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dc.contributor.authorMolina Valero, Juan Alberto
dc.contributor.authorGinzo Villamayor, María José
dc.contributor.authorNovo Pérez, Manuel Antonio
dc.contributor.authorÁlvarez González, Juan Gabriel
dc.contributor.authorMontes, Fernando
dc.contributor.authorMartínez Calvo, Adela
dc.contributor.authorPérez Cruzado, César
dc.date.accessioned2021-02-12T09:57:39Z
dc.date.available2021-02-12T09:57:39Z
dc.date.issued2021
dc.identifier.citationEnviron. Sci. Proc. 2021, 3(1), 38; https://doi.org/10.3390/IECF2020-08066
dc.identifier.urihttp://hdl.handle.net/10347/24418
dc.description.abstractTerrestrial 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
dc.description.sponsorshipThis research was funded by the Spanish Ministry of Science, Innovation and Universities, AGL2016-76769-C2-2-R. JAMV was funded by the Spanish Ministry of Education through the FPU program (FPU16/03057)
dc.language.isoeng
dc.publisherMDPI
dc.rightsCopyright: © 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/)
dc.rightsAtribución 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectForest inventory
dc.subjectLiDAR
dc.subjectRemote sensing
dc.subjectR-package
dc.subjectSoftware
dc.subjectStand-level
dc.subjectTLS
dc.titleFORTLS: An R Package for Processing TLS Data and Estimating Stand Variables in Forest Inventories
dc.typejournal article
dc.identifier.doi10.3390/IECF2020-08066
dc.relation.publisherversionhttps://doi.org/10.3390/IECF2020-08066
dc.type.hasVersionVoR
dc.identifier.essn2673-4931
dc.rights.accessRightsopen access
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Enxeñaría Agroforestal
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Estatística, Análise Matemática e Optimización
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Produción Vexetal e Proxectos de Enxeñaría
dc.description.peerreviewedSI
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2016-76769-C2-2-R/ES/MODELIZACION DEL EFECTO DE LA INTENSIDAD DE PERTURBACION SOBRE LA ESTRUCTURA Y EL STOCK DE CARBONO EN MASAS NATURALES A PARTIR DEL INVENTARIO FORESTAL NACIONAL


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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/)
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 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/)





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