aba_metrics {lidaRtRee} | R Documentation |
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:
for all points: total number
ntot
, percentage of points above minimum heightp_hmin
, percentage of points in height binsH.propZ1_Z2
,for first return points: percentage above minimum height
p_1st_hmin
,for all points above minimum height: height metrics returned by
stdmetrics_z
and intensity metrics returned bystdmetrics_i
for first returns above minimum height:
mCH
andsdCH
as proposed by Bouvier et al.
Usage
aba_metrics(z, i, rn, c, hmin = 2, breaksH = NULL)
.aba_metrics
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 |
Format
An object of class formula
of length 2.
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. 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)
# 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])