogram of tree distribution according to their distance to TLS. Use both half normal and hazard rate functions, without and with dbh as covariate. These probabilities are used for correcting estimation bias caused by lack of detection of trees due to occlusion.
distance.sampling(tree.tls, id.plots = NULL, strata.attributes = NULL)distance.sampling(tree.tls, id.plots = NULL, strata.attributes = NULL)
tree.tls |
Data frame with a list of trees detected and their dbh and horizontal distances from TLS with the same structure and format as |
id.plots |
Optional vector with plot identification encoded as character string or numeric for the plots considered. In this case, |
strata.attributes |
Optional data frame inluding plot radius considered at strata level. It must contain a column named ‘stratum’ (numeric) with encoding coinciding with that used in previous functions ( |
All internal functions related to distance sampling methodologies are fitted with the ds function included in the Distance package.
Detection functions are left-truncated at 1 m, according to Astrup et al., (2014).
Same warning messages as ds function are provided when fits do not converge or another warnings occur.
For further details on these point transects methods and similar sampling methodologies, as well as their application with R, see Buckland et al., (2001); Marques & Buckland, (2003); Miller & Thomas, (2015) and Clark (2016). Examples of distance sampling analyses, as well as lectures, are available at http://examples.distancesampling.org/ and http://workshops.distancesampling.org/.
List containing the following elements:
tree |
Data frame with detection probabilities for every tree and method. |
stratum: stratum identification (coincident with strata of tree.tls). If there are not strata, it will be set as a single stratum encoded as 1 (numeric).
id: plot identification (coincident with id of tree.tls).
tree: tree numbering (coincident with tree of tree.tls).
P.hn: tree detection probability according to half normal function.
P.hn.cov: tree detection probability according to half normal function with dbh as covariate.
P.hr: tree detection probability according to half rate function.
P.hr.cov: tree detection probability according to half rate function with dbh as covariate.
parameters |
Data frame with parameters estimated for detection functions (see references for understanding their meaning). |
P.hn.scale: scale parameter for half normal function (sigma).
P.hn.cov.scale.intercept: alpha.0 parameter of scale parameter for half normal function with dbh as covariate.
P.hn.cov.dbh: alpha.1 parameter of scale parameter for half normal function with dbh as covariate.
P.hr.scale: scale parameter for half rate function (sigma).
P.hr.shape: shape parameter for half rate function (b).
P.hr.cov.scale.intercept: alpha.0 parameter of scale parameter for half normal function with dbh as covariate.
P.hr.cov.dbh: alpha.1 parameter of scale parameter for half normal function with dbh as covariate.
P.hr.cov.shape: shape parameter for half rate function with dbh as covariate (b).
AIC |
Data frame with Akaike information criterions (AIC) of every detection function fit. |
P.hn: AIC of half normal function fit.
P.hn.cov: AIC of half normal function with dbh as covariate fit.
P.hr: AIC of half rate function fit.
P.hr.cov: AIC of half rate function with dbh as covariate fit.
Although this step is optional for other functionalities of FORTLS, such as obtaining metrics and assessing the best plot designs (implemented in metrics.variables, correlations, relative.bias and optimize.plot.design), its inclusion is highly recommended, especially with high rates of occlusions.
Note that this function could be more useful after assessing the best possible plot design with estimation.plot.size, correlations, relative.bias or optimize.plot.design functions.
Juan Alberto Molina-Valero and Adela Martínez-Calvo.
Astrup, R., Ducey, M. J., Granhus, A., Ritter, T., & von Lüpke, N. (2014). Approaches for estimating stand-level volume using terrestrial laser scanning in a single-scan mode. Canadian Journal of Forest Research, 44(6), 666-676. doi:10.1139/cjfr-2013-0535.
Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L., & Thomas, L. (2001). Introduction to distance sampling: estimating abundance of biological populations, Oxford, United Kindown, Oxford University Press.
Clark, R. G. (2016). Statistical efficiency in distance sampling. PloS one, 11(3), e0149298. doi:10.1371/journal.pone.0149298.
Marques, F. F., & Buckland, S. T. (2003). Incorporating covariates into standard line transect analyses. Biometrics, 59(4), 924-935. doi:10.1111/j.0006-341X.2003.00107.x.
Miller, D. L., & Thomas, L. (2015). Mixture models for distance sampling detection functions. PloS one, 10(3), e0118726. doi:10.1371/journal.pone.0118726.
tree.detection.single.scan, tree.detection.several.plots, metrics.variables, simulations.
# Loading example data data(Rioja.data) tree.tls <- Rioja.data$tree.tls # Whithout considering maximum distance ds <- distance.sampling(tree.tls)# Loading example data data(Rioja.data) tree.tls <- Rioja.data$tree.tls # Whithout considering maximum distance ds <- distance.sampling(tree.tls)
Plots empirical linear charts of density (N, trees/ha) and basal area (G, ) estimates (derived from simulated TLS plots) as a function of plot size (estimation-size charts) for different plot designs (circular fixed area, k-tree and angle-count), through continuous size increments (radius, k and BAF respectively). Size increments are set at 0.1 m, 1 tree and 0.1 for fixed area, k-tree and angle-count plot designs, respectively. These size-estimation line charts represent the consistency in predicting the stand variables across different values of radius, k and BAF. Size-estimation charts can be drawn for individual sample plots (including all plots together in the same charts) or for mean values (global mean computed for all the sample plots, or for group means if different strata are considered). Finally,
different plot designs can be compared if specified in the arguments, producing one size-estimation chart per variable (N and G).
estimation.plot.size(tree.tls, plot.parameters = data.frame(radius.max = 25, k.max = 50, BAF.max = 4), dbh.min = 4, average = FALSE, all.plot.designs = FALSE)estimation.plot.size(tree.tls, plot.parameters = data.frame(radius.max = 25, k.max = 50, BAF.max = 4), dbh.min = 4, average = FALSE, all.plot.designs = FALSE)
tree.tls |
Data frame with information of trees detected from TLS point cloud data in the same format as |
plot.parameters |
Optional data frame containing parameters for circular fixed area, k-tree and angle-count plot designs. The parameters are as follows: |
radius.max: maximum plot radius (m) considered for circular fixed area plots. If the radius.max specified is larger than the farthest tree from the plot centre, the horizontal distance from the farthest tree will be considered the maximum radius. By default, the radius.max will be 25 m.
k.tree.max: maximum number of trees considered for k-tree plots. If k.tree.max specified is larger than the maximum number of trees of the densest plot, this number of trees will be considered the maximum k.tree.max. By default, k.tree.max is 50.
BAF.max: maximum basal area factor () considered for angle-count plots. By default, BAF.max is 4.
dbh.min |
Optional minimum dbh (cm) considered for detecting trees. By default it will be set at 4 cm. |
average |
Logical; if |
all.plot.designs |
Logical; if |
If there are strata in the tree.tls argument, they will be differentiated in charts with different colours. Strata must be specified in a numeric column named stratum.
The all.plot.designs argument only works for single strata, and therefore if there are additional strata in the tree.tls argument, they will be considered equal.
The outputs of this function are inspired by Fig. 3 of Brunner and Gizachew (2014).
Invisible NULL
Mean values are relevant when plots are representing homogenous strata.
Note that this is an option for choosing the best plot design when field data are not available. Otherwise, using correlations, relative.bias and optimize.plot.design will be more desirable for obtaining the best possible plot design.
Juan Alberto Molina-Valero and Adela Martínez-Calvo.
Brunner, A., & Gizachew, B. (2014). Rapid detection of stand density, tree positions, and tree diameter with a 2D terrestrial laser scanner. European Journal of Forest Research, 133(5), 819-831.
tree.detection.single.scan, tree.detection.multi.scan, tree.detection.several.plots
# Loading dataset with trees detected from TLS single-scans data("Rioja.data") tree.tls <- Rioja.data$tree.tls # Without strata and plot parameters by default estimation.plot.size(tree.tls) estimation.plot.size(tree.tls, average = TRUE) estimation.plot.size(tree.tls, all.plot.designs = TRUE)# Loading dataset with trees detected from TLS single-scans data("Rioja.data") tree.tls <- Rioja.data$tree.tls # Without strata and plot parameters by default estimation.plot.size(tree.tls) estimation.plot.size(tree.tls, average = TRUE) estimation.plot.size(tree.tls, all.plot.designs = TRUE)
This function computes a set of metrics and variables from the Terrestrial-Based Technologies point cloud data, which have a high potential to be related or used as direct estimates (in the case of variables) of forest attributes at plot level. These can be obtained for different plot designs (circular fixed area, k-tree and angle-count plots). This function also includes methodologies for correcting occlusions generated in TLS single-scan point clouds.
metrics.variables(tree.tls, tree.ds = NULL, tree.field = NULL, plot.design = c("fixed.area", "k.tree", "angle.count"), plot.parameters = data.frame(radius = 25, k = 10, BAF = 2), scan.approach = "single", var.metr = list(tls = NULL, field = NULL), v.calc = "parab", dbh.min = 4, h.min = 1.3, max.dist = Inf, dir.data = NULL, save.result = TRUE, dir.result = NULL)metrics.variables(tree.tls, tree.ds = NULL, tree.field = NULL, plot.design = c("fixed.area", "k.tree", "angle.count"), plot.parameters = data.frame(radius = 25, k = 10, BAF = 2), scan.approach = "single", var.metr = list(tls = NULL, field = NULL), v.calc = "parab", dbh.min = 4, h.min = 1.3, max.dist = Inf, dir.data = NULL, save.result = TRUE, dir.result = NULL)
tree.tls |
Data frame with information about trees detected from terrestrial-based technologies point clouds data in the same format as |
tree.ds |
Optional list containing detection probabilities of trees with distance sampling methods. The format must be the same as the ‘Value’ obtained with |
tree.field |
Data frame with information about trees measured in the field plots. Each row must correspond to a (plot, tree) pair, and it must include at least the following columns:
|
plot.design |
Vector containing the plot designs considered. By default, all plot designs will be considered (circular fixed area, k-tree and angle-count plots). |
plot.parameters |
Data frame containing parameters for circular fixed area, k-tree and angle-count plot designs. If there is a |
radius: plot radius (m) considered for circular fixed area plots. Absence of this argument rules out this plot design.
k.tree: number of trees (trees) considered for k-tree plots. Absence of this argument rules out this plot design.
