| Title: | Trees Geometry and Morphology from Unstructured TLS Data |
|---|---|
| Description: | Tools for 3D point cloud voxelisation, projection, geometrical and morphological description of trees (DBH, height, volume, crown diameter), analyses of temporal changes between different measurement times, distance based clustering and visualisation of 3D voxel clouds and 2D projection. Most analyses and algorithms provided in the package are based on the concept of space exploration and are described in Lecigne et al. (2018, https://doi.org/10.1093/aob/mcx095). |
| Authors: | Bastien Lecigne [aut, cre] |
| Maintainer: | Bastien Lecigne <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.0 |
| Built: | 2025-01-19 04:00:31 UTC |
| Source: | https://github.com/blecigne/voxr |
Computes points angle with an axis (X, Y or Z) and the origin of the 3D cartesian coordinates system.
axis_angle(data, axis, project, message)axis_angle(data, axis, project, message)
data |
a data.frame or data.table containing the x, y, z, ... coordinates of a point cloud or voxel cloud. |
axis |
charecter. Specifying the reference axis to compute the angles: "X", "Y" or "Z". |
project |
character. If specifyed the point cloud is projected into a 2D plan before computing the angles. Can be "xy", "yz" or "xz". Default is without projection. |
message |
logical. If FALSE, messages are disabled. Default = TRUE. @references Lecigne, B., Delagrange, S., & Messier, C. (2018). Exploring trees in three dimensions: VoxR, a novel voxel-based R package dedicated to analysing the complex arrangement of tree crowns. Annals of botany, 121(4), 589-601. |
A vector containing the angle values of the points.
#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- compute angle with the Z axis tls[,angle_z:=VoxR::axis_angle(tls,axis = "Z")] #- compute angle with the X axis with projection in the xy plan tls[,angle_x:=VoxR::axis_angle(tls,axis = "X",project = "xy")] #- round angle values for visualization tls[,angle_z:=round(angle_z)] tls[,angle_x:=round(angle_x)] #- plot the angle with Z axis cols=rev(rainbow(max(tls$angle_z)+1,end=4/6)) # color scale rgl::open3d() rgl::plot3d(tls,col=cols[tls$angle_z+1],add=TRUE) #- plot the angle with X axis cols=rev(rainbow(max(tls$angle_x)+1,end=4/6)) # color scale rgl::open3d() rgl::plot3d(tls,col=cols[tls$angle_x+1],add=TRUE)#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- compute angle with the Z axis tls[,angle_z:=VoxR::axis_angle(tls,axis = "Z")] #- compute angle with the X axis with projection in the xy plan tls[,angle_x:=VoxR::axis_angle(tls,axis = "X",project = "xy")] #- round angle values for visualization tls[,angle_z:=round(angle_z)] tls[,angle_x:=round(angle_x)] #- plot the angle with Z axis cols=rev(rainbow(max(tls$angle_z)+1,end=4/6)) # color scale rgl::open3d() rgl::plot3d(tls,col=cols[tls$angle_z+1],add=TRUE) #- plot the angle with X axis cols=rev(rainbow(max(tls$angle_x)+1,end=4/6)) # color scale rgl::open3d() rgl::plot3d(tls,col=cols[tls$angle_x+1],add=TRUE)
Computes points distance to an axis of the cartesian coordinates system.
axis_distance(data, axis, message)axis_distance(data, axis, message)
data |
a data.frame or data.table containing the x, y, z, ... coordinates of a point cloud or voxel cloud. |
axis |
charecter. Specifying the reference axis to compute the distance: "X", "Y" or "Z". |
message |
logical. If FALSE, messages are disabled. Default = TRUE. |
A vector containing the distance values of the points
Lecigne, B., Delagrange, S., & Messier, C. (2018). Exploring trees in three dimensions: VoxR, a novel voxel-based R package dedicated to analysing the complex arrangement of tree crowns. Annals of botany, 121(4), 589-601.
