Package: FORTLS 1.5.0

Juan Alberto Molina-Valero

FORTLS: Automatic Processing of Terrestrial-Based 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. '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 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, <doi:10.1016/j.envsoft.2022.105337>).

Authors:Juan Alberto Molina-Valero [aut, cph, cre], Adela Martínez-Calvo [aut, com], María José Ginzo Villamayor [aut, com], Manuel Antonio Novo Pérez [aut, com], Juan Gabriel Álvarez-González [aut, ths], Fernando Montes [aut], César Pérez-Cruzado [aut, ths]

FORTLS_1.5.0.tar.gz
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FORTLS.pdf |FORTLS.html✨
FORTLS/json (API)

# Install 'FORTLS' in R:

install.packages('FORTLS', repos = c('https://molina-valero.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/molina-valero/fortls/issues

Pkgdown site:https://molina-valero.github.io

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

Rioja.data - Inventoried Plots Data for a Stand Case Study in La Rioja
Rioja.simulations - Simulated Metrics and Variables for a Stand Case Study in La Rioja

On CRAN:

forest-inventory forest-monitoring lidar-point-cloud cpp

6.48 score 23 stars 11 scripts 295 downloads 38 exports 147 dependencies

Last updated 9 days agofrom:7d2c5ab5b6. Checks:12 OK. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Mar 19 2025
R-4.5-win-x86_64	OK	Mar 19 2025
R-4.5-mac-x86_64	OK	Mar 19 2025
R-4.5-mac-aarch64	OK	Mar 19 2025
R-4.5-linux-x86_64	OK	Mar 19 2025
R-4.4-win-x86_64	OK	Mar 19 2025
R-4.4-mac-x86_64	OK	Mar 19 2025
R-4.4-mac-aarch64	OK	Mar 19 2025
R-4.4-linux-x86_64	OK	Mar 19 2025
R-4.3-win-x86_64	OK	Mar 19 2025
R-4.3-mac-x86_64	OK	Mar 19 2025
R-4.3-mac-aarch64	OK	Mar 19 2025

Exports:angle_count_cpp chunk_size_from_area correlations datatable_grid distance.sampling estimation.plot.size fit_circle_cpp_modified fixed_area_cpp geometric_features geometric_features_point geometric.features height_perc_cpp internal_ransac is_one_row_all_na iterations_RANSAC k_tree_cpp metrics.variables normalize optimize.plot.design random.forest.fit random.forest.sp RANSAC_cpp relative.bias sample_indices save_file_as_laz save_to_tiles simulations sort_sublists species.classification tree.detection.multi.scan tree.detection.several.plots tree.detection.single.scan voxel_grid_downsampling VoxR_vox_update weighted_mean_arit weighted_mean_geom weighted_mean_harm weighted_mean_sqrt

Dependencies:abind askpass base64enc BH bit bit64 boot brew bslib cachem circular class classInt cli colorspace cpp11 crayon crosstalk curl data.table DBI dbscan digest Distance dplyr e1071 evaluate fansi farver fastcluster fastmap FNN fontawesome fs generics geojsonsf geometries geometry ggplot2 glue gtable here highr hms htmltools htmlwidgets httpuv httr isoband jquerylib jsonify jsonlite KernSmooth knitr labeling later lattice lazyeval leafem leaflet leaflet.providers leafpop lidR lifecycle linprog lpSolve magic magrittr mapview MASS Matrix memoise mgcv mime moments mrds munsell mvtnorm nlme nloptr numDeriv openssl optimx pillar pkgconfig plotly plyr png pracma prettyunits progress promises proxy purrr R6 rapidjsonr rappdirs raster RColorBrewer Rcpp RcppArmadillo RcppEigen RcppParallel RcppProgress RcppTOML RCSF reticulate Rfast rgl rlang rlas rmarkdown rprojroot Rsolnp s2 sass satellite scales servr sf sfheaders sp stars stringi stringr svglite sys systemfonts terra tibble tidyr tidyselect tinytex truncnorm tzdb units utf8 uuid vctrs viridisLite VoxR vroom withr wk xfun yaml zigg

Tree-level

Anika Seppelt

Rendered fromtree_level.Rmdusingknitr::rmarkdownon Mar 19 2025.

Last update: 2025-03-19
Started: 2023-01-11

Stand-level

Anika Seppelt

Rendered fromstand_level.Rmdusingknitr::rmarkdownon Mar 19 2025.

Last update: 2023-01-11
Started: 2023-01-11

Plot design optimization

Anika Seppelt

Rendered fromplot_design_optimization.Rmdusingknitr::rmarkdownon Mar 19 2025.

Last update: 2023-01-11
Started: 2023-01-11

Help page	Topics
FORTLS: Automatic Processing of Terrestrial-Based Technologies Point Cloud Data for Forestry Purposes	FORTLS-package FORTLS
Correlation Between Field Estimations and TLS Metrics	correlations
Distance Sampling Methods for Correcting Occlusions Effects	distance.sampling
Assess Consistency of Metrics for Simulated TLS Plots	estimation.plot.size
Compute Metrics and Variables for Terrestrial-Based Technologies Point Clouds	metrics.variables
Relative Coordinates and Density Reduction for Terrestrial-Based Technologies Point Clouds	normalize
Optimize Plot Design Based on Optimal Correlations	optimize.plot.design
Relative Bias Between Field Estimations and TLS metrics	relative.bias
Inventoried Plots Data for a Stand Case Study in La Rioja	Rioja.data
Simulated Metrics and Variables for a Stand Case Study in La Rioja	Rioja.simulations
Compute Metrics and Variables for Simulated TLS and Field Plots	simulations
Tree-Level Variables Estimation	tree.detection.multi.scan
Tree-Level Variables Estimation for Several Plots	tree.detection.several.plots
Tree-Level Variables Estimation for TLS Single-Scan Approach	tree.detection.single.scan

Package: FORTLS 1.5.0

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

Tree-level

Stand-level

Plot design optimization

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)