lidR (version 1.6.1)

lastrees_li2: Individual tree segmentation

Description

Individual tree segmentation using the Li et al. (2012) algorithm (see reference). This method replaces the former algorithm being a slightly closer implementation of the original paper. This method is a growing region method working at the point cloud level. It is an implementation (as strict as possible) of the Li et al. 2012 (see references) algorithm made by the lidR author but with the addition of a parameter hmin to stop over-segmentation for objects that are too low. The classification is done at the point cloud level and the function always returns nothing (NULL). The original point cloud is updated in place with an ID for each point in a new column treeID. Users are free to post-process this output the way they want.

Usage

lastrees_li2(las, dt1 = 1.5, dt2 = 2, R = 2, Zu = 15, hmin = 2,
  speed_up = 10, ...)

Arguments

las

An object of the class LAS.

dt1

numeric. Threshold number 1. See reference page 79 in Li et al. (2012). Default 1.5.

dt2

numeric. Threshold number 2. See reference page 79 in Li et al. (2012). Default 2.

R

numeric. Search radius. See reference page 79 in Li et al. (2012). Default 2. If R = 0 all the points are automatically considered as local maxima and the search step is skipped (much faster).

Zu

numeric. If point elevation is greater than Zu, dt2 is used, otherwise dt1 is used. See reference page 79 in Li et al. (2012). Default 15.

hmin

numeric. Minimum height of a detected tree. Default 2.

speed_up

numeric. Maximum radius of a crown. Any value greater than a crown is good because this parameter does not affect the result. However, it greatly affects the computation speed. The lower the value, the faster the method. Default is 10.

...

Supplementary options. Currently field is supported to change the default name of the new column.

Value

Nothing (NULL), the point cloud is updated by reference. The original point cloud has a new column named treeID containing an ID for each point that refers to a segmented tree.

References

Li, W., Guo, Q., Jakubowski, M. K., & Kelly, M. (2012). A new method for segmenting individual trees from the lidar point cloud. Photogrammetric Engineering & Remote Sensing, 78(1), 75-84.

See Also

Other tree_segmentation: lastrees_dalponte, lastrees_silva, lastrees_watershed, lastrees

Examples

# NOT RUN {
LASfile <- system.file("extdata", "MixedConifer.laz", package="lidR")
las = readLAS(LASfile, select = "xyz", filter = "-drop_z_below 0")
col = pastel.colors(200)

# Li 2012
lastrees_li2(las)
plot(las, color = "treeID", colorPalette = col)

# }