BAF: basal area factor () considered for angle-count plots. Absence of this argument rules out this plot design.
num.trees: number of dominant trees per ha (tree/ha), i.e. those with largest dbh, considered for calculating dominant diameters and heights. In the absence of this argument, the number will be assumed to be 100 trees/ha.
scan.approach |
Character parameter indicating TLS single-scan (‘single’) or TLS multi-scan approach or SLAM point clouds (‘multi’) approaches. If this argument is not specified by the user, it will be set to ‘multi’ approach. |
var.metr |
Optional vector containing all the metrics and variables of interest. By default it will be set as NULL and thus, all the metrics and variables available will be generated. |
v.calc |
Optional parameter to calculate volume when is not included in tree.tls input data. |
dbh.min |
Optional minimum dbh (cm) considered for detecting trees. By default it will be set at 4 cm. |
h.min |
Optional minimum h (m) considered for detecting trees. By default it will be set at 1.3 m. |
max.dist |
Optional argument to specify the maximum horizontal distance considered in which trees will be included. |
dir.data |
Optional character string naming the absolute path of the directory where TXT
files containing TLS point clouds are located. |
save.result |
Optional logical which indicates whether or not the output files described in ‘Output Files’ section must be saved in |
dir.result |
Optional character string naming the absolute path of an existing directory where files described in ‘Output Files’ section will be saved. |
This function also works for several plots. In this case, a column named "id" to identify plots (string character or numeric) must be included in the tree.list.tls database argument. This must coincide with the corresponding "id" assigned in normalize to TXT files saved in dir.data (for more details see normalize). In addition, if there are several strata, they can be processed separately according to plot.parameters values (where each row will represent one stratum). If tree.list.tls does not include a specific "stratum" column, it will be assumed to have only one stratum, which will be encoded according to rownames(plot.parameters)[1].
Using TLS data, this function computes metrics and estimations of variables (see ‘Value’) for plot design specified in the plot.parameters argument. The notation used for variables is based on IUFRO (1959).
At this stage, three plot designs are available:
Circular fixed area plots, simulated only if a radius value is
specified in the plot.parameters argument.
k-tree plots, simulated only if a k.tree value is
specified in the plot.parameters argument.
Angle-count plots, simulated only if a BAF value is
specified in the plot.parameters argument.
Volume is estimated modelling stem profile as a paraboloid and calculating the volumes of revolution; where trees dbh are estimated in tree.detection.single.scan and tree.detection.multi.scan, and total heights are estimated as percentile 99 of z coordinate of points delimited by Voronoi polygons.
Regarding occlusion corrections for estimating variables, apart from distance sampling methods considered in distance.sampling, another occlusion corection based on correcting the shadowing effect (Seidel & Ammer, 2014) has been included to estimate some variables in circular fixed area and k-tree plots. In the case of angle-count plots, occlusion corrections are based on gap probability attenuation with distance to TLS depending on Poisson model (Strahler et al., 2008; Lovell et al., 2011).
List including TLS-based metrics and variables computed for plot designs considered. The list will contain one element per plot design considered (fixed.area.plot, k.tree.plot and angle.count.plot):
fixed.area.plot |
If no value for Stratum, plot identification and radius:
Terrestrial-Based Technologies variables:
Terrestrial-Based Technologies metrics:
|
k.tree.plot |
If no value for Stratum, plot identification and k:
Terrestrial-Based Technologies variables:
Terrestrial-Based Technologies metrics:
|
angle.count.plot |
If no value for Stratum, plot identification and BAF:
Terrestrial-Based Technologies variables:
Terrestrial-Based Technologies metrics:
|
After computing metrics and variables, if the save.result argument is TRUE, the function will save the elements in the list described in ‘Value’ (fixed.area.plot, k.tree.plot and angle.count.plot), which are different from NULL as CSV files. Data frames are written without row names in the dir.result directory by using write.csv function from the utils package. The pattern used for naming these files is ‘metrics.variables.<plot design>.csv’,
being ‘<plot design>’ equal to “fixed.area.plot”, “k.tree.plot” or “angle.count.plot”
according to the plot design.
In order to optimize plot designs and, therefore, for better use of metrics.variables, other functions such as correlations, relative.bias and estimation.plot.size should be used.
This function will be updated as new metrics are developed.
Juan Alberto Molina-Valero, Adela Martínez-Calvo.
IUFRO (1959). Standarization of symbols in forest mensuration. Vienna, Austria, IUFRO.
Lovell, J. L., Jupp, D. L. B., Newnham, G. J., & Culvenor, D. S. (2011). Measuring tree stem diameters using intensity profiles from ground-based scanning lidar from a fixed viewpoint. ISPRS Journal of Photogrammetry and Remote Sensing, 66(1), 46-55. doi:10.1016/j.isprsjprs.2010.08.006
Seidel, D., & Ammer, C. (2014). Efficient measurements of basal area in short rotation forests based on terrestrial laser scanning under special consideration of shadowing. iForest-Biogeosciences and Forestry, 7(4), 227. doi:10.3832/ifor1084-007
Strahler, A. H., Jupp, D. L. B., Woodcock, C. E., Schaaf, C. B., Yao, T., Zhao, F., Yang, X., Lovell, J., Culvenor, D., Newnham, G., Ni-Miester, W., & Boykin-Morris, W. (2008). Retrieval of forest structural parameters using a ground-based lidar instrument (Echidna®). Canadian Journal of Remote Sensing, 34(sup2), S426-S440. doi:10.5589/m08-046
tree.detection.single.scan, tree.detection.multi.scan, tree.detection.several.plots, distance.sampling,
normalize.
# Establishment of working directories (optional) # By default here we propose the current working directory of the R process dir.data <- getwd() dir.result <- getwd() # Loading example data included in FORTLS data("Rioja.data") tree.tls <- Rioja.data$tree.tls tree.tls <- tree.tls[tree.tls$id == "1", ] # Download example of TXT file corresponding to plot 1 from Rioja data set download.file(url = "https://www.dropbox.com/s/w4fgcyezr2olj9m/Rioja_1.txt?dl=1", destfile = file.path(dir.data, "1.txt"), mode = "wb") # Considering distance sampling methods (only for single-scan approaches) # ds <- distance.sampling(tree.tls) met.var.TLS <- metrics.variables(tree.tls = tree.tls, # tree.ds = ds, plot.parameters = data.frame(radius = 10, k = 10, BAF = 2), dir.data = dir.data, dir.result = dir.result)# Establishment of working directories (optional) # By default here we propose the current working directory of the R process dir.data <- getwd() dir.result <- getwd() # Loading example data included in FORTLS data("Rioja.data") tree.tls <- Rioja.data$tree.tls tree.tls <- tree.tls[tree.tls$id == "1", ] # Download example of TXT file corresponding to plot 1 from Rioja data set download.file(url = "https://www.dropbox.com/s/w4fgcyezr2olj9m/Rioja_1.txt?dl=1", destfile = file.path(dir.data, "1.txt"), mode = "wb") # Considering distance sampling methods (only for single-scan approaches) # ds <- distance.sampling(tree.tls) met.var.TLS <- metrics.variables(tree.tls = tree.tls, # tree.ds = ds, plot.parameters = data.frame(radius = 10, k = 10, BAF = 2), dir.data = dir.data, dir.result = dir.result)
This function obtains coordinates relative to the plot centre speciefied for Terrestrial Laser Scanner (TLS) and Mobile Laser Scanner (MLS) point clouds (supplied as LAS or LAZ files). Point clouds obtained from other devices/approaches (e.g. photogrammetry) can be also used, but the guarantee of good performance is likely to be lower. In addition, the point cropping process developed by Molina-Valero et al., (2019) is applied as a criterion for reducing point density homogeneously in space and proportionally to object size when TLS single-scans are provided.
normalize(las, normalized = NULL, x.center = NULL, y.center = NULL, x.side = NULL, y.side = NULL, max.dist = NULL, min.height = NULL, max.height = 50, algorithm.dtm = "knnidw", res.dtm = 0.2, csf = list(cloth_resolution = 0.5), intensity = NULL, RGB = NULL, scan.approach = "single", id = NULL, file = NULL, plot = TRUE, dir.data = NULL, save.result = TRUE, dir.result = NULL)normalize(las, normalized = NULL, x.center = NULL, y.center = NULL, x.side = NULL, y.side = NULL, max.dist = NULL, min.height = NULL, max.height = 50, algorithm.dtm = "knnidw", res.dtm = 0.2, csf = list(cloth_resolution = 0.5), intensity = NULL, RGB = NULL, scan.approach = "single", id = NULL, file = NULL, plot = TRUE, dir.data = NULL, save.result = TRUE, dir.result = NULL)
las |
Character string containing the name of LAS file belonging to TLS point cloud, including .las extension (see ‘Examples’). Planimetric coordinates of point cloud data must be in local, representing TLS scan point the origin with Cartesian coordinates x and y as (0, 0). |
normalized |
Optional argument to establish as |
x.center |
Planimetric x center coordinate of point cloud data. |
y.center |
Planimetric y center coordinate of point cloud data. |
x.side |
x-side (m) of the plot when the plot is square or rectangular. |
y.side |
y-side (m) of the plot when the plot is square or rectangular. |
max.dist |
Optional maximum horizontal distance (m) considered from the plot centre. All points farther than |
min.height |
Optional minimum height (m) considered from ground level. All points below |
max.height |
Optional maximum height (m) considered from ground level. All points above |
algorithm.dtm |
Algorithm used to generate the digital terrain model (DTM) from the TLS point cloud. There are two posible options based on spatial interpolation: ‘tin’ and ‘knnidw’ (see ‘Details’). If this argument is not specified by the user, it will be set to ‘knnidw’ algorithm. |
res.dtm |
Numeric parameter. Resolution of the DTM generated to normalize point cloud (see ‘Details’). If this argument is not specified by the user, it will be set to 0.2 m. |
csf |
List containing parameters of CSF algorithm: |
cloth_resolution: by default 0.5.
scan.approach |
Character parameter indicating TLS single-scan (‘single’) or TLS multi-scan approach or SLAM point clouds (‘multi’) approaches. If this argument is not specified by the user, it will be set to ‘multi’ approach. |
intensity |
Logical parameter useful when point clouds have LiDAR intesinty values. |
RGB |
Logical parameter useful when point clouds are colorized, thus including values of RGB colors. It is based on the Green Leaf Algorithm (GLA) (see ‘Details’). |
id |
Optional plot identification encoded as character string or numeric. If this argument is not specified by the user, it will be set to NULL by default and, as a consequence, the plot will be encoded as 1. |
file |
Optional file name identification encoded as character string or numeric value. If it is null, file will be encoded as |
plot |
Optional logical which indicates whether or not the normalized point cloud will be plot. If this argument is not specified by the user, it will be set to |
dir.data |
Optional character string naming the absolute path of the directory where LAS files containing TLS point clouds are located. |
save.result |
Optional logical which indicates whether or not the output files described in ‘Output Files’ section must be saved in |
dir.result |
Optional character string naming the absolute path of an existing directory where files described in ‘Output Files’ section will be saved. |
Relative coordinates are obtained by means of a normalization process, generating a digital terrain model (DTM) from the TLS point cloud, with the ground height set at 0 m. The DTM is generated by spatial interpolation of ground points classified with the CSF algorithm (Zhang et al., (2016)). Two algorithms are available for that purpose: (i) spatial interpolation based on a Delaunay triangulation, which performs a linear interpolation within each triangle (‘tin’); (ii) spatial interpolation using a k-nearest neighbour (KNN) approach with inverse-distance weighting (IDW) (‘knnidw’). Note that normalization process is based on lidR package functions: classify_ground, grid_terrain and normalize_height.
