wing2015.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/algorithm-snag.r
\name{wing2015}
\alias{wing2015}
\title{Snags Segmentation Algorithm}
\usage{
wing2015(neigh_radii = c(1.5, 1, 2), low_int_thrsh = 50,
  uppr_int_thrsh = 170, pt_den_req = 3, BBPRthrsh_mat = NULL)
}
\arguments{
\item{neigh_radii}{numeric. A vector of three radii used in quantifying local-area centered
neighborhoods. See Wing et al. (2015) reference page 171 and Figure 4. Defaults are 1.5,
1, and 2 for the sphere, small cylinder and large cylinder neighborhoods, respectively.}

\item{low_int_thrsh}{numeric. The lower intensity threshold filtering value. See Wing
et al. (2015) page 171. Default is 50.}

\item{uppr_int_thrsh}{numeric. The upper intensity threshold filtering value. See Wing
et al. (2015) page 171. Default is 170.}

\item{pt_den_req}{numeric. Point density requirement based on plot-level point density
defined classes. See Wing et al. (2015) page 172. Default is 3.}

\item{BBPRthrsh_mat}{matrix. A 3x4 matrix providing the four average BBPR (branch and bole
point ratio) values for each of the three neighborhoods (sphere, small cylinder and large
cylinder) to be used for conditional assessments and classification into the following four snag
classes: 1) general snag 2) small snag 3) live crown edge snag 4) high canopy
cover snag. See Wing et al. (2015) page 172 and Table 2. This matrix must be provided by
the user.}
}
\description{
This function is made to be used in \link{lassnags}. It implements an algorithms for snags segmentation
based on Wing et al (2015) (see references). This is an automated filtering algorithm that utilizes
three dimensional neighborhood lidar point-based intensity and density statistics to remove lidar
points associated with live trees and retain lidar points associated with snags.
}
\details{
Note that this algorithm strictly performs a classification based on user input while
the original publication's methods also included a segmentation step and some pre-
(filtering for first and single returns only) and post-process (filtering for only the
snag classified points prior to segmentation) tasks which are now expected to be performed
by the user. Also, this implementation may have some differences compared with the original
method due to potential mis-interpretation of the Wing et al. manuscript, specifically
Table 2 where they present four groups of conditional assessments with their required
neighborhood point density and average BBPR values (BBPR = branch and bole point ratio;
PDR = point density requirement).\cr\cr
This algorithm attributes each point in the point cloud (\code{snagCls} column) into the
following five snag classes:
\itemize{
\item 0: live tree - not a snag\cr
\item 1: general snag - the broadest range of snag point situations\cr
\item 2: small snag - isolated snags with lower point densities\cr
\item 3: live crown edge snag - snags located directly adjacent or intermixing with live trees crowns \cr
\item 4: high canopy cover snag - snags protruding above the live canopy in dense conditions (e.g.,
canopy cover >= 55\%).
}
The current implementation is known to use a large amount of memory for storing the N x k
integer matrix returning the near neighbor indices for each point in the point cloud.
Improvements are possible in future package versions.
}
\examples{
LASfile <- system.file("extdata", "MixedConifer.laz", package="lidR")
las <- readLAS(LASfile, select = "xyzi", filter="-keep_first") # Wing also included -keep_single

# For the Wing2015 method, supply a matrix of snag BranchBolePtRatio conditional
# assessment thresholds (see Wing et al. 2015, Table 2, pg. 172)
bbpr_thresholds <- matrix(c(0.80, 0.80, 0.70,
                          0.85, 0.85, 0.60,
                          0.80, 0.80, 0.60,
                          0.90, 0.90, 0.55),
                          nrow =3, ncol = 4)

# Run snag classification and assign classes to each point
las <- lassnags(las, wing2015(neigh_radii = c(1.5, 1, 2), BBPRthrsh_mat = bbpr_thresholds))

# Plot it all, tree and snag points...
plot(las, color="snagCls", colorPalette = rainbow(5))

# Filter and plot snag points only
snags <- lasfilter(las, snagCls > 0)
plot(snags, color="snagCls", colorPalette = rainbow(5)[-1])

# Wing et al's (2015) methods ended with performing tree segmentation on the
# classified and filtered point cloud using the watershed method

}
\references{
Wing, Brian M.; Ritchie, Martin W.; Boston, Kevin; Cohen, Warren B.; Olsen, Michael J. 2015.
Individual snag detection using neighborhood attribute filtered airborne lidar data. Remote
Sensing of Environment. 163: 165-179 https://doi.org/10.1016/j.rse.2015.03.013
}
\author{
Implementation by Andrew Sánchez Meador & Jean-Romain Roussel
}
\concept{snags segmentation algorithms}