Type:	Package
Title:	Tools to Process Point Clouds Derived from Terrestrial Laser Scanning
Version:	0.2.6.1
Maintainer:	J. Antonio Guzmán Q. <antguz06@gmail.com>
Description:	A set of tools to process and calculate metrics on point clouds derived from terrestrial LiDAR (Light Detection and Ranging; TLS). Its creation is based on key aspects of the TLS application in forestry and ecology. Currently, the main routines are based on filtering, neighboring features of points, voxelization, canopy structure, and the creation of artificial stands. It is written using data.table and C++ language and in most of the functions it is possible to use parallel processing to speed-up the routines.
License:	GPL (≥ 3)
URL:	https://github.com/Antguz/rTLS
BugReports:	https://github.com/Antguz/rTLS/issues
Depends:	data.table (≥ 1.17.8), R (≥ 4.5.0)
Imports:	alphashape3d, boot, doSNOW (≥ 1.0.16), foreach (≥ 1.4.4), parallel, RcppHNSW (≥ 0.3.0), rgl, sf
Suggests:	knitr, rmarkdown, covr, testthat (≥ 3.0.1)
LinkingTo:	Rcpp, RcppArmadillo, RcppProgress
VignetteBuilder:	knitr
Encoding:	UTF-8
LazyData:	true
LazyDataCompression:	bzip2
RoxygenNote:	7.3.2
SystemRequirements:	GNU make
ByteCompile:	true
Config/testthat/edition:	3
NeedsCompilation:	yes
Packaged:	2026-01-19 04:24:17 UTC; antonio
Author:	J. Antonio Guzmán Q. [cre, aut, ths, cph], Ronny Hernandez [aut], Arturo Sanchez-Azofeifa [dgs, sad]
Repository:	CRAN
Date/Publication:	2026-01-19 08:20:02 UTC

rTLS: Tools to Process Point Clouds Derived From Terrestrial Laser Scanning

Description

rTLS is a package that compiles a set of tools to process and calculate metrics on point clouds derived from terrestrial LiDAR (Light Detection and Ranging). Its creation is based on key aspects of the TLS application in forestry. Currently, the main routines are based on filtering, neighboring features of points, voxelization, optimal sphere or voxel size, and the creation of artificial stands. rTLS is written using data.table and C++ language and in most of the functions it is posible to use parallel processing to speed-up the routines.

Author(s)

Maintainer: J. Antonio Guzmán Q. antguz06@gmail.com (ORCID) [thesis advisor, copyright holder]

Authors:

• Ronny Hernandez ronny.hernandezm@gmail.com (ORCID)

Other contributors:

• Arturo Sanchez-Azofeifa arturo.sanchez@ualberta.ca (ORCID) [degree supervisor, scientific advisor]

See Also

Useful links:

• https://github.com/Antguz/rTLS

• Report bugs at https://github.com/Antguz/rTLS/issues

A TLS scan

Description

A data.table from a TLS scan.

Usage

data(TLS_scan)

Format

A data.table with five columns, which are:

X

the "X" coordinate

Y

the "Y coordinate

Z

the "Z" coordinate

Target_count

The number of received by the same laser shot

Target_index

The rank of the returned pulse in the target count of received by the same laser shot

A data.table where the rows represent the pulse returns and the three columns represent the *XYZ* coordinates, and the target count and index.

Details

A TLS scan conducted using a Reigel VZ400i with a vertical and horizontal resolution of 0.048 and 0.622 degrees (2082 and 580 lines, respectively). The scanner has frame of scanning between 30 and 130.024 degrees zenith and 0 and 359.90 degrees azimuth. At the moment of the scan the roll, pitch, and yaw of the scanner were 1.026, 0.746, -110.019, respectively. The scanner coordinates in this scan are x = 0, y = 0, z = 0.

Examples

data(TLS_scan)
head(TLS_scan)

Artificial Forest Stand

Description

Create an artificial forest stand of a given area using tree point clouds.

