lidaRtRee (version 4.0.8)

aba_metrics: Function for area-based metrics computation

Description

Predefined function usable in cloud_metrics or clouds_metrics. Applies a minimum height threshold to the point cloud and computes the following metrics:

1. for all points: total number ntot, percentage of points above minimum height p_hmin, percentage of points in height bins H.propZ1_Z2,

2. for first return points: percentage above minimum height p_1st_hmin,

3. for all points above minimum height: height metrics returned by stdmetrics_z and intensity metrics returned by stdmetrics_i

4. for first returns above minimum height: mCH and sdCH as proposed by Bouvier et al.

Usage

aba_metrics(z, i, rn, c, hmin = 2, breaksH = NULL)
.aba_metrics

Format

An object of class formula of length 2.

Arguments

z, i, rn, c

Height, Intensity, ReturnNumber and Classification

hmin

numeric. height threshold for low points removal before metrics computation

breaksH

vector. breaks for height histogram proportion computation

References

Bouvier et al. 2015. Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data. Remote Sensing of Environment 156, pp. 322-334. tools:::Rd_expr_doi("10.1016/j.rse.2014.10.004")

See Also

cloud_metrics, stdmetrics, clouds_metrics

Examples

# load LAS file
LASfile <- system.file("extdata", "las_chablais3.laz", package="lidaRtRee")
las_chablais3 <- lidR::readLAS(LASfile)

# set number of threads
lidR::set_lidr_threads(2)
# extract two point clouds from LAS object
llas <- lidR::clip_circle(las_chablais3,
                          c(974350, 974390),
                          c(6581680, 6581680), 10)
# normalize point clouds
llas <- lapply(llas, function(x) {
  lidR::normalize_height(x, lidR::tin())
})
# computes metrics 
m <- clouds_metrics(llas, ~ aba_metrics(
 Z, Intensity, ReturnNumber, Classification, 2
))
head(m[,1:5])