## 1 Introduction
[10] presented a comprehensive framework where common rules in the domain of UAS-based monitoring of ecosystems are identified. By analyzing studies, the researchers found similarities in workflows according to the character of the vegetative properties under investigation. Properties concerning biodiversity, ecosystem structure, plant status, and dynamics were defined. Successful UAS surveys accounted for the choices of sensor and platform, knowledge about the phenomenon under study, and use of the desired resolution and analytical methods. In the study presented herein, the ecosystem structure of vines is monitored. Parameters like leaf surface area (LSA), leaf area index (LAI), and vine height are derived from UAS-collected multispectral and RGB datasets. According to [PERSON] et al. [10], vineyard rows appear as distinct objects that are highly dissimilar to the surrounding objects. In terms of UAS data acquisition, the unique structural appearance of planted rows should lead to a high spatial resolution and low spectral resolution. The preferable classification method comprises an object-based image analysis (OBIA) framework. In order to derive the targeted vegetation heterogeneity components, a passive sensor should be used with a high overlap, followed by SfM processing. By using the resulting 3D point cloud and the respective analytical methods, the heterogeneity parameters of vine height, LSA, and LAI can be derived [10]. [PERSON] et al. [11] proposed the effectiveness of low-cost UAS systems for vigor mapping in vineyards. UAS-collected data can serve as the baseline for photogrammetric processing workflows that are then used to create a variety of 3D datasets. RGB data are useful for spatial variability monitoring within a vineyard when accurately segmented and properly analyzed. However, manually segmenting and digitizing individual vines is a time-consuming and error-prone task. [PERSON] et al. [12] provided a framework for vine canopy recognition using different automatic classification methods like K-means, Ann, random forest, and spectral indices. The results showed that the spectral index method provided the best results for vine canopy detection, since this method is automatic and does not require specific software to calculate the indices. The 2G_RBi index was obtained from the difference of the divergency of the red and blue bands from the green band of the original RGB orthomosaic. The very high resolution of UAS images can also be a challenge in classification due to the high spectral variability between different vegetation classes. In the study of [PERSON] et al. [13], the researchers proposed a method for the automated detection of grapevine 3D canopy structures based on high-resolution UAS imagery. An algorithm was developed where the 3D morphology of vines was investigated, a height estimation was conducted, and missing plants within a row were detected. The algorithm needs no training and is self-adaptive to different crop field conditions. The researchers used a random forest (RF) classification environment where the RF randomly selected a training set, gathered the optimal feature values, and classified weeds and crop rows [13]. The research carried out by [PERSON] et al. [14] shows an approach to plant variability monitoring that uses RGB point clouds for the recognition and classification of plants to investigate the geometric structure of vines using the soil points as a reference and calculating differences to the canopy tops. To obtain information about the tree canopy, a series of 3D point clouds was generated using the SfM technique on images acquired with an RGB sensor on board a UAS. Together with the geometry, each point of the cloud stored the information from the RGB color space.