Usage

artificial_stand(
  files,
  n.trees,
  dimension,
  coordinates = NULL,
  sample = TRUE,
  replace = TRUE,
  overlap = NULL,
  rotation = TRUE,
  degrees = NULL,
  n_attempts = 100,
  progress = TRUE,
  plot = TRUE,
  ...
)

Arguments

Details

When coordinates = NULL, artifical_stand adds, in sequence, random coordinates to each files in the future stand based on the crown area overlap. That is, first a tree from files is randomly located within the stand dimention, then a second tree from files will be located in the future stand based on the crown area overlap from the previous tree, and so on. If during the random location a given tree does not meet the requirements of overlap, new random coordinates will be provided until the requirements are met.

Since artificial_stand will try to add tree to the stand until the requirements are met, this could lead to an infinite loop if the stand dimention is small or if the trees on files are large or many n.trees. Therefore, the use of n_attempts is recommended to avoid this scenario.

Value

A list which contain a data.table (Trees) with the information of the point clouds used and their current coordinates in the stand, and another data.table with that compile all the point clouds used.

Author(s)

J. Antonio Guzmán Q.

See Also

voxels_counting

Examples

#' #Import an example point cloud
path <- system.file("extdata", "pc_tree.txt", package = "rTLS")

#Creates a stand of 4 trees with 10% of overlap
files <- rep(path, 4)
artificial_stand(files, n.trees = 4, dimension = c(15, 15), overlap = 10)

#Creates a stand of 4 trees with their locations
location <- data.table(X = c(5, 10, 10, 5), Y = c(5, 5, 10, 10))
artificial_stand(files, n.trees = 4, dimension = c(15, 15), coordinates = location)

Canopy Structure

Description

Estimates the canopy structure from a discrete returns scan from different TLS.

Usage

canopy_structure(
  TLS.type,
  scan,
  zenith.range,
  zenith.rings,
  azimuth.range,
  vertical.resolution,
  TLS.pulse.counts,
  TLS.resolution = NULL,
  TLS.coordinates = c(0, 0, 0),
  TLS.frame = NULL,
  TLS.angles = NULL,
  threads = 1
)

Arguments

Details

Since scan describes discrete returns measured by the TLS, canopy_structre first simulates the number of pulses emitted based on Danson et al. (2007). The simulated pulses are created based on the TLS properties (TLS.pulse.counts, TLS.resolution, TLS.frame) assuming that the scanner is perfectly balance. Then these pulses are rotated (rotate3D) based on the TLS.angles roll, pitch, and yaw, and move to TLS.coordintates to simulate the positioning of the scanner during the scan. Rotated simulated-pulses of interest and scan returns are then extracted based on the zenith.range and azimuth.range for a given number of zenith.rings, azimuth.rings and vertical profiles. The probability of gap (Pgap) is then estimated using the frequency of pulses and returns. For TLS.type = "multiple", the frequency of returns is estimated using the sum of 1/target count following Lovell et al. (2011).

Using the Pgap estimated per each zenith ring and vertical profile, canopy_structure then estimates the accumulative L(z) profiles based on the closest zenith ring to 57.5 (hinge region) and, if TLS.type = "fixed.angle", the f(z) or commonly named PAVD based on the ratio of the derivative of L(z) and height (z) following Jupp et al. 2009 (Equation 18). If TLS.type is equal to "single" or "multiple", canopy_structure also estimates the normalized average weighted L/LAI, and then PAVD based on the L (hinge angle) at the highest height (LAI) and the ratio between the derivative of L/LAI (average weighted) and the derivative of z (Jupp et al. 2009; Equation 21).

Jupp et al. 2009 excludes the zero zenith or fist ring to conduct the average weighted L/LAI estimations, canopy_structre does not excludes this sections since it depends on the regions of interest of the user. Therefore, user should consider this difference since it may introduce more variability to profile estimations.

Value

For any TLS.type, it returns a data.table with the height profiles defined by vertical.resolution, the gap probability based on the zenith.range and zenith.rings, and the accumulative L(z) profiles based on the closest zenith ring to 57.5 degrees (hinge angle). If TLS.type is equal to "fixed.angle", it returns f(z) or commonly named PAVD based on on the ratio of the derivative of L(z) and the derivative of height (z). If TLS.type is equal to "single" or "multiple", it returns the normalized average weighting L/LAI, and PAVD: based on the L (hinge angle) at the highest height and the ratio between the derivative of L/LAI average weighted and the derivative of z.