The point cropping process reduces the point cloud density proportionally to the likelihood that objects will receive points according to their distance from TLS and their size, which is determined by angle aperture (the farther they are, the lower the density is). The result is an approximately homogeneous point cloud in three-dimensional space (for more details see Molina-Valero et al., (2019)).
The Green Leaf Algorithm (GLA) is calculated according to Louhaichi et al., (2001)as follows:
Those points with values below 0 are clasified as woody parts, thus retained for tree detection in further functions.
Data frame of normalized point cloud including the following columns (each row corresponds to one point):
id |
Plot identification encoded as a character string or numeric in the argument |
file |
File name identification encoded as character string or numeric, corresponding to the normalized and reduced point clouds saved. This coincides with the TXT file in the absolute path specified in |
Coordinates |
Cartesian (according to https://en.wikipedia.org/wiki/Cartesian_coordinate_system notation):
Cylindrical (according to https://en.wikipedia.org/wiki/Cylindrical_coordinate_system notation):
Spherical (according to https://en.wikipedia.org/wiki/Spherical_coordinate_system notation):
|
slope |
Slope of the terrain (rad). |
intensity |
Intensity (only if point cloud has intensity values and specified in arguments). |
R |
Red (only if point cloud is colorized and specified in arguments). |
G |
Green (only if point cloud is colorized and specified in arguments). |
B |
Blue (only if point cloud is colorized and specified in arguments). |
GLA |
Green Leaf Algorithm (only if point cloud is colorized and specified in arguments). |
prob |
selection probability assigned in point cropping process (0-1]. Only the farthest will have probability of 1. |
prob.select |
final selection probability assigned in point cropping process. Selected (1) and discarded point (0). |
At the end of the normalization process, if the save.result argument is TRUE, the function will save the reduced point cloud as TXT file and encoded according to file argument. The format is the same as data frame described in ‘Value’, except for a prob column, which is removed because all points selected after the point cropping process have a final selection probability of 1. The data frame is written without row names in dir.result directory using the vroom_write function in the vroom package.
Note that max.dist, min.height and max.height arguments may be useful for optimizing computing time as well as for removing unnecessary and/or outlier points. These values may be selected more appropriately when inventory data are already available, or the user has some knowledge about autoecology of scanned tree species.
Note also that the linear interpolation algorithm (‘tin’ in this package) showed the highest accuracy in Liang et al., (2018) in DTM generation with single-scans. In this work a DTM resolution of 0.2 m was also considered adequately for square plots of 32 x 32 m.
Juan Alberto Molina-Valero and Adela Martínez-Calvo.
Liang, X., Hyyppä, J., Kaartinen, H., Lehtomäki, M., Pyörälä, J., Pfeifer, N., ... & Wang, Y. (2018). International benchmarking of terrestrial laser scanning approaches for forest inventories. ISPRS journal of photogrammetry and remote sensing, 144, 137-179. doi:10.1016/j.isprsjprs.2018.06.021
Louhaichi, M., Borman, M. M., & Johnson, D. E. (2001). Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International, 16(1), 65-70. doi:10.1080/10106040108542184
Molina-Valero J. A., Ginzo-Villamayor M. J., Novo Pérez M. A., Álvarez-González J. G., & Pérez-Cruzado C. (2019). Estimación del área basimétrica en masas maduras de Pinus sylvestris en base a una única medición del escáner laser terrestre (TLS). Cuadernos de la Sociedad Espanola de Ciencias Forestales, 45(3), 97-116. doi:10.31167/csecfv0i45.19887.
Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., & Yan, G. (2016). An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sensing, 8(6), 501. doi:10.3390/rs8060501.
tree.detection.single.scan, tree.detection.multi.scan, tree.detection.several.plots.
# Establishment of working directories (optional) # By default here we propose the current working directory of the R process dir.data <- getwd() dir.result <- getwd() # TLS SINGLE-SCAN APPROACH # Loading example TLS data (LAZ file) to dir.data download.file("https://www.dropbox.com/s/17yl25pbrapat52/PinusRadiata.laz?dl=1", destfile = file.path(dir.data, "PinusRadiata.laz"), mode = "wb") # Normalizing the whole point cloud data without considering arguments pcd <- normalize(las = "PinusRadiata.laz", id = "PinusRadiata", dir.data = dir.data, dir.result = dir.result) # MLS OR TLS MULTI-SCAN APPROACHES # Loading example MLS data (LAZ file) to dir.data download.file( "www.dropbox.com/scl/fi/es5pfj87wj0g6y8414dpo/PiceaAbies.laz?rlkey=ayt21mbndc6i6fyiz2e7z6oap&dl=1", destfile = file.path(dir.data, "PiceaAbies.laz"), mode = "wb") # Normalizing the whole point cloud data without considering arguments pcd <- normalize(las = "PiceaAbies.laz", id = "PiceaAbies", scan.approach = "multi", dir.data = dir.data, dir.result = dir.result)# Establishment of working directories (optional) # By default here we propose the current working directory of the R process dir.data <- getwd() dir.result <- getwd() # TLS SINGLE-SCAN APPROACH # Loading example TLS data (LAZ file) to dir.data download.file("https://www.dropbox.com/s/17yl25pbrapat52/PinusRadiata.laz?dl=1", destfile = file.path(dir.data, "PinusRadiata.laz"), mode = "wb") # Normalizing the whole point cloud data without considering arguments pcd <- normalize(las = "PinusRadiata.laz", id = "PinusRadiata", dir.data = dir.data, dir.result = dir.result) # MLS OR TLS MULTI-SCAN APPROACHES # Loading example MLS data (LAZ file) to dir.data download.file( "www.dropbox.com/scl/fi/es5pfj87wj0g6y8414dpo/PiceaAbies.laz?rlkey=ayt21mbndc6i6fyiz2e7z6oap&dl=1", destfile = file.path(dir.data, "PiceaAbies.laz"), mode = "wb") # Normalizing the whole point cloud data without considering arguments pcd <- normalize(las = "PiceaAbies.laz", id = "PiceaAbies", scan.approach = "multi", dir.data = dir.data, dir.result = dir.result)
Generation of interactive heatmaps graphically represent the optimal correlations between variables estimated from field data, and metrics derived from TLS data. These data must be derived 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, correlation measures that are currently admissible are Pearson's correlation coefficient and/or Spearman's rho.
optimize.plot.design(correlations, variables = c("N", "G", "V", "d", "dg", "d.0", "h", "h.0"), dir.result = NULL)optimize.plot.design(correlations, variables = c("N", "G", "V", "d", "dg", "d.0", "h", "h.0"), dir.result = NULL)
correlations |
List including the optimal correlations between field estimations and TLS
metrics. The structure and format must be analogous to the
|
variables |
Optional character vector naming field estimations whose optimal correlations
will be represented graphically in the heatmaps generated during the
execution. If this argument is specified by the user, it must include at
least one of the following character strings: “ |
dir.result |
Optional character string naming the absolute path of an existing directory
where files described in ‘Output Files’ section will be saved.
|
This function represents graphically, by means of interactive heatmaps, the strongest correlations (positive or negative) for each plot design and size simulated, between the
estimated variables based on field data specified in the variables
argument, and metrics derived from TLS data, under circular fixed area, k-tree and/or
angle-count plot designs.
Two correlation measures are implemented at present: Pearson’s correlation
coefficient and Spearman’s rho. Hence, only optimal correlations based on correlations arguments will be taken into account during the
execution.
For each correlation measure and plot design, at least one no missing value for optimal correlations must be represented; otherwise, execution will be stopped, and an error message will appear. In addition, at least two different no missing values for optimal correlations are required to ensure that the colour palette is correctly applied when the heatmap is generated.
Invisible NULL.
During the execution, interactive heatmaps graphically representing optimal
correlations values between field estimations and TLS metrics are created and
saved in dir.result directory by means of the saveWidget
function in the htmlwidgets package. The widgets generated allow users
to consult optimal correlations values and TLS metrics to which they correspond
directly on the plots, to zoom and scroll, and so on. The pattern used for
naming these files is ‘opt.correlations.<plot design>.<method>.html’,
where ‘<plot design>’ equals “fixed.area.plot”,
“k.tree.plot” or “angle.count.plot” according to
plot design, and ‘<method>’ equals “pearson” or
“spearman” according to correlation measure.
This function is key to choosing the best possible plot design (in terms of correlation measures) considering all variables of interest before establishing definitive sampling design.
Juan Alberto Molina-Valero and Adela Martínez-Calvo.
