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Automatic Assessment of Individual Stem Shape Parameters in Forest Stands from TLS Point Clouds: Application in Pinus pinaster

Covadonga Prendes Orcid Logo, Elena Canga Orcid Logo, Celestino Ordoñez Orcid Logo, Juan Majada Orcid Logo, Mauricio Acuna Orcid Logo, Carlos Cabo Gomez

Forests, Volume: 13, Issue: 3, Start page: 431

Swansea University Author: Carlos Cabo Gomez

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DOI (Published version): 10.3390/f13030431

Abstract

Tree morphological characteristics, particularly straightness and lean, significantly influence the value of the commercial products that can be obtained. Despite this, they are not usually evaluated in timber field inventories because traditional techniques are labor-intensive and largely subjectiv...

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Published in: Forests
ISSN: 1999-4907
Published: MDPI AG 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa60739
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Here, a non-destructive, fully automated methodology is presented that estimates the parameters for describing straightness and lean using terrestrial laser scanning (TLS) data. It is based on splitting stems into evenly spaced sections and estimating their centers, which are then used to automatically calculate the maximum sagitta, sinuosity, and lean of each tree. The methodology was applied in a breeding trial plot of Pinus pinaster, and the results obtained were compared with field measurements of straightness and lean based on visual classification. The methodology is robust to errors in the estimation of section centers, the basis for calculating shape parameters. Besides, its accuracy compares favorably with traditional field techniques, which often involve problems of misclassification. The new methodology is easy to use, less expensive, and overcomes the drawbacks of traditional field techniques for obtaining straightness and lean measurements. 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spelling 2022-08-25T15:04:56.2498483 v2 60739 2022-08-05 Automatic Assessment of Individual Stem Shape Parameters in Forest Stands from TLS Point Clouds: Application in Pinus pinaster 660108e8078886c3e750d803be23276b Carlos Cabo Gomez Carlos Cabo Gomez true false 2022-08-05 FGSEN Tree morphological characteristics, particularly straightness and lean, significantly influence the value of the commercial products that can be obtained. Despite this, they are not usually evaluated in timber field inventories because traditional techniques are labor-intensive and largely subjective, hence the use of these parameters is limited to research and genetic breeding programs. Here, a non-destructive, fully automated methodology is presented that estimates the parameters for describing straightness and lean using terrestrial laser scanning (TLS) data. It is based on splitting stems into evenly spaced sections and estimating their centers, which are then used to automatically calculate the maximum sagitta, sinuosity, and lean of each tree. The methodology was applied in a breeding trial plot of Pinus pinaster, and the results obtained were compared with field measurements of straightness and lean based on visual classification. The methodology is robust to errors in the estimation of section centers, the basis for calculating shape parameters. Besides, its accuracy compares favorably with traditional field techniques, which often involve problems of misclassification. The new methodology is easy to use, less expensive, and overcomes the drawbacks of traditional field techniques for obtaining straightness and lean measurements. It can be modified to apply to any species and stand typology. Journal Article Forests 13 3 431 MDPI AG 1999-4907 straightness; lean; sinuosity; tree breeding; wood quality 9 3 2022 2022-03-09 10.3390/f13030431 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University This work was funded by the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) within the framework of the RTA2017-00063-C04-02 (2017) project entitled: “Evaluation of relevant characters for the sustainable management of Pinus pinaster Ait. and their interaction under new climatic scenarios”. Carlos Cabo received funding from the UK Natural Environment Research Council (NE/T001194/1), and from the Spanish Government (Ministerio de Universidades) and the European Union (NextGenerationEU), within the project MU-21-UP2021-030 2022-08-25T15:04:56.2498483 2022-08-05T11:42:35.0859858 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography Covadonga Prendes 0000-0001-8920-2976 1 Elena Canga 0000-0003-2132-9991 2 Celestino Ordoñez 0000-0002-6912-6299 3 Juan Majada 0000-0003-0009-4847 4 Mauricio Acuna 0000-0003-1409-5699 5 Carlos Cabo Gomez 6 60739__24857__f1d9ca83646843e59ab8d830207f0712.pdf 60739.pdf 2022-08-05T11:47:32.3931340 Output 2682449 application/pdf Version of Record true Copyright: © 2022 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 true eng https://creativecommons.org/licenses/by/4.0/
title Automatic Assessment of Individual Stem Shape Parameters in Forest Stands from TLS Point Clouds: Application in Pinus pinaster
spellingShingle Automatic Assessment of Individual Stem Shape Parameters in Forest Stands from TLS Point Clouds: Application in Pinus pinaster
Carlos Cabo Gomez
title_short Automatic Assessment of Individual Stem Shape Parameters in Forest Stands from TLS Point Clouds: Application in Pinus pinaster
title_full Automatic Assessment of Individual Stem Shape Parameters in Forest Stands from TLS Point Clouds: Application in Pinus pinaster
title_fullStr Automatic Assessment of Individual Stem Shape Parameters in Forest Stands from TLS Point Clouds: Application in Pinus pinaster
title_full_unstemmed Automatic Assessment of Individual Stem Shape Parameters in Forest Stands from TLS Point Clouds: Application in Pinus pinaster
title_sort Automatic Assessment of Individual Stem Shape Parameters in Forest Stands from TLS Point Clouds: Application in Pinus pinaster
author_id_str_mv 660108e8078886c3e750d803be23276b
author_id_fullname_str_mv 660108e8078886c3e750d803be23276b_***_Carlos Cabo Gomez
author Carlos Cabo Gomez
author2 Covadonga Prendes
Elena Canga
Celestino Ordoñez
Juan Majada
Mauricio Acuna
Carlos Cabo Gomez
format Journal article
container_title Forests
container_volume 13
container_issue 3
container_start_page 431
publishDate 2022
institution Swansea University
issn 1999-4907
doi_str_mv 10.3390/f13030431
publisher MDPI AG
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Geography{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Geography
document_store_str 1
active_str 0
description Tree morphological characteristics, particularly straightness and lean, significantly influence the value of the commercial products that can be obtained. Despite this, they are not usually evaluated in timber field inventories because traditional techniques are labor-intensive and largely subjective, hence the use of these parameters is limited to research and genetic breeding programs. Here, a non-destructive, fully automated methodology is presented that estimates the parameters for describing straightness and lean using terrestrial laser scanning (TLS) data. It is based on splitting stems into evenly spaced sections and estimating their centers, which are then used to automatically calculate the maximum sagitta, sinuosity, and lean of each tree. The methodology was applied in a breeding trial plot of Pinus pinaster, and the results obtained were compared with field measurements of straightness and lean based on visual classification. The methodology is robust to errors in the estimation of section centers, the basis for calculating shape parameters. Besides, its accuracy compares favorably with traditional field techniques, which often involve problems of misclassification. The new methodology is easy to use, less expensive, and overcomes the drawbacks of traditional field techniques for obtaining straightness and lean measurements. It can be modified to apply to any species and stand typology.
published_date 2022-03-09T04:19:06Z
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