Author(s)

J. Antonio Guzmán Q.

References

Danson F.M., Hetherington D., Morsdorf F., Koetz B., Allgower B. 2007. Forest canopy gap fraction from terrestrial laser scanning. IEEE Geosci. Remote Sensing Letters 4:157-160. doi: 10.1109/LGRS.2006.887064

Lovell J.L., Jupp D.L.B., van Gorsel E., Jimenez-Berni J., Hopkinson C., Chasmer L. 2011. Foliage profiles from ground based waveform and discrete point LiDAR. In: SilviLaser 2011, Hobart, Australia, 16–20 October 2011.

Jupp D.L.B., Culvenor D.S., Lovell J.L., Newnham G.J., Strahler A.H., Woodcock C.E. 2009. Estimating forest LAI profiles and structural parameters using a ground-based laser called “Echidna®”. Tree Physiology 29(2): 171-181. doi: 10.1093/treephys/tpn022

Examples

data(TLS_scan)
#Using a multiple return file
#Select the four columns required
TLS_scan <- TLS_scan[, 1:4]

#This will take a while#
canopy_structure(TLS.type = "multiple",
                 scan = TLS_scan,
                 zenith.range = c(50, 70),
                 zenith.rings = 4,
                 azimuth.range = c(0, 360),
                 vertical.resolution = 0.25,
                 TLS.pulse.counts = c(2082, 580),
                 TLS.frame = c(30, 130.024, 0, 359.90),
                 TLS.angles =  c(1.026, 0.760, -110.019))

#Using a single return file

data(TLS_scan)
#Subset to first return observations
TLS_scan <- TLS_scan[Target_index == 1, 1:3]

#This will take a while#
canopy_structure(TLS.type = "single",
                 scan = TLS_scan,
                 zenith.range = c(50, 70),
                 zenith.rings = 4,
                 azimuth.range = c(0, 360),
                 vertical.resolution = 0.25,
                 TLS.pulse.counts = c(2082, 580),
                 TLS.frame = c(30, 130.024, 0, 359.90),
                 TLS.angles =  c(1.026, 0.760, -110.019))

Cartesian to Polar Coordinates

Description

Convert from East-North-Up cartesian coordinates to polar coordinates.

Usage

cartesian_to_polar(cartesian, anchor = c(0, 0, 0), digits = NULL)

Arguments

Details

It assumes that the positive *Z* axis is the reference vector for the zenith angle. Likewise, it assumes that the *Y* axis is the north-south direction (positive to negative) for the azimuth angle. If a point from cartesian presents the same *XY* coordinates than anchor, angles returns NA.

Value

A data.table with the zenith and azimuth angles (degrees), and the distance to the anchor coordinate.

Author(s)

J. Antonio Guzmán Q.

See Also

polar_to_cartesian

Examples

data(pc_tree)
cartesian_to_polar(pc_tree)
anchor <- c(1, 1, 1)
cartesian_to_polar(pc_tree, anchor)

Adaptive RANSAC Circle Fitting

Description

Adaptive random sample consensus for cicle fitting.

Usage

circleRANSAC(
  cloud,
  fpoints,
  pconf,
  poutlier,
  max_iterations,
  threads = 1L,
  plot = TRUE
)

Arguments

Value

A data.table with the *XY* coordinate information of the circle center, the radius, the error based on the least squares fit, and the proportion of inliers.

Author(s)

J. Antonio Guzmán Q.

See Also

tree_metrics, trunk_volume

Examples

#Point cloud
data("pc_tree")

#Subset region at at breast height
sub <- pc_tree[between(Z, 1.25, 1.35),]

#Fit circle
circleRANSAC(sub, fpoints = 0.2, pconf = 0.95, poutlier = c(0.5, 0.5), max_iterations = 100)

Euclidean Distance Between 3D points

Description

Estimate the distance between a point and a group of point.

