## 4 Discussion  
This study has delved into various aspects of vineyard ecosystem analysis, encompassing the derivation of the location of single vines, vine heights, computation of the LAI, and determination of the LSA. Each of these steps presented its own set of challenges and considerations, from the segmentation processes to the technical nuances of data acquisition. One objective is to discern any patterns or relationships among these parameters that could potentially illuminate the dynamics of the vineyard environment. A correlation matrix analysis holds promise in providing valuable insights into the interplay of these variables. The detection of individual grape vines was based on the 3D point cloud of the study vineyard generated in April 2023. In early spring, the vines did not show any leaves, making it possible to scan the woody part of the plants for structure. Based on the assumption that every vine trunk belonged to a single vine, a surface was generated and applied at the base of the trunks. The computation of the distance from the base surface to the top of the trunks made it possible to extract the z maximum for every vine and the respective  
Figure 13: Linear regression of the vine height validation. X, Y, Z coordinates. The validation showed a mean distance from the reference points to the observed location of 10.7 cm. This value shows the real possibility of detecting vines using UAS techniques. Because of computing power constraints, the algorithm was tested on one vineyard row containing 21 vines. In future work, it would be of great interest to test this approach in a different vineyard to check the robustness of the method. One methodological issue is that vines do not grow perfectly vertically. A height-based threshold of 25 cm from the surface was introduced to make sure that the z maximum value would be derived from the trunk and not any branch of the vine. But if the stem were to have an extreme growing angle, the distance to the reference point, which is taken at the ground level of the stems, would be higher. The derivation of vine heights was based on the computation of the NDVI and an OBIA approach for the classification of vines. The surface model was established using a DTM together with a DSM. Usually, to compute a DTM, a LiDAR device is used, since the laser has the ability to penetrate through the tree cover and record the bare soil [31]. For this project, no UAS-LiDAR was available, which made it necessary to compute the nDSM based on photogrammetric UAS datasets. The use of the cloth filtering algorithm in CloudCompare v2.12.4, together with an interpolation method to close holes, made it possible to generate a DTM from the photogrammetry data. This method shows that it is possible to perform a task that is typically performed using a LiDAR device with lower-cost equipment. After the use of the OBIA framework to distinguish the vine morphology from all other objects, it was possible to use the image object outlines to mask the nDSM and obtain the height values only for vine vegetation across the whole vineyard. The R\({}^{2}\) value of the linear regression on the reference measurements and the observed vine heights was 0.83, indicating a strong correlation of the automatically derived vine heights with the real-world scenario. The computation of the LAI and the LSA could not be validated over the course of this study. A follow-up study on LAI and LSA is currently in progress. This procedure can only be carried out post-harvest, since the removal of all leaves could impede grape development. As shown in Figure 14, correlation matrix was used to display possible relationships among the collected parameters in the vineyard. The matrix was computed using the R-Studio software and an applicable R scripting language. Upon examination of the correlation matrix, some associations emerged. The LAI had a correlation coefficient of 0.22 to the observed LSA. The correlation was not strong, but could still imply that the LAI changes with changing leaf cover and pruning techniques.