lidR (version 4.1.2)

snag_wing2015: Snags Segmentation Algorithm

Description

This function is made to be used in segment_snags. 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.

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

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.

low_int_thrsh

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

uppr_int_thrsh

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

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.

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.

Author

Implementation by Andrew Sánchez Meador & Jean-Romain Roussel

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).

This algorithm attributes each point in the point cloud (snagCls column) into the following five snag classes:

• 0: live tree - not a snag
  

• 1: general snag - the broadest range of snag point situations
  

• 2: small snag - isolated snags with lower point densities
  

• 3: live crown edge snag - snags located directly adjacent or intermixing with live trees crowns
  

• 4: high canopy cover snag - snags protruding above the live canopy in dense conditions (e.g., canopy cover >= 55%).

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

Examples

LASfile <- system.file("extdata", "MixedConifer.laz", package="lidR")
# Wing also included -keep_single
poi ="-keep_first -inside 481260 3812920 481310 3812960"
las <- readLAS(LASfile, select = "xyzi", filter = poi)

# 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 <- segment_snags(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 <- filter_poi(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