#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- compute distance to the Z axis tls[,dist:=VoxR::axis_distance(tls,"Z")] #- round distance values for visualization tls[,dist:=round(dist*100)] #- plot the distance to the Z axis cols=rev(rainbow(max(tls$dist)+1,end=4/6)) # color scale rgl::open3d() rgl::plot3d(tls,col=cols[tls$dist+1],add=TRUE)#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- compute distance to the Z axis tls[,dist:=VoxR::axis_distance(tls,"Z")] #- round distance values for visualization tls[,dist:=round(dist*100)] #- plot the distance to the Z axis cols=rev(rainbow(max(tls$dist)+1,end=4/6)) # color scale rgl::open3d() rgl::plot3d(tls,col=cols[tls$dist+1],add=TRUE)
Deprecated function
axis.angle(...)axis.angle(...)
... |
parameters |
Deprecated function
axis.distance(...)axis.distance(...)
... |
parameters |
Computes fractal dimension using the box counting method.
box_counting(data, min_vox_size, store_fit, message)box_counting(data, min_vox_size, store_fit, message)
data |
a data.frame or data.table containing the x, y, z, ... coordinates of a point cloud. |
min_vox_size |
numeric. The minimum size of a voxel. Default = 0.01. |
store_fit |
logical. If TRUE, the parameters linear model's fit are returned. Default = FALSE. |
message |
logical. If FALSE, messages are disabled. Default = TRUE. |
If store_fit = FALSE only the fractal dimension is returned. If store_fit = TRUE
the parameters of the linear model used to estimate the fractal dimension and
a table containing the number of boxes (i.e. voxels) at any resolution are
returned in a list in addition to the fractal dimension.
Seidel, D. (2018). A holistic approach to determine tree structural complexity based on laser scanning data and fractal analysis. Ecology and evolution, 8(1), 128-134.
#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- box counting FD = VoxR::box_counting(tls,store_fit = TRUE) #- fractal dimension FD$fractal_dim #- linear model fit FD$fit_summary #- plot fit plot(log(FD$fit_table$N)~log(1/FD$fit_table$res)) abline(FD$fit_summary)#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- box counting FD = VoxR::box_counting(tls,store_fit = TRUE) #- fractal dimension FD$fractal_dim #- linear model fit FD$fit_summary #- plot fit plot(log(FD$fit_table$N)~log(1/FD$fit_table$res)) abline(FD$fit_summary)
Clustering objects with non common points: two points located within a user defined distance from each other are considered as the parts of a unique object. This function is well suited to be applied to the outputs of the substract_point_clouds function.
Clustering objects with non common points: two points located within a user defined distance from each other are considered as the parts of a unique object. This function is well suited to be applied to the outputs of the substract_point_clouds function.
distance_clustering(data, d_clust, method, C_size, message) distance_clustering(data, d_clust, method, C_size, message)distance_clustering(data, d_clust, method, C_size, message) distance_clustering(data, d_clust, method, C_size, message)
data |
a data.frame or data.table containing the x, y, z, ... coordinates of a point cloud or voxel cloud. |
d_clust |
numeric. The distance required to consider two points as being part of two different clusters. Default = 0.02. |
method |
character. The algorithm to use for clustering. Can be either "D_mat" or "Iter", see details. Default = "D_mat". |
C_size |
(optional) numeric. If |
message |
logical. If FALSE, messages are disabled. Default = TRUE. |
If method == "D_mat" the clustering process is based on building a matrix distance. This is time efficient but use a lot of memory.
If method == "Iter" a slower but memory efficient iterative process is used. In some cases, D_clust can help to speed up the process.
The input data with an additionnal field containing the cluster ID.
The input data with an additionnal field containing the cluster ID.
Lecigne, B., Delagrange, S., & Messier, C. (2018). Exploring trees in three dimensions: VoxR, a novel voxel-based R package dedicated to analysing the complex arrangement of tree crowns. Annals of botany, 121(4), 589-601.
