Package: FORTLS 2.0.2

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], Juan Gabriel Álvarez-González [aut, ths], Fernando Montes [aut], César Pérez-Cruzado [aut, ths]

FORTLS_2.0.2.tar.gz
FORTLS_2.0.2.zip(r-4.7-x86_64)FORTLS_2.0.2.zip(r-4.7-arm64)FORTLS_2.0.2.zip(r-4.6-x86_64)FORTLS_2.0.2.zip(r-4.6-arm64)FORTLS_2.0.2.zip(r-4.5-x86_64)
FORTLS_2.0.2.tgz(r-4.6-x86_64)FORTLS_2.0.2.tgz(r-4.6-arm64)FORTLS_2.0.2.tgz(r-4.5-x86_64)FORTLS_2.0.2.tgz(r-4.5-arm64)
FORTLS_2.0.2.tar.gz(r-4.7-arm64)FORTLS_2.0.2.tar.gz(r-4.7-x86_64)FORTLS_2.0.2.tar.gz(r-4.6-arm64)FORTLS_2.0.2.tar.gz(r-4.6-x86_64)
FORTLS_2.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
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/docs 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:

Conda:

forest-inventoryforest-monitoringlidar-point-cloudsmobile-laser-scanningterrestrial-laser-scanningtimbercpp

7.35 score 33 stars 15 scripts 515 downloads 30 exports 135 dependencies

Last updated from:4d2c7af03a. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK226
linux-devel-x86_64OK289
source / vignettesOK513
linux-release-arm64OK222
linux-release-x86_64OK279
macos-release-arm64OK349
macos-release-x86_64OK511
macos-oldrel-arm64OK298
macos-oldrel-x86_64OK568
windows-devel-arm64OK455
windows-devel-x86_64OK265
windows-release-arm64OK462
windows-release-x86_64OK194
windows-oldrel-x86_64OK242
wasm-releaseOK242

Exports:angle_count_cppcorrelationsdistance.samplingestimation.plot.sizefit_circle_cpp_modifiedfit_circles_maciffixed_area_cppgeometric_features_distgeometric_features_pyheight_perc_cppinstall_fortls_python_depsinternal_ransacis_one_row_all_naiterations_RANSACk_tree_cppmetrics.variablesnormalizeoptimize.plot.designRANSAC_cpprelative.biassample_indicessimulationstree.detection.multi.scantree.detection.several.plotstree.detection.single.scanvoxel_grid_downsamplingweighted_mean_aritweighted_mean_geomweighted_mean_harmweighted_mean_sqrt

Dependencies:abindaskpassbase64encBHbitbit64bootbslibcachemcircularclassclassIntclicodetoolscpp11crayoncrosstalkcurldata.tableDBIdbscandigestDistancedplyre1071evaluatefarverfastclusterfastmapFNNfontawesomefsfuturefuture.applygenericsgeometryggplot2globalsgluegtableherehighrhmshtmltoolshtmlwidgetshttrisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevallidRlifecyclelinproglistenvlpSolvemagicmagrittrMatrixmemoisemgcvmimemomentsmrdsmvtnormnlmenloptrnumDerivopenssloptimxotelparallellypillarpkgconfigplotlypngpracmaprettyunitsprogresspromisesproxypurrrR6rappdirsrasterrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRcppProgressRcppTOMLRCSFRdpackreticulateRfastrglrlangrlasrmarkdownrprojrootRsolnps2S7sassscalessfspstarsstringistringrsysterratibbletidyrtidyselecttinytextruncnormtzdbunitsutf8vctrsviridisLiteVoxRvroomwithrwkxfunyamlzigg

Tree-level variables
Normalization | The arguments of normalize | Defining the point cloud | Adjusting the algorithms applied in normalize function | The output data frame | Tree detection | Data from TLS single-scan approach | Defining the range of diameters and heights of possible trees | Resolution of the TLS | Including further information about the plots | Algorithm to distinguish stem points and foliage points | Algorithms for identification of trees | Estimation of tree attributes | Data from TLS multi-scan approach | Automatic normalization and tree detection of several plots
Rendered fromtree_level.Rmdusingknitr::rmarkdown

Last update: 2026-04-10
Started: 2023-01-11

Plot design optimization
Estimating optimal plot size without field data | Validation with field data and optimizing plot design | Plot simultaion and estimation of metrics and variables | The input data frames | Specifying designs of simulated plots | Further adjustable arguments | Output of the simulations function | Calculation of relative bias | Functions facilitating model-based or model-assisted sampling approaches | Computing correlations | Visualizing correlations
Rendered fromplot_design_optimization.Rmdusingknitr::rmarkdown

Last update: 2025-10-13
Started: 2023-01-11

Stand-level variables
Computing stand-level metrics and variables | Distance sampling (distance.sampling function) | Additional information about trees through field data | Selecting trees to be included in the calculations | Plot design and parameters | Output files | Stand-level metrics | Statistics of the z, rho and r | Stand density (N), volume (V) and basal area (G) | Occlusion correction | Mean and dominant heights (h) and diameters (d)
Rendered fromstand_level.Rmdusingknitr::rmarkdown

Last update: 2025-10-13
Started: 2023-01-11