Usage

euclidean_distance(point, cloud, threads = 1L)

Arguments

Value

A numeric vector describing of point to each row of cloud.

Author(s)

J. Antonio Guzmán Q.

Examples

data("pc_tree")

euclidean_distance(point = c(0, 0, 0), pc_tree)

Filtering of Point Clouds

Description

Filtering of point clouds using different methods

Usage

filter(
  cloud,
  method,
  radius,
  min_neighbours,
  k,
  nSigma,
  edge_length,
  distance = "euclidean",
  threads = 1L,
  verbose = FALSE,
  progress = FALSE,
  ...
)

Arguments

Value

A data.table with the filtered points

Author(s)

J. Antonio Guzmán Q.

Examples

#Load data
data("pc_tree")

#Move pc_tree for comparison
pc_compare <- pc_tree
pc_compare$X <- pc_compare$X - 7

#SOR filter
r1 <- filter(pc_tree, method = "SOR", k = 30, nSigma = 1)
rgl::plot3d(r1, col = "red") #Filter
rgl::points3d(pc_compare, col = "black") #Original

#min_neighbours filter
r2 <- filter(pc_tree, "min_neighbors", radius = 0.02, min_neighbours = 20)
rgl::plot3d(r2, col = "red") #Filter
rgl::points3d(pc_compare, col = "black") #Original

#voxel_center filter
r3 <- filter(pc_tree, method = "voxel_center", edge_length = 0.1)
rgl::plot3d(r3, col = "red") #Filter
rgl::points3d(pc_compare, col = "black") #Original

Geometry features of Neighboring Points.

Description

Estimate geometry features of neighboring points in a cloud.

Usage

geometry_features(
  cloud,
  method,
  radius,
  k,
  max_neighbour,
  distance = "euclidean",
  target = FALSE,
  threads = 1L,
  verbose = FALSE,
  progress = TRUE,
  ...
)

Arguments

Details

The function returns the geometry features of the neighboring points of a given point in cloud. Geometry features are represented by the relative values of the eigenvalues derived from a covariance matrix of the neighboring points. Geometry features are not estimated on target points with less than 3 neighboring points.

Value

A array describing the point of the cloud in rows, the relative eigenvalues in columns, and the radius or k per slide. If method = "radius_search", it add in the first column the number of neighboring points.

Author(s)

J. Antonio Guzmán Q.

Examples

#Create cloud
example <- data.table(X = runif(200, min=0, max=10),
                      Y = runif(200, min=0, max=10),
                      Z = runif(200, min=0, max=10))


#Using knn method with two different k
k_test <- c(5, 10)
geometry_features(example, method = "knn", k = k_test)

#Using radius search method with two different radius
radius_test <- c(3, 4)
geometry_features(example, method = "radius_search", radius = radius_test, max_neighbour = 200)

K Nearest Neighbors

Description

Adapted K nearest neighbors based on RcppHNSW

Usage

knn(
  query,
  ref,
  k,
  distance = "euclidean",
  same = FALSE,
  threads = 1L,
  verbose = FALSE,
  progress = FALSE,
  ...
)

Arguments

Details

This function is based on hnswlib C++ library (Malkov & Yashunin 2016) and its bindings for R (RcppHNSW; Melville 2020) for a fast estimation of neighbors points. It is adapted to simplify the workflow within rTLS. If you use this function, please consider cite the C++ library and RcppHNSW package.

Value

A data.table with three columns describing the indices of the query, ref, and k neighbors and the distances.

Author(s)

J. Antonio Guzmán Q.

References

Malkov, Y. A., & Yashunin, D. A. (2016). Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs. arXiv preprint arXiv:1603.09320.

See Also

radius_search

Examples

#Point cloud
data("pc_tree")

#knn search using k = 3
knn(pc_tree, pc_tree, k = 3, same = TRUE)

Line-AABB

Description

Intersection of a line by an Axis-Aligned Bounding Box.