#- import datasets t0=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) t1=data.table::fread(system.file("extdata", "Tree_t1.asc", package="VoxR")) #- keep only the tree crown t0 = t0[z>=0,] t1 = t1[z>=0,] #- substract t0 to t1 with the hull method diff = VoxR::substract_point_clouds(t0 = t0,t1 = t1, method = "hull") #- clustering the difference between t0 and t1 clust = VoxR::distance_clustering(diff,d_clust = 0.03) #- plot the result (NOTE that colors are redundant) rgl::open3d() rgl::plot3d(clust,col=clust$cluster,add=TRUE) #- import datasets t0=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) t1=data.table::fread(system.file("extdata", "Tree_t1.asc", package="VoxR")) #- keep only the tree crown t0 = t0[z>=0,] t1 = t1[z>=0,] #- substract t0 to t1 with the hull method diff = VoxR::substract_point_clouds(t0 = t0,t1 = t1, method = "hull") #- clustering the difference between t0 and t1 with the matrix distance based method clust = VoxR::distance_clustering(diff,d_clust = 0.03) #- plot the result (NOTE that colors are redundant) rgl::open3d() rgl::plot3d(clust,col=clust$cluster,add=TRUE) #- clustering the difference between t0 and t1 with the iterative method clust = VoxR::distance_clustering(diff,d_clust = 0.03,method = "Iter") #- plot the result (NOTE that colors are redundant) rgl::open3d() rgl::plot3d(clust,col=clust$cluster,add=TRUE) #- clustering the difference between t0 and t1 with the iterative method with maximum object size clust = VoxR::distance_clustering(diff,d_clust = 0.03,method = "Iter",C_size = 1) #- plot the result (NOTE that colors are redundant) rgl::open3d() rgl::plot3d(clust,col=clust$cluster,add=TRUE)#- import datasets t0=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) t1=data.table::fread(system.file("extdata", "Tree_t1.asc", package="VoxR")) #- keep only the tree crown t0 = t0[z>=0,] t1 = t1[z>=0,] #- substract t0 to t1 with the hull method diff = VoxR::substract_point_clouds(t0 = t0,t1 = t1, method = "hull") #- clustering the difference between t0 and t1 clust = VoxR::distance_clustering(diff,d_clust = 0.03) #- plot the result (NOTE that colors are redundant) rgl::open3d() rgl::plot3d(clust,col=clust$cluster,add=TRUE) #- import datasets t0=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) t1=data.table::fread(system.file("extdata", "Tree_t1.asc", package="VoxR")) #- keep only the tree crown t0 = t0[z>=0,] t1 = t1[z>=0,] #- substract t0 to t1 with the hull method diff = VoxR::substract_point_clouds(t0 = t0,t1 = t1, method = "hull") #- clustering the difference between t0 and t1 with the matrix distance based method clust = VoxR::distance_clustering(diff,d_clust = 0.03) #- plot the result (NOTE that colors are redundant) rgl::open3d() rgl::plot3d(clust,col=clust$cluster,add=TRUE) #- clustering the difference between t0 and t1 with the iterative method clust = VoxR::distance_clustering(diff,d_clust = 0.03,method = "Iter") #- plot the result (NOTE that colors are redundant) rgl::open3d() rgl::plot3d(clust,col=clust$cluster,add=TRUE) #- clustering the difference between t0 and t1 with the iterative method with maximum object size clust = VoxR::distance_clustering(diff,d_clust = 0.03,method = "Iter",C_size = 1) #- plot the result (NOTE that colors are redundant) rgl::open3d() rgl::plot3d(clust,col=clust$cluster,add=TRUE)
This function produces a filled voxel cloud of a tree, i.e. a voxels cloud within which
empty objects (e.g. trunk and large branches) are filled. The algorithm was inspired from
the one described by Vonderach et al. (2012) with some modifications. First, the point cloud is
is voxelized with a given (res) voxel resolution. The voxel cloud is then sliced into one voxel
tick layers. Within a single layer different objects are then clustered based on their
distance to each other (see the distance_clustering function for more details). Each
cluster is then filled by addind voxels along the range of Y for each X value of the cluster and reversly along
the range of X for each Y of the cluster. All unique voxels are then returned.
filled_voxel_cloud(data, res, d_clust, estimate_volume, message)filled_voxel_cloud(data, res, d_clust, estimate_volume, message)
data |
a data.frame or data.table containing the x, y, z, ... coordinates of a point cloud. |
res |
numeric. Resolution of a voxel. |
d_clust |
numeric. The distance to use for clustering, see the distance_clustering for more details. |
estimate_volume |
logical. If TRUE the tree volume is computed as done in Vonderach et al. (2012). |
message |
logical. If FALSE, messages are disabled. Default = TRUE. |
If estimate_volume = FLASE a data.frame or data.table containing the voxels coordinates is returned. If
estimate_volume = TRUE a list containing the voxels coordinates and the estimated tree volume
is returned.
