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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:

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Version

Install

install.packages('FORTLS')

Monthly Downloads

361

Version

1.4.0

License

GPL-3

Issues

0

Pull Requests

0

Stars

24

Forks

7

Maintainer

Juan Alberto Molina-Valero

Last Published

January 21st, 2024

Functions in FORTLS (1.4.0)

estimation.plot.size

Assess Consistency of Metrics for Simulated TLS Plots
fixed_area_cpp

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

Calculate dominant diameters and heights for simulations for angle-count plots.
tree.detection.multi.scan

Tree-Level Variables Estimation
tree.detection.several.plots

Tree-Level Variables Estimation for Several Plots
simulations

Compute Metrics and Variables for Simulated TLS and Field Plots
optimize.plot.design

Optimize Plot Design Based on Optimal Correlations
normalize

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

Relative Bias Between Field Estimations and TLS metrics
metrics.variables

Compute Metrics and Variables for Terrestrial-Based Technologies Point Clouds
tree.detection.single.scan

Tree-Level Variables Estimation for TLS Single-Scan Approach
ver_point_cloud_double

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

Calculate weighted geometric mean.
weighted_mean_arit

Calculate weighted arithmetic mean.
weighted_mean_sqrt

Calculate weighted quadratic mean.
weighted_mean_harm

Calculate weighted harmonic mean.
correlations

Correlation Between Field Estimations and TLS Metrics
angle_count_cpp

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

Distance Sampling Methods for Correcting Occlusions Effects
FORTLS-package

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

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

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

Inventoried Plots Data for a Stand Case Study in La Rioja
height_perc_cpp

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