Description Usage Arguments Value References Examples
Computes the horizontal visibility in a viewshed defined as a disk.
1 2 3 4 5 6 7 | h_visibility(
data,
position = c(0, 0, 0),
layer_tickness = 0.1,
angular_res = 1,
scene_radius
)
|
data |
LAS class object containing the xyz coordinates of a 3D point cloud. |
position |
vector of length 3 containing the xyz coordinates of the animal location. Default = c(0,0,0). |
layer_tickness |
numeric. The thickness of the disk that defines the viewshed. |
angular_res |
numeric. The angular resolution of a single sightline. Default = 1. |
scene_radius |
(optional) numeric. Defines the radius of the scene relative to the animal position. Can be used to apply a cut-off distance to visibility analyses. |
A list containing a data.table of the visibility
($visibility
) as a function of distance to the animal location (r), a data.table of
the viewshed ($viewshed
) defined as the radius (r) for each azimuth (phi) with non occluded
sightlines distance set to scene_radius, a data.table ($vegetation_distance
) of the vegetation distance (r)
in each azimuth (phi) for occluded sightlines only and different viewshed metrics ($metrics
).
The metrics are: the viewshed fractal dimension (computed from Chen, 2020), the
viewshed area (i.e. the visible area), the proportion visible (the area visible
/ potential area visible), the viewshed coefficient (the area under the curve of
visibility as a function of distance) and the vegetation fractal dimension.
Chen, Y. (2020). Two Sets of Simple Formulae to Estimating Fractal Dimension of Irregular Boundaries. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/7528703
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | #- import the tree_line_plot dataset
file <- system.file("extdata", "tree_line_plot.laz", package="viewshed3d")
tls <- lidR::readLAS(file)
center <- c(0,0,2) # defines the scene center for the entire process
angle <- 1 # defines the angular resolution for the entire process
#- remove noise to avoid visibility estimates error
tls_clean <- viewshed3d::denoise_scene(tls,method="sd",
filter=6)
#- compute the horizontal visibility.
view.data <- viewshed3d::h_visibility(data = tls_clean,
position = center,
angular_res = angle,
scene_radius = 17)
# viewshed metrics
view.data$metrics
# plot the view sheds in a radial plot
plotrix::radial.plot(view.data$viewshed$r,rp.type = "p",poly.col = "blue",
radial.lim = c(0,max(view.data$viewshed$r)))
|
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