## 2 Materials and Methods
### Data Analysis and Respective Results
#### 2.4.2 Leaf Surface Area, Leaf Area Index, and Vine Height Derivation
Bushes and trees were assigned nDSM standard deviations > 1 because of their free-growing nature, where no pruning takes place. The algorithm classified all image objects above 4 m as high vegetation. Therefore, high trees were >4 m in height, indicating a height-based threshold set by the user. The algorithm then assigned classes to the elevated image objects and isolated the vine vegetation from other entities in the scene. To make the algorithm for vineyard row classification more robust, a multispectral orthomosaic was used. The weighted difference vegetation index (_WDVI_) was calculated as shown in Equations (1) and (2):  
\[WDVI=NIR-a\times red \tag{1}\]  
\[a=\frac{NIR\text{ }soil}{red\text{ }soil} \tag{2}\]  
where _NIR_ represents the processed reflection values of the NIR-band; \(a\) represents the slope of the soil line; and \(red\) represents the processed reflection values of the red-band. The WDVI aided in distinguishing between non-vegetated objects and vegetation, assisting in the classification algorithm by considering the spectral properties specific to grape vines. The index especially considered that the ratio between NIR and red spectral values on bare soil pixels was consistent and independent of soil moisture. Furthermore, the WDVI (Equations (1) and (2)) was less sensitive to inter-row vegetation in comparison to the normalized difference vegetation index (NDVI). Because of this, UAS-based WDVI imagery could yield a better separation of soil and vine pixels [26]. WDVI values > 0 correlated with vegetation and values < 0 correlate with bare soil or human-made objects [27]. Taken together, the mean nDSM values, the nDSM standard deviation, and the WDVI-values were used to create image objects representing vines. In Figure 6, the calculated vine heights are shown. The image objects representing vines were used to mask the nDSM raster surface, resulting in a vine height raster (Figure 7). Figure 6: The image objects created with the multi-threshold segmentation approach. Figure 7: The automatically derived vine heights in meters for possible nutrient supply calculation or other resource optimization problems, like pruning management. green: 81; blue: 122)-(red: 50; green: 62; blue: 114)) to represent grape clusters. Points within the green to yellow color range ((red: 69; green: 114; blue: 66)-(red: 160; green: 156; blue: 67) were categorized as leaf points, representing the vine canopy. Additionally, to facilitate precise analysis at the individual vine level as opposed to a row-based metric, the knowledge of vine trunk locations was utilized. The 3D point cloud was segmented into individual vines based on the locations of trunks derived beforehand. This segmentation approach enabled the computation of geometric parameters for each individual vine. A strip threshold of 0.5 m was applied to the point cloud, resulting in the subdivision of the point cloud into discrete segments, each centered around a detected trunk. This approach ensured that the analysis was conducted at a fine-grained level, allowing for the derivation of accurate leaf surface measurements for each vine. In Figure 8, a snippet of the 3D point cloud is shown. The right image shows the detected grape clusters based on their RGB values that were subtracted from the point cloud in order to calculate the LSA of leaf points only. Leaf points were segmented using a 0.5 m threshold, delineating them between the detected trunk locations. Subsequently, a mesh representing the entire canopy of the vineyard row was generated (Figure 9). This process yielded a segmented mesh for each individual vine within the investigated row. The mesh was then employed to determine the LSA. For further analysis, the one-sided leaf area was used. The LSA was therefore divided by a factor of 2. The LSA calculation was automated through the use of Leica Cyclone 3 DR Software (Leica Geosystems AG, St. Gallen, Switzerland).