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Forests play a central role in the management of the Earth’s climate. Airborne laser scanning (ALS) technologies facilitate the monitoring of large and impassable areas and can be used to monitor the 3D structure of forests. While the ALS-based forest measures have been studied in depth, 3D change detection in forests is still a subject of little attention in the literature due to the challenges introduced by comparing point cloud pairs. In this study, we propose an innovative methodology to (i) automatically perform a 3D change detection of forests on an individual tree level; (ii) estimate tree parameters with allometric equations; and (iii) perform an assessment of the aboveground biomass (AGB) variation over time. The area in which the tests were carried out was hit by an ice storm that occurred in the time interval between the two LiDAR acquisitions; furthermore, field measurements were carried out and used to validate the results. The single-tree segmentation of the point clouds was automatically performed with a local maxima algorithm to detect the treetop, and a decision tree method to define the individual crowns around the local maxima. The multitemporal comparison of the point clouds was based on the identification of single trees, which were matched when there was a correlation between the position of the treetops. For each tree, the DBH (diameter at breast height) and the AGB were also estimated using allometric equations. The results are promising and allowed us to identify the uprooted trees and estimate that about 40% of the AGB of the area under examination had been destroyed, with an RMSE over the estimation ranging between 4% and 21% in four scenarios.

In the current scenario, where environmental concern determines the evolution of passenger cars, hybrid electric vehicles (HEV) represent a hub in the automotive sector to reach net-zero CO2 emissions. To fully exploit the energy conversion potential of advanced powertrains, proper energy management strategies are mandatory. In this work, a simulation study is presented, aiming at developing a new control strategy for a P3 parallel plug-in HEV (PHEV). The simulation model is built on MATLAB/Simulink. The proposed strategy is based on an alternative utilization of the thermal engine and electric motor to provide the vehicle power demand (efficient thermal/electric skipping strategy (ETESS)). An adaptive function is then introduced to develop a charge-blended control strategy. Fuel consumption along different driving cycles is evaluated by applying the novel adaptive-ETESS (A-ETESS). To have a proper comparison, the same adaptive function is built on the equivalent consumption minimization strategy (ECMS). Processor-in-the-loop (PIL) simulations are performed to benchmark the A-ETESS. Simulation results highlighted that the proposed strategy provides for a fuel economy similar to ECMS (worse of about 2.5% on average) and a computational effort reduced by 99% on average, opening the possibility of real-time on-vehicle applications.

Objectives: Specific anti-inflammatory and/or immunomodulating drugs (AIDs) can influence endothelial function which is often impaired in patients with rheumatoid arthritis (RA). We sought to determine whether overall patterns of AID usage are similarly associated with endothelial function.Methods: The reactive hyperaemia index (RHI), a marker of microvascular endothelial function, was measured in 868 RA patients reporting their intake of seven AIDs known to affect endothelial function. Latent class analysis (LCA) was performed to characterise patterns of AID usage. Models for 2–6 classes were compared using the AIC and BIC statistics and Lo-Mendell-Rubin likelihood ratio tests. Associations between the classes and RHI were adjusted for age, gender, body mass index, diabetes, HDL-cholesterol, LDL-cholesterol, family history of ischaemic heart disease, smoking status, RA duration, DAS28 score, steroid dose, existing hypertension, and C-reactive protein.Results: LCA identified five distinct AID usage classes: Class 1, generally low medication usage; Class 2, using either sulfasalazine or non-tumour necrosis factor (TNF) inhibitors; Class 3, methotrexate users; Class 4, TNF-inhibitor users; and Class 5, hydroxychloroquine users. The geometric mean for the RHI for subjects in classes 1 to 5 was 1.92, 1.81, 1.94, 2.10, and 2.07, respectively, with subjects in classes 4 and 5 having better endothelial function than subjects in class 2 (p = 0.003 for each). The glucocorticoid dosage did not influence the classes formed or the association between the classes and the RHI in sensitivity analyses.Conclusion: There were five broad patterns (classes) of AID usage in RA patients. The RHI was relatively lower in users of either sulfasalazine or non-TNF inhibitors. TNF inhibitors or hydroxychloroquine may counteract the negative effects of RA on endothelial function.

