Compute metrics for each tree
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.
tree_metrics(las, func = ~max(Z), attribute = "treeID")
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 |
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:
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).
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.
LAScatalog
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 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.
Other metrics:
cloud_metrics(),
grid_metrics(),
hexbin_metrics(),
point_metrics(),
voxel_metrics()
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.