itd_manual: Individual Tree Detection Algorithm

In 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 manual tree detection. Users can pinpoint the tree top positions manually and interactively using the mouse. This is only suitable for small-sized plots. First the point cloud is displayed, then the user is invited to select a rectangular region of interest in the scene using the mouse button. Within the selected region the highest point will be flagged as 'tree top' in the scene. Once all the trees are labelled the user can exit the tool by selecting an empty region. Points can also be unflagged. The goal of this tool is mainly for minor correction of automatically-detected tree outputs.
This algorithm does not preserve tree IDs from detected and renumber all trees. It also looses all attributes

Usage

manual(detected = NULL, radius = 0.5, color = "red", button = "middle", ...)

Arguments

detected	SpatialPoints* or sf/sfc_POINT' with 2 or 3D points of already found tree tops that need manual correction. Can be NULL
radius	numeric. Radius of the spheres displayed on the point cloud (aesthetic purposes only).
color	character. Colour of the spheres displayed on the point cloud (aesthetic purposes only).
button	Which button to use for selection. One of "left", "middle", "right". lidR using left for rotation and right for dragging using one of left or right will disable either rotation or dragging
...	supplementary parameters to be passed to plot.

See Also

Other individual tree detection algorithms: itd_lmf

Examples

## Not run: 
LASfile <- system.file("extdata", "MixedConifer.laz", package="lidR")
las = readLAS(LASfile)

# Full manual tree detection
ttops = locate_trees(las, manual())

# Automatic detection with manual correction
ttops = locate_trees(las, lmf(5))
ttops = locate_trees(las, manual(ttops))

## End(Not run)

lidR documentation built on Sept. 8, 2023, 5:10 p.m.