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Forests are significant resources from an ecological, economic and social point of view. Their protection and management could greatly benefit from a complete knowledge of the shape and distribution of trees in forest stands. To this purpose, aerial surveys, especially through Airborne Laser Scanning (ALS), were carried out in the last years to acquire point clouds to be used in 3D models aimed at achieving an accurate description of tree crowns and terrain. However, airborne data acquisition is expensive and may provide poor results in case of dense foliage. Further, point cloud resolution is not very high, as models with a grid of 2-3 m are usually obtained. In order to implement more accurate 3D forest models, a feasible solution is the integration of point clouds obtained by aerial acquisition (ALS or photogrammetry) for the treetops and the terrain description, with information from terrestrial surveys. In this paper, we investigated the possible integration of point clouds obtained by Terrestrial Laser Scanner (TLS) with those collected by photogrammetric 3D models based on images captured by Unmanned Aerial Vehicle (UAV) in a test site located in northern Italy, with the aim of creating an accurate dataset of the forest site with high resolution and precision. The limits of ALS and TLS were bridged by aerial photogrammetry at low altitude (and vice versa). A 3D model of the study area was obtained with a resolution of 5 cm and a precision of 3 cm. Such model may be used in a wide range of applications in forestry studies, e.g., the reconstruction of 3D shapes of trees or the analysis of tree growth throught time. The implications of the use of such integrate approach as a support tool for decision-making in forest management are discussed.

In recent decades, photogrammetric and machine learning technologies have become essential for a better understanding of environmental and anthropic issues. The present work aims to respond one of the most topical problems in environmental photogrammetry, i.e., the automatic classification of dense point clouds using the machine learning (ML) technology for the refraction correction on the fluvial water table. The applied methodology for the acquisition of multiple photogrammetric flights was made through UAV drones, also in RTK configuration, for various locations along the Orco River, sited in Piedmont (Italy) and georeferenced with GNSS—RTK topographic method. The authors considered five topographic fluvial cross-sections to set the correction methodology. The automatic classification in ML has found a valid identification of different patterns (Water, Gravel bars, Vegetation, and Ground classes), in specific hydraulic and geomatic conditions. The obtained results about the automatic classification and refraction reduction led us the definition of a new procedure, with precise conditions of validity.

Brazil, the world's largest coffee producer, faces challenges managing the coffee leaf miner (Leucoptera coffeella), a significant pest. This study suggests remote sensing for pest control decisions. Two experimental areas in the Cerrado region of Minas Gerais State were analyzed to spectrally characterize infested plants and estimate the number of mines per plant. Results show the ability to differentiate infested plants with greater reflectance variance in the near infrared at 850nm. The performances of the three machine learning algorithms were compared. Determining the number of mines in the group of most infested plants demonstrated slightly higher precision, achieving an RMSE of 22.69% using the Support Vector Machine algorithm. Conversely, the group of least-infested plants obtained the best result with the Random Forest algorithm, achieving an RMSE of 32.47%. These promising results indicated that CLM can be detected using aerial multispectral imaging data.