# Load field estimations and TLS metrics corresponding to Rioja data set data("Rioja.simulations") # Compute correlations between field estimations and TLS metrics corresponding # to Rioja example, and select optimal correlations results corr <- correlations(simulations = Rioja.simulations, variables = c("N", "G", "d", "dg", "dgeom","dharm", "d.0", "dg.0", "dgeom.0", "dharm.0", "h", "hg", "hgeom", "hharm", "h.0", "hg.0", "hgeom.0", "hharm.0"), save.result = FALSE) opt.corr <- corr$opt.correlations # Establish directory where optimal correlations heatmaps corresponding to Rioja # example will be saved. For instance, current working directory dir.result <- getwd() # Generate heatmaps for optimal correlations between field estimations and TLS # metrics corresponding to Rioja example # Optimal Pearson's and Spearman's correlations for variables by default # optimize.plot.design(correlations = opt.corr, dir.result = dir.result) # Optimal Pearson's and Spearman's correlations for variables 'N' and 'G' optimize.plot.design(correlations = opt.corr, variables = c("N", "G"), dir.result = dir.result)# Load field estimations and TLS metrics corresponding to Rioja data set data("Rioja.simulations") # Compute correlations between field estimations and TLS metrics corresponding # to Rioja example, and select optimal correlations results corr <- correlations(simulations = Rioja.simulations, variables = c("N", "G", "d", "dg", "dgeom","dharm", "d.0", "dg.0", "dgeom.0", "dharm.0", "h", "hg", "hgeom", "hharm", "h.0", "hg.0", "hgeom.0", "hharm.0"), save.result = FALSE) opt.corr <- corr$opt.correlations # Establish directory where optimal correlations heatmaps corresponding to Rioja # example will be saved. For instance, current working directory dir.result <- getwd() # Generate heatmaps for optimal correlations between field estimations and TLS # metrics corresponding to Rioja example # Optimal Pearson's and Spearman's correlations for variables by default # optimize.plot.design(correlations = opt.corr, dir.result = dir.result) # Optimal Pearson's and Spearman's correlations for variables 'N' and 'G' optimize.plot.design(correlations = opt.corr, variables = c("N", "G"), dir.result = dir.result)
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.
relative.bias(simulations, variables = c("N", "G", "V", "d", "dg", "d.0", "h", "h.0"), save.result = TRUE, dir.result = NULL)relative.bias(simulations, variables = c("N", "G", "V", "d", "dg", "d.0", "h", "h.0"), save.result = TRUE, dir.result = NULL)
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
|
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: “ |
save.result |
Optional logical which indicates whether or not the output files described in
‘Output Files’ section must be saved in |
dir.result |
Optional character string naming the absolute path of an existing directory
where files described in ‘Output Files’ section will be saved.
|
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
where and are the values of the field
estimate and its TLS counterpart, respectively, corresponding to plot
for .
fixed.area.plot |
If no “
|
k.tree.plot |
If no “
|
angle.count.plot |
If no “
|
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’).
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.
Juan Alberto Molina-Valero and Adela Martínez-Calvo.
# 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)# 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)
This list includes trees detected with TLS for 16 single scans corresponding to plots located in La Rioja, a region of Spain, in the north of the Iberian Peninsula (first element), as well as those inventoried in the field for these 16 plots (second element). Plot attributes related to stand stratum are also included (third element).
The elements of the list are as follows:
tree.tls: data frame that includes the list of trees detected with tree.detection.single.scan for 16 TLS single-scan sampling points. The following
variables are provided for each tree (see tree.detection.single.scan ‘Value’ for more details):
| [,1] | id | character/numeric |
| [,2] | file | character |
| [,3] | tree | numeric |
| [,4] | x | numeric |
| [,5] | y | numeric |
| [,6:8] | phi, phi.left, phi.right | numeric |
| [,9] | h.dist | numeric |
| [,10] | dbh | numeric |
| [,11] | h | numeric |
| [,12] | v | numeric |
| [,13:16] | n.pts, n.pts.red, n.pts.est, n.pts.red.est | numeric |
| [,15] | partial.occlusion | numeric |
tree.list.field: data frame that includes the list of trees measured in 16 circular fixed area plots of radius 20 m, whose centres coincide with TLS single-scans points. The following variables are provided for each tree:
| [,1] | id | numeric | plot identification (coincident to TLS scans) |
| [,2] | tree | numeric | trees numbering |
| [,3] | Sp | numeric | specie code according to NFI () |
| [,4] | x | numeric | x cartesian coordinate |
| [,5] | y | numeric | 7 cartesian coordinate |
| [,6] | h.dist | numeric | horizontal distance (m) from plot center to tree center |
| [,7] | dbh | numeric | tree diameter (cm) at breast height (1.3 m) |
| [,8] | h | numeric | tree total height (m) |
| [,9] | dead | numeric | dead (1) or not (NA) |
| [,10] | v.user | numeric | stem volume (m^3) estimated with allometric equations |
| [,11] | w.user | numeric | stem biomass (Mg) estimated with allometric equations |
data(Rioja.data)data(Rioja.data)
List with 2 data frames containing 604 observations and 17 variables (tree.tls) and 659 observations and 11 variables (tree.field).
This list contains metrics and variables estimated from field data and TLS data from Rioja.data.
The elements on this list correspond to simulations ‘Value’, as follows:
fixed.area: data frame with TLS metrics and variables estimated on the basis of simulated plots in a fixed area plot design with radius increment of 0.1 m (from smallest possible radius to 20 m). The following variables are provided for each pair (plot, radius) (see simulations ‘Value’ for more details):
| [,1] | id | character/numeric |
| [,2] | radius | numeric |
| [,3:5] | N, G, V | numeric |
| [,6:9] | d, dg, dgeom, dharm | numeric |
| [,10:13] | h, hg, hgeom, hharm | numeric |
| [,14:17] | d.0, dg.0, dgeom.0, dharm.0 | numeric |
| [,18:21] | h.0, hg.0, hgeom.0, hharm.0 | numeric |
| [,22:27] | N.tls, N.hn.tls, N.hr.tls, N.hn.cov.tls, N.hr.cov.tls, N.sh.tls | numeric |
| [,28:31] | num.points, num.points.est, num.points.hom, num.points.hom.est | numeric |
| numeric | ||
| [,32:37] | G.tls, G.hn.tls, G.hr.tls, G.hn.cov.tls, G.hr.cov.tls, G.sh.tls | numeric |
| [,38:43] | V.tls, V.hn.tls, V.hr.tls, V.hn.cov.tls, V.hr.cov.tls, V.sh.tls | numeric |
| [,44:47] | d.tls, dg.tls, dgeom.tls, dharm.tls | numeric |
| [,48:51] | h.tls, hg.tls, hgeom.tls, hharm.tls | numeric |
| [,52:55] | d.0, dg.0, dgeom.0, dharm.0 | numeric |
| [,56:59] | h.0, hg.0, hgeom.0, hharm.0 | numeric |
| [,60:74] | P01, P05, P10, P20, P25, P30, P40, P50, P60, P70, P75, P80, P90, P95, P99 | numeric |
k.tree: data frame with TLS metrics and variables estimated on the basis of simulated plots under k-tree plot design for incremental values of 1 tree (from 1 to largest number of trees in one plot). The following variables are provided for each pair (plot, k) (see simulations ‘Value’ for more details):
| [,1] | id | character/numeric |
| [,2] | k | numeric |
| [,3:5] | N, G, V | numeric |
| [,6:9] | d, dg, dgeom, dharm | numeric |
| [,10:13] | h, hg, hgeom, hharm | numeric |
| [,14:17] | d.0, dg.0, dgeom.0, dharm.0 | numeric |
| [,18:21] | h.0, hg.0, hgeom.0, hharm.0 | numeric |
| [,22:27] | N.tls, N.hn.tls, N.hr.tls, N.hn.cov.tls, N.hr.cov.tls, N.sh.tls | numeric |
| [,28:31] | num.points, num.points.est, num.points.hom, num.points.hom.est | numeric |
| numeric | ||
| [,32:37] | G.tls, G.hn.tls, G.hr.tls, G.hn.cov.tls, G.hr.cov.tls, G.sh.tls | numeric |
| [,38:43] | V.tls, V.hn.tls, V.hr.tls, V.hn.cov.tls, V.hr.cov.tls, V.sh.tls | numeric |
| [,44:47] | d.tls, dg.tls, dgeom.tls, dharm.tls | numeric |
| [,48:51] | h.tls, hg.tls, hgeom.tls, hharm.tls | numeric |
| [,52:55] | d.0, dg.0, dgeom.0, dharm.0 | numeric |
| [,56:59] | h.0, hg.0, hgeom.0, hharm.0 | numeric |
| [,60:74] | P01, P05, P10, P20, P25, P30, P40, P50, P60, P70, P75, P80, P90, P95, P99 | numeric |
angle.count: data frame with TLS metrics and variables estimated on the basis of simulated
plots in an angle-count plot design. They plots are simulated for correlative angle-count plots and incremental values of 0.1 for BAF. The following variables are provided for each pair (plot, BAF) (see simulations ‘Value’ for more details):
| [,1] | id | character/numeric |
| [,2] | BAF | numeric |
| [,3:5] | N, G, V | numeric |
| [,6:9] | d, dg, dgeom, dharm | numeric |
| [,10:13] | h, hg, hgeom, hharm | numeric |
| [,14:17] | d.0, dg.0, dgeom.0, dharm.0 | numeric |
| [,18:21] | h.0, hg.0, hgeom.0, hharm.0 | numeric |
| [,22:23] | N.tls, N.pam.tls | numeric |
| [,24:27] | num.points, num.points.est, num.points.hom, num.points.hom.est | numeric |
| numeric | ||
| [,28:29] | G.tls, G.pam.tls | numeric |
| [,30:31] | V.tls, V.pam.tls | numeric |
| [,32:35] | d.tls, dg.tls, dgeom.tls, dharm.tls | numeric |
| [,48:51] | h.tls, hg.tls, hgeom.tls, hharm.tls | numeric |
| [,36:39] | d.0, dg.0, dgeom.0, dharm.0 | numeric |
| [,40:43] | h.0, hg.0, hgeom.0, hharm.0 | numeric |
| [,44:62] | P01, P05, P10, P20, P25, P30, P40, P50, P60, P70, P75, P80, P90, P95, P99 | numeric |
data(Rioja.simulations)data(Rioja.simulations)
List with 3 data frames containing 2224 observations and 74 variables (simulations.fixed.area.plot), 272 observations and 74 variables (simulations.k.tree.plot), and 576 observations and 62 variables (simulations.angle.count.plot).
Computes TLS metrics derived from simulated TLS plots and variables estimated on the basis of simulated field plots. Real TLS and field data from the same
set of plots are required in order to build simulated plots. Three different
plot designs are currently available: circular fixed area, k-tree and angle-count.