In the present work, three LiDAR technologies (Faro Focus 3D X130—Terrestrial Laser Scanner, TLS-, Kaarta Stencil 2–16—Mobile mapping system, MMS-, and DJI Zenmuse L1—Airborne LiDAR sensor, ALS-) have been tested and compared in order to assess the performances in surveying built heritage in vegetated areas. Each of the mentioned devices has their limits of usability, and different methods to capture and generate 3D point clouds need to be applied. In addition, it has been necessary to apply a methodology to be able to position all the point clouds in the same reference system. While the TLS scans and the MMS data have been geo-referenced using a set of vertical markers and sphere measured by a GNSS receiver in RTK mode, the ALS model has been geo-referenced by the GNSS receiver integrated in the unmanned aerial system (UAS), which presents different characteristics and accuracies. The resulting point clouds have been analyzed and compared, focusing attention on the number of points acquired by the different systems, the density, and the nearest neighbor distance.

Monitoring the world’s areas that are more vulnerable to natural hazards has become crucial worldwide. In order to reduce disaster risk, effective tools and relevant land cover (LC) data are needed. This work aimed to generate a high-resolution LC map of flood-prone rural villages in southwest Niger using multispectral drone imagery. The LC was focused on highly thematically detailed classes. Two photogrammetric flights of fixed-wing unmanned aerial systems (UAS) using RGB and NIR optical sensors were realized. The LC input dataset was generated using structure from motion (SfM) standard workflow, resulting in two orthomosaics and a digital surface model (DSM). The LC system is composed of nine classes, which are relevant for estimating flood-induced potential damages, such as houses and production areas. The LC was generated through object-oriented supervised classification using a random forest (RF) classifier. Textural and elevation features were computed to overcome the mapping difficulties due to the high spectral homogeneity of cover types. The training-test dataset was manually defined. The segmentation resulted in an F1_score of 0.70 and a median Jaccard index of 0.88. The RF model performed with an overall accuracy of 0.94, with the grasslands and the rocky clustered areas classes the least performant.

This research presents a virtual tour performed on the oppidum of Ulaca, one of the most relevant archaeological sites of the Iberian Peninsula during the Late Iron Age (ca. 400–50 BC). Beyond the clear benefits of the tool to the interpretation, dissemination, and knowledge of the mentioned archaeological site and its surroundings, the novelty of this research is the implementation of the platform in alternative scenarios and purposes. In this way, the present work verifies how the access to multi-source and spatially geolocated information in the same tool (working as a geospatial database) allows the promotion of cross-sectional investigations in which different specialists intervene. This peculiarity is also considered useful to promote tourism with an interest beyond the purely historical/archaeological side. Likewise, the possibility of storing and managing a large amount of information in different formats facilitates the investigation in the contexts of excavations and archaeological or environmental works. In this sense, the use of this kind of tool for the study of cultural landscapes is especially novel. In order to better contextualize the potential of the virtual tour presented here, an analysis about the challenges and possibilities of implementing this tool in environments such as the Ulaca oppidum is performed. The selected site stands out for: (i) being in a unique geological, environmental and ecological context, allowing us to appreciate how human beings have modified the landscape over time; (ii) presenting numerous visible archaeological remains with certain conservation problems; and (iii) not having easy access for visitors.

A three-dimensional survey of natural caves is often a difficult task due to the roughness of the investigated area and the problems of accessibility. Traditional adopted techniques allow a simplified acquisition of the topography of caves characterized by an oversimplification of the geometry. Nowadays, the advent of LiDAR and Structure from Motion applications eased three-dimensional surveys in different environments. In this paper, we present a comparison between other three-dimensional survey systems, namely a Terrestrial Laser Scanner, a SLAM-based portable instrument, and a commercial photo camera, to test their possible deployment in natural caves survey. We presented a comparative test carried out in a tunnel stretch to calibrate the instrumentation on a benchmark site. The choice of the site is motivated by its regular geometry and easy accessibility. According to the result obtained in the calibration site, we presented a methodology, based on the Structure from Motion approach that resulted in the best compromise among accuracy, feasibility, and cost-effectiveness, that could be adopted for the three-dimensional survey of complex natural caves using a sequence of images and the structure from motion algorithm. The methods consider two different approaches to obtain a low resolution complete three-dimensional model of the cave and ultra-detailed models of most peculiar cave morphological elements. The proposed system was tested in the Gazzano Cave (Piemonte region, Northwestern Italy). The obtained result is a three-dimensional model of the cave at low resolution due to the site’s extension and the remarkable amount of data. Additionally, a peculiar speleothem, i.e., a stalagmite, in the cave was surveyed at high resolution to test the proposed high-resolution approach on a single object. The benchmark and the cave trials allowed a better definition of the instrumentation choice for underground surveys regarding accuracy and feasibility.

