lidR (version 2.1.4)

pitfree: Digital Surface Model Algorithm

Description

This function is made to be used in grid_canopy. It implements the pit-free algorithm developed by Khosravipour et al. (2014), which is based on the computation of a set of classical triangulations at different heights (see references). The subcircle tweak replaces each point with 8 points around the original one. This allows for virtual 'emulation' of the fact that a lidar point is not a point as such, but more realistically a disc. This tweak densifies the point cloud and the resulting canopy model is smoother and contains fewer 'pits' and empty pixels.

Usage

pitfree(thresholds = c(0, 2, 5, 10, 15), max_edge = c(0, 1),
  subcircle = 0)

Arguments

thresholds

numeric. Set of height thresholds according to the Khosravipour et al. (2014) algorithm description (see references)

max_edge

numeric. Maximum edge length of a triangle in the Delaunay triangulation. If a triangle has an edge length greater than this value it will be removed. The first number is the value for the classical triangulation (threshold = 0, see also dsmtin), the second number is the value for the pit-free algorithm (for thresholds > 0). If max_edge = 0 no trimming is done (see examples).

subcircle

numeric. radius of the circles. To obtain fewer empty pixels the algorithm can replace each return with a circle composed of 8 points (see details).

References

Khosravipour, A., Skidmore, A. K., Isenburg, M., Wang, T., & Hussin, Y. A. (2014). Generating pit-free canopy height models from airborne lidar. Photogrammetric Engineering & Remote Sensing, 80(9), 863-872.

See Also

Other digital surface model algorithms: dsmtin, p2r

Examples

# NOT RUN {
LASfile <- system.file("extdata", "MixedConifer.laz", package="lidR")
las <- readLAS(LASfile)
col <- height.colors(50)

# Basic triangulation and rasterization of first returns
chm <- grid_canopy(las, res = 0.5, dsmtin())
plot(chm, col = col)

# Khosravipour et al. pitfree algorithm
chm <- grid_canopy(las, res = 0.5, pitfree(c(0,2,5,10,15), c(0, 1.5)))
plot(chm, col = col)

# }
# NOT RUN {
# Potentially complex concave subset of point cloud
x = c(481340, 481340, 481280, 481300, 481280, 481340)
y = c(3812940, 3813000, 3813000, 3812960, 3812940, 3812940)
las2 = lasclipPolygon(las,x,y)
plot(las2)

# Since the TIN interpolation is done within the convex hull of the point cloud
# dummy pixels are interpolated that are strictly correct according to the interpolation method
# used, but meaningless in our CHM
chm <- grid_canopy(las2, res = 0.5, pitfree())
plot(chm, col = col)

chm = grid_canopy(las2, res = 0.5, pitfree(max_edge = c(3, 1.5)))
plot(chm, col = col)
# }