During the simulation process, plots with incremental values for radius, k and
BAF are simulated for circular fixed area, k-tree and angle-count designs, respectively,
according to the parameters specified in the plot.parameters argument. For
TLS metrics, different method are included for correcting occlusions generated in
TLS point clouds.
simulations(tree.tls, tree.ds = NULL, tree.field, plot.design = c("fixed.area", "k.tree", "angle.count"), plot.parameters = data.frame(radius.max = 25, k.max = 50, BAF.max = 4), scan.approach = "single", var.metr = list(tls = NULL, field = NULL), v.calc = "parab", dbh.min = 4, h.min = 1.3, max.dist = Inf, dir.data = NULL, save.result = TRUE, dir.result = NULL)simulations(tree.tls, tree.ds = NULL, tree.field, plot.design = c("fixed.area", "k.tree", "angle.count"), plot.parameters = data.frame(radius.max = 25, k.max = 50, BAF.max = 4), scan.approach = "single", var.metr = list(tls = NULL, field = NULL), v.calc = "parab", dbh.min = 4, h.min = 1.3, max.dist = Inf, dir.data = NULL, save.result = TRUE, dir.result = NULL)
tree.tls |
Data frame with information about trees detected from TLS point cloud data. The
structure and format must be analogous to output returned by
|
tree.ds |
An optional list containing results arises from the application of distance
sampling methodologies. The structure and format must be analogous to output
returned by
|
tree.field |
Data frame with information about trees measured in the field plots. Each row must correspond to a (plot, tree) pair, and it must include at least the following columns:
|
plot.design |
Vector containing the plot designs considered. By default, all plot designs will be considered (circular fixed area, k-tree and angle-count plots). |
plot.parameters |
Optional list containing parameters for circular fixed area, k-tree and angle-count plot designs. User can set all or any of the following parameters specifying them as named elements of the list:
If this argument is specified by the user, it must include at least one of
the following elements: |
scan.approach |
Character parameter indicating TLS single-scan (‘single’) or TLS multi-scan approach or SLAM point clouds (‘multi’) approaches. If this argument is not specified by the user, it will be set to ‘multi’ approach. |
var.metr |
Optional vector containing all the metrics and variables of interest. By default it will be set as NULL and thus, all the metrics and variables available will be generated. |
v.calc |
Optional parameter to calculate volume when is not included in tree.tls input data. |
dbh.min |
Optional minimum dbh (cm) considered for detecting trees. By default it will be set at 4 cm. |
h.min |
Optional minimum h (m) considered for detecting trees. By default it will be set at 1.3 m. |
max.dist |
Optional argument to specify the maximum horizontal distance considered in which trees will be included. |
dir.data |
Optional character string naming the absolute path of the directory where TXT
files containing TLS point clouds are located. |
save.result |
Optional logical which indicates wheter or not the output files described in
‘Output Files’ section must be saved in |
dir.result |
Optional character string naming the absolute path of an existing directory
where files described in ‘Output Files’ section will be saved.
|
Using real TLS and field data from the same set of plots, this function enables construction of simulated plots under different plot designs and computation of the corresponding TLS metrics and estimated variables. The notation used for variables is based on IUFRO (1959).
At this stage, three plot designs are available:
Circular fixed area plots, simulated only if a radius.max value is
specified in the plot.parameters argument.
k-tree plots, simulated only if a k.tree.max value is
specified in the plot.parameters argument.
Angle-count plots, simulated only if a BAF.max value is
specified in the plot.parameters argument.
For each real plot, a simulation process is run under each of the plot designs
specified by means of elements of the plot.parameters argument. Although there
are some minor differences depending on the plot design, the rough outline of
the simulation process is similar for all, and it consists of the
following main steps:
Define an increasing sequence of the plot design parameter (radius,
k or BAF) according to the maximum value and, if applicable, the positive
increment set in plot.parameters argument.
Build simulated plots for each parameter value in the previous sequence based on either TLS or field data.
Compute either TLS metrics or variables estimated on the basis of simulated plots for each parameter value (see ‘Value’ section for details). For the simulated TLS plots, note that in addition to the counterparts of variables computed for the simulated field plots, the function also computes the following:
Metrics related to the number of points belonging to normal tree sections.
Metrics with occlusion corrections based on the following:
Distance sampling methodologies (Astrup et al., 2014) for
circular fixed area and k-tree plot designs, if the distance.sampling
argument is not NULL.
Correction of the shadowing effect (Seidel & Ammer, 2014) for circular fixed area and k-tree plot designs.
Gap probability attenuation with distance to TLS (Strahler et al., 2008; Lovell et al., 2011) for angle-count plot design.
Height percentiles derived from z coordinates of TLS point clouds relative to ground level.
List with field estimates and TLS metrics for plot designs considered. It will contain one element per plot design considered (fixed.area.plot, k.tree.plot and angle.count.plot)
fixed.area.plot |
If no value for Plot identification and radius:
Variables estimated on the basis of simulated field plots:
TLS variables derived from simulated TLS plots:
TLS metrics derived from simulated TLS plots:
|
k.tree.plot |
If no value for Plot identification and k:
Estimated variables based on simulated field plots:
TLS variables derived from simulated TLS plots:
TLS metrics derived from simulated TLS plots:
|
angle.count.plot |
If no value for Plot identification and BAF:
Estimated variables based on simulated field plots:
TLS variables derived from simulated TLS plots:
TLS metrics derived from simulated TLS plots:
|
At the end of the simulation process, if the save.result argument is
TRUE, the function will print all the elements described in
‘Value’ section and which are different from NULL to files. Data frames are
written without row names in dir.result directory using
the write.csv function from the utils package. The pattern used
for naming these files is ‘simulations.<plot design>.csv’, where
‘<plot design>’ is equal to “fixed.area.plot”,
“k.tree.plot” or “angle.count.plot” according to
plot design.
The simulation process implemented in this function is computationally intensive.
Although the function currently uses the vroom function from the vroom package for reading large files and contains fast implementations of
several critical calculations (C++ via Rcpp package), long
computation times may be required when a large number of plots are considered,
number of points in TLS point clouds are very high, or the radius, k
or BAF sequences used in the simulation process are very long.
Using reduced point clouds (according to point cropping process implemented in the
normalize function), rather than original ones, may be
recommended in order to cut down on computing time. Another possibility would
be to specify large increments for radius
and BAF, and/or low maximum values for radius, number of trees and BAF in the plot.parameters argument. This
would make the function more efficient, though there may be a notable
loss of detail in the results generated.
Juan Alberto Molina-Valero and Adela Martínez-Calvo.
Astrup, R., Ducey, M. J., Granhus, A., Ritter, T., & von Lüpke, N. (2014). Approaches for estimating stand level volume using terrestrial laser scanning in a single-scan mode. Canadian Journal of Forest Research, 44(6), 666-676. doi:10.1139/cjfr-2013-0535
IUFRO (1959). Standarization of symbols in forest mensuration. IUFRO, Wien, 32 pp.
Lovell, J. L., Jupp, D. L. B., Newnham, G. J., & Culvenor, D. S. (2011). Measuring tree stem diameters using intensity profiles from ground-based scanning lidar from a fixed viewpoint. ISPRS Journal of Photogrammetry and Remote Sensing, 66(1), 46-55. doi:10.1016/j.isprsjprs.2010.08.006
Seidel, D., & Ammer, C. (2014). Efficient measurements of basal area in short rotation forests based on terrestrial laser scanning under special consideration of shadowing. iForest-Biogeosciences and Forestry, 7(4), 227. doi:10.3832/ifor1084-007
Strahler, A. H., Jupp, D. L. B., Woodcock, C. E., Schaaf, C. B., Yao, T., Zhao, F., Yang, X., Lovell, J., Culvenor, D., Newnham, G., Ni-Miester, W., & Boykin-Morris, W. (2008). Retrieval of forest structural parameters using a ground-based lidar instrument (Echidna®). Canadian Journal of Remote Sensing, 34(sup2), S426-S440. doi:10.5589/m08-046
tree.detection.single.scan, tree.detection.multi.scan, tree.detection.several.plots, distance.sampling,
normalize.
# Load information of trees detected from TLS point clouds data corresponding to # plots 1 and 2 from Rioja data set data("Rioja.data") example.tls <- subset(Rioja.data$tree.tls, id < 3) # Compute detection probabilities using distance sampling methods example.ds <- distance.sampling(example.tls) # Load information of trees measured in field plots corresponding to plot 1 and 2 # from Rioja data set example.field <- subset(Rioja.data$tree.field, id < 3) # Establish directory where TXT file containing TLS point cloud corresponding to # plot 1 from Rioja data set is located. For instance, current working directory dir.data <- getwd() # Download example of TXT file corresponding to plots 1 and 2 from Rioja data set download.file(url = "https://www.dropbox.com/s/w4fgcyezr2olj9m/Rioja_1.txt?dl=1", destfile = file.path(dir.data, "1.txt"), mode = "wb") download.file(url = "https://www.dropbox.com/s/sghmw3zud424s11/Rioja_2.txt?dl=1", destfile = file.path(dir.data, "2.txt"), mode = "wb") # Establish directory where simulation results corresponding to plots 1 and 2 # from Rioja data set will be saved. For instance, current working directory dir.result <- getwd() # Compute metrics and variables for simulated TLS and field plots corresponding # to plots 1 and 2 from Rioja data set # Without occlusion correction based on distance sampling methods sim <- simulations(tree.tls = example.tls, tree.field = example.field, plot.parameters = data.frame(radius.max = 10, k.max = 20, BAF.max = 2), dir.data = dir.data, dir.result = dir.result)# Load information of trees detected from TLS point clouds data corresponding to # plots 1 and 2 from Rioja data set data("Rioja.data") example.tls <- subset(Rioja.data$tree.tls, id < 3) # Compute detection probabilities using distance sampling methods example.ds <- distance.sampling(example.tls) # Load information of trees measured in field plots corresponding to plot 1 and 2 # from Rioja data set example.field <- subset(Rioja.data$tree.field, id < 3) # Establish directory where TXT file containing TLS point cloud corresponding to # plot 1 from Rioja data set is located. For instance, current working directory dir.data <- getwd() # Download example of TXT file corresponding to plots 1 and 2 from Rioja data set download.file(url = "https://www.dropbox.com/s/w4fgcyezr2olj9m/Rioja_1.txt?dl=1", destfile = file.path(dir.data, "1.txt"), mode = "wb") download.file(url = "https://www.dropbox.com/s/sghmw3zud424s11/Rioja_2.txt?dl=1", destfile = file.path(dir.data, "2.txt"), mode = "wb") # Establish directory where simulation results corresponding to plots 1 and 2 # from Rioja data set will be saved. For instance, current working directory dir.result <- getwd() # Compute metrics and variables for simulated TLS and field plots corresponding # to plots 1 and 2 from Rioja data set # Without occlusion correction based on distance sampling methods sim <- simulations(tree.tls = example.tls, tree.field = example.field, plot.parameters = data.frame(radius.max = 10, k.max = 20, BAF.max = 2), dir.data = dir.data, dir.result = dir.result)
Detects trees from point clouds corresponding to TLS multi-scan approaches and SLAM devices. For each tree detected, the function calculates the central coordinates and estimates the diameter at 1.3 m above ground level (which is known as dbh, diameter at breast height) and classifies it as fully visible or partially occluded. Finally, the function obtains the number of points belonging to normal sections of trees (those corresponding to dbh +/- 5 cm) and estimates them for both original and reduced (with random selection process) point clouds.
