Package: FORTLS 1.4.0

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], Arunima Singh [aut, com], Gokul Kottilapurath Surendran [aut, com], Juan Gabriel Álvarez-González [aut, ths], Fernando Montes [aut], César Pérez-Cruzado [aut, ths]

FORTLS_1.4.0.tar.gz
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FORTLS_1.4.0.tgz(r-4.4-x86_64)FORTLS_1.4.0.tgz(r-4.4-arm64)FORTLS_1.4.0.tgz(r-4.3-x86_64)FORTLS_1.4.0.tgz(r-4.3-arm64)
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FORTLS_1.4.0.tgz(r-4.4-emscripten)FORTLS_1.4.0.tgz(r-4.3-emscripten)
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'))

Peer review:

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

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-inventoryforest-monitoringlidar-point-cloud

6.18 score 21 stars 12 scripts 292 downloads 21 exports 130 dependencies

Last updated 10 months agofrom:b2f25c1ae2. Checks:OK: 7 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 18 2024
R-4.5-win-x86_64NOTENov 18 2024
R-4.5-linux-x86_64NOTENov 18 2024
R-4.4-win-x86_64OKNov 18 2024
R-4.4-mac-x86_64OKNov 18 2024
R-4.4-mac-aarch64OKNov 18 2024
R-4.3-win-x86_64OKNov 18 2024
R-4.3-mac-x86_64OKNov 18 2024
R-4.3-mac-aarch64OKNov 18 2024

Exports:angle_count_cppcorrelationsdistance.samplingestimation.plot.sizefixed_area_cppheight_perc_cppk_tree_cppmetrics.variablesncr_point_cloud_doublenormalizeoptimize.plot.designrelative.biassimulationstree.detection.multi.scantree.detection.several.plotstree.detection.single.scanver_point_cloud_doubleweighted_mean_aritweighted_mean_geomweighted_mean_harmweighted_mean_sqrt

Dependencies:abindaskpassbase64encBHbitbit64bootbslibcachemcircularclassclassIntclicolorspacecpp11crayoncrosstalkcurldata.tableDBIdbscandigestDistancedplyre1071evaluatefansifarverfastclusterfastmapFNNfontawesomefsgenericsgeometryggplot2gluegtableherehighrhmshtmltoolshtmlwidgetshttrisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevallidRlifecyclelinproglpSolvemagicmagrittrMASSMatrixmemoisemgcvmimemomentsmrdsmunsellmvtnormnlmenloptrnumDerivopenssloptimxpillarpkgconfigplotlypngpracmaprettyunitsprogresspromisesproxypurrrR6rappdirsrasterRColorBrewerRcppRcppArmadilloRcppEigenRcppGSLRcppParallelRcppProgressRcppTOMLRcppZigguratRCSFreticulateRfastrglrlangrlasrmarkdownrprojrootRsolnps2sassscalessfspstarsstringistringrsysterratibbletidyrtidyselecttinytextruncnormtzdbunitsutf8vctrsviridisLiteVoxRvroomwithrwkxfunyaml

Tree-level

Rendered fromtree_level.Rmdusingknitr::rmarkdownon Nov 18 2024.

Last update: 2023-09-11
Started: 2023-01-11

Stand-level

Rendered fromstand_level.Rmdusingknitr::rmarkdownon Nov 18 2024.

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

Plot design optimization

Rendered fromplot_design_optimization.Rmdusingknitr::rmarkdownon Nov 18 2024.

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