lidR (version 2.1.4)

lmf: Individual Tree Detection Algorithm

Description

This function is made to be used in tree_detection. It implements an algorithm for tree detection based on a local maximum filter. The windows size can be fixed or variable and its shape can be square or circular. The internal algorithm works either with a raster or a point cloud. It is deeply inspired by Popescu & Wynne (2004) (see references).

Usage

lmf(ws, hmin = 2, shape = c("circular", "square"))

Arguments

ws

numeric or function. Length or diameter of the moving window used to detect the local maxima in the units of the input data (usually meters). If it is numeric a fixed window size is used. If it is a function, the function determines the size of the window at any given location on the canopy. The function should take the height of a given pixel or point as its only argument and return the desired size of the search window when centered on that pixel/point.

hmin

numeric. Minimum height of a tree. Threshold below which a pixel or a point cannot be a local maxima. Default is 2.

shape

character. Shape of the moving window used to find the local maxima. Can be "square" or "circular".

References

Popescu, Sorin & Wynne, Randolph. (2004). Seeing the Trees in the Forest: Using Lidar and Multispectral Data Fusion with Local Filtering and Variable Window Size for Estimating Tree Height. Photogrammetric Engineering and Remote Sensing. 70. 589-604. 10.14358/PERS.70.5.589.

See Also

Other individual tree detection algorithms: manual

Examples

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

# point-cloud-based
# =================

# 5x5 m fixed window size
ttops <- tree_detection(las, lmf(5))

x <- plot(las)
add_treetops3d(x, ttops)

# variable windows size
f <- function(x) { x * 0.07 + 3}
ttops <- tree_detection(las, lmf(f))

x <- plot(las)
add_treetops3d(x, ttops)

# raster-based
# ============

# 5x5 m fixed window size
chm <- grid_canopy(las, res = 1, p2r(0.15))
kernel <- matrix(1,3,3)
chm <- raster::focal(chm, w = kernel, fun = median, na.rm = TRUE)

ttops <- tree_detection(chm, lmf(5))

plot(chm, col = height.colors(30))
plot(ttops, add = TRUE)

# variable window size
f <- function(x) { x * 0.07 + 3 }
ttops <- tree_detection(chm, lmf(f))

plot(chm, col = height.colors(30))
plot(ttops, add = TRUE)
# }