In these exercises the most basic functions of the rlas and lidR R packages by Jean-Romain are demonstrated. See the documentation for these packages from rlas and lidR.
The materials in this repository are based mostly on the above mentioned documentation with some edits and new parts to adapt them to using these libraries in CSC's Puhti supercluster.
The original 2019 CSC's course page: Lidar data analysis in Taito, with PDAL and R The same material works in Puhti supercomputer as well.
The data for these excerises is basic NLS lidar data. In Puhti the lidar data can be found from /appl/data/geo/mml/laserkeilaus/2008_latest/
The original NLS lidar files might not workd with lidR, because of scale errors. For fixing this, use e.g. las2las
fix one file with:
las2las -i /scratch/<PROJECT>/mml/laserkeilaus/2008_17/2017/T522/1/T5224F1.laz -rescale 0.01 0.01 0.01 -auto_reoffset -o ~/outfolder/T5224F1.laz
all with (in same directory):
las2las -i ~/original_las_dir/*.laz -rescale 0.01 0.01 0.01 -auto_reoffset -olaz -odir ~/outdir/
The most up-to-date version of the exercises is in this repository. Download the contents of this repository as a zip file to your project's folders or your home folder in Puhti and unzip it with unzip R_lidar_2019.zip. Then connect to Puhti using NoMachine and start RStudio. Open the R_lidar.Rproj project from the r_exercise folder you just unziped.
Note that all the necessary software packages are already installed in the Puhti supercluster and thus their installation is not covered in these exercises. To see a description of the installed R spatial packages ready installed see: https://docs.csc.fi/apps/r-env/