This research focuses on the study of the ruins of a large building known as “El Torreón” (the Tower), belonging to the Ulaca oppidum (Solosancho, Province of Ávila, Spain). Different remote sensing and geophysical approaches have been used to fulfil this objective, providing a better understanding of the building’s functionality in this town, which belongs to the Late Iron Age (ca. 300–50 BCE). In this sense, the outer limits of the ruins have been identified using photogrammetry and convergent drone flights. An additional drone flight was conducted in the surrounding area to find additional data that could be used for more global interpretations. Magnetometry was used to analyze the underground bedrock structure and ground penetrating radar (GPR) was employed to evaluate the internal layout of the ruins. The combination of these digital methodologies (surface and underground) has provided a new perspective for the improved interpretation of “El Torreón” and its characteristics. Research of this type presents additional guidelines for better understanding of the role of this structure with regards to other buildings in the Ulaca oppidum. The results of these studies will additionally allow archaeologists to better plan future interventions while presenting new data that can be used for the interpretation of this archaeological complex on a larger scale.

The growth of location-based services (LBS) has increased rapidly in last years, mainly due to the possibility to exploit low-cost sensors installed in portable devices, such as smartphones and tablets. This work aims to show a low-cost multi-sensor platform developed by the authors in which an ultra-wideband (UWB) indoor positioning system is added to a classical global navigation satellite systems–inertial navigation system (GNSS-INS) integration, in order to acquire different synchronized data for further data fusion analysis in order to exploit seamless positioning. The data fusion is based on an extended Kalman filter (EKF) and on a geo-fencing approach which allows the navigation solution to be provided continuously. In particular, the proposed algorithm aims to solve a navigation task of a pedestrian user moving from an outdoor space to an indoor environment. The methodology and the system setup is presented with more details in the paper. The data acquired and the real-time positioning estimation are analysed in depth and compared with ground truth measurements. Particular attention is given to the UWB positioning system and its behaviour with respect to the environment. The proposed data fusion algorithm provides an overall horizontal and 3D accuracy of 35 cm and 45 cm, respectively, obtained considering 5 different measurement campaigns.

This article explores the application of Hölder exponent analysis for the identification and delineation of single tree crowns from very high-resolution (VHR) imagery captured by unmanned aerial vehicles (UAV). Most of the present individual tree crown detection (ITD) methods are based on canopy height models (CHM) and are very effective as far as an accurate digital terrain model (DTM) is available. This prerequisite is hard to accomplish in some environments, such as alpine forests, because of the high tree density and the irregular topography. Indeed, in such conditions, the photogrammetrically derived DTM can be inaccurate. A novel image processing method supports the segmentation of crowns based only on the parameter related to the multifractality description of the image. In particular, the multifractality is related to the deviation from a strict self-similarity and can be treated as the information about the level of inhomogeneity of considered data. The multifractals, even if well established in image processing and recognized by the scientific community, represent a relatively new application in VHR aerial imagery. In this work, the Hölder exponent (one of the parameters related to multifractal description) is applied to the study of a coniferous forest in the Western Alps. The infrared dataset with 10 cm pixels is captured by a UAV-mounted optical sensor. Then, the tree crowns are detected by a basic workflow. This consists of the thresholding of the image on the basis of the Hölder exponent. Then, the single crowns are segmented through a multiresolution segmentation approach. The ITD segmentation was validated through a two-level validation analysis that included a visual evaluation and the computing of quantitative measures based on 200 reference crowns. The results were checked against the ITD performed in the same area but using only spectral, textural, and elevation information. Specifically, the visual assessment included the estimation of the producer’s and user’s accuracies and the F1 score. The quantitative measures considered are the root mean square error (RMSE) (for the area, the perimeter, and the distance between centroids) and the over-segmentation and under-segmentation indices, the Jaccard index, and the completeness index. The F1 score indicates positive results (over 73%) as well as the completeness index that does not exceed 0.23 on a scale of 0 to 1, taking 0 as the best result possible. The RMSE of the extension of crowns is 3 m2, which represents only 14% of the average extension of reference crowns. The performance of the segmentation based on the Hölder exponent outclasses those based on spectral, textural, and elevation information. Despite the good results of the segmentation, the method tends to under-segment rather than over-segment, especially in areas with sloping. This study lays the groundwork for future research into ITD from VHR optical imagery using multifractals.

The access and the use of the global navigation satellite system (GNSS) pseudo-range and carrier-phase measurements mobile devices as smartphones and tablets with an Android operating system has transformed the concept of accurate positioning with mobile devices. In this work, the comparison of positioning performances obtained with a smartphone and an external mass-market GNSS receiver both in real-time and post-processing is made. Particular attention is also paid to accuracy and precision of positioning results, also analyzing the possibility of estimating the phase ambiguities as integer values (fixed positioning) that it is still challenging for mass-market devices. The precisions and accuracies obtained with the mass-market receiver were about 5 cm and 1 cm both for real-time and post-processing solutions, respectively, while those obtained with a smartphone were slightly worse (few meters in some cases) due to the noise of its measurements.

