## 4 Yield
\begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Platform** & **Sensor Type Used \({}^{*}\)** & **Aim of the Study** \\ \hline
[105] & Aircraft - * & LiDAR—Leica ALS60 sensor & Quantifying the height and volume of olive trees \\ \hline
[113] & Aircraft - & LiDAR—Leica ALS60 sensor & Quantifying pruning residual biomass \\ \hline
[109] & Aircraft - & LiDAR—Leica ALS60 & Computation of tree height, crown base height, \\  & & LiDAR—Leica ALS50-II & crown diameters, crown area \\ \hline
[106] & Aircraft - & LiDAR - & Computation of tree height, crown base height and \\  & & & crown diameters \\ \hline
[108] & UAV MD4-1000 & Olympus PEN E-PM1 (RGB) & Tree detection and computation of position, \\  & & & projected canopy area, canopy height and volume \\ \hline \end{tabular} \({}^{*}\)LiDAR = light detection and ranging; \({}^{*}\) = missing information; \({}^{*}\) RGB = red-green-blue.  
\end{table} 
Table 2: References dealing with pruning. The remote sensing (RS) platform used, the type of sensor and the main objectives of the researches are also indicated. from a biogeochemical model (BIOME-BGC) [130] to simulate the net primary production (NPP), which was finally converted into yield. Similarly, [PERSON] et al. [131] developed a multi-step methodology for estimating olive grove GPP by modeling it in a complex agricultural context, simulating the behavior of both primary ecosystem components: ground vegetation and olive trees, separately. Daily temperatures and precipitation for the two years examined (2010-2011) were obtained from ancillary sensors linked to the eddy covariance station, while RS imagery derived from IKONOS, ETM+ together with multi temporal NDVI values from MODIS. GPP was estimated using the C-Fix model, and the modeling performances were compared with GPP data derived from eddy covariance measurements. [PERSON] et al. [132] proposed a method to assess compensation from an insurance based on yield losses in olive growing by using satellite-based weather meteorological indices. The authors, presenting a case study in Andalusia (region of Spain), investigated which phases of the satellite-based vegetation health indices have the highest relationship with olive oil yields. Some indices were tested on MODIS' imagery: the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI) and the Vegetation Health Index (VHI). Meteorological indices for temperature and precipitation are used as benchmarks. Results showed that the relationship between olive oil yields and the indices is more pronounced for inflorescence development, flowering, fruit growth, and oil accumulation stages. [PERSON] et al. [133] evaluated olive annual yield forecast based on tree canopy measurements. In particular, they proposed relating trees' manually measured canopy volume to their crown area obtained from UAV (RGB) imagery for producing yield thematic maps for all olive grove categories (except super-intensive groves). In particular, manual (or ground) measurements and UAV data were taken by selecting random trees from olive groves irrigated intensive, irrigated traditional, and rain fed traditional. [PERSON] and [PERSON] [123] assessed olive trees' geometrical characteristics (height, area, and canopy volume) in non-linear hilly olive groves to develop a yield forecast model using MS UAV imagery. Tree crowns were isolated and classified using GEOBIA calculating the area of the tree canopy, the mean, the minimum and the maximum height, NDVI, and the slope for each of them. Each of these variables was tested to verify the correlation with the yield. The mean NDVI of the tree canopy, canopy volume, and the average slope in correspondence to each tree were used to implement a yield forecasting model. This model can be used in Mediterranean regions (such as Greece), where hilly non-linear olive tree groves represent a common way of cultivation. [PERSON] et al. [134] aimed at obtaining an approximate early production estimation of olive groves based on the canopy radius extracted from UAV (RGB) imagery.