## 3 Results and Discussion
### Remote Sensing Applications  
#### 3.4.1 Tree Segmentation and Parameters Extraction  
Remote sensing techniques have become integral to agricultural monitoring, offering non-invasive and efficient means to gather essential information for optimizing cultivation practices. In this context, RS has proven particularly useful for segmenting individual almond trees and determining important parameters such as tree height, crown diameter, and biomass [34]. This subsection delves into a comprehensive review of studies centered on tree segmentation and parameter extraction in almond orchards. The spatial resolution is a pivotal factor in TSPE, and, consequently, the choice of data platform is critical. [PERSON] et al. [35] conducted a study exclusively employing satellite imagery. Their work focused on predicting the leaf area index (LAI) in almond orchards using VIs derived from Landsat imagery. For this purpose, the study uses ground measurements of LAI obtained with a mule lightbar (MLB) and compares them with VIs calculated from Landsat imagery. The results show that the EVI is the most accurate index for predicting LAI, with an \(R^{2}\) of 0.78. Another study was recorded in connection with the use of satellite data in combination with UAV data. [PERSON] et al. [36] used satellite data to estimate geometric and structural parameters, bypassing the time-consuming procedures associated with LiDAR or UAV photogrammetry. They estimated critical orchard parameters using LiDAR data. They then interpolated these data using block kriging at different resolutions from PlanetScope (3 m) and Sentinel-2 (10 m). The  
Figure 8: Spectral indices employed in studies according to remote sensing applications. TSPE: tree segmentation and parameters extraction; IC: imagery classification; HMDD: health monitoring and disease detection; WM: water management; OTR: other; CCCI: Canopy Chlorophyll Content Index; CWSI: Crop Water Stress Index; EVI: Enhanced Vegetation Index; GLI: Green Leaf Index; GNDVI: Green Normalized Difference Vegetation Index; GRVI: Green-Red Vegetation Index; MCARI: Modified Chlorophyll Absorption in Reflectance Index; NDRE: Normalized Difference Red Edge Index; NDWI: Normalized Difference Water Index; NDVI: Normalized Difference Vegetation Index; PSRI: Plant Senescence Reflectance Index; REN: Red Edge Normalized Difference Vegetation Index; SAVI: Soil Adjusted Vegetation Index; and WDRVI: Wide Dynamic Range Vegetation Index. results showed that NDVI and GNDVI had the strongest correlations with geometric and structural parameters. Studies using MAV platforms and LiDAR data for vegetation characterization include the work by [PERSON] et al. [37], who developed methods employing small-footprint full-waveform LiDAR to estimate foliage-height profiles and gap probability. The results contribute to the calibration of full-waveform LiDAR data, enhancing applications in vegetation mapping, snow mass estimation, and soil moisture assessment. In studies involving UAVs, structural parameters were efficiently collected on a large scale, with tree height being a commonly reported parameter in six studies. Tree crown area and volume, reported in four studies each, along with the number of trees, reported in three studies, underscore the versatility of UAVs in acquiring critical orchard data. [PERSON] et al. [38] explored tree classification using unsupervised and supervised methods, combining the Hue, Saturation, and Value (HSV) and Gray Level Concurrence Matrix (GLCM) approaches to achieve optimal results. [PERSON] et al. [39] used object-based image analysis (OBIA) on photogrammetric point clouds to effectively identify almond trees and characterize their geometric features. Their tree height extraction algorithm achieved an \(R^{2}\) of 0.94. [PERSON] et al. [3] focused on monitoring flower density and flowering times for different almond tree cultivars using color photogrammetric point clouds. [PERSON] et al.