locate_trees: Individual tree detection

In lidR: Airborne LiDAR Data Manipulation and Visualization for Forestry Applications

Individual tree detection

Description

Individual tree detection function that find the position of the trees using several possible algorithms.

Usage

locate_trees(las, algorithm, uniqueness = "incremental")

Arguments

las	An object of class LAS or LAScatalog. Can also be a raster from raster, stars or terra representing a canopy height model, in which case it is processed like a regularly-spaced point cloud.
algorithm	An algorithm for individual tree detection. lidR has: lmf and manual. More experimental algorithms may be found in the package lidRplugins.
uniqueness	character. A method to compute a unique ID. Can be 'incremental', 'gpstime' or 'bitmerge'. See section 'Uniqueness'. This feature must be considered as 'experimental'.

Value

locate_trees returns an sf object with POINT Z geometries. The table of attributes contains a column treeID with an individual ID for each tree. The height of the trees (Z) are also repeated in the table of attribute to be analysed as an attribute and not as a coordinate.

Uniqueness

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 chunk/file is processed independently of the others and potentially in parallel on different computers. Thus, the index always restarts at 1 on each chunk/file. Worse, in a tree segmentation process, a tree that is located exactly between 2 chunks/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. It also ensures that two halves of a tree on the edge of a processing chunk will be assigned the same ID.

incremental

Number from 0 to n. This method does not ensure uniqueness of the IDs. This is the legacy method.

gpstime

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 expected 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 of getting ID attribution errors.

bitmerge

This method uses the XY coordinates of the highest point (apex) of a tree to create a single 64-bit number with a bitwise operation. First, XY coordinates are converted to 32-bit integers using the scales and offsets of the point cloud. For example, if the apex is at (10.32, 25.64) with a scale factor of 0.01 and an offset of 0, the 32-bit integer coordinates are X = 1032 and Y = 2564. Their binary representations are, respectively, (here displayed as 16 bits) 0000010000001000 and 0000101000000100. X is shifted by 32 bits and becomes a 64-bit integer. Y is kept as-is and the binary representations are unionized into a 64-bit integer like (here displayed as 32 bit) 00000100000010000000101000000100 that is guaranteed to be unique. However R does not support 64-bit integers. The previous steps are done at C++ level and the 64-bit binary representation is reinterpreted into a 64-bit decimal number to be returned in R. The IDs thus generated are somewhat weird. For example, the tree ID 00000100000010000000101000000100 which is 67635716 if interpreted as an integer becomes 3.34164837074751323479078607289E-316 if interpreted as a decimal number. This is far from optimal but at least it 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 least it works and deals with some of the limitations of R.

Examples

LASfile <- system.file("extdata", "MixedConifer.laz", package="lidR")
las <- readLAS(LASfile, select = "xyz", filter = "-inside 481250 3812980 481300 3813030")

ttops <- locate_trees(las, lmf(ws = 5))

#plot(las) |> add_treetops3d(ttops)

lidR documentation built on Sept. 8, 2023, 5:10 p.m.