Usage

line_AABB(orig, end, AABB_min, AABB_max)

Arguments

Details

The interaction of a line with a AABB may result in five scenarios: i) the line is not intercepted by a AAABB (0), ii) the origin and end of the line falls within the AABB (1), iii) the origin point of the line falls within the AABB both not the end point (2), iv) the end point of the line falls within the AABB both not the origin point (3), and v) the line is intercepted by the AABB (4).

Value

An numeric vector of length two, describing if the line was intercepted or not, and the length of the intercepted line within in the AABB. See details.

Author(s)

J. Antonio Guzmán Q.

See Also

lines_interception, voxels,

Examples

#Create origins and end paths
orig <- data.table(X = c(0, 0, 0, 0, 0),
                   Y = c(-0.45, -0.25, 0, 0.25, 0.45),
                   Z = c(-1, -0.25, 0, -1, -1))

end <- data.table(X = c(0, 0, 0, 0, 0),
                  Y = c(-0.45, -0.25, 0, 0.25, 0.45),
                  Z = c(-0.75, 0.25, 1, 0, 1))

#Create the AABB
AABB <- matrix(c(0, 0, 0), ncol = 3)
edge_length <- c(1, 1, 1)

AABB_min <- c(AABB[1, 1] - edge_length[1]/2,
              AABB[1, 2] - edge_length[2]/2,
              AABB[1, 3] - edge_length[3]/2)

AABB_max <- c(AABB[1, 1] + edge_length[1]/2,
              AABB[1, 2] + edge_length[2]/2,
              AABB[1, 3] + edge_length[3]/2)

#Plot
cube <- rgl::cube3d()
cube <- rgl::scale3d(cube, edge_length[1]/2,
                           edge_length[2]/2,
                           edge_length[3]/2)
box <- rgl::translate3d(cube, AABB[1, 1], AABB[1, 2], AABB[1, 3])
rgl::shade3d(box, col= "green", alpha = 0.6)
rgl::points3d(orig, size = 4, col = "black")
rgl::points3d(end, size = 4, col = "red")

#Line no intercepted
rgl::lines3d(c(orig[1, 1], end[1, 1]),
             c(orig[1, 2], end[1, 2]),
             c(orig[1, 3], end[1, 3]), col = "grey")

line_AABB(orig[1,], end[1,], AABB_min, AABB_max)

#Both ends falls inside
rgl::lines3d(c(orig[2, 1], end[2, 1]),
             c(orig[2, 2], end[2, 2]),
             c(orig[2, 3], end[2, 3]), col = "red")

line_AABB(orig[2,], end[2,], AABB_min, AABB_max)

#Oring falls inside, but not the end.
rgl::lines3d(c(orig[3, 1], end[3, 1]),
             c(orig[3, 2], end[3, 2]),
             c(orig[3, 3], end[3, 3]), col = "blue")

line_AABB(orig[3,], end[3,], AABB_min, AABB_max)

#End falls inside, but not the orig
rgl::lines3d(c(orig[4, 1], end[4, 1]),
             c(orig[4, 2], end[4, 2]),
             c(orig[4, 3], end[4, 3]), col = "green")

line_AABB(orig[4,], end[4,], AABB_min, AABB_max)

#Some segments of the line are intercepted
rgl::lines3d(c(orig[5, 1], end[5, 1]),
             c(orig[5, 2], end[5, 2]),
             c(orig[5, 3], end[5, 3]), col = "black")

line_AABB(orig[5,], end[5,], AABB_min, AABB_max)

Intersection of Lines by AABBs

Description

Intersection of lines by several Axis-Aligned Bounding Boxs.

Usage

lines_interception(orig, end, AABBs, edge_length, threads = 1, progress = TRUE)

Arguments

Value

It returns a data.table with nine columns: 1-5 columns with the counts for the code of intersection (see line_AABB), and 6-9 columns with sum the path length of intersection. The number of rows match with nrow(AABBs).