Societal Impact Statement Arbuscular mycorrhizal fungi (AMF) impact the relationships between plants, aphids (insects that feed on plant phloem), and their natural enemies (insects that prey on or parasitize aphids). The presence of AMF influences the growth and population of different aphid species and affects the development of aphid‐killing wasps and their attraction to plants. This research has been conducted also considering the insects' feeding strategy and their feeding specialization. This study provides novel perspectives on how these fungi shape interactions in the natural world, offering potential insights for the development of sustainable pest management strategies in agriculture. Summary Arbuscular mycorrhizal fungi (AMF) are major root symbionts regulating plant physiology. Their presence affects the performance of aboveground insect herbivores in relation to their feeding strategy and their feeding specialization. For example, the effect of the arbuscular mycorrhizal (AM) symbiosis on chewing insects, positive for specialists and negative for generalists, has been previously demonstrated. Conversely, the impact of AMF on phloem‐suckers with relatively different levels of specialization remains unexplored. We tested the influence of the AM Funneliformis mosseae on the fitness of the specialist aphid Acyrthosiphon pisum and the generalist aphid Myzus persicae on Vicia faba plants. Further, we investigated the effects of AMF on the higher trophic level, the aphid parasitoids Aphidius ervi (specialist) and Aphidius colemani (generalist), by evaluating plant attractiveness and parasitoid fitness. To support the results of behavioral and biological bioassays we characterized the photosynthetic parameters, the volatilome and the transcriptome of tested plants. Mycorrhizal plants proved unsuitable for the generalist M. persicae but enhanced the fitness of the specialist A. pisum. The AM symbiosis had no effects on the behavioral response of A. colemani and enhanced the attraction and fitness of A. ervi. Volatilome and transcriptome profiling corroborated the results of bioassay highlighting a bottom‐up effect of the AMF across a plant–aphid–parasitoid system.

Following the enormous technological developments of LiDAR (Light Detection And Ranging) sensors, it is currently easier to find them commercially in the UA (Uncrewed Aerial Systems) sector. In particular, with the Zenmuse L1 by DJI (Dà-Jiāng Innovations) the market has grown globally, mainly due to the compactness of the product that is easily compatible with UAS. The L1 sensor can record up to three returns of the emanating signal, so it can acquire a larger amount of points, such as those below the vegetation. Therefore, in addition to the geometric information of the points, the Zenmuse L1 point clouds also provide other information, such as the number of echo returns from 1 to 3. This data could be exploited to improve the automatic extraction of the digital terrain model (DTM) from the point clouds, hopefully leading to the avoidance of manual correction. This research aims to focus on evaluating whether the addition of the return number feature can affect the identification of the ground points through different computational methods and can improve the time efficiency of state-of-the-art algorithms.

Underwater photogrammetry presents unique challenges due to the optical properties of water that, if not correctly taken into account, might affect the quality of the survey and the related 2D and 3D products. It is recognized nowadays the importance to train newcomers to underwater surveying, and extend and consolidate the knowledge of best practices for underwater data acquisition. Starting from this consideration, we propose the development of POSER, a 3D simulation framework designed to facilitate the teaching of underwater imaging principles. The project, an ISPRS Educational and Capacity Building Initiative, is built upon the open-source platform Blender, incorporating realistic modelling of the physical properties of water, including light refraction, scattering, and absorption phenomena, to simulate underwater surveying conditions. We foster a learning-by-doing approach, providing users with ready-to-use application scenarios inspired by real-life case studies. They will cover a range of application fields, from marine ecology to archaeology and subsea metrology, and allow users to address the complexities of underwater surveying practices. This paper introduces POSER to the community, presenting its educational vocation and describing its constituent components.

Benchmark datasets have become increasingly widespread in the scientific community as a method of comparison, validation, and improvement of theories and techniques thanks to more affordable means for sharing. While this especially holds for test sites and data collected above the water, publicly accessible benchmark activities for geospatial analyses in the underwater environment are not very common. Applying geomatic techniques underwater is challenging and expensive, especially when dealing with deep water and offshore operations. Moreover, benchmarking requires ground truth data for which, in water, several open issues exist concerning geometry and radiometry. Recognizing this scientific and technological challenge, the NAUTILUS (uNder And throUgh waTer datasets for geospatIaL stUdieS) project aims to create guidelines for new multi-sensor/cross-modality benchmark datasets. The project focuses on (i) surveying the actual needs and gaps in through and under-the-water geospatial applications through a questionnaire and interviews, (ii) launching a unique publicly available database collecting already existing datasets scattered across the web and literature, (iii) designing and identifying proper test site(s) and methodologies to deliver to the extended underwater community a brand-new multi-sensor/cross-modality benchmark dataset. The project outputs are available to researchers and practitioners in underwater measurements-related domains, as they can now access a comprehensive tool providing a synthesis of open questions and data already available. In doing so, past research efforts to collect and publish datasets have received additional credit and visibility.

