FindTreesCHM: Individual tree detection whitin the LiDAR-derived Canopy...

In rLiDAR: LiDAR Data Processing and Visualization

Description

Detects and computes the location and height of individual trees within the LiDAR-derived Canopy Height Model (CHM). The algorithm implemented in this function is local maximum with a fixed window size.

Usage

FindTreesCHM(chm,fws,minht)

Arguments

chm	A LiDAR-derived Canopy Height Model (CHM) raster file.
fws	A single dimension (in raster grid cell units) of fixed square window size, e.g. 3, 5, 7 and so on. Default is 5.
minht	Height threshold. Detect individual trees above specified height threshold, e.g. 1.37, 2.0, 3.5 m and so on. Default is 1.37 m.

Value

Returns A matrix with four columns (tree id, xy coordinates, and height).

Author(s)

Carlos Alberto Silva

Examples

# Importing the LiDAR-derived CHM raster file
data(chm) # or set a CHM. e.g. chm<-raster("CHM_stand.asc") 

# Smoothing CHM
schm<-CHMsmoothing(chm, "mean", 5)

# Setting the fws:
fws<-5 # dimention 5x5

# Setting the specified height above ground for detectionbreak
minht<-8.0

# Getting the individual tree detection list
treeList<-FindTreesCHM(schm, fws, minht)
summary(treeList)

# Plotting the individual tree location on the CHM
library(raster)
plot(chm, main="LiDAR-derived CHM")
library(sp) 
XY<-SpatialPoints(treeList[,1:2]) # Spatial points
plot(XY, add=TRUE, col="red")        # plotthing tree location

rLiDAR documentation built on Oct. 5, 2021, 3 a.m.