## 1 Introduction  
The olive tree is one of the oldest species in the Mediterranean area [1, 2, 3] which has been spreading throughout history and shaped the recognizable Mediterranean landscape [4, 5]. Resistance to extreme climatic conditions and adaptability to different types of poorly fertile soils ensure the olive's social, ecological, and economic benefits [6, 7]. Sustainability and preservation of olive trees are especially important [8] due to the frequent impacts of various economic activities such as industry, wildfires [9], and tourism sector activities [10, 11]. Preservation mostly depends on sustainable environmental management and precision agriculture (PA) methodologies [12, 13, 14, 15]. Geospatial technologies and their products have become easily available and widely accessible in everyday life, which has advanced and accelerated their use in various research [16, 17, 18, 19]. The development of advanced geospatial technologies has enabled PA in the olive management sector, especially in the context of precise canopy extraction for management [20, 21, 22], monitoring [23, 24, 25], assessment of crop quality [26], disease detection [27], and preventive actions [28]. Therefore, geospatial technologies have a meaningful role in the observation and analysis of various processes located on the Earth's surface. Unmanned aerial vehicles (UAVs) are objects that can be controlled remotely or have a pre-set programmed flight plan [29, 30], and their main advantage is the competency to collect very high spatial resolution data [31; 32]. The use of UAVs has become necessary for most scientists in terms of collecting high spatial resolution data necessary for conducting interdisciplinary research [33; 34; 35]. UAVs have been applied significantly for remote sensing of vegetation and natural regions. UAV-based RS applications provide considerably better resolution and more flexibility in choosing acceptable payloads and appropriate spatiotemporal resolution than satellite-based RS applications [36]. Due to the enhancement of geomorphic elements over the landscape, the creation of UAV-derived datasets, such as orthomosaics and high-resolution digital elevation models, enables the characterization and geometrical analysis of the channels, improving their identification in areas with vegetation [37]. In the process of remote sensing, multispectral (MS) sensors are one of the most powerful tools for a better understanding of environmental processes [38]. Moreover, they are one of the most frequently used sensors, which was primarily influenced by their exceptional applicability in forestry, PA, and other economic activities that are closely related to vegetation [34]. MS sensors provide insight into the part of the electromagnetic spectrum that is invisible to the human eye, and in terms of remote sensing, they represent a significant contribution with the aim of better understanding environmental processes [38; 39]. MS sensors play a leading role in PA, mostly due to the possibility of extracting vegetation indices (VIs) [40]. VIs are one of the more widely used indicators within the geospatial analysis and PA [13; 41; 42] and are defined as the result of a combination of two or more spectral bands [43; 44; 45]. Analysis of VIs is generally performed with the use of MS sensors, enabling the classification of the study area based on spectral characteristics [46; 47]. VIs are primarily used to distinguish observed crops or analyze the canopies of various plants [15; 48]. A lot of authors use VIs in order to reach some new knowledge and conclusions regarding the vegetation in agriculture sectors. In [48], three VIs (GNDVI, NDVI, SAVI) is used to monitor the vegetation phases of vineyard and tomato crops. In [19] for PA, five VIs models (NDVI, GNDVI, SAVI, BNDVI, GSAVI) are derived to extract the chestnut canopies. In [13], WorldView-3 satellite imagery is used to extract and analyze VIs inside olive tree canopies.