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I'm still fairly new to using LiDAR data and R, so please let me know if this isn't clear.

I am using the lidR package to segment individual trees over a large landscape. I have been using the catalog function to process the data in reasonable chunks, and have created and saved the CHMs for each chunk.

What I can't figure out is how to use the lastrees function on the catalog and call the correct CHM to use with the algorithm. Is there a way to do this within the package, maybe using catalog apply (still haven't figured that out), or am I better off attacking it some other way?

If there is a way to process the CHMs to make this easier, I'm willing to try that as well!

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    Actually there is no built-in function (yet) to segment trees over large landscape in lidR because it is more complex to do than it might appear. Moreover your question is not related to point-cloud processing but more to raster processing and yet you will find almost no tool in lidR for that. – JRR May 22 at 11:37
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    There is nothing impossible actually but you should first mention what kind of output you are expecting. Do you want a raster-based tree segmentation or a point-cloud-based segmentation? I guess the first one and consequently you should study how to process large raster with raster instead of large point cloud with lidR – JRR May 22 at 11:42
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    @Andre Silva the difficulty come with the edge of the processed chunks. Processing the dataset into independent chunks implies that there is no easy wall-to-wall continuity. At the edge of a chunk you will have the first half of a tree labelled 123 and the second half of the same tree from another chunk will be labelled 456. Wall-to-wall continuity is the reason why lastrees does not have a LAscatalog version yet. – JRR May 28 at 15:37
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In order to answer, let’s put aside important, but broad issues:

  • The fact that identifying and segmenting trees is a very complex analysis which depends on many things (things related to the type of vegetation, and quality and amount of available data, for example).
  • That processing 'large point clouds' in R is a real concern (due to memory limitation), and still depends on how large the study area is, how large the point cloud is; the goal of the analysis; if the point cloud can be preliminarily thinned, filtered, spatially sampled; the hardware; etc.
  • That the question is not entirely clear; if working with a catalog and using CHMs are mandatory; the context of the analysis; type of output; reproducible code, etc.

Regarding using lastrees with a catalog, as explained by JRR, it is not currently possible (version 2.0.3):

the difficulty come with the edge of the processed chunks. Processing the dataset into independent chunks implies that there is no easy wall-to-wall continuity. At the edge of a chunk you will have the first half of a tree labelled 123 and the second half of the same tree from another chunk will be labelled 456. Wall-to-wall continuity is the reason why lastrees does not have a LAscatalog version yet.

Using a CHM with lastrees is possible as long as one chooses a segmentation algorithm which takes it as an argument. For example, silva2016 (not me), dalponte2016, watershed, etc. The following is a well crafted answer about this: Exporting crown boundaries from tree segmentation in R?. Also, carefully read the package's documentation. It will explain that such algorithms can be run independent of the point cloud; i.e., at most, the LiDAR data can be used to register the results from the CHM-based segmentation.

And since this is a subject which still is in the academic frontier, to deepen exploring other possibilities, the scientific literature is our friend. For example, see the articles recommended in Extracting tree crown areas from remote sensing data (visual images and LiDAR).

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    Thank you very much, there was a lot I was unfamiliar with and this has cleared things up- the lack of clarity with the question was certainly due to my fundamental misunderstandings of the process with the edges. Thank you! – KLH May 29 at 0:57

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