tlsInventory: Extract forest inventory metrics from a point cloud

In tiagodc/TreeLS: Terrestrial Point Cloud Processing of Forest Data

Extract forest inventory metrics from a point cloud

Description

Estimation of diameter and height tree-wise for normalized point clouds with assigned stem points.

Usage

tlsInventory(
  las,
  dh = 1.3,
  dw = 0.5,
  hp = 1,
  d_method = shapeFit(shape = "circle", algorithm = "ransac", n = 15, n_best = 20)
)

Arguments

las	LAS object.
dh	numeric - height layer (above ground) to estimate stem diameters, in point cloud units.
dw	numeric - height layer width, in point cloud units.
hp	numeric - height percentile to extract per tree (0-1). Use 1 for top height, i.e. the highest point.
d_method	parameterized shapeFit function, i.e. method to use for diameter estimation.

Examples

file = system.file("extdata", "pine_plot.laz", package="TreeLS")
tls = readTLS(file) %>%
  tlsNormalize %>%
  tlsSample

map = treeMap(tls, map.hough())
tls = treePoints(tls, map, trp.crop(circle=FALSE))
tls = stemPoints(tls, stm.hough())

dmt = shapeFit(shape = 'circle', algorithm='ransac', n=20)
inv = tlsInventory(tls, d_method = dmt)
tlsPlot(tls, inv)

tiagodc/TreeLS documentation built on June 29, 2023, 7:40 p.m.