Package: FORTLS 1.4.0
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:
FORTLS_1.4.0.tar.gz
FORTLS_1.4.0.zip(r-4.5)FORTLS_1.4.0.zip(r-4.4)FORTLS_1.4.0.zip(r-4.3)
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)
FORTLS_1.4.0.tar.gz(r-4.5-noble)FORTLS_1.4.0.tar.gz(r-4.4-noble)
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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
- 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
forest-inventoryforest-monitoringlidar-point-cloud
Last updated 10 months agofrom:b2f25c1ae2. Checks:OK: 7 NOTE: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 18 2024 |
R-4.5-win-x86_64 | NOTE | Nov 18 2024 |
R-4.5-linux-x86_64 | NOTE | Nov 18 2024 |
R-4.4-win-x86_64 | OK | Nov 18 2024 |
R-4.4-mac-x86_64 | OK | Nov 18 2024 |
R-4.4-mac-aarch64 | OK | Nov 18 2024 |
R-4.3-win-x86_64 | OK | Nov 18 2024 |
R-4.3-mac-x86_64 | OK | Nov 18 2024 |
R-4.3-mac-aarch64 | OK | Nov 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.Rmd
usingknitr::rmarkdown
on Nov 18 2024.Last update: 2023-09-11
Started: 2023-01-11
Stand-level
Rendered fromstand_level.Rmd
usingknitr::rmarkdown
on Nov 18 2024.Last update: 2023-01-11
Started: 2023-01-11
Plot design optimization
Rendered fromplot_design_optimization.Rmd
usingknitr::rmarkdown
on Nov 18 2024.Last update: 2023-01-11
Started: 2023-01-11