Vonderach, C., Voegtle, T., & Adler, P. (2012). Voxel-based approach for estimating urban tree volume from terrestrial laser scanning data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 39, 451-456.
#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t1.asc", package="VoxR")) #- keep the tree trunk tls=tls[z<=0] #- run filled voxel voxelisation filled = VoxR::filled_voxel_cloud(tls,0.02) #- run usual voxelisation voxels = VoxR::vox(tls,0.02) #- compare filled voxel cloud to empty voxel cloud VoxR::plot_voxels(filled,res = 0.02) VoxR::plot_voxels(voxels,res = 0.02) #- compare the volume estimate from Vonderach et al. 2012 to estimate based on voxel volume #- run filled voxel voxelisation with volume estimation filled = VoxR::filled_voxel_cloud(tls,0.01,estimate_volume = TRUE) #- compare volumes filled$estimated_volume # Vonderach nrow(filled$filled_voxels)*0.01^3 # voxel based#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t1.asc", package="VoxR")) #- keep the tree trunk tls=tls[z<=0] #- run filled voxel voxelisation filled = VoxR::filled_voxel_cloud(tls,0.02) #- run usual voxelisation voxels = VoxR::vox(tls,0.02) #- compare filled voxel cloud to empty voxel cloud VoxR::plot_voxels(filled,res = 0.02) VoxR::plot_voxels(voxels,res = 0.02) #- compare the volume estimate from Vonderach et al. 2012 to estimate based on voxel volume #- run filled voxel voxelisation with volume estimation filled = VoxR::filled_voxel_cloud(tls,0.01,estimate_volume = TRUE) #- compare volumes filled$estimated_volume # Vonderach nrow(filled$filled_voxels)*0.01^3 # voxel based
Implements the Statistical Outliers Removal (SOR) filter available in
CloudCompare.
Computes the distance of each point to its k nearest neighbours and considers
a point as noise if it is further than the average distance (for the entire point cloud)
plus sigma times the standard deviation away from other points.
filter_noise(data, k, sigma, store_noise, message)filter_noise(data, k, sigma, store_noise, message)
data |
a data.frame or data.table containing the x, y, z, ... coordinates of a point cloud. |
k |
numeric. The number of nearest neighbours to use. Default = 5. |
sigma |
numeric. The multiplier of standard deviation to consider a point as noise. Default = 1.5. |
store_noise |
logical. Should the noisy points be retained ? Default = FALSE. |
message |
logical. If FALSE, messages are disabled. Default = TRUE. |
If store_noise = TRUE the input data is returned with an additional field ("Noise")
where points that are classified as noise points are labaled with 2 and the points not classified as noise are labeled as 1.
If store_noise = FALSE only the points that were not classified as noise are returned.
#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- run noise filter clean=VoxR::filter_noise(tls,store_noise = TRUE) #- plot the result (noise in red) rgl::open3d() rgl::plot3d(clean,col=clean$Noise,add=TRUE)#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- run noise filter clean=VoxR::filter_noise(tls,store_noise = TRUE) #- plot the result (noise in red) rgl::open3d() rgl::plot3d(clean,col=clean$Noise,add=TRUE)
Retains one point of the original point cloud within a voxel of given size.
filter_point_density(data, res, message)filter_point_density(data, res, message)
data |
a data.frame or data.table containing the x, y, z, ... coordinates of a point cloud. |
res |
numeric. The voxel resolution. |
message |
logical. If FALSE, messages are disabled. Default = TRUE. |
a data.frame or data.table with reduced point sensity.
#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- keep one point in 2cm voxels filtered=VoxR::filter_point_density(tls,0.02) rgl::open3d() rgl::plot3d(filtered,add=TRUE) #- keep one point in 10cm voxels filtered=VoxR::filter_point_density(tls,0.1) rgl::open3d() rgl::plot3d(filtered,add=TRUE)#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- keep one point in 2cm voxels filtered=VoxR::filter_point_density(tls,0.02) rgl::open3d() rgl::plot3d(filtered,add=TRUE) #- keep one point in 10cm voxels filtered=VoxR::filter_point_density(tls,0.1) rgl::open3d() rgl::plot3d(filtered,add=TRUE)
Deprecated function
level(...)level(...)
