Skip to content
GitLab
Select Git revision
  • master default
  • dev
Find file BlameHistoryPermalink
cloudTreeMetrics.Rd 2.43 KiB
1 
% Generated by roxygen2: do not edit by hand
2 
% Please edit documentation in R/metrics.R
3 
\name{cloudTreeMetrics}
4 
\alias{cloudTreeMetrics}
5 
\title{Computation of tree metrics}
6 
\usage{
7 
cloudTreeMetrics(llasn, XY, plot.radius, res = 0.5,
8 
  func = stdTreeMetrics, ...)
9 
}
10 
\arguments{
11 
\item{llasn}{list of LAS objects (from lidR package)}
12
13 
\item{XY}{a dataframe or matrix with XY coordinates of plot centers}
14
15 
\item{plot.radius}{numeric. plot radius in same unit as coordinates of LAS objects}
16
17 
\item{res}{numeric. resolution of canopy height model computed with \code{\link{points2DSM}} before tree segmentation}
18
19 
\item{func}{a function to be applied to the attributes of extracted trees (return from internal call to treeExtraction function to compute plot level metrics}
20
21 
\item{...}{other parameters to be passed to \code{\link{treeSegmentation}}}
22 
}
23 
\value{
24 
a dataframe with tree metrics in columns corresponding to LAS objects of the list (lines)
25 
}
26 
\description{
27 
This function performs tree detection in each of LAS objects contained in a list and then computes summary statistics based on compute tree metrics for list of LAS objects (should be normalized clouds, with buffer around area of interest).
28 
- calls the tree detection and extraction functions on each object
29 
- remove the detected trees located outside of the regions of interest defined by their centers and radius
30 
- computes summary statistics of remaining detected trees.
31 
}
32 
\examples{
33 
data(laschablais3)
34
35 
# extract three point clouds from LAS object
36 
llas <- list()
37 
llas[["A"]] <- lidR::lasclipCircle(laschablais3, 974350, 6581680, 10)
38 
llas[["B"]] <- lidR::lasclipCircle(laschablais3, 974390, 6581680, 10)
39 
llas[["C"]] <- lidR::lasclipCircle(laschablais3, 974350, 6581640, 10)
40 
# normalize point clouds
41 
llas <- lapply(llas, function(x) {lidR::lasnormalize(x, lidR::tin())})
42
43 
# compute tree metrics
44 
cloudTreeMetrics(llas,
45 
                 cbind(c(974350, 974390, 974350), c(6581680, 6581680, 6581640)),
46 
                 10, res=0.5)
47
48 
# compute metrics with user-defined function
49 
# number of detected trees between 20 and 30 meters and their mean height
50 
user.func <- function(x, area.ha=NA)
51 
{
52 
  dummy <- x$h[which(x$h>10 & x$h<20)]
53 
  data.frame(Tree.between.20.30=length(dummy)/area.ha, Tree.meanH=mean(dummy))
54 
}
55 
 cloudTreeMetrics(llas, cbind(c(974350, 974390, 974350), c(6581680, 6581680, 6581640)), 10, res=0.5, func=user.func)
56 
}
57 
\seealso{
58 
\code{\link{treeSegmentation}}, \code{\link{treeExtraction}}, \code{\link{stdTreeMetrics}}
59 
}
60