Control over the coupling between multiple modes of a frequency comb is an important step toward measurement-based quantum computation with a continuous-variable system. We demonstrate the creation of square-ladder correlation graphs in a microwave comb with 95 modes. The graphs are engineered through precise control of the relative phase of three pumps applied to a Josephson parametric oscillator. Experimental measurement of the mode scattering matrix is in good agreement with theoretical predictions based on a linearized equation of motion of the parametric oscillator. The digital methods used to create and measure the correlations are easily scaled to more modes and more pumps, with the potential to tailor a specific correlation graph topology.

In the framework of mapping underwater environments, Posidonia Oceanica (PO) is a fundamental indicator for environmental monitoring and related studies and analyses for climate change. The POSEIDON project aims to study, develop and test new methodologies to provide knowledge, as well as ultra-high-resolution (UHR) and beyond ultra-high-resolution (BUHR) mapping and monitoring data for the conservation of the PO and related meadows and banquettes in the Mediterranean area. The project is funded by the National Recovery and Resilience Plan and stems from the recommendation of international organizations (European Environmental Directives, Mediterranean PO Network, International Partnership for Blue Carbon, Intergovernmental Oceanographic Commission of UNESCO in the Save the Wave project). Starting from new integrated survey methods and legacy data, the PO, ichthyofauna and seagrass banquettes will be mapped to understand their evolution and extract indicators and useful trends. Furthermore, the produced information will be stored and shared in a webGIS and a dedicated app to prevent unexpected damages during sailing or other activities. POSEIDON will use imaging sensors (RGB and multi/hyperspectral cameras) to extract information. With specific lighting equipment for significant water depths, these sensors will be conducted (by scuba divers or Autonomous Underwater Vehicles) next to the object to acquire BUHR data. Finally, to monitor the fauna (fish, other species, invertebrates), terrestrial and underwater videogrammetry will be applied using mono and stereo-cameras with object detection algorithms.

For the machining of industrial components, their accurate alignment on operative machines is a crucial step with significant impacts on the whole process, especially when dealing with large-scale elements. To achieve this alignment, a trained operator is needed along with expensive equipment, such as laser trackers, whose correct use requires the measurements of many significant points and is time-consuming. The lack of a precise alignment comes with possible incorrect parts machining, resulting in quality concerns and potentially expensive rework. To face this problem, collaborative real-time photogrammetry is an effective solution for guiding untrained users in correctly positioning various measured and codified components to generate the best solution for machining high-volume parts. The solution proposed in this contribution is part of the TACCO project, co-financed by EIT Manufacturing (co-funded by the EU). It takes advantage of the TACCO system, where auxiliary components (scale bars, dome-shaped components, coded targets and a cross-reference system) are placed on the object to be measured for their correct positioning in order to obtain an optimal photogrammetric reconstruction. This allows the reduction of the error ellipsoids on several uncoded targets, which are calculated through an iterative simulation carried out in a MATLAB environment. Unskilled operators are then guided through an Augmented Reality headset for their correct placement on the real-world object and for defining the positions and orientations from which to acquire images. This methodology will lead to an accurate measurement of the target points to be machined and, thus, a precise alignment process for the subsequent machining operations.