... |
parameters |
Deprecated function
obj.rec(...)obj.rec(...)
... |
parameters |
Visualization of a projected voxel cloud.
plot_projection(data, var, th, palette)plot_projection(data, var, th, palette)
data |
a data.frame or data.table containing the output of the project_voxels function: x, y, number of voxels, number of points and ratio of a projected voxel cloud. |
var |
character. The variable to plot: "nvox" for the number of voxels per pixel, "npts" for the number of points or "ratio" for the ratio npts/nvox. Default is "nvox". |
th |
numeric between 0 and 1. A quantile threshold that defines the maximum value of |
palette |
a color palette to use for plotting. |
Lecigne, B., Delagrange, S., & Messier, C. (2018). Exploring trees in three dimensions: VoxR, a novel voxel-based R package dedicated to analysing the complex arrangement of tree crowns. Annals of botany, 121(4), 589-601.
#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- voxelisation voxels = VoxR::vox(tls,0.05) #- project into the xy plan project = VoxR::project_voxels(voxels,"xy") #- plot the number of voxels VoxR::plot_projection(project,var = "nvox") #- plot the number of points VoxR::plot_projection(project,var = "npts") #- plot the ratio npts/nvox VoxR::plot_projection(project,var = "ratio") #- plot the number of voxels with different color palette VoxR::plot_projection(project,palette = terrain.colors) #- plot the number of voxels with a 95% percentile threshold VoxR::plot_projection(project,th = 0.95)#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- voxelisation voxels = VoxR::vox(tls,0.05) #- project into the xy plan project = VoxR::project_voxels(voxels,"xy") #- plot the number of voxels VoxR::plot_projection(project,var = "nvox") #- plot the number of points VoxR::plot_projection(project,var = "npts") #- plot the ratio npts/nvox VoxR::plot_projection(project,var = "ratio") #- plot the number of voxels with different color palette VoxR::plot_projection(project,palette = terrain.colors) #- plot the number of voxels with a 95% percentile threshold VoxR::plot_projection(project,th = 0.95)
Voxel cloud visualization.
plot_voxels(data, res, type, lcol, fcol, lwd, alpha, plot, message)plot_voxels(data, res, type, lcol, fcol, lwd, alpha, plot, message)
data |
a data.frame or data.table containing at least the voxel cloud x, y, z coordinates. |
res |
numeric. The voxel resolution. If not provided, the function will guess it. |
type |
character. How to represent a voxel ? If "w" only the voxel hedges are plotted, if "p" plain voxels are plotted, if "b" both hedges and plain voxels are plotted. Default = "b". |
lcol |
the line color for |
fcol |
the facets color for |
lwd |
numeric. The line width for |
alpha |
numeric. The transparency of the voxel faces for |
plot |
logical. Plot the voxels ? See return for mesh capture. Default = TRUE. |
message |
logical. Removes the message from the resolution guessing. Default = FALSE. |
If plot = TRUE, the 3D plot of voxels is plotted. At anytime the mesh object that enables
to plot the voxels can be captured and to be plotted using the
shade3d function from rgl.
The returned object is a list containing the 3D mesh of the voxel cloud and all additionnal fields
of the input data.
