CrownMetrics {rLiDAR} | R Documentation |
Compute individual tree crown metrics from lidar data
CrownMetrics(xyziId)
xyziId |
A 5-column matrix with the x, y, z coordinates, intensity and the tree id classification for the LiDAR point cloud. |
# List of the individual tree crown metrics:
TotalReturns: Total number of returns
ETOP - UTM Easting coordinate of the tree top
NTOP - UTM Northing coordinate of the tree top
EMIN - Minimum UTM Easting coordinate
NMIN - Minimum UTM Northing coordinate
EMAX - Maximum UTM Easting coordinate
NMAX - Maxmium UTM Northing coordinate
EWIDTH - Tree crown width 01
NWIDTH - Tree crown width 02
HMAX - Maximum Height
HMEAN - Mean height
HSD - Standard deviation of height
HCV - Coefficient of variation of height
HMOD - Mode of height
H5TH - 5th percentile of height
H10TH - 10th percentile of height
H20TH - 20th percentile of height
H25TH - 25th percentile of height
H30TH - 30th percentile of height
H40TH - 40th percentile of height
H50TH - 50th percentile of height
H60TH - 60th percentile of height
H70TH - 70th percentile of height
H75TH - 75th percentile of height
H80TH - 80th percentile of height
H90TH - 90th percentile of height
H95TH - 95th percentile of height
H99TH - 99th percentile of height
IMAX - Maximum intensity
IMEAN - Mean intensity
ISD - Standard deviation of intensity
ICV - Coefficient of variation of intensity
IMOD - Mode of intensity
I5TH - 5th percentile of intensity
I10TH - 10th percentile of intensity
I20TH - 20th percentile of intensity
I25TH - 25th percentile of intensity
I30TH - 30th percentile of intensity
I40TH - 40th percentile of intensity
I50TH - 50th percentile of intensity
I60TH - 60th percentile of intensity
I70TH - 70th percentile of intensity
I75TH - 75th percentile of intensity
I80TH - 80th percentile of intensity
I90TH - 90th percentile of intensity
I95TH - 95th percentile of intensity
I99TH - 99th percentile of intensity
Returns A matrix of the LiDAR-based metrics for the individual tree detected.
Carlos Alberto Silva
#=======================================================================# # Individual tree detection using K-means cluster #=======================================================================# # Importing LAS file: LASfile <- system.file("extdata", "LASexample1.las", package="rLiDAR") # Reading LAS file LAS<-readLAS(LASfile,short=TRUE) # Setting the xyz coordinates and subsetting the data xyzi<-subset(LAS[,1:4],LAS[,3] >= 1.37) # Finding clusters (trees) clLAS<-kmeans(xyzi[,1:2], 32) # Set the tree id vector Id<-as.factor(clLAS$cluster) # Combining xyzi and tree id xyziId<-cbind(xyzi,Id) #=======================================================================# # Computing individual tree LiDAR metrics #=======================================================================# TreesMetrics<-CrownMetrics(xyziId) head(TreesMetrics)