Globalisation has contributed to rapid economic growth but has also exposed vulnerabilities such as the spread of pests in agriculture. An example is the Popillia Japonica Newman beetle, introduced to Italy in 2014, which has caused significant economic losses, mainly affecting vine cultures. Reliable identification of pests is essential for its management, but it is time-consuming and laborious. This has prompted growing interest in image-based methods, supported by computer vision (CV), which can significantly improve efficiency in insect detection. This study aims to evaluate a CV algorithm's effectiveness in identifying adult specimens of Popillia using Near-Infrared sensors on Uncrewed Aerial Systems (UAS). The project, conducted in two vineyards in northern Italy, intends to establish a replicable and standardised data acquisition protocol for future monitoring activities. Insects detected by the CV-based method are validated by manual counting performed by entomologists. In a GIS environment, prescription maps are generated in near real-time to identify where the vineyard is most affected and to guide the drone spraying treatment only on the areas in which the threshold is exceeded. The study demonstrates effective semi-automated monitoring, with a clear correlation between CV-based and manual insect measurements, as indicated by the Pearson correlation coefficients ranging from 0.89 to 0.96. Although the CV-based method may overestimate insect numbers, it provides valuable insights for targeted pest management interventions and damage assessment. The project outcomes offer a promising approach to safeguarding agriculture against invasive species, enhancing regional economic resilience while minimising the spread of insecticide, the required time, and human interaction with harmful substances.

When facing unregulated human interventions, the tumultuous backdrop of wars, and the relentless impact of climate change, a significant effect is held on those structures that stand as custodians of our cultural identity and historical legacy. The need to document their status is an essential first step toward preservation and restoration. This contribution will analyze and compare diverse datasets acquired through three distinct methods: Terrestrial Laser Scanner, Apple’s iPhone 15 Pro with no additional tool, and iPhone 15 Pro using Pix4D viDoc RTK Rover to assess the quality and accuracy of point clouds, as well as to determine the feasibility of each technique for the expeditious documentation of endangered built heritage. The use of mass-distributed low-cost sensors provides a rapid and cost-effective means to capture detailed 3D models of built heritage while, at the same time, democratizing the process of documentation. Low-cost sensors not only facilitate documentation but also enhance the efficiency of conservation efforts. The precision of LiDAR sensors helps identify structural vulnerabilities, allowing conservationists to prioritize interventions and allocate resources wisely. Furthermore, detailed imagery will enable conservationists to monitor subtle structural changes over time, acting as an early warning system against potential threats. As an additional aid, the RTK Rover enables centimeter-level positioning accuracy while maintaining a compact and fast deployable design, and the direct measurement of Ground Control Points (GCPs) significantly streamlines the surveying process and equipment footprint.

Time series analysis of medium-resolution multispectral satellite imagery is critical to investigate forest disturbance dynamics at the landscape scale. In particular, the spatial, temporal, and radiometric consistency of Landsat time series data provides unprecedented insight into past disturbances that occurred over the last four decades. Several Landsat time series-based algorithms have been developed to automate the detection of forest disturbances. However, automated detection of non-stand-replacing disturbances based on Landsat time series remains a challenging task due to the difficulty of effectively separating them from spectral noise. Here, we present the High-dimensional detection of Landscape Dynamics (HILANDYN) algorithm, which exploits spatial and spectral information provided by Landsat time series to detect forest disturbance dynamics retrospectively. A novel and unsupervised procedure for changepoint detection in high-dimensional time series allows HILANDYN to perform the temporal segmentation of inter-annual time series into linear trends. The algorithm embeds a noise filter to remove spurious changepoints caused by residual spectral noise in the time series. We tested HILANDYN to detect disturbances that occurred in the forests of the European Alps over a period of 39 years, i.e. between 1984 and 2022, and evaluated its accuracy using a validation dataset of 3000 plots randomly located inside and outside the disturbed patches. We compared HILANDYN with the Bayesian Estimator of Abrupt change, Seasonality, and Trend (BEAST), which is a well-established and high-performing time series-based algorithm for changepoint detection. The quantitative results highlighted that the number of bands, i.e. original Landsat bands and spectral indices, included in the high-dimensional time series and the threshold controlling the significance of changepoints strongly influenced the user’s accuracy (UA). Conversely, changes in the combinations of bands primarily affected the producer’s accuracy (PA). HILANDYN achieved an F1 score of 0.801, which increased to 0.833 when we activated the noise filter, allowing the algorithm to balance UA (83.1%) and PA (83.5%). The qualitative results showed that disturbed forest patches detected by HILANDYN were characterized by a high spatio-temporal consistency, regardless of the disturbance severity. Furthermore, our algorithm was able to detect forest patches associated with secondary disturbances, such as salvage logging, that occur in close succession with respect to the primary event. The comparison with BEAST evidenced a similar sensitivity of the algorithms to non-stand-replacing events, as both achieved comparable PA. However, BEAST struggled to balance UA and PA when using a single parameter set, achieving a maximum F1 score of 0.717. Moreover, the computational efficiency of BEAST in processing high-dimensional time series was very limited due to its univariate nature based on the Bayesian approach. The adaptability of HILANDYN to detect a wide range of disturbance severities using a single parameter set and its computational efficiency in handling high-dimensional time series promotes its scalability to large study areas characterized by heterogeneous ecological conditions.

