stdmetrics function

Predefined standard metrics functions

Predefined metrics functions intended to me used in *_metrics function such as pixel_metrics , cloud_metrics , crown_metrics , voxel_metrics and so on. Each function comes with a convenient shortcuts for lazy coding. The lidR package aims to provide an easy way to compute user-defined metrics rather than to provide them. However, for efficiency and to save time, sets of standard metrics have been predefined (see details). Every function can be computed by every *_metrics functions however stdmetrics* are more pixel-based metrics, stdtreemetrics are more tree-based metrics and stdshapemetrics are more point-based metrics. For example the metric zmean computed by stdmetrics_z makes sense when computed at the pixel level but brings no information at the voxel level. data

Format

An object of class formula of length 2.

An object of class formula of length 2.

An object of class formula of length 2.

An object of class formula of length 2.

An object of class formula of length 2.

An object of class formula of length 2.

An object of class formula of length 2.

An object of class formula of length 2.

stdmetrics(x, y, z, i, rn, class, dz = 1, th = 2, zmin = 0)

stdmetrics_z(z, dz = 1, th = 2, zmin = 0)

stdmetrics_i(i, z = NULL, class = NULL, rn = NULL)

stdmetrics_rn(rn, class = NULL)

stdmetrics_pulse(pulseID, rn)

stdmetrics_ctrl(x, y, z)

stdtreemetrics(x, y, z)

stdshapemetrics(x, y, z)

.stdmetrics

.stdmetrics_z

.stdmetrics_i

.stdmetrics_rn

.stdmetrics_pulse

.stdmetrics_ctrl

.stdtreemetrics

.stdshapemetrics

Arguments

• x, y, z, i: Coordinates of the points, Intensity
• rn, class: ReturnNumber, Classification
• dz: numeric. Layer thickness metric entropy
• th: numeric. Threshold for metrics pzabovex. Can be a vector to compute with several thresholds.
• zmin: numeric. Lower bound of the integral for zpcumx metrics. See wiki page and Wood et al. (2008) reference.
• 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 elevation
• i: refers to the intensity
• rn: refers to the return number
• q: refers to quantile
• a: refers to the ScanAngleRank or ScanAngle
• n: 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 also 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)

• stdmetrics: is a combination of stdmetrics_ctrl + stdmetrics_z + stdmetrics_i + stdmetrics_rn
• stdtreemetrics: is a special function that works with crown_metrics . Actually, it won't fail with other functions but the output makes more sense if computed at the individual tree level.
• stdshapemetrics: is a set of eigenvalue based feature described in Lucas et al, 2019 (see references).

Examples

LASfile <- system.file("extdata", "Megaplot.laz", package="lidR")
las <- readLAS(LASfile, select = "*", filter = "-keep_random_fraction 0.5")

# All the predefined metrics
m1 <- pixel_metrics(las, ~stdmetrics(X,Y,Z,Intensity,ReturnNumber,Classification,dz=1), res = 40)

# Convenient shortcut
m2 <- pixel_metrics(las, .stdmetrics, res = 40)

# Basic metrics from intensities
m3 <- pixel_metrics(las, ~stdmetrics_i(Intensity), res = 40)

# All the metrics from intensities
m4 <- pixel_metrics(las, ~stdmetrics_i(Intensity, Z, Classification, ReturnNumber), res = 40)

# Convenient shortcut for the previous example
m5 <- pixel_metrics(las, .stdmetrics_i, res = 40)

# Combine some predefined function with your own new metrics
# Here convenient shortcuts are no longer usable.
myMetrics = function(z, i, rn)
{
  first  <- rn == 1L
  zfirst <- z[first]
  nfirst <- length(zfirst)
  above2 <- sum(z > 2)

  x <- above2/nfirst*100

  # User's metrics
  metrics <- list(
     above2aboven1st = x,       # Num of returns above 2 divided by num of 1st returns
     zimean  = mean(z*i),       # Mean products of z by intensity
     zsqmean = sqrt(mean(z^2))  # Quadratic mean of z
   )

  # Combined with standard metrics
  return( c(metrics, stdmetrics_z(z)) )
}

m10 <- pixel_metrics(las, ~myMetrics(Z, Intensity, ReturnNumber), res = 40)

# Users can write their own convenient shorcuts like this:
.myMetrics = ~myMetrics(Z, Intensity, ReturnNumber)
m11 <- pixel_metrics(las, .myMetrics, res = 40)

References

M. Woods, K. Lim, and P. Treitz. Predicting forest stand variables from LiDAR data in the Great Lakes – St. Lawrence forest of Ontario. The Forestry Chronicle. 84(6): 827-839. https://doi.org/10.5558/tfc84827-6

Lucas, C., Bouten, W., Koma, Z., Kissling, W. D., & Seijmonsbergen, A. C. (2019). Identification of Linear Vegetation Elements in a Rural Landscape Using LiDAR Point Clouds. Remote Sensing, 11(3), 292.