The coastal system, with its physical chemical and biological components, is the place where the lithosphere, atmosphere and hydrosphere intersect and interact, and in which human activity has a considerable impact on the balance of the whole ecosystem. The aim of this study is to assess the health of the coastal environment in a natural protected area in Policoro (Italy). Sand samples have been collected and analysed to detect the presence of environmental contaminants and pollutants (heavy metals). A photogrammetric survey was carried out using an unmanned aerial vehicle (UAV), and an updated orthophoto of the area was obtained using the Structure from Motion (SfM) processing technique. Geostatistical techniques have been used to assess the distribution of the contaminants in the study area.
The progress of scientific research and technological innovation are contributing to an increase in the use of rapid systems for monitoring and identifying geo-environmental processes related to natural and/or anthropogenic activities. The aim of this study is identifying autumnal beach litter using ground-based IR thermography. Starting from quarterly autumn monitoring data of air temperature and sandy soil surface temperature, an empirical equation between the two environmental matrices (air and sandy soil) is obtained. This will allow the calculation of the sandy soil surface temperature knowing only the air temperature. Therefore, it will be possible to know in advance the thermal response of the sandy soil, thus creating a thermal blank of the beach. Using an IR thermal camera, it is possible for a quicker identification of thermal anomalies of the coastal area potentially connected to the presence of pollution due to the anthropogenic origin (particularly plastic material). The test area is located in the area of the Coastal Dunes Regional Natural Park of Ostuni–Fasano in Apulia (southern Italy).
This paper introduces a mobile robotic system equipped with RBG (Red, Green, Blue) and IRT (InfraRed Thermography) sensors, designed in a joint industrial research project, which aims to develop low-cost technologies for the survey of dangerous scenarios in remote mode. The methodology adopted for mapping a landslide scenario, based on aerial and terrestrial unmanned systems, is presented. The UAV (Unmanned Aerial Vehicle) has been employed to map the damaged buildings and the landslide slope, by planning ten survey missions; the UGV (Unmanned Ground Vehicle) has been employed as a complementary mobile photogrammetry technique to obtain highresolution ground data, by planning three missions, each consisting of two passes along the same paths simulating a two-camera setting. UAV and UGV-derived datasets have been processed using the Structure-from-Motion technique in order to obtain photogrammetry products such as ortho-photomosaics, digital terrain models, point clouds and 3D models of the building. Aerial and terrestrial models have been integrated, using GCPs (ground control points) coordinates and natural/artificial control points, and have been exploited for the spatial analysis.An accuracy assessment has been carried out in CloudCompare using the Cloud-to-Cloud function and local modelling: the aerial and terrestrial models of buildings facades have been independently compared with a reference model, obtained from a stationary photogrammetry survey. The results highlight the higher reliability of the terrestrial model for accurate surfaces reconstruction, proving the potential of terrestrial mobile photogrammetry as an effective lowcost mapping solution for hazardous environments. Moreover, UGV mission settings, datasets characteristics and potential improvements are discussed in order to portray the benefits and limitations of this approach.
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