The prevalence of malnutrition in patients is very high in Europe (>70%). An Italian FADOI-SINPE survey revealed that malnutrition is an underestimated problem by internists. The AMIDO study examines malnutrition in Acqui Terme Hospital (Alessandria, Italy) Internal Medicine Department patients. Patients were screened for malnutrition using the Malnutrition Universal Screening Tool (MUST) upon admission. 268 patients (49.63% male) were enrolled, with a median age of 83 years. At admission, the prevalence of malnutrition risk was 48.88%. The proportion of patients who came from home resulted to have a slight decreasing trend among the three subgroups according to MUST (84.67%, 82.76%, and 73.53% in increasing order of MUST score respectively), but was not statistically significant (P=0.10). Dementia [odds ratio (OR): 6.36; 95% confidence interval (CI): 2.52-18.63], heart failure (OR: 2.45; 95% CI: 1.33-4.57), neoplasm (OR: 2.24; 95% CI: 1.08-4.77) and infectious diseases (OR: 2.27; 95% CI: 1.226-4.274) increase malnutrition odds. Increasing attention to malnutrition risk is crucial for patients with dementia, neoplasm, heart failure, and infectious diseases, which raise malnutrition risk probability.

Abstract Radical polymers are actively being investigated in different applications such as batteries. In order to store higher energy, radical polymers with high redox potential are searched. This work describes the synthesis of 2,2,5,5‐tetramethylpyrrolin−N−oxyl methacrylate monomer (PROXYL−methacrylate) and its polymerization to get a PolyPROXYL−methacrylate (PPMA) by free radical polymerization. The structures of the monomer and the percentage of redox active groups of the polymer were determined by mean of NMR and UV‐Vis spectroscopy, respectively. Cyclic voltammetry of the novel redox polymer revealed a half‐wave potential (E1/2) of 3.7 V vs Li+/Li stable at different scan rates, which was ascribed to the efficient swelling and insoluble properties of PPMA. The polyPROXYL−methacrylate polymer exhibits higher nominal redox potential than the reference radical polymer polyTEMPO−methacrylate (PTMA). Li−metal lab‐scale cells fabricated with the PPMA cathode active material performed at different C‐rates (up to 50 C) with reversible charge/discharge and a specific capacity output of 80 mAh g−1 at 1 C, along with 96 % capacity retention after more than 100 cycles. Finally, the comparison between PPMA and PTMA redox polymers evidenced the superior performances of the PROXYL with respect to TEMPO in terms of energy density and half‐wave potential.

In this study, we tested two plotless sampling methods, the ordered distance method and point-centred quarter method, to estimate the tree density and basal area in some managed Alpine forests in northeastern Italy. We selected nine independent forest stands, classified according to the spatial distribution patterns of trees (cluster, random, regular). A plotless sampling survey was simulated within the selected stands and the tree density and basal area were estimated by applying both the ordered distance method and point-centred quarter method. We compared the estimates, in terms of accuracy and precision, between the two methods and against estimates obtained from a simulated survey based on a plot-based sampling method. The point-centred quarter method outperformed the ordered distance method in terms of both accuracy and precision, showing higher robustness towards the bias related to non-random spatial patterns. However, both the plotless methods we tested can provide unbiased accuracy of estimates which, in addition, do not differ from estimates of plot-based sampling. The satisfactory results are encouraging for further tests over other Italian Alpine as well as Apennine forests. If confirmed, the plotless sampling method, especially the point-centred quarter method, could represent an effective alternative whenever plot-based sampling is deemed redundant, or expensive.

