Digital Surface Model Algorithm

This function is made to be used in grid_canopy. It implements an algorithm for digital surface model computation based on a points-to-raster method: for each pixel of the output raster the function attributes the height of the highest point found. 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.

p2r(subcircle = 0, na.fill = NULL)

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).


function. A function that implements an algorithm to compute spatial interpolation to fill the empty pixel often left by points-to-raster methods. lidR has knnidw, tin, and kriging (see also grid_terrain for more details).

See Also

Other digital surface model algorithms: dsmtin, pitfree

  • p2r
LASfile <- system.file("extdata", "MixedConifer.laz", package="lidR")
las <- readLAS(LASfile)
col <- height.colors(50)

# Points-to-raster algorithm with a resolution of 1 meter
chm <- grid_canopy(las, res = 1, p2r())
plot(chm, col = col)

# Points-to-raster algorithm with a resolution of 0.5 meters replacing each
# point by a 20 cm radius circle of 8 points
chm <- grid_canopy(las, res = 0.5, p2r(0.2))
plot(chm, col = col)

# }
chm <- grid_canopy(las, res = 0.5, p2r(0.2, na.fill = tin()))
plot(chm, col = col)
# }
Documentation reproduced from package lidR, version 2.1.0, License: GPL-3

Community examples

Looks like there are no examples yet.