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In the present work, we have obtained a detailed, complete view of surface deformation at a volcano-tectonic structure belonging to the Krafla Fissure Swarm (NE Iceland), through the integration of field mapping and the Aerial Structure from Motion technique (ASfM), as well as through orthomosaics and Digital Surface Models (DSMs). The documented, 2.5-km-long deformation zone is marked by a range of features, such as topographic bulging, parallel extension fractures, and narrow grabens affected by local floor uplift, which we have explained as the effect of the northward, shallow propagation of a dyke from the Krafla magma chamber. Our lithostratigraphic field survey of the area, at a 1: 5000 scale, indicates that major changes in the pattern of surface deformation take place across the contacts between deposits with different rheological properties. The transition from very stiff lavas to softer hyaloclastites produces changes from extensional fracturing to normal faulting, though the width of the deformation zone does not change. We have also documented a number of NE-SW, left-lateral and NNW-SSE, right-lateral strike-slip fault zones that are rotated in clockwise and anticlockwise sense to the main NNE-SSW graben trend, and extend outwards as much as about 17 m. The graben zone terminates to the north by splitting into two strike-slip fault zones. These were likely generated at the front of the dyke tip during its propagation, and were later on bypassed by the advancement of the dyke. As the dyke entered the soft hyaloclastites, propagation was slowed down and the dyke became thicker.

In the present work, we applied the use of an Unmanned Aerial Vehicle (UAV) - a quadcopter - and the Aerial Structure from Motion digital photogrammetry image processing technique (ASfM) to study volcano-tectonics and tectonic features in an active Icelandic rift. Data have been collected in order to evaluate the Holocene deformation in the Northern Volcanic Zone of Iceland. We mapped 397 structures, mainly related to extension fractures and subordinately normal faults in the Theistareykir Fissure Swarm, obtaining 1098 and 21 structural data, respectively. This allowed to reconstruct an overall spreading direction of N108° during Holocene times, and to calculate a stretch of 1.013 regarding 8–10 ka old lava units. Deformation in the area is related to both dyke intrusions and extensional tectonics. Furthermore, detailed geological-structural field and UAV surveys were also performed in two test areas in order to determine data accuracy and the associated reliability of this approach. In addition to the above, different flight heights were tested, suggesting that photo collection with a 12.4 MPx camera at 100 m is efficient to study fracture dilation and kinematics.

Anthropogenic Marine Debris (AMD) is one of the major environmental issues of our planet to date, and plastic accounts for 80% of total AMD. Beaches represent one of the main marine compartment where AMD accumulates, but few and scattered regional assessments are available from literature reporting quantitative estimation of AMD distributed on the shorelines. However, accessing information on the AMD accumulation rate on beaches, and the associated spatiotemporal oscillations, would be crucial to refining global estimation on the dispersal mechanisms. In our work, we address this issue by proposing an ad-hoc methodology for monitoring and automatically quantifying AMD, based on the combined use of a commercial Unmanned Aerial Vehicle (UAV) (equipped with an RGB high-resolution camera) and a deep-learning based software (i.e.: PlasticFinder). Remote areas were monitored by UAV and were inspected by operators on the ground to check and to categorise all AMD dispersed on the beach. The high-resolution images obtained from UAV allowed to visually detect a percentage of the objects on the shores higher than 87.8%, thus providing suitable images to populate training and testing datasets, as well as gold standards to evaluate the software performance. PlasticFinder reached a Sensitivity of 67%, with a Positive Predictive Value of 94%, in the automatic detection of AMD, but a limitation was found, due to reduced sunlight conditions, thus restricting to the use of the software in its present version. We, therefore, confirmed the efficiency of commercial UAVs as tools for AMD monitoring and demonstrated - for the first time - the potential of deep learning for the automatic detection and quantification of AMD.