Terrestrial Point Cloud Processing of Forest Data

Algorithms for tree detection, noise removal, stem modelling, 3D visualization and manipulation of terrestrial 'LiDAR' (but not only) point clouds, currently focusing on high performance applications for forest inventory - being fully compatible with the 'LAS' infrastructure provided by 'lidR'. For in depth descriptions of the stem classification and segmentation algorithms check out Conto et al. (2017) .


High performance R functions for forest inventory based on Terrestrial Laser Scanning (but not only) point clouds.

Description

This package is a refactor of the methods described in this paper.

The algorithms were rewritten in C++ and wrapped in R functions through Rcpp. The algorithms were reviewed and enhanced, new functionalities introduced and the rebuilt functions now work upon lidR's LAS objects infrastructure.

This is an ongoing project and new features will be introduced often. For any questions or comments please contact me through github. Suggestions, ideas and references of new algorithms are always welcome - as long as they fit into TreeLS' scope.

Main functionalities

  • Tree detection at plot level
  • Stem points detection at single tree and plot levels
  • Stem segmentation at single tree and plot levels

Coming soon:

  • lidR wrappers for writing TLS data with extra header fields
  • Eigen decomposition feature detection for trees and stems
  • Tree modelling based on robust cylinder fitting
  • 3D interactive point cloud manipulation

Installation

Requirements

  • devtools: run install.packages('devtools', dependencies = TRUE) from the R console
  • Rcpp compiler:
    • on Windows: install Rtools for your R version - make sure to add it to your system's path
    • on Mac: install Xcode
    • on Linux: be sure to have r-base-dev installed

Install TreeLS latest version

On the R console, run:

devtools::install_github('tiagodc/TreeLS')

Legacy code

For anyone still interested in the old implementations of this library (fully developed in R, slow but suitable for research), you can still use it. In order to do it, uninstall any recent instances of TreeLS and reinstall the legacy version:

devtools::install_github('tiagodc/TreeLS', ref='old')

Not all features from the old package were reimplemented using Rcpp, but I'll get there.

Usage

Example of full processing pipe until stem segmentation for a forest plot:

library(TreeLS)

# open artificial sample file
file = system.file("extdata", "pine_plot.laz", package="TreeLS")
tls = readTLS(file)

# normalize the point cloud
tls = tlsNormalize(tls, keepGround = T)
plot(tls, color='Classification')

# extract the tree map from a thinned point cloud
thin = tlsSample(tls, voxelize(0.05))
map = treeMap(thin, map.hough(min_density = 0.03))

# visualize tree map in 2D and 3D
xymap = treePositions(map, plot = TRUE)
plot(map, color='Radii')

# classify stem points
tls = stemPoints(tls, map)

# extract measures
seg = stemSegmentation(tls, sgmt.ransac.circle(n = 15))

# view the results
tlsPlot(tls, seg)
tlsPlot(tls, seg, map)

News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("TreeLS")

1.0 by Tiago de Conto, 2 months ago


https://github.com/tiagodc/TreeLS


Browse source code at https://github.com/cran/TreeLS


Authors: Tiago de Conto [aut, cre]


Documentation:   PDF Manual  


GPL-3 license


Imports rgl, raster

Depends on data.table, magrittr, lidR

Linking to Rcpp, BH, RcppEigen


See at CRAN