grid_tincanopy
Canopy height model based on a triangulation.
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
- x
A LAS object
- res
numeric. Resolution of the canopy height model.
- thresholds
numeric. Set of height thresholds. If
thresholds = 0the 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 = 0no 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
LAScatalogthe 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 LAScatalog
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
# 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)
# }