Interval metrics

Compute the percentage of points falling within user-defined height intervals.

Usage

metrics_interval(
  z,
  zintervals = c(0, 0.15, 2, 5, 10, 20, 30),
  zmin = NA_real_,
  right = FALSE
)

.metrics_interval

Format

An object of class formula of length 2.

Arguments

z

A numeric vector of heights (e.g., `Z` from a LAS object).

zintervals

Numeric, strictly increasing. Interior breakpoints for height intervals (e.g., c(0, 0.15, 2, 5, 10, 20, 30)). The full set of breaks used is c(-Inf, zintervals, Inf), producing:

• a "below" bin: (-Inf, zintervals[1])

• interior bins: [zintervals[i], zintervals[i+1])

• an "above" bin: [zintervals[length(zintervals)], Inf)

With right = FALSE (used internally), each bin includes its left boundary and excludes its right boundary, except the open-ended top bin which includes all values \(\ge\) the last breakpoint.

zmin

Optional numeric scalar. If provided, values are filtered as z > zmin before binning (strictly greater).

right

Logical, default `FALSE`. Passed to `hist()` to control whether intervals are right-closed (`TRUE`) or right-open (`FALSE`).

Value

A named list of percentages (numeric) for each interval.

Details

Internally, counts are computed with:

  breaks <- c(-Inf, zintervals, Inf)
  hist(z, breaks = breaks, plot = FALSE, right = FALSE)

Using `right = FALSE` enforces left-closed, right-open bins (`[a,b)`), which guarantees, for example, that `0` is included in `[0, next)` rather than the "below" bin. Proportions are the counts divided by the number of points remaining after optional `zmin` filtering, multiplied by 100.

Output names follow:

• pz_below_<first> for the below bin

• pz_<a>-<b> for each interior [a,b) bin

• pz_above_<last> for the above bin

Examples

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


m1 <- cloud_metrics(las, ~metrics_interval(z = Z))

m2 <- pixel_metrics(las, ~metrics_interval(z = Z, zintervals = c(0, 5, 10)), res = 20)