lidR (version 1.2.1)

grid_canopy: Canopy surface model

Description

Creates a canopy surface model using a LiDAR point cloud. For each pixel the function returns the highest point found. This basic method could be improved by replacing each LiDAR return with a small disk. An interpolation for empty pixels is also available.

Usage

grid_canopy(.las, res = 2, subcircle = 0, na.fill = "none", ...)

Arguments

.las

An object of class LAS

res

numeric. The size of a grid cell in LiDAR data coordinates units. Default is 2 meters i.e. 4 square meters.

subcircle

numeric radius of the circles. To obtain fewer empty pixels the algorithm can replace each return with a circle composed of 8 points before computing the maximum elevation in each pixel.

na.fill

character. name of the algorithm used to interpolate the data and fill the empty pixels. Can be "knnidw", "delaunay" or "kriging" (see details).

...

extra parameters for the algorithm used to interpolate the empty pixels (see details)

Value

It returns a data.table with the class lasmetrics, which enables easier plotting and RasterLayer casting.

Details

The algorithm relies on the 'local maximum'. For each pixel the function returns the highest point found. This method implies that the resulting surface model can contain empty pixels. Those 'holes' can be filled by interpolation. Internally, the interpolation is based on the same method used in the function grid_terrain. Therefore the documentation for grid_terrain is also applicable to this function. (see also examples)

See Also

grid_metrics as.raster

Other grid_alias: grid_density

Examples

# NOT RUN {
LASfile <- system.file("extdata", "Megaplot.laz", package="lidR")
lidar = readLAS(LASfile)

# Local maximum algorithm with a resolution of 2 meters
lidar %>% grid_canopy(2) %>% plot

# Local maximum algorithm with a resolution of 1 meter replacing each
# point by a 20 cm radius circle of 8 points
lidar %>% grid_canopy(1, 0.2) %>% plot

# Local maximum algorithm with a resolution of 1 meter replacing each
# point by a 10 cm radius circle of 8 points and interpolating the empty
# pixels using the 3-nearest neighbours and an inverse-distance weighting.
grid_canopy (lidar, 1, subcircle = 0.1, na.fill = "knnidw", k = 3) %>% plot

# }
# NOT RUN {
grid_canopy(lidar, 1, na.fill = "knnidw", k = 3) %>% plot
grid_canopy(lidar, 1, subcircle = 0.1, na.fill = "delaunay") %>% plot
# }