The unmanned aerial vehicle (UAV) photogrammetric survey of an archaeological site has proved itself to be particularly efficient. In order to obtain highly accurate and reliable results, it is necessary to design carefully the flight plan and the geo-referencing, while also evaluating the indicators of the accuracy rate. Using as a test case a UAV photogrammetric survey conducted on the archaeological site of the Roman Amphitheatre of Avella (Italy), in this paper, we propose a pipeline to assess the accuracy of the results according to some quality indicators. The flight configuration and the georeferencing chosen is then be checked via the residuals on the ground control points (GCPs), evenly distributed on the edges and over the entire area. With the aim of appraising the accuracy of the final model, we will suggest a method for the outlier detection, taking into account the statistical distribution (both global and of portion of the study object) of the reprojection errors. A filter to reduce the noise within the model will then be implemented through the detection of the angle formed by homologous rays, in order to reach a compromise between the number of the usable points and the reduction of the noise linked to the definition of the 3D model.

Coastal dunes provide the hinterland with natural protection from marine dynamics. The specialized plant species that constitute dune vegetation communities are descriptive of the dune evolution status, which in turn reveals the ongoing coastal dynamics. The aims of this paper were to demonstrate the applicability of a low-cost unmanned aerial system for the classification of dune vegetation, in order to determine the level of detail achievable for the identification of vegetation communities and define the best-performing classification method for the dune environment according to pixel-based and object-based approaches. These goals were pursued by studying the north-Adriatic coastal dunes of Casal Borsetti (Ravenna, Italy). Four classification algorithms were applied to three-band orthoimages (red, green, and near-infrared). All classification maps were validated through ground truthing, and comparisons were performed for the three statistical methods, based on the k coefficient and on correctly and incorrectly classified pixel proportions of two maps. All classifications recognized the five vegetation classes considered, and high spatial resolution maps were produced (0.15 m). For both pixel-based and object-based methods, the support vector machine algorithm demonstrated a better accuracy for class recognition. The comparison revealed that an object approach is the better technique, although the required level of detail determines the final decision.

The evaluation of the structural efficiency of airport infrastructures is a complex task. Faulting is one of the most important indicators of rigid pavement performance. The aim of our study is to provide a new method for faulting detection and computation on jointed concrete pavements. Nowadays, the assessment of faulting is performed with the use of laborious and time-consuming measurements that strongly hinder aircraft traffic. We proposed a field procedure for Terrestrial Laser Scanner data acquisition and a computation flow chart in order to identify and quantify the fault size at each joint of apron slabs. The total point cloud has been used to compute the least square plane fitting those points. The best-fit plane for each slab has been computed too. The attitude of each slab plane with respect to both the adjacent ones and the apron reference plane has been determined by the normal vectors to the surfaces. Faulting has been evaluated as the difference in elevation between the slab planes along chosen sections. For a more accurate evaluation of the faulting value, we have then considered a few strips of data covering rectangular areas of different sizes across the joints. The accuracy of the estimated quantities has been computed too.

Terrestrial laser scanning (TLS) is a relatively new, versatile, and efficient technology for landslide monitoring. The evaluation of uncertainty of the surveyed data is not trivial because the final accuracy of the point position is unknown. An a priori evaluation of the accuracy of the observed points can be made based on both the footprint size and of the resolution, as well as in terms of effective instantaneous field of view (EIFOV). Such evaluations are surely helpful for a good survey design, but the further operations, such as cloud co-registration, georeferencing and editing, digital elevation model (DEM) creation, and so on, cause uncertainty which is difficult to evaluate. An assessment of the quality of the survey can be made evaluating the goodness of fit between the georeferenced point cloud and the terrain model built using it. In this article, we have considered a typical survey of a landsliding slope. We have presented an a priori quantitative assessment and we eventually analyzed how good the comparison is of the computed point cloud to the actual ground points. We have used the method of cross-validation to eventually suggest the use of a robust parameter for estimating the reliability of the fitting procedure. This statistic can be considered for comparing methods and parameters used to interpolate the DEM. Using kriging allows one to account for the spatial distribution of the data (including the typical anisotropy of the survey of a slope) and to obtain a map of the uncertainties over the height of the grid nodes. This map can be used to compute the estimated error over the DEM-derived quantities, and also represents an “objective” definition of the area of the survey that can be trusted for further use.

This paper addresses the problems arising from the use of data acquired with two different remote sensing techniques—high-resolution satellite imagery (HRSI) and terrestrial laser scanning (TLS)—for the extraction of digital elevation models (DEMs) used in the geomorphological analysis and recognition of landslides, taking into account the uncertainties associated with DEM production. In order to obtain a georeferenced and edited point cloud, the two data sets require quite different processes, which are more complex for satellite images than for TLS data. The differences between the two processes are highlighted. The point clouds are interpolated on a DEM with a 1 m grid size using kriging. Starting from these DEMs, a number of contour, slope, and aspect maps are extracted, together with their associated uncertainty maps. Comparative analysis of selected landslide features drawn from the two data sources allows recognition and classification of hierarchical and multiscale landslide components. Taking into account the uncertainty related to the map enables areas to be located for which one data source was able to give more reliable results than another. Our case study is located in Southern Italy, in an area known for active landslides.

The paper reports the results of a photogrammetric survey made using an Unmanned Aerial Vehicle (UAV) in the archaeological site of the Roman Amphitheatre in Avella (Avellino, Italy). The aim of the study is to verify which modality of image acquisition (if only nadiral images or nadiral plus Oblique images), together with the method of Global Positioning Satellite System (GNSS) survey of the Ground Control Points (GCP) is able to produce the better 3D model, in terms of accuracy, in order to extract traditional graphic drawings (plan, elevation and section), suited to the required representation scales (1 : 100 and 1 : 50). The accuracy in georeferencing was evaluated analysing the residues on the GCPs; subsequently, a more detailed analysis of the accuracy of the final 3D model was performed analysing the residuals on the image coordinates, also called re-projection error. The method developed is based on the statistical analysis of the different models, built changing the GCPs survey method and the photogrammetric shots acquired. The results of our analysis show that the photogrammetric survey is more ‘stable’ using only nadiral images and that the nRTK technique allows results comparable to those obtained with static measurements, both in precision and in reliability. Moreover, if the GCPs are measured in nRTK mode, taking into consideration the graphical error, the maximum representation scale is 1 : 100, whereas the use of static technique makes it possible to describe major details, at a scale of 1 : 50.