In recent years, smartglasses for augmented reality are becoming increasingly popular in professional contexts. However, no commercial solutions are available for the agricultural field, despite the potential of this technology to help farmers. Many head-wearable devices in development possess a variety of features that may affect the smartglasses wearing experience. Over the last decades, dairy farms have adopted new technologies to improve their productivity and profit. However, there remains a gap in the literature as regards the application of augmented reality in livestock farms. Head-wearable devices may offer invaluable benefits to farmers, allowing real-time information monitoring of each animal during on-farm activities. The aim of this study was to expand the knowledge base on how augmented reality devices (smartglasses) interact with farming environments, focusing primarily on human perception and usability. Research has been conducted examining the GlassUp F4 smartglasses during animal selection process. Sixteen participants performed the identification and grouping trials in the milking parlor, reading different types of contents on the augmented reality device optical display. Two questionnaires were used to evaluate the perceived workload and usability of the device. Results showed that the information type could influence the perceived workload and the animal identification process. Smart glasses for augmented reality were a useful tool in the animal genetic improvement program offering promising opportunities for adoption in livestock operations in terms of assessing data consultation and information about animals.

Italy is deeply afflicted by geo-hydrological risk, where a predominant part of its area is under risk.Recently, advanced research and sensors have played an important role in realizing automatic systems for landslide monitoring and to alerting. The cost of these systems, considering all parts, limits their use and the cost of each sensor influences the investment. Geodetic global navigation satellite system (GNSS) receivers, which are commonly used to realize these activities, are an appropriate example; however, their costs are quite high.The aim of this research was to test the actual performance of a mass-market GNSS receiver, with the purpose of verifying if such type of sensors can be used for landslide monitoring. In particular, the benefits due to the coupling between mass-market receivers and products offered by a network of GNSS permanent stations (e.g. Virtual RINEX) have been investigated.To verify the capability of these sensors to detect a deformation, under the minimum deformation detectable point of view, a special slide was built, in order to conduct a dedicated test. The support was moved both in horizontal and vertical directions with high precision, with purpose to detect correctly a three-dimensional movement.Tests were carried out considering the dependence of some factors: the most important are the acquisition time and the distance from the GNSS reference station. Interesting is the use of virtual reference station based on GNSS network, as a reference station: the performance achieved using Virtual RINEX for the control of movements and deformations are analysed. The accuracy and precision of movement determination were evaluated and compared, for each test, considering the different factors. The tests and results are described in this contribution.

The geomatic survey in the speleological field is one of the main activities that allows for the adding of both a scientific and popular value to cave exploration, and it is of fundamental importance for a detailed knowledge of the hypogean cavity. Today, the available instruments, such as laser scanners and metric cameras, allow us to quickly acquire data and obtain accurate three-dimensional models, but they are still expensive, require a careful planning phase of the survey, as well as some operator experience for their management. This work analyzes the performance of a smartphone device for a close-range photogrammetry approach for the extraction of accurate three-dimensional information of an underground cave. The image datasets that were acquired with a high-end smartphone were processed using the Structure from Motion (SfM)-based approach for dense point cloud generation: different image-matching algorithms implemented in a commercial and an open source software and in a smartphone application were tested. In order to assess the reachable accuracy of the proposed procedure, the achieved results were compared with a reference dense point cloud obtained with a professional camera or a terrestrial laser scanner. The approach has shown a good performance in terms of geometrical accuracies, computational time and applicability.

The available sight distance (ASD) is that part of the roadway ahead which is visible to the driver, and should be of sufficient length to allow a vehicle traveling at the designated speed to stop before reaching a stationary object in its path. It is fundamental in assessing road safety of a project or on an existing road section. Unfortunately, an accurate estimation of the available sight distance is still an issue on existing roads, above all due to the lack of information regarding the as-built condition of the infrastructure. Today, the geomatics field already offers different solutions for collecting 3D information about environments at different scales, integrating multiple sensors, but the main issue regarding existing mobile mapping systems (MMSs) is their high cost. The first part of this research focused on the use of a low-cost MMS as an alternative for obtaining 3D information about infrastructure. The obtained model can be exploited as input data of specific algorithms, both on a GIS platform and in a numerical computing environment to estimate ASD on a typical urban road. The aim of the investigation was to compare the performances of the two approaches used to evaluate the ASD, capturing the complex morphology of the urban environment.

