Measuring tree heights is a critical step for assessing ecological and agricultural parameters, including biomass, carbon stock, and canopy volume. In extensive areas exceeding a few hectares, traditional terrestrial measurement methods are often prohibitively expensive in terms of time and cost. This study introduces a GIS-based methodology for estimating olive tree (Olea europaea L.) heights using very-high-resolution (VHR) satellite imagery. The approach integrates a mathematical model that incorporates slope and aspect information derived in a GIS environment from a large-scale Digital Elevation Model. By leveraging sun position data embedded in satellite image metadata, a dedicated geometric model was developed to calculate tree heights. Comparative analyses with a drone-based 3D model demonstrated the statistical reliability of the proposed methodology. While this study focuses on olive trees due to their unique canopy structure, the method could also be applied to other tree species or even to buildings and other vertically developed structures on the ground. Future developments aim to enhance efficiency and usability through the creation of a specialized GIS tool, making it a valuable resource for environmental monitoring, sustainable agricultural management, and broader spatial analysis applications.

Modern surveying is increasingly focused on fast data acquisition and processing using lightweight, low-cost equipment, particularly for the continuous monitoring of structures and infrastructures. This study investigates the use of LiDAR and RGB sensors embedded in Apple and Android smartphones, paired with an innovative device, the viDoc RTK Rover, for centimeter-level surveying. Three case studies were selected, each characterized by different materials, functional uses, and environmental contexts. The methodology centers on evaluating final accuracy during both the data acquisition and processing phases. Coordinates of target points were obtained directly via the viDoc device and indirectly through dense point clouds. Validation was conducted using a geodetic GNSS receiver. Results demonstrate that, in most cases, the system achieves accuracy comparable to traditional surveying methods. The findings confirm that these emerging tools offer a reliable and efficient solution for rapid 3D surveys with centimeter-level accuracy.

In recent years, the possibility of using interoperable global constellations, the growing number of Continuously Operating Reference Stations (CORS) and the technological progress of instrumentation, computing algorithms and GNSS products are significantly marking the evolution of the various satellite survey techniques and the diffusion of mass-market technologies contributing to innovation transfers in different sectors including smart cities, smart mobility, connected automated driving, precision farming and others (Egea-Roca et al., 2022).Currently, the study of low-cost GNSS systems for navigation and precision positioning especially utilised in monitoring applications is the focus of numerous research activities (Joubert et al., 2020; Raza et al., 2022; Bellone et al., 2016; Hamza et al., 2020).The aim of this work is to test the performance of some of the latest generation multi-constellation and multi-frequency GNSS medium and low-cost sensors, evaluating their possible application in the mentioned fields. Differential and undifferential techniques were compared (Dardanelli et al., 2021; Ocalan et al.,2016); Precise Point Positioning (PPP) has become a valid alternative to differential methods allowing to obtain comparable accuracy offering greater flexibility (Lin, 2021). The multi-constellation permanent stations network GPS-Umbria was utilised for differential mode tests (Radicioni and Stoppini, 2019).The tests were carried out in different modes (static and kinematic) and operating conditions; various intermediate and low-cost sensors were employed, while the data of a high precision geodetic receiver were used as reference for the comparison of the different solutions.

This article presents and analyzes the methodologies and results of the survey of an Etruscan archaeological site (the Necropolis of Palazzone) with tombs dug into the ground on a slope belonging to the Perugia hill, in central Italy. The survey presented particular difficulties, since the underground rooms are numerous, small, and not illuminated, and it required the creation of a unique geometric model, including both the underground and the external area. Therefore, different techniques and tools of geomatics were used: GNSS, total station 3D survey, terrestrial laser scanning, and digital photogrammetry, with the consequent need to manage and integrate a large amount of data. It was also possible to compare the results acquired with different methods, such as the digital terrain model of the external surface obtained by unmanned aerial vehicle photogrammetry or by kinematic GNSS survey. The survey had a dual purpose: to acquire documentation of the archaeological site geometry to be imported into GIS; and to support knowledge of the geomorphologic aspects of the area, as the tombs—carved into the sedimentary formations—allow us to observe and study the local stratigraphy in detail, while the geomatic surveys put the rooms in geometric relation to each other in 3D space.

The accurate definition of tree growth descriptors is a crucial step in enhancing orchard management, allowing cultivar identification within an orchard and in new genotype selection for breeding programs. In apple, almond, and olive orchards, Terrestrial Laser Scanning (TLS) technologies have been already used to identify different architectural groups, but not in hazelnut yet. This study utilized TLS to investigate the canopy structure of hazelnut trees of four different Italian varieties, with and without leaves. TLS proved to be a sensor capable of collecting three-dimensional data from hazelnut field trials and allowed the definition and selection of hazelnut plant descriptors by morphological traits and morphological indexes. Nineteen descriptors, eight morphologic traits and 11 morphological indexes have been identified as reliable suitable descriptors of hazelnut cultivar and in breeding evaluations, according to Biodiversity, FAO and CIHEAM. Many of the selected descriptors are related to the tree habit, vigour and branching density. Two useful indexes have also been defined: Canopy Uprightness (CU) Index and the Index of Canopy Opening (ICO). The descriptors allowed us to distinguish the four studied hazelnut cultivars based on their growth habit; in particular the cultivar Tonda Gentile delle Langhe showed a growth habit that is a lot different from that of the other ones.

The accurate estimation of tree canopy volume is fundamental in precision agriculture for quantifying vegetation structure, biomass, and productivity in perennial cropping systems. This study investigates a shadow geometry approach for estimating olive tree canopy volumes from a single, very high-resolution WorldView-3 multispectral image. The method integrates multispectral classification for canopy and shadow delineation with a geometric model that infers canopy height from shadow measurements, accounting for solar position and terrain morphology. Two classification strategies were evaluated: object-based image analysis (OBIA) and pixel-based (PB) classification, each applied to the original eight-band multispectral image and to a derived dataset enriched with vegetation indices (NDVI—Normalized Difference Vegetation Index; NDRE—Normalized Difference Red Edge Index) and principal component analysis (PCA) components. The canopy volume was estimated by integrating classified canopy and shadow areas with shadow-derived canopy height. The methodology was tested in a Mediterranean olive orchard and validated against UAV-derived point clouds for approximately 700 trees. The results indicate that the approach captures spatial variability in canopy structure. The Object-Based Image Analysis (OBIA) applied to filtered PCA-enhanced imagery achieved the highest accuracy in canopy volume estimation (RMSE = 2.04 m³; R² = 0.56), outperforming the alternative pixel-based (PB) classification applied to the original multispectral data. Overall, the study demonstrates the potential of single-image WorldView-3 data for rapid and scalable three-dimensional canopy characterization in precision agriculture.