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Evaluation of Canopy Growth in Rainfed Olive Hedgerows Using UAV-LiDAR

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Author
Cantón-Martínez, Susana
Mesas Carrascosa, Francisco Javier
Rosa Navarro, Raúl de la
López-Granados, Francisca
León, Lorenzo
Pérez Porras, Fernando
Páez Cano, Francisco César
Torres-Sánchez, Jorge
Publisher
MDPI
Date
2024
Subject
Remote sensing
Olive growing systems
Drone
Olive breeding
3D point cloud
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Abstract
Hedgerow cultivation systems have revolutionized olive growing in recent years because of the mechanization of harvesting. Initially applied under irrigated conditions, its use has now extended to rainfed cultivation. However, there is limited information on the behavior of olive cultivars in hedgerow growing systems under rainfed conditions, which is a crucial issue in the context of climate change. To fill this knowledge gap, a rainfed cultivar trial was planted in 2020 in Southern Spain to compare ‘Arbequina’, ‘Arbosana’, ‘Koroneiki’, and ‘Sikitita’, under such growing conditions. One of the most important traits in low-water environments is the canopy growth. Because traditional canopy measurements are costly in terms of time and effort, the use of light detection and ranging (LiDAR) sensor onboard an uncrewed aerial vehicle (UAV) was tested. Statistical analyses of data collected in November 2022 and January 2023 revealed high correlations between UAV-LiDAR metrics and field measurements for height, projected area, and crown volume, based on validation with measurements from 36 trees. These results provide a solid basis for future research and practical applications in rainfed olive growing, while highlighting the potential of UAV-LiDAR technology to characterize tree canopy structure efficiently.
URI
http://hdl.handle.net/10396/29935
Fuente
Cantón-Martínez, S.; Mesas-Carrascosa, F.J.; Rosa, R.d.l.; López-Granados, F.; León, L.; Pérez-Porras, F.; Páez, F.C.; Torres-Sánchez, J. Evaluation of Canopy Growth in Rainfed Olive Hedgerows Using UAV-LiDAR. Horticulturae 2024, 10, 952.
Versión del Editor
https://doi.org/10.3390/horticulturae10090952
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