LW_segmentation_dbscan: Computes indices for leaf / wood segmentation

In Blecigne/lidUrb: Terrestrial and Mobile Laser Scaning processing

Computes indices for leaf / wood segmentation

Description

This function implements the method presented in Wan et al. 2020 but replace the connected component algorithm by a dbscan clustering.

Usage

LW_segmentation_dbscan(
  las,
  k = 10L,
  search_radius = 0.05,
  dbscan_eps = 0.03,
  min_cluster_size = 10L,
  reclass_trunk_th = 0.5
)

Arguments

las	a LAS file.
k	integer. The number of nearest neighbors to use in the geometric features computation
search_radius	numeric. The searching distance to compute geometric features. Note that 2*search_radius is used to compute linearity that which provides better estimates.
dbscan_eps	numeric. Sets the esp parameter to pass to the dbscan function.
min_cluster_size	integer. The minimal size of a cluster required to compute the indices. Points located in small clusters recieve a p_wood and SoD of their nearest well classified point.
reclass_trunk_th	numeric. The minimal Threshold to reclass the lower clusters (i.e. the clusters with at least one point with Z <= reclass_trunk_th) as wood, i.e. a p_wood = SoD = 1. A negative value desable the reclassification.

Value

a LAS with two additionnal fields, p_wood and SoD, corresponding to a wood classification index. p_wood was introduced in Wang et al. 2019 and range from 0 to 1 with 1 being very high probability of wood. SoD was introduced in Wan et al. 2020 and range between -1 and 1, higher probability of wood class close to 1.

References

• Wang, D., Momo Takoudjou, S., & Casella, E. (2020). LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR. Methods in Ecology and Evolution, 11(3), 376-389.

• Wan, P., Zhang, W., Jin, S., Wang, T., Yang, S., & Yan, G. (2020). Plot-level wood-leaf separation of trees using terrestrial LiDAR data based on a segmentwise geometric feature classification method. Methods in Ecology and Evolution, 12, 2473–2486.

Examples

# import the data
file = system.file("extdata", "tree_leaves.las", package="lidUrb")
las = lidR::readLAS(file)

# clean and reduce point density
las = lidUrb::filter_noise(las,k = 6L, sigma = 0.8)
las = lidUrb::reduce_point_density(las,0.02)

# compute two criterias for wood and leaves classification
las = LW_segmentation_dbscan(las)

# plot the two wood indexes criterias
lidR::plot(las,color = "p_wood",legend = TRUE)
lidR::plot(las,color = "SoD",legend = TRUE)

# assign a class base on p_wood
las@data[,wood := as.numeric(p_wood >= 0.9)]
# plot the hard classification
lidR::plot(las,color="wood",size=2,colorPalette = c("chartreuse4","cornsilk2"))

# assign a class base on p_wood
las@data[,wood := as.numeric(SoD >= 0.99)]
# plot the hard classification
lidR::plot(las,color="wood",size=2,colorPalette = c("chartreuse4","cornsilk2"))

Blecigne/lidUrb documentation built on Feb. 19, 2024, 9:12 a.m.