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FORTLS

Automatic Processing of Close-Range Technologies Point Cloud Data for Forestry Purposes

Process automation of point cloud data derived from terrestrial-based technologies such as Terrestrial Laser Scanner (TLS) or Mobile Laser Scanner (MLS). 'FORTLS' enables (i) detection of trees and estimation of tree-level attributes (e.g. diameters and heights), (ii) estimation of stand-level variables (e.g. density, basal area, mean and dominant height), (iii) computation of metrics related to important forest attributes estimated in Forest Inventories (FIs) at stand-level, and (iv) optimization of plot design for combining TLS data and field measured data. Documentation about 'FORTLS' is described in Molina-Valero et al. (2022, https://doi.org/10.1016/j.envsoft.2022.105337).

Get the lat stable version of FORTLS from GitHub (included in the master branch)

remotes::install_github("Molina-Valero/FORTLS", ref = "devel", dependencies = TRUE)

Acknowledgements

FORTLS it is being developed at Czech University of Life Sciences Prague and University of Santiago de Compostela.

Development of the FORTLS package is being possible thanks to the following fellowships/projects:

Climate Change Adaptation of Forests in the Brdy Highland LIFE21-CCA-CZ-LIFE-Adapt-Brdy/101074426
Design of forest monitoring systems on a regional scale [ED431F 2020/02] supported by the Regional Government of Galicia
Ramón Areces Foundation Grants for Postdoctoral Studies XXXV Call for Expansion of Studies Abroad in Life and Matter Sciences

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Install

install.packages('FORTLS')

Monthly Downloads

361

Version

1.5.1

License

GPL-3

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Repository

https://github.com/Molina-Valero/FORTLS

Homepage

https://molina-valero.github.io/FORTLS/

Maintainer

Juan Alberto Molina-Valero

Last Published

May 1st, 2025

Functions in FORTLS (1.5.1)

FORTLS: Automatic Processing of Terrestrial-Based Technologies Point Cloud Data for Forestry Purposes

VoxR_vox_update

This function was updated to return also the input data with the computed voxels

Apply RANSAC algorithm to estimate diameters.

Correlation Between Field Estimations and TLS Metrics

distance.sampling

Distance Sampling Methods for Correcting Occlusions Effects

chunk_size_from_area

This function compute the chunk size from the area of the las-catalog

This function create overlapping polygons using the data.table R package

angle_count_cpp

Calculate dominant diameters and heights for simulations for angle-count plots.

Rioja.simulations

Simulated Metrics and Variables for a Stand Case Study in La Rioja

Inventoried Plots Data for a Stand Case Study in La Rioja

geometric_features_point

This function obtains geometric features at point level

estimation.plot.size

Assess Consistency of Metrics for Simulated TLS Plots

Calculate dominant diameters and heights for simulations for angle-count plots.

fit_circle_cpp_modified

This function fit a circle based on 3 points

iterations_RANSAC

Function that performs the "RANSAC_cpp" N-times

internal_ransac

Apply RANSAC algorithm to estimate diameters.

height_perc_cpp

Calculate dominant diameters and heights for simulations for angle-count plots.

geometric.features

This function fit a circle based on 3 points

is_one_row_all_na

This function was updated to return also the input data with the computed voxels

geometric_features

This function obtains geometric features at point level

save_file_as_laz

Save a file as a .laz file

random.forest.fit

Define the path to the folder containing the Python scripts relative to the package directory

Save to tiles and receive the metadata

metrics.variables

Compute Metrics and Variables for Terrestrial-Based Technologies Point Clouds

Relative Bias Between Field Estimations and TLS metrics

Calculate dominant diameters and heights for simulations for angle-count plots.

random.forest.sp

Define the path to the folder containing the Python scripts relative to the package directory

Relative Coordinates and Density Reduction for Terrestrial-Based Technologies Point Clouds

optimize.plot.design

Optimize Plot Design Based on Optimal Correlations

weighted_mean_geom

Calculate weighted geometric mean.

tree.detection.multi.scan

Tree-Level Variables Estimation

tree.detection.several.plots

Tree-Level Variables Estimation for Several Plots

weighted_mean_harm

Calculate weighted harmonic mean.

species.classification

Species classification

voxel_grid_downsampling

Voxel down sampling

Compute Metrics and Variables for Simulated TLS and Field Plots

tree.detection.single.scan

Tree-Level Variables Estimation for TLS Single-Scan Approach

Secondary function to use and sort the sublists before using as input to the Rcpp function

weighted_mean_arit

Calculate weighted arithmetic mean.

weighted_mean_sqrt

Calculate weighted quadratic mean.