Digital Surface Model Algorithm
This function is made to be used in rasterize_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)
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).na.fill: 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 rasterize_terrain for more details).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 <- rasterize_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 <- rasterize_canopy(las, res = 0.5, p2r(0.2)) plot(chm, col = col) ## Not run: chm <- rasterize_canopy(las, res = 0.5, p2r(0.2, na.fill = tin())) plot(chm, col = col) ## End(Not run)
Other digital surface model algorithms: dsm_pitfree, dsm_tin