stdmetrics
Predefined standard metrics functions
Predefined functions usable in grid_metrics, grid_hexametrics, lasmetrics, tree_metrics,
and their convenient shortcuts. The philosophy of the lidR
package is to provide an easy way
to compute user-defined metrics rather than to provide them. However, for efficiency and to save time, a set of
standard metrics has been predefined. To use these functions please read the Details and Examples sections.
- Keywords
- datasets
Usage
stdmetrics(x, y, z, i, a, rn, class, dz = 1).stdmetrics
stdmetrics_z(z, dz = 1)
.stdmetrics_z
stdmetrics_i(i, z = NULL, class = NULL, rn = NULL)
.stdmetrics_i
stdmetrics_rn(rn, class = NULL)
.stdmetrics_rn
stdmetrics_pulse(pulseID, rn)
.stdmetrics_pulse
stdmetrics_ctrl(x, y, z, a)
.stdmetrics_ctrl
stdtreemetrics(x, y, z)
.stdtreemetrics
Arguments
- x, y, z, i, a
Coordinates of the points, Intensity and ScanAngle
- rn, class
ReturnNumber, Classification
- dz
Layer thickness for metrics requiring this data, such as entropy
- pulseID
The number referencing each pulse
Details
The function names, their parameters and the output names of the metrics rely on a nomenclature chosen for brevity:
z
: refers to the elevationi
: refers to the intensityrn
: refers to the return numberq
: refers to quantilea
: refers to the ScanAnglen
: refers to a number (a count)p
: refers to a percentage
For example the metric named zq60
refers to the elevation, quantile, 60 i.e. the 60th percentile of elevations.
The metric pground
refers to a percentage. It is the percentage of points classified as ground.
The function stdmetric_i
refers to metrics of intensity. A description of each existing metric can be found
on the lidR wiki page.
Some functions have optional parameters. If these parameters are not provided the function
computes only a subset of existing metrics. For example stdmetrics_i
requires the intensity
values, but if the elevation values are provided it can compute additional metrics such as cumulative
intensity at a given percentile of height.
Each function has a convenient associated variable. It is the name of the function, with a
dot before the name. This enables the function to be used without writing parameters. The cost
of such a feature is inflexibility. It corresponds to a predefined behaviour (see examples)
stdtreemetrics
is a special function which works with tree_metrics. Actually
it won't fail with other functions but the output make sense more sense if computed at the
individual tree level.
Format
An object of class expression
of length 1.
See Also
grid_metrics lasmetrics grid_hexametrics grid_metrics3d tree_metrics
Examples
# NOT RUN {
LASfile <- system.file("extdata", "Megaplot.laz", package="lidR")
lidar = readLAS(LASfile, all = TRUE)
# All the predefined functions
lidar %>% grid_metrics(stdmetrics(X,Y,Z,Intensity, ScanAngle,
ReturnNumber, Classification,
dz = 1))
# Convenient shortcut
lidar %>% grid_metrics(.stdmetrics)
# Basic metrics from intensities
lidar %>% grid_metrics(stdmetrics_i(Intensity))
# All the metrics from intensities
lidar %>% grid_metrics(stdmetrics_i(Intensity, Z, Classification, ReturnNumber))
# Convenient shortcut for the previous example
lidar %>% grid_metrics(.stdmetrics_i)
# Compute the metrics only on first return
lidar %>% lasfilterfirst %>% grid_metrics(.stdmetrics_z)
# Compute the metrics with a threshold at 2 meters
lidar %>% lasfilter(Z > 2) %>% grid_metrics(.stdmetrics_z)
# Works also with lasmetrics and grid_hexametrics
lidar %>% lasmetrics(.stdmetrics)
lidar %>% grid_hexametrics(.stdmetrics)
# Combine some predefined function with your own new metrics
# Here convenient shortcuts are no longer usable.
myMetrics = function(z, i)
{
metrics = list(
zwimean = sum(z*i)/sum(i), # Mean elevation weighted by intensities
zimean = mean(z*i), # Mean products of z by intensity
zsqmean = sqrt(mean(z^2)) # Quadratic mean
)
return( c(metrics, stdmetrics_z(z)) )
}
lidar %>% grid_metrics(myMetrics(Z, Intensity))
# You can write your own convenient shorcuts like this:
.myMetrics = expression(myMetrics(Z,Intensity))
lidar %>% grid_metrics(.myMetrics)
# }