lidR (version 3.1.1)

tree_metrics: Compute metrics for each tree

Description

Once the trees are segmented, i.e. attributes exist in the point cloud that reference each tree, computes a set of user-defined descriptive statistics for each individual tree. This is the "tree version" of grid_metrics.

Usage

tree_metrics(las, func = ~max(Z), attribute = "treeID")

Arguments

las

An object of class LAS or LAScatalog.

func

formula. An expression to be applied to each tree. It works like in grid_metrics voxel_metrics or delineate_crowns and computes, in addition to tree locations a set of metrics for each tree.

attribute

character. The column name of the attribute containing tree IDs. Default is "treeID"

Value

A SpatialPoinsDataFrame that references the xy-position with a table of attributes that associates the z-elevation (highest points) of the trees and the id of the trees, plus the metrics defined by the user.

Working with a <code>LAScatalog</code>

This section appears in each function that supports a LAScatalog as input.

In lidR when the input of a function is a LAScatalog the function uses the LAScatalog processing engine. The user can modify the engine options using the available options. A careful reading of the engine documentation is recommended before processing LAScatalogs. Each lidR function should come with a section that documents the supported engine options.

The LAScatalog engine supports .lax files that significantly improve the computation speed of spatial queries using a spatial index. Users should really take advantage a .lax files, but this is not mandatory.

Supported processing options

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.

Details

By default the function computes the xyz-coordinates of the highest point of each tree and uses xy as tree coordinates in SpatialPoinsDataFrame. z is stored in the table of attributes along with the id of each tree. All the other attributes are user-defined attributes: The following existing functions contain a small set of pre-defined metrics:

• stdmetrics_tree

Users must write their own functions to create their own metrics. tree_metrics will dispatch the LiDAR data for each segmented tree in the user-defined function. Functions are defined without the need to consider each segmented tree i.e. only the point cloud (see examples).

See Also

Other metrics: cloud_metrics(), grid_metrics(), hexbin_metrics(), point_metrics(), voxel_metrics()

Examples

# NOT RUN {
LASfile <- system.file("extdata", "MixedConifer.laz", package="lidR")
las = readLAS(LASfile, filter = "-drop_z_below 0")

# NOTE: This dataset is already segmented
# plot(las, color = "treeID", colorPalette = pastel.colors(200))

# Default computes only Z max
metrics = tree_metrics(las)

# User-defined metrics - mean height and mean intensity for each tree
metrics = tree_metrics(las, ~list(Zmean = mean(Z), Imean = mean(Intensity)))

# Define your own new metrics function
myMetrics = function(z, i)
{
  metrics = list(
     imean = mean(i),
     imax  = max(i),
     npoint = length(z)
   )

   return(metrics)
}

metrics = tree_metrics(las, ~myMetrics(Z, Intensity))

# predefined metrics (see ?stdmetrics)
metrics = tree_metrics(las, .stdtreemetrics)
# }