## 2 Tree Detection and Counting
Bottom left and right photos, respectively, show applications of vegetation indices (VIs) Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) on olive tree canopies. Images were derived from a dataset used in [43]. canopies and eliminate the spectral disturbances of soil background. The results, showing an overall accuracy in tree extraction of about 96%, demonstrated the reliability of WorldView-3 data relying on high spectral, radiometric, and spatial resolution. In the last decade, machine learning algorithms attracted much attention in the RS research framework [60], demonstrating their effectiveness in mapping crops [61] and weeds [41; 62], disease detection [63; 64], and land cover assessment [65; 66; 67; 68; 69]. An example of the application of machine learning supervised algorithms for olive tree detection is given by [PERSON] et al. [25], which combined pre-processing, segmentation, feature extraction, and classification techniques. The pre-processing step provided for eliminating noise. It was followed by segmentation using the K-means clustering algorithm. Several supervised machine learning algorithms were tested: Naive Bayes (NB) [67; 70; 71], Support Vector Machine (SVM) [72; 73], Multi-layer Perceptrons, and Random Forest (RF) [74]. Results showed RF outperforming other algorithms with an overall accuracy of 97.5%. [PERSON] et al. [42] compared the performance of three different GEOBIA approaches based on four machine learning algorithms: K-Nearest Neighbour (KNN), SVM, RF, and NB. In particular, a supervised classification of MS UAV imagery was carried out in three different software suites: Orfco ToolBox (OTB, www.orfeo-toolbox.org (accessed on 14 March 2022)) [75], Scikit-learn [76; 77] and eCognition Developer. As for olive tree class, the highest value of accuracies was reached by SVM performed on eCognition with about 96% of F-score. [PERSON] [78] mapped olive trees using Quickbird's pan sharpened images and evaluated pixel- and object-based classification approaches through the comparison of four classification algorithms: Minimum Distance (MD), Spectral Angle Mapper (SAM), Maximum Likelihood (ML) and Decision Tree (DT). Results showed ML and DT were the two most precise classifiers and that greater weight given to the Red and NIR bands in the pan sharpening process increases the accuracy of canopy delineation. No difference was observed between pixel-based and object-based analyses. However, [PERSON] and [PERSON] [79] highlighted that the object-based classification approaches demonstrated better performance than pixel-based approaches in mapping individual landscape features. Also, [PERSON] et al. [80] tested ML and SVM classification algorithms within pixel-based and GEOBIA-based classification approaches for mapping olive trees in UAV imagery. Results showed SVM algorithm performed better in both approaches and particularly more accurately in the GEOBIA approach reducing shadow problems. [PERSON] et al. [81] demonstrated the usefulness of combining different types of information and data in olive tree detection. VHR imagery and synthetic aperture radar (SAR) data were used for implementing an object-based classification to map olive groves by integrating data from different sensors: multispectral Airborne Digital Sensor (ADS40), Radar Aeroporte Multispectral d'Etude des Signatures (RAMSES SAR) and TerraSAR-X. The processing chain for object-based olive grove mapping was followed: the multi resolution segmentation algorithm was used for defining objects, features calculation was executed for each object (shape, texture, spectral reflectance in optical imagery, and backscattering in SAR imagery), training and validation of the classifier used, and RF. Results showed that although SAR imagery alone did not contribute to the production of satisfying olive maps, its combination with MS imagery increased the mapping accuracy of RF to 90.5%. Also, [PERSON] et al. [82] investigated the combined use of MS and SAR satellite imagery to detect olive trees.