## 2 Materials and Methods
### Data Analysis and Respective Results
#### 2.4.2 Leaf Surface Area, Leaf Area Index, and Vine Height Derivation  
The canopy architecture of the vines plays an important role in the domain of precision viticulture. Having geometric parameters of the vines at hand enables viticulturists to perform biomass calculations to assess vine heights and leaf surface area metrics that are of great importance when applying the right amount of fertilizer and for the optimization of canopy treatment practices. Information about canopy architecture at the vine level enables farmers to undertake regulation procedures targeted at a vine-specific location rather than a larger scale of their vineyard [11]. Automated derivations of vine heights were produced for the entire study area. In an effort to obtain canopy-associated metrics, a specific row of grapevines was designated as the experimental area. Rather than conducting a comprehensive analysis covering the entire vineyard, this targeted approach was based on considerations of time efficiency and the limitations posed by finite computational resources. Vine row localization is an important first task in precision viticulture that enables researchers to shift the focus from the vineyard level to the single-vine level by determining the exact extent of the plants under investigation [22]. To discriminate the vine rows from other objects in the scene, the 3D point cloud was used. A cloth filter algorithm was applied to the 3D point cloud that extracted ground from non-ground points by applying a cloth simulation [23]. The scene  
Figure 5: (**Left**): The maximal values of the mesh to cloud distance calculation represent the tips of the individual vine trunks. (**Right**): The nadir view of the calculated maximal values of the mesh to cloud distance calculation. selection was utilized to illustrate relief-like terrain, with the cloth resolution adjusted to 1.5 cm to match the cell size of the DSM. A z-classification threshold of 0.15 m was set. The algorithm flipped the original point cloud and then constructed a flexible plane over the surface, turning it \"upside down\". By analyzing the distances between the knots of the cloth and the points in the 3D point cloud, the final shape of the cloth could be determined and used for point cloud classification in two sets, namely, ground and non-ground points [24]. Two surfaces were generated. A digital terrain model (DTM) indicated the topography of the ground surface and a DSM indicated the total height of objects, stored per pixel. Both surfaces had a cell size of 1.5 cm. By performing a raster difference calculation, a normalized digital surface model (nDSM) was created. The nDSM represented the relative height of objects above the bare ground surface [25]. The DTM represented the absolute heights relative to mean sea level, referenced to a reference geoid. In the current study, the reference geoid was the GRS80 ellipsoid. The nDSM surface showed the absolute heights of all objects in the research area. In order to distinguish the vine vegetation from other objects in the scene, a multi-threshold segmentation approach was used. The multi-threshold segmentation algorithm was implemented in eCognition Developer 10.3 (Trimble, Munich, Germany). This procedure segments image objects in a top-down segmentation approach based on pixel value thresholds. The thresholds are user-defined and are based on the knowledge that all elevated objects are possible candidate vines, and all non-elevated objects are not considered for further analysis. The scene was segmented to isolate elevated image objects from non-elevated ones. To classify the vine row image objects and analyze their geometric and spectral features, a class assignment method was utilized. This approach involved employing multiple thresholds for the classification of the image objects. Classes were assigned to the elevated image objects. The classification algorithm makes use of the nDSM raster surface and the respective standard deviation values. Pruned vineyard rows were assigned nDSM standard deviations < 1.