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cran / FORTLS

:exclamation: This is a read-only mirror of the CRAN R package repository. FORTLS — Automatic Processing of Terrestrial-Based Technologies Point Cloud Data for Forestry Purposes. Homepage: https://github.com/Molina-Valero/FORTLS Report bugs for this package: https://github.com/Molina-Valero/FORTLS/issues
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Tree location issue in tree.detection function #1

Open ShingObt opened 3 years ago

ShingObt commented 3 years ago

I have a question related to tree.detection function in FORTLS.

I first normalized the TLS point cloud with normalize function. Then identified individual trees with tree.detection function. The code is as follows.

las.norm=normalize("Block1_plot1.las",save.result = TRUE)
id.tree=tree.detection(las.norm)

yet the XY coordinates of the individual trees do not coincide with the distribution of the normalized point cloud.

# randomly select 20,000 points from normalized point cloud.
id=sample(seq(1,dim(las.norm)[1]),20000)
x.sample=las.norm$x[id]
y.sample=las.norm$y[id]
plot(x.sample,y.sample)# plot XY

# overlay individual tree location acquired by tree.detection
points(id.tree$x,id.tree$y,col="red",cex=3)

Rplot Black points are the XY location of 20000 points in the normalized point cloud. Red points are the XY location of the trees. I expected that the tree location is close to the region with dense point cloud (thick black in the plot) but some of the tree locations are omitted and many tree centers are identified along the edge of the plot.

Could you let me know the cause of the spatial disagreement between the normalized point cloud and identified tree location?

For reference, I show the 3D plot of the point cloud. Screen Shot 2021-09-07 at 11 47 37 PM

Thank you for your help!

gaborcsardi commented 3 years ago

Hi, this is a read only mirror of CRAN, please see the package authors in the DESCRIPTION file. Look for Maintainer, BugReports and URL. Thanks!