get_raster_eigen_treelocs: Obtain tree information by rasterizing point cloud values of...

In bi0m3trics/spanner: Utilities to support landscape-, forest-, and tree-related data analysis

Obtain tree information by rasterizing point cloud values of relative neighborhood density and verticality within a slice of a normalized point cloud

Description

get_raster_eigen_treelocs returns a data.frame containing TreeID, X, Y, Z, Radius and Error in the same units as the .las

Usage

get_raster_eigen_treelocs(
  las,
  res = 0.05,
  pt_spacing = 0.0254,
  dens_threshold = 0.2,
  neigh_sizes = c(0.333, 0.166, 0.5),
  eigen_threshold = 0.6666,
  grid_slice_min = 0.6666,
  grid_slice_max = 2,
  minimum_polygon_area = 0.025,
  cylinder_fit_type = "ransac",
  max_dia = 0.5,
  SDvert = 0.25,
  n_best = 25,
  n_pts = 20,
  inliers = 0.9,
  conf = 0.99,
  max_angle = 20
)

Arguments

las	LAS Normalized las object.
res	numeric Pixel width of rasterized point cloud metrics.
pt_spacing	numeric Subsample spacing for graph connections.
dens_threshold	numeric Minimum point density in raster cell to be considered as potential tree bole.
eigen_threshold	numeric Minimum average verticality in raster cell to be considered as potential tree bole.
grid_slice_min	numeric Lower bound of point cloud slice in normalized point cloud.
grid_slice_max	numeric Upper bound of point cloud slice in normalized point cloud.
minimum_polygon_area	numeric Smallest allowable polygon area of potential tree boles.
cylinder_fit_type	character Choose "ransac" or "irls" cylinder fitting.
max_dia	numeric The max diameter (in m) of a resulting tree (use to eliminate commission errors).
SDvert	numeric The standard deviation threshold below whihc polygons will be considered as tree boles.
n_best	integer number of "best" ransac fits to keep when evaluating the best fit.
n_pts	integer number of point to be selected per ransac iteraiton for fitting.
inliers	integer expected proportion of inliers among cylinder points
conf	numeric confidence level
max_angle	numeric maximum tolerated deviation, in degrees, from vertical.
neigh_size	numeric Vector for verticality and relative density (small and large neighborhoods) calculations

Details

For terrestrial and mobile lidar datasets, tree locations and estimates of DBH are provided by rasterizing individual point cloud values of relative neighborhood density (at 0.3 and 1 m radius) and verticality within a slice of the normalized point cloud around breast height (1.34 m). The algorithim then uses defined threshold values to classify the resulting rasters and create unique polygons from the resulting classified raster. These point-density and verticality polygons were selected by their intersection with one another, resulting in a final set of polygons which were used to clip out regions of the point cloud that were most likely to represent tree boles. A RANSAC cylinder fitting algorithm was then used to estimate the fit of a cylinder to individual bole points. Cylinder centers and radius were used as inputs to an individual tree segmentation

Value

sf object An sf object containing the following seed information: TreeID, X, Y, Z, Radius, and Error in the same units as the .las

Examples

## Not run: 
# Set the number of threads to use in lidR
set_lidr_threads(8)

# Download and read an example laz
getExampleData("DensePatchA")
LASfile = system.file("extdata", "DensePatchA.laz", package="spanner")
las = readTLSLAS(LASfile, select = "xyzcr", "-filter_with_voxel 0.01")
# Don't forget to make sure the las object has a projection
projection(las) = sp::CRS("+init=epsg:26912")

# Pre-process the example lidar dataset by classifying the ground points
# using lidR::csf(), normalizing it, and removing outlier points
# using lidR::ivf()
las = classify_ground(las, csf(sloop_smooth = FALSE,
                                class_threshold = 0.5,
                                cloth_resolution = 0.5, rigidness = 1L,
                                iterations = 500L, time_step = 0.65))
las = normalize_height(las, tin())
las = classify_noise(las, ivf(0.25, 3))
las = filter_poi(las, Classification != LASNOISE)

# Plot the non-ground points, colored by height
plot(filter_poi(las, Classification != 2), color = "Z")

# Perform a deep inspection of the las object. If you see any
# red text, you may have issues!
las_check(las)

# Find individual tree locations and attribute data
myTreeLocs = get_raster_eigen_treelocs(las = las, res = 0.05,
                                       pt_spacing = 0.0254,
                                       dens_threshold = 0.2,
                                       neigh_sizes = c(0.333, 0.166, 0.5),
                                       eigen_threshold = 0.5,
                                       grid_slice_min = 0.6666,
                                       grid_slice_max = 2.0,
                                       minimum_polygon_area = 0.025,
                                       cylinder_fit_type = "ransac",
                                       max_dia = 0.5,
                                       SDvert = 0.25,
                                       n_pts = 20,
                                       n_best = 25)

# Plot the tree information over a CHM
plot(lidR::grid_canopy(las, res = 0.2, p2r()))
points(myTreeLocs$X, myTreeLocs$Y, col = "black", pch = 16,
       cex = myTreeLocs$Radius^2 * 10, asp = 1)


## End(Not run)

bi0m3trics/spanner documentation built on June 9, 2025, 3:57 p.m.