Description: Process automation of Terrestrial Laser Scanner (TLS) point cloud data derived from single scans. 'FORTLS' enables (i) detection of trees and estimation of diameter at breast height (dbh), (ii) estimation of some stand 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. (2020, <doi:10.3390/IECF2020-08003>).
Imports: dbscan, Distance, ggvoronoi, htmlwidgets, lidR, moments,
plotly, progress, Rcpp(>= 1.0.7), raster, scales, sp, tidyr,

plotly, progress, Rcpp(>= 1.0.7), raster, RCSF, scales, sp,

Title: Automatic Processing of Terrestrial-Based Technologies Point
Description: Process automation of point cloud data derived from terrestrial-based technologies such as Terrestrial Laser Scanner (TLS) or Simultaneous Localization and Mapping (SLAM). '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>).
Imports: dbscan, Distance, htmlwidgets, lidR, methods, moments, plotly,
progress, raster, Rcpp(>= 1.0.7), RCSF, scales, sf, sp, tidyr,
Suggests: devtools, kableExtra, knitr, rmarkdown, testthat

Title: Two Dimensional Functional Principal Component Analysis
Description: Compute the two dimension functional principal component scores for a series of two dimension images.

Title: Three Dimensional Functional Component Analysis
Description: Run three dimensional functional principal component analysis and return the three dimensional functional principal component scores. The details of the method are explained in Lin et al.(2015) <doi:10.1371/journal.pone.0132945>.
Depends: graphics, grDevices, stats, utils