lidR (version 1.6.1)

lastrees_dalponte: Individual tree segmentation

Description

Individual tree segmentation using Dalponte et al. (2016) algorithm (see reference). This is a local maxima + growing region algorithm. It is based on the constraints proposed by Dalponte and Coomes (see references). This algorithm exists in the package itcSegment. This version is identical to the original but with superfluous code removed and rewritten efficiently. Consequently it is hundreds to millions times faster. Note that this algorithm strictly performs a segmentation, while the original method as implemented in itcSegment and described in the manuscript also performs a pre- and post-process when these tasks are expected to be done by the user in separate functions.

Usage

lastrees_dalponte(las, chm, treetops, th_tree = 2, th_seed = 0.45,
  th_cr = 0.55, max_cr = 10, extra = FALSE, ...)

Arguments

las

An object of the class LAS. If missing extra is turned to TRUE automatically.

chm

RasterLayer. Image of the canopy. Can be computed with grid_canopy or grid_tincanopy or read it from an external file.

treetops

RasterLayer or data.frame containing the position of the trees. Can be computed with tree_detection or read from an external file.

th_tree

numeric. Threshold below which a pixel cannot be a tree. Default 2.

th_seed

numeric. Growing threshold 1. See reference in Dalponte et al. 2016. A pixel is added to a region if its height is greater than the tree height multiplied by this value. It should be between 0 and 1. Default 0.45.

th_cr

numeric. Growing threshold 2. See reference in Dalponte et al. 2016. A pixel is added to a region if its height is greater than the current mean height of the region multiplied by this value. It should be between 0 and 1. Default 0.55.

max_cr

numeric. Maximum value of the crown diameter of a detected tree (in pixels). Default 10.

extra

logical. By default the function classifies the original point cloud by reference and return nothing (NULL) i.e. the original point cloud is automatically updated in place. If extra = TRUE an additional RasterLayer used internally can be returned.

...

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 refer to a segmented tree. If extra = TRUE algorithms return a RasterLayer used internally.

References

Dalponte, M. and Coomes, D. A. (2016), Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data. Methods Ecol Evol, 7: 1236<U+2013>1245. doi:10.1111/2041-210X.12575.

See Also

Other tree_segmentation: lastrees_li2, 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)

chm = grid_canopy(las, res = 0.5, subcircle = 0.3)
chm = as.raster(chm)
kernel = matrix(1,3,3)
chm = raster::focal(chm, w = kernel, fun = mean, na.rm = TRUE)

ttops = tree_detection(chm, 5, 2)
lastrees_dalponte(las, chm, ttops)
plot(las, color = "treeID", colorPalette = col)

# }