## 2 Tree Detection and Counting
The authors used Sentinel-1's and Sentinel-2's free multi-temporal/multi-sensor images. They used data acquired between the dates from April 2017 to May 2018, freely downloaded from the Copernicus Open Access Hub [83]. Different combinations of SAR, MS, and NDVI data were stacked, and supervised classification based on the RF algorithm was tested. The combinations were Sentinel-1-Sentinel-2, Sentinel-1--NDVI, and Sentinel-2--NDVI. Results showed that dual data fusion of Sentinel-1-Sentinel-2, Sentinel-1--NDVI, and Sentinel-2--NDVI gave an overall accuracy of 73.33%. In particular, the addition of NDVI to Sentinel-1 and Sentinel-2 data positively affected accuracy. The alternative to counting trees before image post-processing was demonstrated by [PERSON] et al. [31], who proposed a method for counting olive trees in real-time during UAV (RGB) flight. This process involved the segmentation of each image (the two techniques, color-based and stereo vision-based segmentation algorithms, were tested), geolocation of each tree, and image overlay corrections. The embedded computer on the UAV processed the images on-the-fly, and sent the relevant findings, such as the geographic coordinates of each tree, to the ground station. Therefore, the results can be visualized in real-time at the ground station. Such a method, combined with existing cloud services, would allow end-users to have rapid access to user-friendly data without needing specific software, using only the browser. Results showed a capacity to classify trees with an accuracy of 99%. In recent years, deep learning (DL) techniques have grown in popularity in RS application in agriculture, among which include weeds detection [84], crop classification [85], and tree detection, as shown in [86; 87; 32; 88]. Applications of DL for the detection of olive trees are also very recent. [PERSON] et al. [24] tested low-cost UAV (RGB) imagery in the semantic segmentation model U2-Net for olive tree crown extraction. U2-Net was compared to other DL models. Despite the positive results obtained, the model showed some limits. The first limit derives from the camera used to investigate only the visible, making it difficult to distinguish different vegetation that shares a similar spectrum. Other limits are the presence of several \"vertices\" and crowns belonging to the same tree, and the presence of adjacent crowns very close together, according to the density of the planting scheme. [PERSON] et al. [89] used DL to map olive groves in two regions of Morocco, one semi-arid and the other sub-humid, comparing the effectiveness of high-resolution imagery, PlanetScope 3-m MS imagery, and VHR imagery and WorldView-2 and WorldView-3 RGB imagery with 0.5/0.3-m of spatial resolution. Images were acquired for the years 2018-2019. The objective was to test the model generalizability under different levels of spatial variability. Results showed that VHR imagery had better performances in capturing the texture feature of olive groves in both sub-humid and semi-arid regions. In contrast, PlanetScope imagery could only detect olive groves with larger canopy sizes and smaller tree spacings. The temporal information was not helpful for mapping using PlanetScope images of young, smaller, and more spaced olive groves in the semi-arid region. At the same time, the result was better in mapping olive trees in a sub-humid region where older trees had prominent texture features. Table 1 below summarizes the research focused on tree detection and counting. This is the topic with the largest number of articles identified which, moreover, collectively covered the use of different RS platforms (satellites, aircraft, and UAVs) and the different types of sensors, from those producing PAN and RGB images to MS and SAR.