Medicinal plants have been explored worldwide as potential alternatives for the prevention and treatment of different diseases, including viral infections. Baccharis crispa Spreng. (Asteraceae) is a native medicinal species widely used in South America. Given the influence of genetic and environmental factors on secondary metabolites biosynthesis and accumulation, this study aimed to evaluate the in vitro antiviral activity of four wild populations of B. crispa from Córdoba, Argentina, and assess the variability in their bioactivity and chemical composition. The cytotoxicity of chloroform, ethanol, and aqueous extracts from aerial parts was evaluated by the neutral red uptake method. Antiviral and virucidal activity against herpes simplex virus type 1 (HSV-1) and chikungunya virus (CHIKV) were assessed via plaque-forming unit (PFU) reduction assay. Phytochemical analyses of the extracts were conducted using HPLC-ESI- MS/MS. The Puesto Pedernera population showed the strongest antiviral activity, with inhibition rates of 82% for CHIKV and 79% against HSV-1, as well as potent virucidal effects, reducing PFU formation by up to 5 logarithms for both viruses. Remarkably, ethanol extract exhibited the least toxicity and strongest inhibitory activity. Villa del Parque population was inactive. We identified 38 secondary metabolites, predominantly phenolic acids (12) and flavonoids (18), in varying proportions. Delphinidin and delphinidin-3-glucoside are described for the first time in the species. Differences in phytochemical profiles were observed among extract types and populations. Key phenolic compounds showed moderate positive correlations with the evaluated bioactivities, emphasizing the complexity of phytochemical properties and interactions. These results highlight the therapeutic potential of B. crispa extracts against viral infections and underscore the importance of considering the geographical source of plant material in bioactivity evaluations.

Background: Antimicrobial Photodynamic Therapy (aPDT) has demonstrated effectiveness against various Candida biofilms, typical resistant to conventional treatments. Some strategies have shown to enhance the photoactivity of some photosensitizers (PS), such as the use of a multiple irradiation scheme or the combination with drugs that improve the penetration of the PS through the microbial membrane. Purpose: Having demonstrated the photodynamic antibiofilm activity of some natural anthraquinones (AQs), we selected rubiadin 1-methyl ether (R-1ME) that showed a low photo-reduction percentage (%R) on the biofilm mass, with the aim to improve its effect. Study Design: Experimental in vitro photo-stimulation protocols have been developed, which include successive light exposures and the combination of this AQ with a commonly used antifungal such as Amphotericin B (AmB). Methods: The biofilms reduction was quantified by Crystal Violet staining. Reactive oxygen and nitrosative species were observed as action mechanism, alongside an assessment of antioxidant response through superoxide dismutase enzyme activation and total antioxidant system capacity. Results: Applying R-1ME to C. tropicalis biofilms with sequential 15-min irradiation sessions at varied incubation intervals (0, 3, 6, 24, 27, and 30 h) yielded a substantial photo-reduction (62.9%R) on biofilm mass, even halving the bioactive concentration of R-1ME. Moreover, combining R-1ME with AmB under this irradiation pattern produced an even greater impact (82%R) at concentrations below the Minimal Inhibitory Concentration. Evident redox imbalances in the biofilm were linked to this photosensitized activity. Conclusions: A new strategy was found to improve the activity of a natural PS on fungal biofilms, by combining it with antifungal drugs, under a staged irradiation scheme, which, in turn, required low doses of the PS and the antifungal to achieve this improved photo-reduction.

