lidR | R package for airborne LiDAR data manipulation | Navigation library

I have a question regarding to the TIN method used in LidR. My point cloud data is look like this figure. When I compute DSM using TIN method, the height value is not at the highest peak of first return. Does the value used in triangulation is an average value from several first return within one pixel? Is there any ways to retrieve the DSM from the highest first return using LidR or another software? Thank you.

• Michael -

I am expecting to retrieve clarification about the TIN algorithm used to generate DSM in LidR package.

Lidar data is simply 3d coordinates, usually in las file format. Сontent example

To get a circular subset of a las-dataset with specific area, I would like to use lidR::clip_circular(). To do so, I first calculate the central point of my las-dataset, then I define the radius I want to use, to get a subset of exactly 500m^2 from the centroid of my las-dataset. The operation works and does not throw any error, the result however is not correct, see base::print() at the end of my short code snippet.

I have tried to use lidR::clip_roi() as well, providing a polygon representing my region of interest, but got the same, incorrect result. Now I do not have any clue how to go on. I could imagine, that it is about the crs I am using (EPSG:25832) or because the area is circular and not rectangular...

The lidR package has a neat way to work with huge (pointcloud) datasets: The catalog function (doc here) avoids loading the dataset to memory and can treat mosaics [datasets that are spread across multiple (non-overlapping) tiles] as single dataset. It loads required tiles on-the-fly during computations in an intelligent way. It is great to avoid working with huge files (multiple GBs) and keep memory requirements lean if only working with small parts of the dataset.

Is there a similar convenient/memory-efficient/"lidR-catalog-way" to process large raster mosaics in R? Or more put in a more general way: Is there a way to work with mosaic raster datasets in R without merging them first?

I am aware of the mosaic (doc) and merge functions, which allow me to merge my tiled raster mosaic into a single raster dataset. I also found that gdal will do so a lot faster and memory efficient. Here is a R-snippet for this:

I created a local package with personal functions to be easily used within R. One of these is aimed to be used in the lidR package within a wrapper function (i.e. grid_metrics). For this reason I took the scheme of this script as a reference, exporting both the long name (e.g. my_metrics(param1, param2,...)) and the lazy one (e.g. .my_metrics), because I really like its ease of use.

Nevertheless, if I load my package and then call the lazy function

I have large point clouds (each with several million points) and want to compute geometric attributes for all points. In order to do so, I need to calculate eigenvalues. How can I do this as quickly as possible? My data is stored in *.las files and I read them in with the package lidR. I also use this package to calculate point metrics. According to this post, I implemented this version:

Is there a way to export a *.las point cloud in R to a orthomosaic? I loaded my las-file containing the points with the package lidR. I want to export a tif which shows the point cloud from above in RGB, similar to what an orthophoto would look like. The data was collected using a terrestrial laser scanner.

point cloud

this is what I want

I have a possibility to do processing on a HPC environment, where task managing and resource allocation are controlled by SLURM batch job system. However, I have not found the right configurations how to utilize the allocated resources within R efficiently. I have tried to allocate 20 CPU's to one task in SLURM, using plan(multicore) -function of future package in R. After running test runs with different count of CPUs allocated, efficiency statistics suggested that with these settings only one of the CPU's allocated was used during the test runs.

Slurm bash script presented below

Is there a way to change the projection (like st_transform from sf package) of LiDAR point cloud while remaining in R?

With lidR package you can set but not modify the point cloud projection. I know las2las from lastools could do the job (and this part is open) but I would like to keep it simple, within R and without hard copying the input/output of this process.

Any advice?