## 2 Tree Detection and Counting
Providing rapid and reliable methods functional in both simple and complex scenarios is also highlighted in [43; 48; 49]. [PERSON] et al. [48] proposed a quick and easy semi-automated classification for mapping olive plantations on a sub-tree scale using GEOBIA on MS UAV imagery and based on a binary map (i.e., containing the two classes \"Olive trees\" and \"Other\"). A region-growing segmentation algorithm, the Multiresolution Segmentation algorithm (MRS) [50], implemented in the eCognition suite, was applied for the image segmentation. According to a trial-and-error procedure, the segmentation parameters were chosen. As stated by [PERSON] et al. [51], the real challenge is defining appropriate segmentation parameters (typically based on spectral homogeneity, size, or both) for image objects of varying sizes, shapes, and spatial distribution that make up a scene so that segments can be generated to meet user requirements [52]. In tree detection, segmentation allows isolating the objects of interest, i.e., the tree crowns (or canopies) from the rest of the image or rather from the background data represented by the soil. Trees were segmented out of the background by exploiting thresholding values or using morphological operations of opening and closing, region growing, edge detection, and clustering [29]. Olive groves were previously divided into subsets based on dimensional and geometric properties of the trees, such as density, size, shape, and structure observed on the field. The choice of classifying individual internally segmented canopies gives the possibility, by applying VIs such as NDVI, to reveal extreme values of indices linked to known stress conditions. Similarly, [PERSON] et al. [43] implemented a semi-automatic workflow to process MS UAV imagery for detecting and extracting olive and citrus trees' canopies with the final production of vigor maps (Figure 4). The three study sites tested were heterogeneous regarding tree plantation distances, crop management, crop composition, tree age, height, and crown diameters. In two of the study sites, the olive trees were present in an olive grove and a windbreak in a citrus grove. The authors proposed a GEOBIA unsupervised classification approach, implemented in the software eCognition Developer, consisting of an MRS segmentation of the UAV MS image and subsequent classification of trees based on VIs (NDVI, SAVI, and Chlorophyll Vegetation Index--CVI) and digital surface models (DSM and Canopy Height Model, CHM). Olive and citrus trees were classified by assigning a class to an object which fell within one or more chosen conditions, also called rules, based on values attributed to features as threshold values of VIs and topographic data. The use of DSM and CHM contributing to the distinction of different tree species, or simply separating tree crowns from the spectrally similar vegetation that covered the ground, improved the results obtained (ranging between 0.85 and 0.90 using the F-score accuracy parameter). [PERSON] et al. [49] implemented a GEOBIA semi-automatic workflow for olive tree detection and extraction, testing the reliability of WorldView-3 satellite imagery and deriving some VIs to analyze olive trees' vegetative vigor: NDVI, Normalized Difference Red Edge Index (NDRE) [53], Modified Soil Adjusted Vegetation Index 2 (MSAVI 2) [54,55], Modified Chlorophyll Absorption Ratio Index Improved (MCARI2) [56], and NDVI 2 [57,58,59]. The study sites tested were different: the first olive grove suffered various diseases, which reduced its development, while the second was well managed. The GEOBIA approach was implemented in the Imagine object of the Erdas Imagine suite (Hexagon Geospatial, Madison, WI, USA) as a semi-automatic process to extract single olive tree  
Figure 4: Top left and right respectively show olive trees and the spectral signature of an olive tree canopy.