tree.detection.multi.scan(data, single.tree = NULL, dbh.min = 4, dbh.max = 200, h.min = 1.3, ncr.threshold = 0.1, tls.precision = NULL, density.reduction = 2, stem.section = c(0.7, 3.5), stem.range = NULL, breaks = NULL, slice = 0.1, understory = NULL, bark.roughness = 1, den.type = 1, d.top = NULL, plot.attributes = NULL, plot = TRUE, save.result = TRUE, dir.result = NULL)tree.detection.multi.scan(data, single.tree = NULL, dbh.min = 4, dbh.max = 200, h.min = 1.3, ncr.threshold = 0.1, tls.precision = NULL, density.reduction = 2, stem.section = c(0.7, 3.5), stem.range = NULL, breaks = NULL, slice = 0.1, understory = NULL, bark.roughness = 1, den.type = 1, d.top = NULL, plot.attributes = NULL, plot = TRUE, save.result = TRUE, dir.result = NULL)
data |
Data frame with same description and format as indicated for |
single.tree |
Optional argument to indicate if there is only one tree. |
dbh.min |
Optional minimum dbh (cm) considered for detecting trees. By default it will be set at 4 cm. |
dbh.max |
Optional maximum dbh (cm) considered for detecting trees. By default it will be set at 200 cm. |
h.min |
Optional minimum h (m) considered for detecting trees. By default it will be set at 1.3 m. |
ncr.threshold |
Local surface variation (also known as normal change rate, NCR). By default it will be set as 0.1. For better understanding of this argument see ‘Details’. |
tls.precision |
Average point cloud precision in cm. |
density.reduction |
Density reduction intensity. |
stem.section |
Section free of noise (shurb, branches, etc.) considered to detect trees. If not specified, an automatic internal algorithm will be applied (see ‘Details’). |
stem.range |
Section considered to estimate straightness tree attributes. |
breaks |
Height above ground level (m) of slices considered for detecting trees. By default it will be considered all possible sections from 0.4 m to maximum height by 0.3 m intervals (+/- 5 cm). |
slice |
Slice width considered for detecting trees. By default it will be considered as 0.1 m. |
understory |
Optional argument to indicate if there is dense understory vegetation. |
bark.roughness |
Bark roughness established in 3 degrees (1 < 2 < 3). By default it will be considered as 1. |
den.type |
Numeric argument indicating the dendrometic type used to estimate volumen when there are not sections enough to fit a taper equation. Dendrometrics types available are the following: cylinder = 0, paraboloid = 1 (by default), cone = 2 and neiloid = 3. |
d.top |
Top stem diameter (cm) considered to estimate commercial timber volume. |
plot.attributes |
Data frame with attributes at plot level. It must contain a column named |
plot |
Optional logical which indicates whether or not the normalized point cloud will be plot. If this argument is not specified by the user, it will be set to |
save.result |
Optional logical which indicates whether or not the output files described in ‘Output Files’ section should be saved in |
dir.result |
Optional character string naming the absolute path of an existing directory where the files described in ‘Output Files’ section will be saved. |
Slices determined by breaks argument are clustered using the DBSCAN algorithm (Ester et al., 1996) on the horizontal plane according to Cartesian coordinates (x, y). Before and after this process, several algorithms are used to remove noisy points and apply classification criteria to select the clusters of trees.
dbh is directly estimated for the section of 1.3 m above ground level, and estimated from other sections using dbh~breaks linear regression. Finally, the mean value of all estimates is provided in ‘Value’ as the dbh of the tree section.
Volume is estimated modelling stem profile as a paraboloid and calculating the volumes of revolution; where trees dbh are estimated in tree.detection.single.scan, and total heights are estimated as percentile 99 of z coordinate of points delimited by Voronoi polygons.
The number of points corresponding to a normal section (+/- 5 cm) is estimated in proportion to dbh, using the average number of points per radius unit as reference. In this respect, only tree sections fully visible at 1.3 m above ground level will be considered for estimating the average number of points.
Local surface variation (also known as normal change rate ,NCR), is a quantitative measure of curvature feature (Pauly et al., 2002). This is useful for distinguishing points belonging to fine branches and foliage (e.g. leaves, shrubs) and stem points (e.g. Jin et al., 2016; Zhang et al., 2019). Just as we considered 5 cm as suitable for calculating local surface variation for the stem separation in forests, according to other authors (Ma et al., 2015; Xia et al., 2015), we also established the NCR threshold as 0.1, according to Zhang et al. (2019). However, this argument (ncr.threshold) may be modified in order to use more appropriate values.
Data frame with the following columns for every tree detected (each row corresponds to one tree detected):
id |
Optional plot identification encoded as a character string or numeric. If this argument is not specified by the user, it will be set to NULL by default and, as a consequence, the plot will be encoded as 1. |
file |
Optional file name identification encoded as character string or numeric. If it is null, the file will be encoded as |
tree |
tree numbering |
Coordinates |
Cartesian (according to https://en.wikipedia.org/wiki/Cartesian_coordinate_system notation): |
x: distance on x axis (m) of tree centre.
y: distance on y axis (m) of tree centre.
Azimuthal angles:
phi: angular coordinate (rad) of tree centre.
h.dist |
horizontal distance (m) from plot centre to tree centre. |
\emph{dbh} |
estimated tree diameter (cm) at breast height (1.3 m). |
\emph{h} |
estimated tree total height (m). |
\emph{h.com} |
estimated commercial tree height (m) according to the top diameter defined in the argument |
\emph{v} |
estimated tree stem volume (m^3). |
\emph{v.com} |
estimated commercial tree stem volume (m^3) according to the top diameter defined in the argument |
n.pts |
number of points corresponding to a normal section (+/- 5 cm) in the original point cloud. |
n.pts.red |
number of points corresponding to a normal section (+/- 5 cm) in the point cloud reduced by the point cropping process. |
n.pts.est |
number of points estimated for a normal section (+/- 5 cm) in the original point cloud. |
n.pts.red.est |
number of points estimated for a normal section (+/- 5 cm) in the point cloud reduced by the point cropping process. |
partial.occlusion |
yes (1) or no (0) |
At the end of the tree detection process, if the save.result argument is TRUE, the function will save the data frame described in ‘Value’ as a CSV file named ‘tree.tls.csv’. The data frame will be written without row names in the dir.result directory by using write.csv function from the utils package.
Although tree.detection.multi.scan also works with reduced point clouds, thus reducing the computing time, use of the original point cloud is recommended in order to detect more trees. This will also depend on forest conditions, especially those related to visibility. The more distant the trees are, the lower the density of points will be, and using reduced point clouds will therefore complicate detection of the most distant trees.
Note that dbh.min and dbh.max are important for avoiding outlier values when inventory data are used for reference purposes. Otherwise, knowledge about the autoecology of species could be used for filtering anomalous values of dbh.
The argument breaks = 1.3 could be sufficient for detecting trees visible at dbh, involving lower computational cost. However, those trees not detected at dbh, may be estimated from lower and/or higher sections. Considering the three default sections in the argument breaks = c(1.0, 1.3, 1.6) maintains a good balance in the case study of this package.
Juan Alberto Molina-Valero and Adela Martínez-Calvo.
Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd (Vol. 96, No. 34, pp. 226-231).
Jin, S., Tamura, M., & Susaki, J. (2016). A new approach to retrieve leaf normal distribution using terrestrial laser scanners. J. Journal of Forestry Research, 27(3), 631-638. doi:10.1007/s11676-015-0204-z
Ma, L., Zheng, G., Eitel, J. U., Moskal, L. M., He, W., & Huang, H. (2015). Improved salient feature-based approach for automatically separating photosynthetic and nonphotosynthetic components within terrestrial lidar point cloud data of forest canopies. IEEE Transactions Geoscience Remote Sensing, 54(2), 679-696. doi:10.1109/TGRS.2015.2459716
Pauly, M., Gross, M., & Kobbelt, L. P., (2002). Efficient simplification of point-sampled surfaces. In IEEE Conference on Visualization. (pp. 163-170). Boston, USA. doi:10.1109/VISUAL.2002.1183771
Xia, S., Wang, C., Pan, F., Xi, X., Zeng, H., & Liu, H. (2015). Detecting stems in dense and homogeneous forest using single-scan TLS. Forests. 6(11), 3923-3945. doi:10.3390/f6113923
Zhang, W., Wan, P., Wang, T., Cai, S., Chen, Y., Jin, X., & Yan, G. (2019). A novel approach for the detection of standing tree stems from plot-level terrestrial laser scanning data. Remote Sens. 11(2), 211. doi:10.3390/rs11020211
normalize, tree.detection.single.scan, tree.detection.several.plots, distance.sampling, estimation.plot.size, simulations, metrics.variables
# Establishment of working directories (optional) # By default here we propose the current working directory of the R process dir.data <- getwd() dir.result <- getwd() # Loading example data of TLS multi-scan approach point cloud (LAZ file) to dir.data download.file( "www.dropbox.com/scl/fi/es5pfj87wj0g6y8414dpo/PiceaAbies.laz?rlkey=ayt21mbndc6i6fyiz2e7z6oap&dl=1", destfile = file.path(dir.data, "PiceaAbies.laz"), mode = "wb") # Normalizing the whole point cloud data without considering arguments pcd <- normalize(las = "PiceaAbies.laz", id = "PiceaAbies", scan.approach = "multi", dir.data = dir.data, dir.result = dir.result) # Tree detection without considering arguments tree.tls <- tree.detection.multi.scan(data = pcd, slice = 0.2, breaks = c(1, 1.3, 1.6), dir.result = dir.result)# Establishment of working directories (optional) # By default here we propose the current working directory of the R process dir.data <- getwd() dir.result <- getwd() # Loading example data of TLS multi-scan approach point cloud (LAZ file) to dir.data download.file( "www.dropbox.com/scl/fi/es5pfj87wj0g6y8414dpo/PiceaAbies.laz?rlkey=ayt21mbndc6i6fyiz2e7z6oap&dl=1", destfile = file.path(dir.data, "PiceaAbies.laz"), mode = "wb") # Normalizing the whole point cloud data without considering arguments pcd <- normalize(las = "PiceaAbies.laz", id = "PiceaAbies", scan.approach = "multi", dir.data = dir.data, dir.result = dir.result) # Tree detection without considering arguments tree.tls <- tree.detection.multi.scan(data = pcd, slice = 0.2, breaks = c(1, 1.3, 1.6), dir.result = dir.result)
This function integrates both, the normalize and tree.detection.single.scan or tree.detection.multi.scan functions, generating the same ‘Output Files’ as indicated for these, and it returs the same ‘Value’ as described for tree.detection.single.scan or tree.detection.multi.scan respectively. However, this function is designed for working with several plots, producing a list of all scans considered automatically from LAS files.
