itd_lmf: Individual Tree Detection Algorithm

In Jean-Romain/lidR: Airborne LiDAR Data Manipulation and Visualization for Forestry Applications

Individual Tree Detection Algorithm

Description

This function is made to be used in locate_trees. 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"), ws_args = "Z")

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. By default function takes 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. This can be controled with the 'ws_args' parameter
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".
ws_args	list. Named list of argument for the function 'ws' if 'ws' is a function.

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: itd_manual

Examples

LASfile <- system.file("extdata", "MixedConifer.laz", package="lidR")
las <- readLAS(LASfile, select = "xyzi", filter = "-inside 481250 3812980 481300 3813050")

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

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

#plot(las) |> add_treetops3d(ttops)

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

#plot(las) |> add_treetops3d(ttops)

# Very custom variable windows size
f <- function(x, y, z) { x * 0.07 + y * 0.01 + z}
ws_args <- list(x = "Z", y = "Intensity", z = 3)
ttops <- locate_trees(las, lmf(f, ws_args = ws_args))

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

chm <- rasterize_canopy(las, res = 1, p2r(0.15), pkg = "terra")
ttops <- locate_trees(chm, lmf(5))

plot(chm, col = height.colors(30))
plot(sf::st_geometry(ttops), add = TRUE, col = "black", cex = 0.5, pch = 3)

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

plot(chm, col = height.colors(30))
plot(sf::st_geometry(ttops), add = TRUE, col = "black", cex = 0.5, pch = 3)

Jean-Romain/lidR documentation built on April 6, 2024, 9:41 p.m.