grid_tincanopy: Canopy height model based on a triangulation.

Description

Canopy height model based on a triangulation of first returns. Depending on the inputs this function computes a simple Delaunay triangulation of the first returns with a linear interpolation within each triangle. This function also enables the use of 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 reference).

Usage

grid_tincanopy(x, res = 0.5, thresholds = c(0, 2, 5, 10, 15),
  max_edge = c(0, 1), subcircle = 0, filter = "-keep_first")

Arguments

A LAS object

res

numeric. Resolution of the canopy height model.

thresholds

numeric. Set of height thresholds. If thresholds = 0 the algorithm is a strict rasterization of the triangulation of the first returns. However, if an array is passed to the function it becomes the Khosravipour et al. pit-free algorithm.

max_edge

numeric. Maximum edge-length of a triangle in the Delaunay triangulation. If a triangle has an edge greater than this value it will be removed. It is used to drive the pit-free algorithm (see reference) and to trim dummy interpolation on non-convex areas. The first number is the value for the classical triangulation (threshold = 0), the second number is the value for the pit-free algorithm for (thresholds > 0). If max_edge = 0 no trimming will be done.

subcircle

numeric. Radius of the circles. To obtain fewer pits the algorithm can replace each return with a circle composed of 8 points before computing the triangulation (see also grid_canopy).

filter

character. Streaming filter while reading the files (see readLAS). If the input is a LAScatalog the function readLAS is called internally. The user cannot manipulate the lidar data directly but can use streaming filters instead.

Value

Returns a data.table of class lasmetrics, which enables easier plotting and RasterLayer casting.

Use with a <code>LAScatalog</code>

When the parameter x is a LAScatalog the function processes the entire dataset in a continuous way using a multicore process. Parallel computing is set by default to the number of core available in the computer. A buffer is required. The user can modify the global options using the function catalog_options. lidR support .lax files. Computation speed will be significantly improved with a spatial index.

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.

Examples

Run this code

# NOT RUN {
LASfile = system.file("extdata", "Tree.laz", package="lidR")
las = readLAS(LASfile, Classification = FALSE, Intensity = FALSE, filter = "-drop_z_below 0")

# Basic triangulation and rasterization
chm1 = grid_tincanopy(las, thresholds = 0, max_edge = 0)

# Khosravipour et al. pitfree algorithm
chm2 = grid_tincanopy(las, thresholds = c(0,2,5,10,15), max_edge = c(0, 1.5))

plot(chm1)
plot(chm2)
# }

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