Operationalizing the use of TLS in forest inventories: the R package FORTLS
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Título: | Operationalizing the use of TLS in forest inventories: the R package FORTLS |
Autor/a: | Molina Valero, Juan Alberto Martínez Calvo, Adela Ginzo Villamayor, María José Novo Pérez, Manuel Antonio Álvarez González, Juan Gabriel Montes, Fernando Pérez Cruzado, César |
Centro/Departamento: | Universidade de Santiago de Compostela. Departamento de Estatística, Análise Matemática e Optimización Universidade de Santiago de Compostela. Departamento de Matemáticas Universidade de Santiago de Compostela. Departamento de Produción Vexetal e Proxectos de Enxeñaría |
Palabras chave: | Forest monitoring | Forest stands parameters | LiDAR | Precision forestry | Remote sensing | Terrestrial-based-technologies | |
Data: | 2022 |
Editor: | Elsevier |
Cita bibliográfica: | Environmental Modelling & Software 150 (2022) 105337 |
Resumo: | Terrestrial Laser Scanning (TLS) devices show great potential for application in Forest Inventories (FIs) as they are capable of registering high resolution point clouds rapidly and automatically. Nevertheless, operational use of TLS for FI purposes has been hampered by the absence of algorithms for processing the acquired data, particularly in the single-scan mode, as occlusions result in loss of information. The R package FORTLS has been developed to overcome this obstacle, as it automates the processing of single-scan TLS point cloud data for forestry purposes and includes several features that deal with occlusions. FORTLS makes use of the main advantage of the single-scan scenario in FI, thus improving the efficiency of data acquisition and post-processing. All of these features of the FORTLS package are potentially valuable for the operational use of TLS in FIs, in combination with inference techniques derived from model-based and model-assisted approaches |
Versión do editor: | https://doi.org/10.1016/j.envsoft.2022.105337 |
URI: | http://hdl.handle.net/10347/27624 |
DOI: | 10.1016/j.envsoft.2022.105337 |
E-ISSN: | 1364-8152 |
Dereitos: | © 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) Atribución 4.0 Internacional |
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