FORTLS-package {FORTLS}

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

Description

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>).

Details

Usage of FORTLS includes the following functionalities:

• Tree detection: this is the first and necessary step for the other functionalities of FORTLS. This can be achieved using the following functions:

  1. normalize: mandatory first step for obtaining the relative coordinates of a TLS point cloud.

  2. tree.detection.single.scan: detects as many trees as possible from a normalized TLS single-scan point clouds.

  3. tree.detection.multi.scan: detects as many trees as possible from a normalized TLS multi-scan, SLAM, or similar terrestrial-based technologies point clouds.

  4. tree.detection.several.plots: includes the two previous functions for a better workflow when there are several plots to be sequentially analyzed.

• Estimation of variables when no field data are available: this is the main functionality of FORTLS and can be achieved using the following functions:

  1. distance.sampling: optional function which can be used for considering methodologies for correcting occlusion effects in estimating variables.

  2. estimation.plot.size: enables the best plot design to be determined on the basis of TLS data only.

  3. metrics.variables: is used for estimating metrics and variables potentially related to forest attributes at stand level.

• Estimation of variables when field data are available: this is the main and most desirable functionality of FORTLS and can be achieved using the following functions:

  1. distance.sampling: as before.

  2. simulations: computes simulations of TLS and field data for different plot designs. This is a prior step to the next functions.

  3. relative.bias: uses simulations output to assess the accuracy of direct estimations of variables according to homologous TLS and field data.

  4. correlations: uses simulations output to assess correlations among metrics and variables obtained from TLS data, and variables of interest estimated from field data.

  5. optimize.plot.design: using correlations output, represents the best correlations for variables of interest according to the plot design. It is thus possible to select the best plot design for estimating forest attributes from TLS data.

  6. metrics.variables: as before, but in this case plot parameters will be choosen on the basis of field data and better estimates will therefore be obtained.

Author(s)

Maintainer: Juan Alberto Molina-Valero [e-mail] [copyright holder]

Authors:

• María José Ginzo Villamayor [contributor]

• Manuel Antonio Novo Pérez [contributor]

• Adela Martínez-Calvo [contributor]

• Juan Gabriel Álvarez-González [contributor]

• Fernando Montes [contributor]

• César Pérez-Cruzado [contributor]

References

Molina-Valero J. A., Ginzo-Villamayor M. J., Novo Pérez M. A., Martínez-Calvo A., Álvarez-González J. G., Montes F., & Pérez-Cruzado C. (2019). FORTLS: an R package for processing TLS data and estimating stand variables in forest inventories. The 1st International Electronic Conference on Forests — Forests for a Better Future: Sustainability, Innovation, Interdisciplinarity. doi:10.3390/IECF2020-08066