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dc.contributor | Escuela de Ingeniería Agraria y Forestal | es_ES |
dc.contributor.author | Pereira-Obaya, Dimas | |
dc.contributor.author | Cabo, Carlos | |
dc.contributor.author | Ordóñez, Celestino | |
dc.contributor.author | Rodríguez Pérez, José Ramón | |
dc.contributor.other | Ingeniería Cartografica, Geodesica y Fotogrametria | es_ES |
dc.date | 2024 | |
dc.date.accessioned | 2024-04-03T07:11:00Z | |
dc.date.available | 2024-04-03T07:11:00Z | |
dc.identifier.citation | Pereira-Obaya, D.; Cabo, C.; Ordóñez, C.; Rodríguez-Pérez, J.R. A Handheld Laser-Scanning-Based Methodology for Monitoring Tree Growth in Chestnut Orchards. Sensors 2024, 24, 1717. https://doi.org/10.3390/s24061717 | es_ES |
dc.identifier.issn | 1424-8220 | |
dc.identifier.other | https://www.mdpi.com/1424-8220/24/6/1717 | es_ES |
dc.identifier.uri | https://hdl.handle.net/10612/19333 | |
dc.description.abstract | [EN] Chestnut and chestnut byproducts are of worldwide interest, so there is a constant need to develop faster and more accurate monitoring techniques. Recent advances in simultaneous localization and mapping (SLAM) algorithms and user accessibility have led to increased use of handheld mobile laser scanning (HHLS) in precision agriculture. We propose a tree growth monitoring methodology, based on HHLS point cloud processing, that calculates the length of branches through spatial discretization of the point cloud for each tree. The methodology was tested by comparing two point clouds collected almost simultaneously for each of a set of sweet chestnut trees. The results obtained indicated that our HHLS method was reliable and accurate in efficiently monitoring sweet chestnut tree growth. The same methodology was used to calculate the growth of the same set of trees over 37 weeks (from spring to winter). Differences in week 0 and week 37 scans showed an approximate mean growth of 0.22 m, with a standard deviation of around 0.16 m reflecting heterogeneous tree growth. | es_ES |
dc.language | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Ingeniería forestal | es_ES |
dc.subject.other | Sweet chestnut | es_ES |
dc.subject.other | MLS | es_ES |
dc.subject.other | SLAM | es_ES |
dc.subject.other | 3-D point cloud | es_ES |
dc.subject.other | Tree growth monitoring | es_ES |
dc.title | A Handheld Laser-Scanning-Based Methodology for Monitoring Tree Growth in Chestnut Orchards | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.identifier.doi | 10.3390/s24061717 | |
dc.description.peerreviewed | SI | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.essn | 1424-8220 | |
dc.journal.title | Sensors | es_ES |
dc.volume.number | 24 | es_ES |
dc.issue.number | 6 | es_ES |
dc.page.initial | 1717 | es_ES |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es_ES |
dc.subject.unesco | 3106 | es_ES |
dc.description.project | This work was supported by UK NERC project (NE/T001194/1): ‘Advancing 3D Fuel Mapping for Wildfire Behaviour and Risk Mitigation Modelling’ and by the Spanish Knowledge Generation project (PID2021-126790NB-I00): ‘Advancing carbon emission estimations from wildfires applying artificial intelligence to 3D terrestrial point clouds’. This work was supported by the company VITICAMPO, SL (grant id: 2021/00009/001; T132). | es_ES |
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