Author(s)

J. Antonio Guzmán Q.

See Also

line_AABB, voxels

Examples

#Create points with paths
n <- 20
orig <- data.table(X = runif(n, min = -5, max = 5),
                   Y = runif(n, min = -5, max = 5),
                   Z = runif(n, min = -5, max = 5))

end <- data.table(X = runif(n, min = -5, max = 5),
                  Y = runif(n, min = -5, max = 5),
                  Z = runif(n, min = -5, max = 5))

#Create a potential AABB
AABBs <- data.table(X = 0, Y = 0, Z = 0)
edge_length <- c(2, 2, 2)

#Plot

cube <- rgl::cube3d()
cube <- rgl::scale3d(cube,
                     edge_length[1]/2,
                     edge_length[2]/2,
                     edge_length[3]/2)
box <- rgl::translate3d(cube, AABBs[[1]], AABBs[[2]], AABBs[[3]])
rgl::shade3d(box, col= "green", alpha = 0.6)
rgl::points3d(orig, size = 5, col = "black")
rgl::points3d(end, size = 5, col = "red")

for(i in 1:nrow(orig)) {
rgl::lines3d(c(orig[[1]][i], end[[1]][i]),
             c(orig[[2]][i], end[[2]][i]),
             c(orig[[3]][i], end[[3]][i]), col = "grey")
}


#Estimation
lines_interception(orig, end, AABBs, edge_length, progress = FALSE)

Minimum Distance Between Points

Description

Estimate the minimum distance between points in a point cloud.

Usage

min_distance(
  cloud,
  distance = "euclidean",
  threads = 1L,
  verbose = FALSE,
  progress = FALSE,
  ...
)

Arguments

Value

A numeric vector describing the minimum distance between points.

Author(s)

J. Antonio Guzmán Q.

Examples

data("pc_tree")

#Estimate the minimum distance of a sample o 100 points
min_distance(pc_tree)

A Tree Point Cloud

Description

A data.table from a point cloud of a tree with a spatial point resolution of 0.05 mm.

Usage

data(pc_tree)

Format

A data.table with three columns, which are:

X

the "X" coordinate

Y

the "Y coordinate

Z

the "Z" coordinate

A data.table where the rows represent the points and the three columns represent the *XYZ* coordinates.

References

Guzman, Sharp, Alencastro, Sanchez-Azofeifa. 2018. To be published.

Examples

data(pc_tree)
head(pc_tree)

Plot Method for Voxels

Description

The plot method for objects of class "voxels" created using the voxels function.

Usage

plot_voxels(
  voxels,
  add.points = TRUE,
  add.voxels = TRUE,
  border = TRUE,
  points.size = 1,
  points.col = "black",
  fill.col = "forestgreen",
  line.lwd = 0.5,
  line.col = "black",
  alpha = 0.1,
  ...
)

Arguments

Value

A 3D plot of a point cloud and voxels.

Author(s)

J. Antonio Guzmán Q.

See Also

voxels, voxels_counting, summary_voxels

Examples

data("pc_tree")

###Create cubes of a size of 7x7x3.5.
vox <- voxels(pc_tree, edge_length = c(7, 7, 3.5))
plot_voxels(vox)

Polar to Cartesian Coordinates

Description

Convert from polar to cartesian coordinates.

Usage

polar_to_cartesian(polar, threads = 1, digits = NULL)

Arguments

Value

A data.table with three columns describing the *XYZ* of the cartesian coordinates.

Author(s)

J. Antonio Guzmán Q.

See Also

cartesian_to_polar

Examples

#Creates a hemisphere of points each 2 degrees

zenith <- seq(0, 90, 2)
azimuth <- seq(0, 360, 2)
hemi <- CJ(zenith, azimuth)
hemi$distance <- 1
hemicloud <- polar_to_cartesian(hemi)
rgl::plot3d(hemicloud)

Radius Search of Points

Description

Adapted radius searching of points based on RcppHNSW

Usage

radius_search(
  query,
  ref,
  radius,
  max_neighbour,
  distance = "euclidean",
  same = FALSE,
  threads = 1L,
  verbose = FALSE,
  progress = FALSE,
  ...
)

Arguments

Details

This function is based on hnswlib C++ library (Malkov & Yashunin 2016) and its bindings for R (RcppHNSW; Melville 2020) for a fast estimation of neighbors points. It is adapted to simplify the workflow within rTLS. If you use this function, please consider cite the C++ library and RcppHNSW package.

