Individual tree detection function that find the position of the trees using several possible algorithms.
find_trees(las, algorithm, uniqueness = "incremental")An object of class LAS or LAScatalog. Can also be a RasterLayer
representing a canopy height model, in which case it is processed like a regularly-spaced point cloud.
An algorithm for individual tree detection. lidR has: lmf and manual. More experimental algorithms may be found in the package lidRplugins.
character. A method to compute a unique ID. Can be 'incremental', 'gpstime' or 'bitmerge'. See section 'Uniqueness'. This feature must be considered as 'experimental'.
A SpatialPointsDataFrame with an attribute Z for the tree tops and treeID with an individual ID for each tree.
By default the tree IDs are numbered from 1 to n, n being the number of trees found. The problem
with such incremental numbering is that, while it ensures a unique ID is assigned for each tree in
a given point-cloud, it also guarantees duplication of tree IDs in different tiles or chunks when
processing a LAScatalog. This is because each file is processed independently of the others and potentially
in parallel on different computers. Thus, the index always restarts at 1 on each file or chunk. Worse,
in a tree segmentation process, a tree that is located exactly between 2 files will have two different
IDs for its two halves.
This is why we introduced some uniqueness strategies that are all imperfect and that should be seen as experimental. Please report any troubleshooting. Using a uniqueness-safe strategy ensures that trees from different files will not share the same IDs. Moreover, it also means that two halves of a tree on the edge of a processing chunk will be assigned the same ID.
Number from 0 to n. This method does not ensure uniqueness of the IDs. This is the legacy method.
This method uses the gpstime of the highest point of a tree (apex) to create a unique ID. This ID is not an integer but a 64-bit decimal number which is suboptimal but at least it is exepected to be unique if the gpstime attribute is consistent across files. If inconsistencies with gpstime are reported (for example gpstime records the week time and was reset to 0 in a coverage that takes more than a week to complete), there is a (low) probability to get ID attribution errors.
This method uses the XY coordinates of the highest point (apex) of a tree to create a single number with a bitwise operation. First, XY coordinates are converted to integers using the scales and offsets of the point-cloud. Then the ID is computed with X * 2^32 + Y to combine twice the 32-bits of information into a 64-bit number. For example, if the apex is at (10.32, 25.64) with a scale factor of 0.01 and an offset of 0, the integer coordinates are X = 1032 and Y = 2564 and the ID is 4432406252036. Such methods return a 64-bit integer but because 64-bit integers do not exist in R it is converted to a 64-bit decimal number that is guaranteed to be unique if all files have the same offsets and scale factors.
All the proposed options are suboptimal because they either do not guarantee uniqueness in all cases (inconsistencies in the collection of files), or they imply that IDs are based on non-integers or meaningless numbers. But at the very least we expect this to work for simple cases.
Supported processing options for a LAScatalog (in bold). For more details see the
LAScatalog engine documentation:
chunk size: How much data is loaded at once.
chunk buffer*: Mandatory to get a continuous output without edge effects. The buffer is always removed once processed and will never be returned either in R or in files.
chunk alignment: Align the processed chunks.
progress: Displays a progression estimation.
output files: Supported templates are {XLEFT}, {XRIGHT},
{YBOTTOM}, {YTOP}, {XCENTER}, {YCENTER} {ID} and,
if chunk size is equal to 0 (processing by file), {ORIGINALFILENAME}.
select: Load only attributes of interest.
filter: Read only points of interest.
# NOT RUN {
LASfile <- system.file("extdata", "MixedConifer.laz", package="lidR")
las <- readLAS(LASfile, select = "xyz", filter = "-drop_z_below 0")
ttops <- find_trees(las, lmf(ws = 5))
x = plot(las)
add_treetops3d(x, ttops)
# }
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