tree.detection.several.plots(las.list, id.list = NULL, file = NULL, scan.approach = "single", pcd.red = NULL, normalized = NULL, center.coord = NULL, x.side = NULL, y.side = NULL, max.dist = NULL, min.height = NULL, max.height = 50, algorithm.dtm = "knnidw", res.dtm = 0.2, csf = list(cloth_resolution = 0.5), intensity = NULL, RGB = NULL, single.tree = NULL, dbh.min = 4, dbh.max = 200, h.min = 1.3, ncr.threshold = 0.1, tls.resolution = NULL, tls.precision = NULL, density.reduction = 2, stem.section = c(0.7, 3.5), stem.range = NULL, breaks = NULL, slice = 0.1, understory = NULL, bark.roughness = 1, den.type = 1, d.top = NULL, plot.attributes = NULL, plot = NULL, dir.data = NULL, save.result = TRUE, dir.result = NULL)tree.detection.several.plots(las.list, id.list = NULL, file = NULL, scan.approach = "single", pcd.red = NULL, normalized = NULL, center.coord = NULL, x.side = NULL, y.side = NULL, max.dist = NULL, min.height = NULL, max.height = 50, algorithm.dtm = "knnidw", res.dtm = 0.2, csf = list(cloth_resolution = 0.5), intensity = NULL, RGB = NULL, single.tree = NULL, dbh.min = 4, dbh.max = 200, h.min = 1.3, ncr.threshold = 0.1, tls.resolution = NULL, tls.precision = NULL, density.reduction = 2, stem.section = c(0.7, 3.5), stem.range = NULL, breaks = NULL, slice = 0.1, understory = NULL, bark.roughness = 1, den.type = 1, d.top = NULL, plot.attributes = NULL, plot = NULL, dir.data = NULL, save.result = TRUE, dir.result = NULL)
las.list |
Character vector containing the names of all LAS files for analysis and belonging to TLS point cloud, including .las extension (see ‘Examples’) |
id.list |
Optional vector with plots identification encoded as character string or numeric. If this argument is not specified by the user, it will be set to NULL by default and, as a consequence, the plots will be encoded with correlative numbers from 1 to n plots. |
file |
Optional vector containing files name identification encoded as character string or numeric value. If it is null, file will be encoded as |
scan.approach |
Character parameter indicating TLS single-scan (‘single’) or TLS multi-scan approach or SLAM point clouds (‘multi’) approaches. If this argument is not specified by the user, it will be set to ‘multi’ approach. |
pcd.red |
Optional argument to indicate if point cloud density must be reduced to detect trees. |
normalized |
Optional argument to establish as |
center.coord |
Planimetric x and y center coordinate of the plots. They has to be introduced as a data frame object with the following columns names: 'id', 'x' and 'y'. They represent plot id, and center coordinates respectively. |
x.side |
x-side (m) of the plot when the plot is square or rectangular. |
y.side |
y-side (m) of the plot when the plot is square or rectangular. |
max.dist |
Optional maximum horizontal distance (m) considered from the plot centre. All points farther than |
min.height |
Optional minimum height (m) considered from ground level. All points below |
max.height |
Optional maximum height (m) considered from ground level. All points above |
algorithm.dtm |
Algorithm used to generate the digital terrain model (DTM) from the TLS point cloud. There are two posible options based on spatial interpolation: ‘tin’ and ‘knnidw’ (see ‘Details’). If this argument is not specified by the user, it will be set to ‘knnidw’ algorithm. |
res.dtm |
Numeric parameter. Resolution of the DTM generated to normalize point cloud (see ‘Details’). If this argument is not specified by the user, it will be set to 0.2 m. |
csf |
List containing parameters of CSF algorithm: |
cloth_resolution: by default 0.5.
intensity |
Logical parameter useful when point clouds have intensity values. It may be useful in some internal process to filter data. |
RGB |
Logical parameter useful when point clouds are colorized, thus including values of RGB colors. It is based on the Green Leaf Algorithm (GLA) (see ‘Details’). |
single.tree |
Optional argument to indicate if there is only one tree. |
dbh.min |
Optional minimum dbh (cm) considered for detecting trees. By default it will be set at 4 cm. |
dbh.max |
Optional maximum dbh (cm) considered for detecting trees. By default it will be set at 200 cm. |
h.min |
Optional minimum h (m) considered for detecting trees. By default it will be set at 1.3 m. |
ncr.threshold |
Local surface variation (also known as normal change rate, NCR). By default it will be set as 0.1. For better understanding of this argument see ‘Details’. |
tls.resolution |
List containing parameters of TLS resolution. This can be defined by the angle aperture: |
horizontal.angle: horizontal angle aperture (degrees).
vertical.angle: vertical angle aperture (degrees).
point.dist: distance (mm) between two consecutive points.
tls.dist: distance (m) from TLS at which two consecutive points are separated by point.dist.
If this argument is not specified by the user, it will be set to NULL by default and, as a consequence the function will stop giving an error message.
tls.precision |
Optional argument indicating the average point cloud precision in cm. |
density.reduction |
Density reduction intensity. |
stem.section |
Section free of noise (shurb, branches, etc.) considered to detect trees. If not specified, an automatic internal algorithm will be applied (see ‘Details’). |
breaks |
Height above ground level (m) of slices considered for detecting trees. By default it will be considered all possible sections from 0.1 m to maximum height by 0.3 m intervals (+/- 5 cm). |
stem.range |
Section considered to estimate straightness tree attributes. |
slice |
Slice width considered for detecting trees. By default it will be considered as 0.1 m. |
understory |
Optional argument to indicate if there is dense understory vegetation. |
bark.roughness |
Bark roughness established in 3 degrees (1 < 2 < 3). By default it will be considered as 2. |
den.type |
Numeric argument indicating the dendrometic type used to estimate volumen when there are not sections enough to fit a taper equation. Dendrometrics types available are the following: cylinder = 0, paraboloid = 1 (by default), cone = 2 and neiloid = 3. |
d.top |
Top stem diameter (cm) considered to estimate commercial timber volume. |
plot.attributes |
Data frame with attributes at plot level. It must contain a column named |
plot |
Optional logical which indicates whether or not the normalized point cloud will be plot. If this argument is not specified by the user, it will be set to |
dir.data |
Optional character string naming the absolute path of the directory where LAS files containing TLS point clouds are located. |
save.result |
Optional logical which indicates whether or not the output files described in ‘Output Files’ section should be saved in the |
dir.result |
Optional character string naming the absolute path of an existing directory where files described in ‘Output Files’ section will be saved. |
See normalize, tree.detection.single.scan and tree.detection.multi.scan for further details.
Data frame with the same description and format as tree.detection.single.scan and tree.detection.multi.scan ‘Values’. In this case, the id of plots will be encoded with correlative numbers from 1 to n, where n is the number of LAS files included in files argument, and file column will be encoded as id, but including .las extension.
At the end of the tree detection process, if the save.result argument is TRUE, the function will save both, the reduced point clouds as TXT files encoded according to file column of ‘Value’; and the data frame with the tree list described in ‘Value’ as CSV file (see normalize and tree.detection.single.scan or tree.detection.multi.scan ‘Output files’). All outputs are written without row names in the dir.result directory using vroom_write function from vroom package.
This function has been developed for working with several plots, which will be the most common situation in forest inventory approaches. Nevertheless, several LAS files are not provided as examples due to problems with memory capacity.
Juan Alberto Molina-Valero and Adela Martínez-Calvo.
normalize, tree.detection.single.scan,tree.detection.multi.scan, distance.sampling, estimation.plot.size, simulations, metrics.variables.
# Establishment of working directories (optional) # By default here we propose the current working directory of the R process dir.data <- getwd() dir.result <- getwd() # Loading example data (LAZ files) to dir.data download.file( "www.dropbox.com/scl/fi/hzzrt0a39crdy6uvcj9el/PinusSylve1.laz?rlkey=svpwvorkm8889fgbnj14ns1f2&dl=1", destfile = file.path(dir.data, "PinusSylvestris1.laz"), mode = "wb") download.file( "www.dropbox.com/scl/fi/zeszze31jh5m1g4o3ns1o/PinusSylve2.laz?rlkey=wx72bi6ggdc7wedwgzupekp9k&dl=1", destfile = file.path(dir.data, "PinusSylvestris2.laz"), mode = "wb") # Obtaining a vector with names of LAZ files located in dir.data files <- list.files(pattern = "laz$", path = dir.data) # Tree detection (TLS single-scan aproach) tree.tls <- tree.detection.several.plots(las.list = c("PinusSylvestris1.laz", "PinusSylvestris2.laz"), id = c("PinusSylvestris1", "PinusSylvestris2"), tls.resolution = list(point.dist = 7.67, tls.dist = 10), breaks = c(1, 1.3, 1.6), stem.section = c(0.5, 4.5))# Establishment of working directories (optional) # By default here we propose the current working directory of the R process dir.data <- getwd() dir.result <- getwd() # Loading example data (LAZ files) to dir.data download.file( "www.dropbox.com/scl/fi/hzzrt0a39crdy6uvcj9el/PinusSylve1.laz?rlkey=svpwvorkm8889fgbnj14ns1f2&dl=1", destfile = file.path(dir.data, "PinusSylvestris1.laz"), mode = "wb") download.file( "www.dropbox.com/scl/fi/zeszze31jh5m1g4o3ns1o/PinusSylve2.laz?rlkey=wx72bi6ggdc7wedwgzupekp9k&dl=1", destfile = file.path(dir.data, "PinusSylvestris2.laz"), mode = "wb") # Obtaining a vector with names of LAZ files located in dir.data files <- list.files(pattern = "laz$", path = dir.data) # Tree detection (TLS single-scan aproach) tree.tls <- tree.detection.several.plots(las.list = c("PinusSylvestris1.laz", "PinusSylvestris2.laz"), id = c("PinusSylvestris1", "PinusSylvestris2"), tls.resolution = list(point.dist = 7.67, tls.dist = 10), breaks = c(1, 1.3, 1.6), stem.section = c(0.5, 4.5))
Detects trees from TLS point clouds corresponding to a single scan. For each tree detected, the function calculates the central coordinates and estimates the diameter at 1.3 m above ground level (which is known as dbh, diameter at breast height) and classifies it as fully visible or partially occluded. Finally, the function obtains the number of points belonging to normal sections of trees (those corresponding to dbh +/- 5 cm) and estimates them for both original and reduced (with point cropping process) point clouds.