#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- voxelisation voxels = VoxR::vox(tls,res=0.05) #- plot the voxels VoxR::plot_voxels(voxels) #- capture the voxels mesh to plot with color scale ###- number of points in the voxel voxels_mesh = VoxR::plot_voxels(voxels,plot = FALSE) # capture the mesh colors=rev(rainbow(max(voxels_mesh$additionnal$npts),end=4/6)) # color scale rgl::open3d() rgl::shade3d(voxels_mesh$mesh,col=colors[round(voxels_mesh$additionnal$npts)] ,lit=FALSE,alpha=0.5) # plot ###- distance from the crow center # compute distnce voxels[,distance:=round(VoxR::point_distance(voxels[,1:3],c(mean(x),mean(y),mean(z)))*100)] voxels_mesh = VoxR::plot_voxels(voxels,plot = FALSE) # capture mesh cols=rev(rainbow(max(voxels_mesh$additionnal$distance),end=4/6)) # color scale rgl::open3d() rgl::shade3d(voxels_mesh$mesh,col=cols[round(voxels_mesh$additionnal$distance)] ,lit=FALSE,alpha=0.5) # plot#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- voxelisation voxels = VoxR::vox(tls,res=0.05) #- plot the voxels VoxR::plot_voxels(voxels) #- capture the voxels mesh to plot with color scale ###- number of points in the voxel voxels_mesh = VoxR::plot_voxels(voxels,plot = FALSE) # capture the mesh colors=rev(rainbow(max(voxels_mesh$additionnal$npts),end=4/6)) # color scale rgl::open3d() rgl::shade3d(voxels_mesh$mesh,col=colors[round(voxels_mesh$additionnal$npts)] ,lit=FALSE,alpha=0.5) # plot ###- distance from the crow center # compute distnce voxels[,distance:=round(VoxR::point_distance(voxels[,1:3],c(mean(x),mean(y),mean(z)))*100)] voxels_mesh = VoxR::plot_voxels(voxels,plot = FALSE) # capture mesh cols=rev(rainbow(max(voxels_mesh$additionnal$distance),end=4/6)) # color scale rgl::open3d() rgl::shade3d(voxels_mesh$mesh,col=cols[round(voxels_mesh$additionnal$distance)] ,lit=FALSE,alpha=0.5) # plot
Voxel cloud visualization when voxel cloud includes the empty voxels. Filled voxels are plotted as plain vertices and only the edges of empty voxels are plotted.
plot_voxels_full_grid(data, res, ecol, fcol, lwd, alpha, plot, message)plot_voxels_full_grid(data, res, ecol, fcol, lwd, alpha, plot, message)
data |
a data.frame or data.table containing at least the voxel cloud x, y, z coordinates. |
res |
numeric. The voxel resolution. If not provided, the function will guess it. |
ecol |
color for the edges of empty voxels. |
fcol |
color for the facets of filled voxels. |
lwd |
numeric. The line width for the edges of empty voxels. |
alpha |
numeric. The transparency of the voxel facets for filled voxels. |
plot |
logical. Plot the voxels ? See return for mesh capture. Default = TRUE. |
message |
logical. If FALSE removes the message from the resolution guessing. |
At anytime the mesh object that enables to plot the voxels can be captured to plot it using
the shade3d function from rgl. The returned object is a list containing
the 3D mesh of filled and empty voxels separately.
#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- voxelisation with full.grid option voxels=VoxR::vox(tls,0.3,full.grid = TRUE) #- plot the voxels VoxR::plot_voxels_full_grid(voxels)#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- voxelisation with full.grid option voxels=VoxR::vox(tls,0.3,full.grid = TRUE) #- plot the voxels VoxR::plot_voxels_full_grid(voxels)
Computes the distance of a set of points to a user defined point.
point_distance(data, point, message)point_distance(data, point, message)
data |
a data.frame or data.table containing the x, y, z, ... coordinates of a point cloud or voxel cloud. |
point |
a vector of length 3 containing the x, y and z coordintes of the reference point. |
message |
logical. If FALSE, messages are disabled. Default = TRUE. |
A vector containing the distance values of the points.
Lecigne, B., Delagrange, S., & Messier, C. (2018). Exploring trees in three dimensions: VoxR, a novel voxel-based R package dedicated to analysing the complex arrangement of tree crowns. Annals of botany, 121(4), 589-601.
#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- compute distance to the crown centre tls[,dist:=VoxR::point_distance(tls,c(mean(x),mean(y),mean(z)))] #- round distance values for visualization tls[,dist:=round(dist*100)] #- plot the distance to crown centre cols=rev(rainbow(max(tls$dist)+1,end=4/6)) # color scale rgl::open3d() rgl::plot3d(tls,col=cols[tls$dist+1],add=TRUE)#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- compute distance to the crown centre tls[,dist:=VoxR::point_distance(tls,c(mean(x),mean(y),mean(z)))] #- round distance values for visualization tls[,dist:=round(dist*100)] #- plot the distance to crown centre cols=rev(rainbow(max(tls$dist)+1,end=4/6)) # color scale rgl::open3d() rgl::plot3d(tls,col=cols[tls$dist+1],add=TRUE)
Deprecated function
point.distance(...)point.distance(...)
... |
parameters |
Deprecated function
project(...)project(...)