Value

A data.table with three columns describing the indices of the query and ref points and the distances.

Author(s)

J. Antonio Guzmán Q.

References

Malkov, Y. A., & Yashunin, D. A. (2016). Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs. arXiv preprint arXiv:1603.09320.

See Also

radius_search

Examples

#Point cloud
data("pc_tree")


#Radius search of 1
radius_search(pc_tree, pc_tree, radius = 1, max_neighbour = 100)

Rotate a plane of coordinates

Description

Rotate a plane of coordinates to a given angle.

Usage

rotate2D(plane, angle, threads = 1)

Arguments

Value

A data.table with the rotation applied to plane.

Author(s)

J. Antonio Guzmán Q.

Examples

data(pc_tree)

plot(pc_tree[,1:2])

#Rotate in 45 degrees using Z axis of the cloud
plot(rotate2D(pc_tree[,1:2], angle = 45))

Rotate a Point Cloud

Description

Rotate point cloud based on the roll, pitch, and yaw angles.

Usage

rotate3D(cloud, roll = 0, pitch = 0, yaw = 0, threads = 1)

Arguments

Details

The *XYZ* coordinates are transformed to E-N-U coordinates (ENU system, East-North-Up).

Value

A data.table with the rotation applied to cloud.

Author(s)

J. Antonio Guzmán Q.

Examples

data(pc_tree)
rgl::plot3d(pc_tree)
rgl::plot3d(rotate3D(pc_tree, roll = 45, pitch = 45, yaw = 0))

Stand Counting

Description

Applies the voxels_counting function on a grid base point cloud.

Usage

stand_counting(
  cloud,
  xy.res,
  z.res = NULL,
  points.min = NULL,
  min_size,
  edge_sizes = NULL,
  length_out = 10,
  bootstrap = FALSE,
  R = NULL,
  progress = TRUE,
  parallel = FALSE,
  threads = NULL
)

Arguments

Value

A data.table with the summary of the voxels per grid created with their features.

Author(s)

J. Antonio Guzmán Q.

See Also

voxels_counting, voxels, summary_voxels

Examples

data(pc_tree)

#Applying stand_counting.

stand_counting(pc_tree, xy.res = c(4, 4), min_size = 3)


#Applying stand_counting using bootstrap in the H index.

stand_counting(pc_tree,
               xy.res = c(4, 4),
               min_size = 3,
               bootstrap = TRUE,
               R = 10)

Voxels Summary

Description

Create a summary objects of class "voxels" created using the voxels.

Usage

summary_voxels(voxels, edge_length = NULL, bootstrap = FALSE, R = NULL)

Arguments

Details

The function provides 12 main statistics of the voxels. Specifically, the first three columns represent the edge length of the voxels, the following three columns (ei. N_voxels, Volume, Surface) describe the number of voxels created, the total volume that they represent, and the surface area that they cover. Following columns represent the mean (Density_mean) and sd (Density_sd) of the density of points per voxel (e.g. points/m2). Columns 9:12 provide metrics calculated using the Shannon Index. Specifically, H describe the entropy, H_max the maximum entropy, Equitavility the ratio between H and Hmax, and Negentropy describe the product of Hmax - H. If bootstrap = TRUE four more columns are created (13:16). These represent the mean and sd of the H index estimated using bootstrap (H_boot_mean and H_boot_sd), the Equtavility_boot as the ratio of the ratio between H_boot_sd and Hmax, and Negentropy_boot as the product Hmax - H_boot_mean.

Value

A data.table with with the summary of voxels.