tree.detection.single.scan(data, single.tree = NULL, dbh.min = 4, dbh.max = 200, h.min = 1.3, ncr.threshold = 0.1, tls.resolution = list(), tls.precision = NULL, density.reduction = 2, stem.section = c(0.7, 3.5), stem.range = NULL, breaks = NULL, slice = 0.1, understory = NULL, bark.roughness = 1, den.type = 1, d.top = NULL, plot.attributes = NULL, plot = TRUE, save.result = TRUE, dir.result = NULL)tree.detection.single.scan(data, single.tree = NULL, dbh.min = 4, dbh.max = 200, h.min = 1.3, ncr.threshold = 0.1, tls.resolution = list(), tls.precision = NULL, density.reduction = 2, stem.section = c(0.7, 3.5), stem.range = NULL, breaks = NULL, slice = 0.1, understory = NULL, bark.roughness = 1, den.type = 1, d.top = NULL, plot.attributes = NULL, plot = TRUE, save.result = TRUE, dir.result = NULL)
data |
Data frame with same description and format as indicated for |
single.tree |
Optional argument to indicate if there is only one tree. |
dbh.min |
Optional minimum dbh (cm) considered for detecting trees. By default it will be set at 4 cm. |
dbh.max |
Optional maximum dbh (cm) considered for detecting trees. By default it will be set at 200 cm. |
h.min |
Optional minimum h (m) considered for detecting trees. By default it will be set at 1.3 m. |
ncr.threshold |
Local surface variation (also known as normal change rate, NCR). By default it will be set as 0.1. For better understanding of this argument see ‘Details’. |
tls.resolution |
List containing parameters of TLS resolution. This can be defined by the angle aperture: |
horizontal.angle: horizontal angle aperture (degrees).
vertical.angle: vertical angle aperture (degrees).
point.dist: distance (mm) between two consecutive points.
tls.dist: distance (m) from TLS at which two consecutive points are separated by point.dist.
If this argument is not specified by the user, it will be set to NULL by default and, as a consequence the function will stop giving an error message.
tls.precision |
Average point cloud precision in cm. |
density.reduction |
Density reduction intensity. |
stem.section |
Section free of noise (shurb, branches, etc.) considered to detect trees. If not specified, an automatic internal algorithm will be applied (see ‘Details’). |
stem.range |
Section considered to estimate straightness tree attributes. |
breaks |
Height above ground level (m) of slices considered for detecting trees. By default it will be considered all possible sections from 0.1 m to maximum height by 0.3 m intervals (+/- 5 cm). |
slice |
Slice width considered for detecting trees. By default it will be considered as 0.1 m. |
understory |
Optional argument to indicate if there is dense understory vegetation. |
bark.roughness |
Bark roughness established in 3 degrees (1 < 2 < 3). By default it will be considered as 1. |
den.type |
Numeric argument indicating the dendrometic type used to estimate volumen when there are not sections enough to fit a taper equation. Dendrometrics types available are the following: cylinder = 0, paraboloid = 1 (by default), cone = 2 and neiloid = 3. |
d.top |
Top stem diameter (cm) considered to estimate commercial timber volume. |
plot.attributes |
Data frame with attributes at plot level. It must contain a column named |
plot |
Optional logical which indicates whether or not the normalized point cloud will be plot. If this argument is not specified by the user, it will be set to |
save.result |
Optional logical which indicates whether or not the output files described in ‘Output Files’ section should be saved in |
dir.result |
Optional character string naming the absolute path of an existing directory where the files described in ‘Output Files’ section will be saved. |
Slices determined by breaks argument are clustered using the DBSCAN algorithm (Ester et al., 1996) on the horizontal plane according to Cartesian coordinates (x, y). Before and after this process, several algorithms are used to remove noisy points and apply classification criteria to select the clusters of trees.
dbh is directly estimated for the section of 1.3 m above ground level, and estimated from other sections using dbh~breaks linear regression. Finally, the mean value of all estimates is provided in ‘Value’ as the dbh of the tree section.
The number of points corresponding to a normal section (+/- 5 cm) is estimated in proportion to dbh, using the average number of points per radius unit as reference. In this respect, only tree sections fully visible at 1.3 m above ground level will be considered for estimating the average number of points.
Local surface variation (also known as normal change rate ,NCR), is a quantitative measure of curvature feature (Pauly et al., 2002). This is useful for distinguishing points belonging to fine branches and foliage (e.g. leaves, shrubs) and stem points (e.g. Jin et al., 2016; Zhang et al., 2019). Just as we considered 5 cm as suitable for calculating local surface variation for the stem separation in forests, according to other authors (Ma et al., 2015; Xia et al., 2015), we also established the NCR threshold as 0.1, according to Zhang et al. (2019). However, this argument (ncr.threshold) may be modified in order to use more appropriate values.
Data frame with the following columns for every tree detected (each row corresponds to one tree detected):
id |
Optional plot identification encoded as a character string or numeric. If this argument is not specified by the user, it will be set to NULL by default and, as a consequence, the plot will be encoded as 1. |
file |
Optional file name identification encoded as character string or numeric. If it is null, the file will be encoded as |
tree |
tree numbering |
Coordinates |
Cartesian (according to https://en.wikipedia.org/wiki/Cartesian_coordinate_system notation): |
x: distance on x axis (m) of tree centre.
y: distance on y axis (m) of tree centre.
Azimuthal angles:
phi: angular coordinate (rad) of tree centre.
phi.left: angular coordinate (rad) of left border of tree section.
phi.right: angular coordinate (rad) of right border of tree section
h.dist |
horizontal distance (m) from plot centre to tree centre. |
\emph{dbh} |
estimated tree diameter (cm) at breast height (1.3 m). |
\emph{h} |
estimated tree total height (m). |
\emph{h.com} |
estimated commercial tree height (m) according to the top diameter defined in the argument |
\emph{v} |
estimated tree stem volume (m^3). |
\emph{v.com} |
estimated commercial tree stem volume (m^3) according to the top diameter defined in the argument |
n.pts |
number of points corresponding to a normal section (+/- 5 cm) in the original point cloud. |
n.pts.red |
number of points corresponding to a normal section (+/- 5 cm) in the point cloud reduced by the point cropping process. |
n.pts.est |
number of points estimated for a normal section (+/- 5 cm) in the original point cloud. |
n.pts.red.est |
number of points estimated for a normal section (+/- 5 cm) in the point cloud reduced by the point cropping process. |
partial.occlusion |
yes (1) or no (0) |
At the end of the tree detection process, if the save.result argument is TRUE, the function will save the data frame described in ‘Value’ as a CSV file named ‘tree.tls.csv’. The data frame will be written without row names in the dir.result directory by using write.csv function from the utils package.
Although tree.detection also works with reduced point clouds, thus reducing the computing time, use of the original point cloud is recommended in order to detect more trees. This will also depend on forest conditions, especially those related to visibility. The more distant the trees are, the lower the density of points will be, and using reduced point clouds will therefore complicate detection of the most distant trees.
Note that dbh.min and dbh.max are important for avoiding outlier values when inventory data are used for reference purposes. Otherwise, knowledge about the autoecology of species could be used for filtering anomalous values of dbh.
The argument breaks = 1.3 could be sufficient for detecting trees visible at dbh, involving lower computational cost. However, those trees not detected at dbh, may be estimated from lower and/or higher sections. Considering the three default sections in the argument breaks = c(1.0, 1.3, 1.6) maintains a good balance in the case study of this package.
Juan Alberto Molina-Valero and Adela Martínez-Calvo.
Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd (Vol. 96, No. 34, pp. 226-231).
Jin, S., Tamura, M., & Susaki, J. (2016). A new approach to retrieve leaf normal distribution using terrestrial laser scanners. J. Journal of Forestry Research, 27(3), 631-638. doi:10.1007/s11676-015-0204-z
Ma, L., Zheng, G., Eitel, J. U., Moskal, L. M., He, W., & Huang, H. (2015). Improved salient feature-based approach for automatically separating photosynthetic and nonphotosynthetic components within terrestrial lidar point cloud data of forest canopies. IEEE Transactions Geoscience Remote Sensing, 54(2), 679-696. doi:10.1109/TGRS.2015.2459716
Pauly, M., Gross, M., & Kobbelt, L. P., (2002). Efficient simplification of point-sampled surfaces. In IEEE Conference on Visualization. (pp. 163-170). Boston, USA. doi:10.1109/VISUAL.2002.1183771
Xia, S., Wang, C., Pan, F., Xi, X., Zeng, H., & Liu, H. (2015). Detecting stems in dense and homogeneous forest using single-scan TLS. Forests. 6(11), 3923-3945. doi:10.3390/f6113923
Zhang, W., Wan, P., Wang, T., Cai, S., Chen, Y., Jin, X., & Yan, G. (2019). A novel approach for the detection of standing tree stems from plot-level terrestrial laser scanning data. Remote Sens. 11(2), 211. doi:10.3390/rs11020211
normalize, tree.detection.multi.scan, tree.detection.several.plots, distance.sampling, estimation.plot.size, simulations, metrics.variables
# Establishment of working directories (optional) # By default here we propose the current working directory of the R process dir.data <- getwd() dir.result <- getwd() # Loading example data (LAZ file) to dir.data download.file("https://www.dropbox.com/s/17yl25pbrapat52/PinusRadiata.laz?dl=1", destfile = file.path(dir.data, "PinusRadiata.laz"), mode = "wb") # Normalizing the whole point cloud data without considering arguments pcd <- normalize(las = "PinusRadiata.laz", id = "PinusRadiata", dir.data = dir.data, dir.result = dir.result) # Tree detection without considering arguments # For this case study, TLS resolution was established as: # point.dist = 6.34 mm and tls.dist = 10 m tree.tls <- tree.detection.single.scan(data = pcd, tls.resolution = list(point.dist = 6.34, tls.dist = 10), breaks = c(1, 1.3, 1.6), dir.result = dir.result)# Establishment of working directories (optional) # By default here we propose the current working directory of the R process dir.data <- getwd() dir.result <- getwd() # Loading example data (LAZ file) to dir.data download.file("https://www.dropbox.com/s/17yl25pbrapat52/PinusRadiata.laz?dl=1", destfile = file.path(dir.data, "PinusRadiata.laz"), mode = "wb") # Normalizing the whole point cloud data without considering arguments pcd <- normalize(las = "PinusRadiata.laz", id = "PinusRadiata", dir.data = dir.data, dir.result = dir.result) # Tree detection without considering arguments # For this case study, TLS resolution was established as: # point.dist = 6.34 mm and tls.dist = 10 m tree.tls <- tree.detection.single.scan(data = pcd, tls.resolution = list(point.dist = 6.34, tls.dist = 10), breaks = c(1, 1.3, 1.6), dir.result = dir.result)