UAV-LiDAR and RGB Imagery Reveal Large Intraspecific Variation in Tree-Level Morphometric Traits across Different Pine Species Evaluated in Common Gardens

Visualitza/ Obre
Data de publicació
2022Autor/a
Rodríguez Puerta, Francisco
Chambel, Maria Regina
Climent, Jose
Citació recomanada
Lombardi, Erica;
Rodríguez Puerta, Francisco;
Santini, Filippo;
Chambel, Maria Regina;
Climent, Jose;
Resco de Dios, Víctor;
Voltas Velasco, Jordi;
.
(2022)
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UAV-LiDAR and RGB Imagery Reveal Large Intraspecific Variation in Tree-Level Morphometric Traits across Different Pine Species Evaluated in Common Gardens.
Remote Sensing, 2022, vol. 14, 5904.
https://doi.org/10.3390/rs14225904.
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Remote 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.
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