... |
parameters |
Project a voxel cloud in a 2D plan formed by two axes of the cartesian coordiantes system.
project_voxels(data, plan, message)project_voxels(data, plan, message)
data |
a data.frame or data.table containing the x, y, z, ... coordinates of a voxel cloud. |
plan |
character. Defines the projection plan: "xy", "xz" or "yz". Default = "xy". |
message |
logical. If FALSE, messages are disabled. Default = TRUE. |
A data frame of a 2D point cloud containing : x, y coordinates of the pixels and the number of voxels (nvox), number of points (npts), ratio npts/nvox contained in each pixel.
Lecigne, B., Delagrange, S., & Messier, C. (2018). Exploring trees in three dimensions: VoxR, a novel voxel-based R package dedicated to analysing the complex arrangement of tree crowns. Annals of botany, 121(4), 589-601.
#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- voxelisation voxels = VoxR::vox(tls,0.05) #- project into the xy plan project_xy = VoxR::project_voxels(voxels,"xy") VoxR::plot_projection(project_xy) # plot projection #- project into the xz plan project_xy = VoxR::project_voxels(voxels,"xz") VoxR::plot_projection(project_xy) # plot projection#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- voxelisation voxels = VoxR::vox(tls,0.05) #- project into the xy plan project_xy = VoxR::project_voxels(voxels,"xy") VoxR::plot_projection(project_xy) # plot projection #- project into the xz plan project_xy = VoxR::project_voxels(voxels,"xz") VoxR::plot_projection(project_xy) # plot projection
Deprecated function
raster.proj(...)raster.proj(...)
... |
parameters |
Deprecated function
sub.obj(...)sub.obj(...)
... |
parameters |
Identify the point that are unique to one of two point clouds to detect the changes that occured between two measuring times (e.g. growth, branches losses, branch motion). Two methods are available (see details).
substract_point_clouds(t0, t1, method, dist, message)substract_point_clouds(t0, t1, method, dist, message)
t0 |
a data.frame or data.table containing the x, y, z, ... coordinates of a point cloud or voxel cloud acquired at time 0. |
t1 |
a data.frame or data.table containing the x, y, z, ... coordinates of a point cloud or voxel cloud acquired at time 1. |
method |
character. The method to use to identify the difference between t0 and t1. Can be either "hull" or "distance", see details. |
dist |
numeric. The threshold distance to consider a point is unique to t1 if |
message |
logical. If FALSE, messages are disabled. Default = TRUE. |
If method = "hull", the convex hull that wraps t0 is constructed and the difference between t1 and t0 are
the points outside the convex hull. If method = "distance", the points in t1 that are distant (i.e. further than
dist) from the points in t0 are returned.
a data.frame or data.table containing the x, y, z, ... coordinates of points that are unique to t1.
t0 and t1 must be registered in the same coordinates system.
@references Lecigne, B., Delagrange, S., & Messier, C. (2018). Exploring trees in three dimensions: VoxR, a novel voxel-based R package dedicated to analysing the complex arrangement of tree crowns. Annals of botany, 121(4), 589-601.
#- import datasets t0=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) t1=data.table::fread(system.file("extdata", "Tree_t1.asc", package="VoxR")) #- keep only the tree crown t0 = t0[z>=0,] t1 = t1[z>=0,] ####- substract t0 to t1 with the hull method diff = VoxR::substract_point_clouds(t0 = t0,t1 = t1, method = "hull") #- plot the result (t0 in black, the difference between t1 and t0 in red) rgl::open3d() rgl::plot3d(t0,add=TRUE) rgl::plot3d(diff,col="red",add=TRUE) ####- substract t0 to t1 with the distance based method diff = substract_point_clouds(t0 = t0,t1 = t1, method = "distance",dist = 0.1) #- plot the result (t0 in black, the difference between t1 and t0 in red) rgl::open3d() rgl::plot3d(t0,add=TRUE) rgl::plot3d(diff,col="red",add=TRUE)#- import datasets t0=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) t1=data.table::fread(system.file("extdata", "Tree_t1.asc", package="VoxR")) #- keep only the tree crown t0 = t0[z>=0,] t1 = t1[z>=0,] ####- substract t0 to t1 with the hull method diff = VoxR::substract_point_clouds(t0 = t0,t1 = t1, method = "hull") #- plot the result (t0 in black, the difference between t1 and t0 in red) rgl::open3d() rgl::plot3d(t0,add=TRUE) rgl::plot3d(diff,col="red",add=TRUE) ####- substract t0 to t1 with the distance based method diff = substract_point_clouds(t0 = t0,t1 = t1, method = "distance",dist = 0.1) #- plot the result (t0 in black, the difference between t1 and t0 in red) rgl::open3d() rgl::plot3d(t0,add=TRUE) rgl::plot3d(diff,col="red",add=TRUE)
Deprecated function
surface(...)surface(...)
