FORTLS: An R Package for Processing TLS Data and Estimating Stand Variables in Forest Inventories
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Title: | FORTLS: An R Package for Processing TLS Data and Estimating Stand Variables in Forest Inventories |
Author: | Molina Valero, Juan Alberto Ginzo Villamayor, María José Novo Pérez, Manuel Antonio Álvarez González, Juan Gabriel Montes, Fernando Martínez Calvo, Adela Pérez Cruzado, César |
Affiliation: | Universidade de Santiago de Compostela. Departamento de Enxeñaría Agroforestal Universidade de Santiago de Compostela. Departamento de Estatística, Análise Matemática e Optimización Universidade de Santiago de Compostela. Departamento de Produción Vexetal e Proxectos de Enxeñaría |
Subject: | Forest inventory | LiDAR | Remote sensing | R-package | Software | Stand-level | TLS | |
Date of Issue: | 2021 |
Publisher: | MDPI |
Citation: | Environ. Sci. Proc. 2021, 3(1), 38; https://doi.org/10.3390/IECF2020-08066 |
Abstract: | Terrestrial Laser Scanning (TLS) enables rapid, automatic, and detailed 3D representation of surfaces with an easily handled scanner device. TLS, therefore, shows great potential for use in Forest Inventories (FIs). However, the lack of well-established algorithms for TLS data processing hampers operational use of the scanner for FI purposes. Here, we present FORTLS, which is an R package specifically developed to automate TLS point cloud data processing for forestry purposes. The FORTLS package enables (i) detection of trees and estimation of their 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 tree attributes estimated in FIs at stand level, and (iv) optimization of plot design for combining TLS data and field measured data. FORTLS can be used with single-scan TLS data, thus, improving data acquisition and shortening the processing time as well as increasing sample size in a cost-efficient manner. The package also includes several features for correcting occlusion problems in order to produce improved estimates of stand variables. These features of the FORTLS package will enable the operational use of TLS in FIs, in combination with inference techniques derived from model-based and model-assisted approaches |
Publisher version: | https://doi.org/10.3390/IECF2020-08066 |
URI: | http://hdl.handle.net/10347/24418 |
DOI: | 10.3390/IECF2020-08066 |
E-ISSN: | 2673-4931 |
Rights: | Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses /by/4.0/) Atribución 4.0 Internacional |
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Except where otherwise noted, this item's license is described as Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses /by/4.0/)