CHMsmoothing: LiDAR-derived Canopy Height Model (CHM) smoothing

In rLiDAR: LiDAR Data Processing and Visualization

Description

LiDAR-derived Canopy Height Model (CHM) smoothing is used to eliminate spurious local maxima caused by tree branches.

Usage

CHMsmoothing(chm, filter, ws, sigma)

Arguments

chm	A LiDAR-derived Canopy Height Model (CHM) RasterLayer or SpatialGridDataFrame file.
filter	Filter type: mean, median, maximum or Gaussian. Default is mean.
ws	The dimension of a window size, e.g. 3,5, 7 and so on. Default is 5.
sigma	Used only when filter parameter is equal to Gaussian, e.g. 0.5, 1.0, 1.5 and so on. Default is 0.67.

Value

Returns a CHM-smoothed raster.

Author(s)

Carlos Alberto Silva.

See Also

focal in the raster package.

Examples

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

#=======================================================================#
# Example 01: Smoothing the CHM using a Gaussian filter
#=======================================================================#
# Set the ws:
ws<-3 # dimension 3x3

# Set the filter type
filter<-"Gaussian"

# Set the sigma value
sigma<-0.6

# Smoothing CHM
sCHM<-CHMsmoothing(chm, filter, ws, sigma)

#=======================================================================# 
# Example 02: Smoothing the CHM using a mean filter
#=======================================================================#
# Set the ws:
ws<-5 # dimension 5x5

# Set the filter type
filter<-"mean"

# Smoothing and plotting LiDAR-derived CHM 
sCHM<-CHMsmoothing(chm, filter, ws)

rLiDAR documentation built on Oct. 5, 2021, 9:07 a.m.