... |
parameters |
Estimates a set of morphological parameters from a tls point cloud of a tree.
tree_metrics( data, dbh, height, crown_diameter, crown_proj_area, volume, message )tree_metrics( data, dbh, height, crown_diameter, crown_proj_area, volume, message )
data |
a data.frame or data.table containing the x, y, z, ... coordinates of a point cloud. |
dbh |
numeric and optional. Estimate tree DBH ? |
height |
numeric and optional. Estimate tree height ? |
crown_diameter |
numeric and optional. Estimate tree average crown diameter ? |
crown_proj_area |
numeric and optional. Estimate tree crown projected area ? |
volume |
numeric and optional. Estimate tree volume ? |
message |
logical. If FALSE, messages are disabled. Default = TRUE. |
If none of dbh,height,crown_diameter,
crown_proj_area and volume are passed, all parameters are computed. However, the
user can select a set of parameters by declaring wich parameters should be computed (all other are not).
The tree DBH is estimated as the diameter of a circle fitted to the point cloud between 1.2m and 1.4m above the ground. The tree height is computed as the elevation difference between the lowest and the highest points of the point cloud. Two values are provided for crown parameter. First a 2D convex hull is used to identify the external points of the crown. Then, a first estimate of the crown diameter ("distant_points") is computed as the average distance of each point to the further point. A second estimate ("circle_fitting") correspond to the diameter of a circle fitted to the crown external points. The crown projected area is computed as the area of a 2D convex hull that wraps the projected crown. The volume is computed as the volume of a 3D convex hull that wraps the point cloud.
a list containing the estimated value for each parameter.
#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- compute all metrics VoxR::tree_metrics(tls) #- compute DBH only VoxR::tree_metrics(tls,dbh = TRUE) #- compute DBH and height VoxR::tree_metrics(tls,dbh = TRUE,height = TRUE)#- import tls data tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- compute all metrics VoxR::tree_metrics(tls) #- compute DBH only VoxR::tree_metrics(tls,dbh = TRUE) #- compute DBH and height VoxR::tree_metrics(tls,dbh = TRUE,height = TRUE)
Voxelisation of 3D point cloud recording the number of points within each voxels.
vox(data, res, full.grid, message)vox(data, res, full.grid, message)
data |
a data.frame or data.table containing the x, y, z, ... coordinates of a point cloud. |
res |
numeric. Resolution of a voxel. |
full.grid |
logical. If TRUE empty voxels contained in the tree bounding box are returned. If FALSE, only filled voxels are returned. Default = FALSE. |
message |
logical. If FALSE, messages are disabled. Default = TRUE. |
A data.frame or data.table containing the x, y, z coordinates of the voxel center and the number of points within each voxel of a voxel cloud.
Lecigne, B., Delagrange, S., & Messier, C. (2018). Exploring trees in three dimensions: VoxR, a novel voxel-based R package dedicated to analysing the complex arrangement of tree crowns. Annals of botany, 121(4), 589-601.
#- import file tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- resolution = 0.02m voxels_002 = VoxR::vox(tls,res=0.02) # voxelisation VoxR::plot_voxels(voxels_002) # voxels plot #- resolution = 0.2m voxels_02 = VoxR::vox(tls,res=0.2) # voxelisation VoxR::plot_voxels(voxels_02) # voxels plot#- import file tls=data.table::fread(system.file("extdata", "Tree_t0.asc", package="VoxR")) #- resolution = 0.02m voxels_002 = VoxR::vox(tls,res=0.02) # voxelisation VoxR::plot_voxels(voxels_002) # voxels plot #- resolution = 0.2m voxels_02 = VoxR::vox(tls,res=0.2) # voxelisation VoxR::plot_voxels(voxels_02) # voxels plot