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dc.contributor.authorLombardi, Erica
dc.contributor.authorRodríguez Puerta, Francisco
dc.contributor.authorSantini, Filippo
dc.contributor.authorChambel, Maria Regina
dc.contributor.authorCliment, Jose
dc.contributor.authorResco de Dios, Víctor
dc.contributor.authorVoltas Velasco, Jordi
dc.date.accessioned2022-11-24T13:02:14Z
dc.date.available2022-11-24T13:02:14Z
dc.date.issued2022
dc.identifier.issn2072-4292
dc.identifier.urihttp://hdl.handle.net/10459.1/84323
dc.description.abstractRemote sensing is increasingly used in forest inventories. However, its application to assess genetic variation in forest trees is still rare, particularly in conifers. Here we evaluate the potential of LiDAR and RGB imagery obtained through unmanned aerial vehicles (UAVs) as highthroughput phenotyping tools for the characterization of tree growth and crown structure in two representative Mediterranean pine species. To this end, we investigated the suitability of these tools to evaluate intraspecific differentiation in a wide array of morphometric traits for Pinus nigra (European black pine) and Pinus halepensis (Aleppo pine). Morphometric traits related to crown architecture and volume, primary growth, and biomass were retrieved at the tree level in two genetic trials located in Central Spain and compared with ground-truth data. Both UAV-based methods were then tested for their accuracy to detect genotypic differentiation among black pine and Aleppo pine populations and their subspecies (black pine) or ecotypes (Aleppo pine). The possible relation between intraspecific variation of morphometric traits and life-history strategies of populations was also tested by correlating traits to climate factors at origin of populations. Finally, we investigated which traits distinguished better among black pine subspecies or Aleppo pine ecotypes. Overall, the results demonstrate the usefulness of UAV-based LiDAR and RGB records to disclose tree architectural intraspecific differences in pine species potentially related to adaptive divergence among populations. In particular, three LiDAR-derived traits related to crown volume, crown architecture, and main trunk—or, alternatively, the latter (RGB-derived) two traits—discriminated the most among black pine subspecies. In turn, Aleppo pine ecotypes were partly distinguishable by using two LiDAR-derived traits related to crown architecture and crown volume, or three RGB-derived traits related to tree biomass and main trunk. Remote-sensing-derived-traits related to main trunk, tree biomass, crown architecture, and crown volume were associated with environmental characteristics at the origin of populations of black pine and Aleppo pine, thus hinting at divergent environmental stress-induced local adaptation to drought, wildfire, and snowfall in both species.ca_ES
dc.description.sponsorshipThis work was partly supported by the Spanish Government, grant numbers RTI2018-094691-B-C31 and RTI2018-094691-B-C33 (MCIU/AEI/FEDER, EU). E. Lombardi was supported by a AGAUR FI-2021 pre-doctoral fellowship (with the support from the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund).ca_ES
dc.language.isoengca_ES
dc.publisherMDPIca_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094691-B-C31/ES/PAPEL DE LA EVOLUCION DE LOS FENOTIPOS INTEGRADOS EN LA RESILIENCIA DE LOS PINOS MEDITERRANEOS EN UN AMBIENTE CAMBIANTE/ca_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094691-B-C33/ES/RESILIENCIA DE LOS PINOS MEDITERRANEOS EN UN AMBIENTE CAMBIANTE: INTEGRACION FENOTIPICA DE DEFENSAS QUIMICAS Y FISICAS, RESPUESTAS CLIMATICAS Y SINDROMES ADAPTATIVOS/ca_ES
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.3390/rs14225904ca_ES
dc.relation.ispartofRemote Sensing, 2022, vol. 14, 5904ca_ES
dc.rightscc-by (c) Erica Lombardi et al., 2022ca_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAleppo pineca_ES
dc.subjectBlack pineca_ES
dc.subjectCrown architectureca_ES
dc.subjectClimate adaptationca_ES
dc.subjectIntraspecific variabilityca_ES
dc.subjectLiDARca_ES
dc.subjectRemote sensingca_ES
dc.subjectRGBca_ES
dc.titleUAV-LiDAR and RGB Imagery Reveal Large Intraspecific Variation in Tree-Level Morphometric Traits across Different Pine Species Evaluated in Common Gardensca_ES
dc.typeinfo:eu-repo/semantics/articleca_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.3390/rs14225904


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cc-by (c) Erica Lombardi et al., 2022
Except where otherwise noted, this item's license is described as cc-by (c) Erica Lombardi et al., 2022