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This paper describes the results of a field investigation with the objective of evaluating the possibility to produce drone-derived 3D digital point clouds sufficiently dense and accurate to determine discontinuity surface roughness characteristics. A discontinuous rock mass in Italy was chosen as the investigation site and Structure from Motion and Multi-View Stereo techniques adopted for producing three-dimensional point clouds from the two-dimensional image sequences. Since the roughness of discontinuities depends on direction, scale and resolution of the sampling, data were always collected along the maximum slope gradient. The scale effect was evaluated by analysing discontinuity profiles of different lengths (10 cm, 30 cm, 60 cm and 100 cm), with measurements taken from drone flights flown at different distances from the rocky slopes (10 m, 20 m and 30 m). The accuracy of the derived joint roughness coefficients was evaluated by direct comparison with discontinuity profiles measured during fieldwork using conventional techniques and from contemporaneous terrestrial laser scanning. Results from this research show that 3D digital point clouds, derived from the processing of drone-flight images, were successfully used for reliable representation of discontinuity roughness for profiles longer than 60 cm, whereas less reliable results were achieved for shorter profile lengths. This, even if strictly related to this case study since several factors can affect the minimum profile length, represents a significant contribution to improve the knowledge on the use of remotely captured data for characterising the discontinuities in natural or man-made rock outcrops, particularly where access difficulties do not allow conventional engineering-geological surveys to be undertaken. [ABSTRACT FROM AUTHOR]

Xylella fastidiosa (Xf) is a well-known bacterial plant pathogen mainly transmitted by vector insects and is associated with serious diseases affecting a wide variety of plants, both wild and cultivated; it is known that over 350 plant species are prone to Xf attack. In olive trees, it causes olive quick decline syndrome (OQDS), which is currently a serious threat to the survival of hundreds of thousands of olive trees in the south of Italy and in other countries in the European Union. Controls and countermeasures are in place to limit the further spreading of the bacterium, but it is a tough war to fight mainly due to the invasiveness of the actions that can be taken against it. The most effective weapons against the spread of Xf infection in olive trees are the detection of its presence as early as possible and attacks to the development of its vector insects. In this paper, image processing of high-resolution visible and multispectral images acquired by a purposely equipped multirotor unmanned aerial vehicle (UAV) is proposed for fast detection of Xf symptoms in olive trees. Acquired images were processed using a new segmentation algorithm to recognize trees which were subsequently classified using linear discriminant analysis. Preliminary experimental results obtained by flying over olive groves in selected sites in the south of Italy are presented, demonstrating a mean Sørensen–Dice similarity coefficient of about 70% for segmentation, and 98% sensitivity and 93% precision for the classification of affected trees. The high similarity coefficient indicated that the segmentation algorithm was successful at isolating the regions of interest containing trees, while the high sensitivity and precision showed that OQDS can be detected with a low relative number of both false positives and false negatives. [ABSTRACT FROM AUTHOR]