According to the Annual Fire Report 2013 (European Commission-Joint Research Centre, 2014), there have been 873 forest fires in Europe, in 2013, for a total of 340559 ha of territory. A comparison of this data to that of the previous years, highlights that, when the intended goal is that of preserving the environment and saving human lives, the importance of the correct management of forest fires can not be underestimated. In the past years, the European Union has invested in the development of the INSPIRE Directive (Infrastructure for Spatial Information in Europe) to support environmental policies. Furthermore, the EU is currently working on developing "ad hoc" infrastructures for the safe management of forests and fires. The AF3 EU project (Advanced Forest Fire Fighting), financed by the FP7, addresses the issue of developing innovative tools to handle all stages of forest fires. The project develops a single control center for the coordination of monitoring, manoeuvring, and post-fire operations. The SDI platform (Spatial Data Infrastructure) represents another component which was designed in the context of this project. It is based on a GIS (Geographic Information System) which is able to efficiently integrate multi-modal data. Following an analysis of the state of the art of information systems for forest fire-fighting, and in light of the end-user requirements analyzed within the AF3 project, we propose a geo-topographic database based on the INSPIRE Directive and developed on opensource platforms, which provides interoperability of the data and allows forecasting and monitoring of high-risk areas, decision making, damage estimation, and post-fire management.

Nell’articolo verrà esposta una procedura geodetica di trasformazione dal DATUM catastale, verso il DATUM Roma 1940 oltre che ETRF2000, messa a punto presso il Politecnico di Torino – DIATI. Essa è stata applicata nella Regione Piemonte, realtà particolarmente complessa per la consistente presenza di sistemi d’asse di piccola estensione (circa 540), dopo una sperimentazione eseguita nell’ambito di una collaborazione scientifica tra l’Agenzia del Territorio e il Politecnico di Torino – DIATI e in base ad un accordo tra Politecnico di Torino, Regione Piemonte e CSI Piemonte.

Performing two independent surveys in 2016 and 2017 over a flat sample plot (6700 m 2 ), we compare snow-depth measurements from Unmanned-Aerial-System (UAS) photogrammetry and from a new high-resolution laser-scanning device (MultiStation) with manual probing, the standard technique used by operational services around the world. While previous comparisons already used laser scanners, we tested for the first time a MultiStation, which has a different measurement principle and is thus capable of millimetric accuracy. Both remote-sensing techniques measured point clouds with centimetric resolution, while we manually collected a relatively dense amount of manual data (135 pt in 2016 and 115 pt in 2017). UAS photogrammetry and the MultiStation showed repeatable, centimetric agreement in measuring the spatial distribution of seasonal, dense snowpack under optimal illumination and topographic conditions (maximum RMSE of 0.036 m between point clouds on snow). A large fraction of this difference could be due to simultaneous snowmelt, as the RMSE between UAS photogrammetry and the MultiStation on bare soil is equal to 0.02 m. The RMSE between UAS data and manual probing is in the order of 0.20–0.30 m, but decreases to 0.06–0.17 m when areas of potential outliers like vegetation or river beds are excluded. Compact and portable remote-sensing devices like UASs or a MultiStation can thus be successfully deployed during operational manual snow courses to capture spatial snapshots of snow-depth distribution with a repeatable, vertical centimetric accuracy.

Nowadays, the RPAS (Remotely Piloted Aircraft Systems) or UAV (Unmanned Aerial Vehicle) are often used, with onboard calibrated digital camera for photogrammetric purpose such as DTM and DSM, orthophotos and map realization etc., combining the digital images with a minimum sufficient number of ground control points using semiautomatic image-matching techniques combined with traditional bundle-block approach (Barazzetti et al, 2012;. Chiabrando et al, 2012; Haala et al, 2011;. Lingua et al 2008;. Remondino et al., 2011). In this case, the RPAS performances allows to obtain high quality product , considering the pixel size and the accuracy of the DTM/DSM which could be obtained with automatic procedures. This is a good condition for semi-automatic procedure using a bundle-block photogrammetric approach. But is it possible to realize a direct photogrammetry? And what are the limits? Several navigation sensors (GPS/GNSS e IMU-MEMS) are embedded onto RPAS in order to realize an autonomous flight. The quality of these sensors, in term of accuracy, depends on the model of RPAS and its purpose. The navigation solution (position and attitude) is estimated by the internal RPAS sensor and can be employed to directly georeferencing the images, in order to produce an easy and quick description and analysis of the overlooked area. In this paper, the authors describes an investigation over the limits of some commercial RPASs, defining a dedicated procedure to valuate their performance, especially considering the use of RPAS for direct photogrammetry. The first results encourage the use of RPAS for geomatic applications, because the cost and the quality of the obtained product are quite interesting.