# lmf

##### Individual Tree Detection Algorithm

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)
# }
```

*Documentation reproduced from package lidR, version 2.2.5, License: GPL-3*