viewshed3d (version 3.2.0)

viewsheds: Computes cumulated viewsheds

Description

Computes cumulated viewsheds within a 3D point cloud and return a voxel cloud accounting for the number of times each voxel was visible.

Usage

viewsheds(data, positions, angular_res, vox_res, cut_off, pb)

Arguments

data

LAS class object containing the xyx coordinates of a 3D point cloud.

positions

data.frame or data.table with 3 columns containing the xyz coordinates of the animal locations from which the viewsheds will be computed.

angular_res

numeric. The angular resolution of a single sightline. Default = 1.

vox_res

numeric. The resolution of the output voxel cloud. Default = 0.2.

cut_off

(optional) numeric. Defines a cut-off distance for each individual viewshed. Speeds up the process when viewsheds is applied to big datasets.

pb

logical. If FALSE, desables the progress bar.

Value

A LAS class object containing the coordinates of the voxel cloud (X, Y, Z), and the number of times each voxel was visible from any position (N_visible).

Details

Sightline directions in each viewshed are computed from the method described by Malkin (2016). This ensures that every sightline explores a similar portion of the 3d space.

References

Malkin, Z. (2016). A new method to subdivide a spherical surface into equal-area cells. arXiv:1612.03467.

Examples

# NOT RUN {
#- import the tree_line_plot dataset
file = system.file("extdata", "tree_line_plot.laz", package="viewshed3d")
tls = lidR::readLAS(file,select="xyz")

#- remove noise to avoid visibility estimates error
tls_clean <- viewshed3d::denoise_scene(tls,method="sd",
                                       filter=6)

#- RECONSTRUCT THE GROUND
#- classify ground points
class=lidR::classify_ground(tls_clean, lidR::csf(rigidness = 1L,
                                           class_threshold = 0.1,
                                           sloop_smooth = TRUE), FALSE)

#- reconstruct the ground. No need for a very fine ground reconstruction.
ground = lidR::grid_terrain(class, 0.05, lidR::knnidw())

#- build the final scene
reconstructed = na.omit(raster::as.data.frame(ground, xy = TRUE))
names(reconstructed)=c("X","Y","Z")
recons=rbind(lidR::LAS(na.omit(reconstructed)),tls_clean)

#- CREATE THE POSITIONS WITH RANDOM POINTS
N_positions = 10 #- how many points ?
height = 2 #- points height relative to the ground
positions=data.table::data.table(reconstructed[runif(N_positions,
                                               1,nrow(reconstructed)),])
positions[,Z:=Z+height]

#- compute the cumulated viewsheds from the positions
cumulated=viewshed3d::viewsheds(data=recons,
                                positions = positions ,
                                angular_res = 1,
                                vox_res = 0.2)

#- plot the result
x=lidR::plot(cumulated,color="N_visible",size=3,
             colorPalette=viridis::cividis(nrow(positions)+1),trim=6)

#- add the positions
lidR::add_treetops3d(x,sp::SpatialPointsDataFrame(positions,positions),
                     radius=0.5,col="red",add=TRUE)
# }