GNSS NRTK-UAV Photogrammetry and LiDAR Point Clouds for Geometric Features Extraction of Olive Orchard

A thorough understanding of tree geometric features is essential for the precise management of olive groves, facilitating the assessment of tree vigor, plant water consumption, and pruning needs in relation to the availability of light in the different portions of the canopy. This study aims to evaluate alternative methodologies for surveying and geometric features extraction of olive trees, such as UAV photogrammetry and laser scanning survey, with the goal of mitigating some limitations associated with traditional measurement methods. The investigated methodologies differ in terms of data acquisition time, post-processing requirements and results accuracy. Laser scanning surveys involve lengthy data acquisition times for extensive or complex objects such as olive groves but allow to achieve centimetric/sub-centimetric accuracy with relatively simple post-processing steps, if appropriately designed. Regarding UAV-based photogrammetric surveys, flight planning and images acquisition process are rapid, although the point cloud reconstruction may require more time depending on the images number, images resolution and computational capabilities, with an achievable centimetric accuracy. Traditional methodologies, relying on manual operations and handheld tools, are characterized by high subjectivity, low accuracy (decametric), and significant time consumption. Instead, the use of advanced geomatic methodologies such as laser scanning and UAV photogrammetry enable a more objective and accurate determination of the geometric features of the trees. The results showed a good performance of the methods proposed for evaluating the tree height and the actual volume of the canopy. The UA V survey showed a not very good performance to assess the trunk characteristics. Anyway, the measurements conducted on the point cloud resulted less time-consuming per each tree and more punctual than lidar and manual ones.