Author(s)

J. Antonio Guzmán Q.

See Also

voxels, voxels_counting, plot_voxels

Examples

data("pc_tree")

#Apply a summary on a object of class "voxels" using bootstrap with 1000 replicates.
vox <- voxels(pc_tree, edge_length = c(0.5, 0.5, 0.5))
summary_voxels(vox, bootstrap = TRUE, R = 1000)

#Apply a summary on a product from 'voxels' using bootstrap with 1000 replicates.
vox <- voxels(pc_tree, edge_length = c(0.5, 0.5, 0.5), obj.voxels = FALSE)
summary_voxels(vox, edge_length = c(0.5, 0.5, 0.5), bootstrap = TRUE, R = 1000)

Tree Metrics

Description

Estimate the tree height, crown area, and the diameter at breast height of a tree point cloud

Usage

tree_metrics(cloud, region.diameter = NULL, relocateZ = TRUE)

Arguments

Details

The tree height is estimated based on the maximum value of *Z*, the crown area is calculated applying a convex hull on the point cloud, while the DBH is calculated extracting the area of the convex hull on the subset of points between region.diameter, and then estimating the diameter of a circle. For another estimation of DBH try circleRANSAC or for irregular trucks try trunk_volume.

Value

A data.table with the tree height, crown area, and diameter

Author(s)

J. Antonio Guzman Q. and Ronny Hernandez

See Also

circleRANSAC, trunk_volume

Examples

data("pc_tree")
tree_metrics(pc_tree)

Tree Trunk Volume

Description

Estimates the tree trunk volume of a point cloud using the ashape3d package.

Usage

trunk_volume(cloud, max.height = NULL, alpha = 0.2, plot = TRUE, ...)

Arguments

Details

This is an adaptation of the code develop by Lafarge & Pateiro-Lopez (2017) based on Edelsbrunner & Mucke (1994) for the quick extraction of the tree trunk volume. Therefore, if you use this code we kindly suggest to cite these documents in your research.

Value

A numeric vector with the estimated trunk volume.

Author(s)

J. Antonio Guzmán Q.

References

Lafarge, T., Pateiro-Lopez, B. (2017). Implementation of the 3D Alpha-Shape for the Reconstruction of 3D Sets from a Point Cloud. Available at https://CRAN.R-project.org/package=alphashape3d.

Edelsbrunner, H., Mucke, E. P. (1994). Three-Dimensional Alpha Shapes. ACM Transactions on Graphics, 13(1), pp.43-72.

See Also

tree_metrics, circleRANSAC

Examples

data("pc_tree")

#Estimates the trunk volume of a height lower than 1.75.
trunk_volume(pc_tree, max.height = 1.75)

Voxelization of a Point Cloud

Description

Create cubes of a given distance in a point cloud though their voxelization. It use a modify version of the code used in Greaves et al. 2015.

Usage

voxels(cloud, edge_length, threads = 1L, obj.voxels = TRUE)

Arguments

Details

Voxels are created from the negative to the positive *XYZ* coordinates.

Value

If obj.voxels == TRUE, it return an object of class "voxels" which contain a list with the points used to create the voxels, the parameter edge_length, and the voxels created. If FALSE, it returns a data.table with the coordinates of the voxels created and the number of points in each voxel.

Author(s)

J. Antonio Guzmán Q.

References

Greaves, H. E., Vierling, L. A., Eitel, J. U., Boelman, N. T., Magney, T. S., Prager, C. M., & Griffin, K. L. (2015). Estimating aboveground biomass and leaf area of low-stature Arctic shrubs with terrestrial LiDAR. Remote Sensing of Environment, 164, 26-35.

See Also

voxels_counting, plot_voxels, summary_voxels

Examples

data("pc_tree")

###Create cube of a size of 0.5.
voxels(pc_tree, edge_length = c(0.5, 0.5, 0.5))

Voxels Counting

Description

Creates cube like voxels of different size on a point cloud using the voxels function, and then return a summary_voxels of their features.

Usage

voxels_counting(
  cloud,
  edge_sizes = NULL,
  min_size,
  length_out = 10,
  bootstrap = FALSE,
  R = NULL,
  progress = TRUE,
  parallel = FALSE,
  threads = NULL
)

Arguments

Value

A data.table with the summary of the voxels created with their features.

Author(s)

J. Antonio Guzmán Q.

See Also

voxels, summary_voxels, plot_voxels

Examples

data(pc_tree)

#Applying voxels counting.
voxels_counting(pc_tree, min_size = 2)

#Voxels counting using bootstrap on the H indexes with 1000 repetitions.
voxels_counting(pc_tree, min_size = 2, bootstrap = TRUE, R = 1000)