Generate CHM or DSM from LiDAR Data

Builds a height surface from the point cloud with lidR::rasterize_canopy. The tool decides what to produce by calling detect_normalized, which reads the first LAS/LAZ file in the folder:

If the mean |Z| of the ground points (class 2) is below 1, the data counts as height-normalized and the tool produces a CHM.
Otherwise, including when there are no ground points, the tool produces a DSM.

Parameter	Type	Required	Description
`in_las_folder`	directory	Yes	Folder containing the input LAS/LAZ tiles.
`out_folder`	directory (output)	Yes	Folder for the output rasters.
`style`	`tin` \| `pitfree`	Yes	Surface algorithm. The default is `tin`. An invalid value falls back to `tin`.
`processes`	int	No	Requested number of parallel workers.

`style`	lidR algorithm	Tile output	Merged output
`tin`	`dsmtin(max_edge = 3 × cell_size)`	`<tile>_TIN_CHM.tif` / `<tile>_TIN_DSM.tif`	`Merged_TIN_CHM.tif` / `Merged_TIN_DSM.tif`
`pitfree`	`pitfree(thresholds = c(0, 3, 5, 10, 15, 20), max_edge = c(0, 3 × cell_size), subcircle = cell_size)`. Overlap points are also dropped.	`<tile>_PitF_CHM.tif` / `<tile>_PitF_DSM.tif`	`Merged_PitF_CHM.tif` / `Merged_PitF_DSM.tif`

from beratools.tools.rpy_CHM_DSM_by import rpy_chm_dsm

# DSM from the original tiles
rpy_chm_dsm(in_las_folder="tests/data/LiDAR",
            out_folder="tests/data/LiDAR/Raster_Output",
            style="pitfree")

# CHM from the output of Normalize LiDAR Data
rpy_chm_dsm(in_las_folder="tests/data/LiDAR/Normalized_PC",
            out_folder="tests/data/LiDAR/Raster_Output",
            style="tin")

Command line

bt chm_dsm   # show the parameters
bt chm_dsm --in_las_folder <las_dir> --out_folder <out_dir> --style pitfree