Arsenic (As) contamination poses significant environmental and health concerns globally, particularly in regions with high exposure levels due to anthropogenic activities. As phytoremediation, particularly through the hyperaccumulator fern Pteris vittata, offers a promising approach to mitigate arsenic pollution. Bacteria and mycorrhizal fungi colonizing P. vittata roots are involved in As metabolism and resistance and plant growth promotion under stressful conditions. A total of 45 bacterial strains were isolated from bulk soil and the rhizosphere of mycorrhizal P. vittata growing in an industrial As-polluted site. Bacteria were characterized by their plant-beneficial traits, tolerance to sodium arsenate and arsenite, and the occurrence of As-resistant genes. This study highlights differences between the culturable fraction of the microbiota associated with the rhizosphere of mycorrhizal P. vittata plants and the bulk soil. Moreover, several strains showing arsenate tolerance up to 600 mM were isolated. All the bacterial strains possessed arsC genes, and about 70% of them showed arrA genes involved in the anaerobic arsenate respiration pathway. The possible exploitation of such bacterial strains in strategies devoted to the assisted phytoremediation of arsenic highlights the importance of such a study in order to develop effective in situ phytoremediation strategies.

The first edition of the SUNRISE (Seashore and UNderwater documentation of aRchaeological herItage palimpSests and Environment) summer school was carried out in Marina di Ragusa in Sicily (Italy) from 3rd to 9th September 2022. It was jointly organized by Politecnico di Torino, IUAV, University of Sassari, FBK, University of Udine and the University of Modena and Reggio Emilia with the support of SIFET, ISPRS Student Consortium, private companies (Images, Microgeo, Stonex, Leica and Geomax) and the municipality of Santa Croce Camerina.The five days of summer school were attended by 20 students from Europe, Asia and USA. After the first day of lectures focused on the theoretical basis of surveying, photogrammetry, LiDAR, and SLAM, the field activities took place in a submerged and terrestrial scenario. The underwater surveying involved a submerged amphora, and the terrestrial activity was focused on the Arab bath of Mezzagnone, a 6th-century AD building that has been fully preserved until today. The paper deal with this experience and underline the followed approach. Finally some results achieved by the students are reported.

Recently rapid mapping techniques based on UAV photogrammetry increasingly help on-site archaeological documentation works. Multi-temporal data are specifically crucial in diachronic investigation research, and for this purpose the data co-registration and integration can support the accurate 3D digitization of excavation phases and make coherent topological relation among them and phases-related stratigraphic units’ data. In this framework, the level of automation and accuracy control are challenging aspects to streamline the documentation process during excavation activities, however all experimentation phases must be tested and validated in the actual archaeological context, where the boundary conditions are typically demanding. This research is developed during the collaboration project with Cà Foscari University of Venice, started in 2022 campaign, in the excavation site of Tell Zeyd, in Iraqi Kurdistan, The Tell Zeyd Archaeological Project (ZAP) aims to study the rural landscape of the hinterland of Mosul in the long Islamic period, from the Arab conquest in the 7th century to the disintegration of the Ottoman Empire with the First World War, as an ideal observatory on the characteristics of the settlement in its spatial organisation, places of worship, production installations and facilities for storing foodstuffs. The research aims to present the preliminary test and results on an experimental documentation activity based on multi-scale UAV mapping strategy and training with the archaeological expert group, and particularly for automatic co-registration of multi-temporal data, considering different images datasets epochs belonging to subsequent excavation phases.

Landscape heritage, especially if it does not arouse great public echoes, needs great attention, starting from knowledge and metric documentation processes to which reality-based sensing techniques often contribute significantly. The primary purpose of this work is to reflect on the possibility of identifying submerged built heritages, which are sometimes characterised by precarious safety conditions due to abandonment, through multispectral photogrammetric technologies with primary data acquired by UAVs. The experience carried out in an impervious alpine territory foresees the close relationship of integration of photogrammetric techniques in the visible and the multispectral ranges, with the integration of terrestrial scanning solutions from slam-based mobile systems, to validate the results provided by the analysis of the spectral signatures of different kind of soils.