Normalize LiDAR Data

Height-normalizes the point cloud with lidR::normalize_height: the terrain is interpolated from the ground points (class 2) and subtracted from every point, so Z becomes height above ground. Noise (class 7) is dropped when the tiles are read. The data must be ground-classified, so run Ground Classify Using CSF first if it isn't.

Parameter	Type	Required	Description
`in_las_folder`	directory	Yes	Folder containing the ground-classified LAS/LAZ tiles.
`out_folder`	directory (output)	Yes	Folder in which the `Normalized_PC/` subfolder is created.
`style`	`tin` \| `idw` \| `kriging`	Yes	Terrain interpolation algorithm: `tin()`, `knnidw()` or `kriging()`. The default is `tin`. An invalid value falls back to `tin`.
`processes`	int	No	Requested number of parallel workers.

Output: one <out_folder>/Normalized_PC/n_<tile>.las per input tile. Point Generate CHM or DSM from LiDAR Data at the Normalized_PC folder to get a CHM.

from beratools.tools.rpy_normalized_PC import rpy_normalized_PC

rpy_normalized_PC(in_las_folder="tests/data/LiDAR",
                  out_folder="tests/data/LiDAR",
                  style="tin")

Command line

bt normalize   # show the parameters
bt normalize --in_las_folder <las_dir> --out_folder <out_dir> --style tin