Unmanned Aerial Vehicles (UAV)-based remote sensing offers great possibilities to acquire in a fast and easy way field data for precision agriculture applications. This field of study is rapidly increasing due to the benefits and advantages for farm resources management, particularly for studying crop health. This paper reports some experiences related to the analysis of cultivations (vineyards and tomatoes) with Tetracam multispectral data. The Tetracam camera was mounted on a multi-rotor hexacopter. The multispectral data were processed with a photogrammetric pipeline to create triband orthoimages of the surveyed sites. Those orthoimages were employed to extract some Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Soil Adjusted Vegetation Index (SAVI), examining the vegetation vigor for each crop. The paper demonstrates the great potential of high-resolution UAV data and photogrammetric techniques applied in the agriculture framework to collect multispectral images and evaluate different VI, suggesting that these instruments represent a fast, reliable, and cost-effective resource in crop assessment for precision farming applications.

The availability of high-resolution Digital Surface Models of coastal environments is of increasing interest for scientists involved in the study of the coastal system processes. Among the range of terrestrial and aerial methods available to produce such a dataset, this study tests the utility of the Structure from Motion (SfM) approach to low-altitude aerial imageries collected by Unmanned Aerial Vehicle (UAV). The SfM image-based approach was selected whilst searching for a rapid, inexpensive, and highly automated method, able to produce 3D information from unstructured aerial images. In particular, it was used to generate a dense point cloud and successively a high-resolution Digital Surface Models (DSM) of a beach dune system in Marina di Ravenna (Italy). The quality of the elevation dataset produced by the UAV-SfM was initially evaluated by comparison with point cloud generated by a Terrestrial Laser Scanning (TLS) surveys. Such a comparison served to highlight an average difference in the vertical values of 0.05 m (RMS = 0.19 m). However, although the points cloud comparison is the best approach to investigate the absolute or relative correspondence between UAV and TLS methods, the assessment of geomorphic features is usually based on multi-temporal surfaces analysis, where an interpolation process is required. DSMs were therefore generated from UAV and TLS points clouds and vertical absolute accuracies assessed by comparison with a Global Navigation Satellite System (GNSS) survey. The vertical comparison of UAV and TLS DSMs with respect to GNSS measurements pointed out an average distance at cm-level (RMS = 0.011 m). The successive point by point direct comparison between UAV and TLS elevations show a very small average distance, 0.015 m, with RMS = 0.220 m. Larger values are encountered in areas where sudden changes in topography are present. The UAV-based approach was demonstrated to be a straightforward one and accuracy of the vertical dataset was comparable with results obtained by TLS technology.

The feasibility of unmanned-aerial-vehicle-based photogrammetry was assessed for the reconstruction of high-resolution topography and geomorphic features of quarries by nadir and off-nadir imagery. The test site was a quarry located in the rural area of Turi (Bari, southern Italy). Two processing scenarios were created. Nadir images were initially used, and images acquired with off-nadir angles were added. An accurate set of ground control points (GCPs) were surveyed for both georeferencing purposes and validation processes. In the reconstruction of the surfaces, an accuracy of a few centimeters was achieved in the final positioning of point clouds representing the main geometries of quarry environment. However, greatest differences were found along the edges or the lines characterized by sudden slope changes. To better understand such results, some characteristic quarry shapes depicted by both the scenarios were compared to those surveyed by a total station used as an independent benchmark technique. It allowed to define the benefits introduced by the joint use of nadir and oblique images in the delineation of quarry shapes, surface discontinuities and better descriptions of sub-vertical walls. Beside the evaluation of benefits introduced by use of oblique cameras, the effectiveness of the proposed methodology was also discussed with alternative technologies. Unmanned aerial platforms represent an effective solution, with the need for few accurate GCPs.

The present paper explores the combination of unmanned aerial vehicle (UAV) photogrammetry and three-dimensional geomechanical modeling in the investigation of instability processes of long sectors of coastal rocky cliffs. The need of a reliable and detailed reconstruction of the geometry of the cliff surfaces, beside the geomechanical characterization of the rock materials, could represent a very challenging requirement for sub-vertical coastal cliffs overlooking the sea. Very often, no information could be acquired by alternative surveying methodologies, due to the absence of vantage points, and the fieldwork could pose a risk for personnel. The case study is represented by a 600 m long sea cliff located at Sant’Andrea (Melendugno, Apulia, Italy). The cliff is characterized by a very complex geometrical setting, with a suggestive alternation of 10 to 20 m high vertical walls, with frequent caves, arches and rock-stacks. Initially, the rocky cliff surface was reconstructed at very fine spatial resolution from the combination of nadir and oblique images acquired by unmanned aerial vehicles. Successively, a limited area has been selected for further investigation. In particular, data refinement/decimation procedure has been assessed to find a convenient three-dimensional model to be used in the finite element geomechanical modeling without loss of information on the surface complexity. Finally, to test integrated procedure, the potential modes of failure of such sector of the investigated cliff were achieved. Results indicate that the most likely failure mechanism along the sea cliff examined is represented by the possible propagation of shear fractures or tensile failures along concave cliff portions or over-hanging due to previous collapses or erosion of the underlying rock volumes. The proposed approach to the investigation of coastal cliff stability has proven to be a possible and flexible tool in the rapid and highly-automated investigation of hazards to slope failure in coastal areas.

The occurrence of extreme windstorms and increasing heat and drought events induced by climate change leads to severe damage and stress in coniferous forests, making trees more vulnerable to spruce bark beetle infestations. The combination of abiotic and biotic disturbances in forests can cause drastic environmental and economic losses. The first step to containing such damage is establishing a monitoring framework for the early detection of vulnerable plots and distinguishing the cause of forest damage at scales from the management unit to the region. To develop and evaluate the functionality of such a monitoring framework, we first selected an area of interest affected by windthrow damage and bark beetles at the border between Italy and Austria in the Friulian Dolomites, Carnic and Julian Alps and the Carinthian Gailtal. Secondly, we implemented a framework for time-series analysis with open-access Sentinel-2 data over four years (2017–2020) by quantifying single-band sensitivity to disturbances. Additionally, we enhanced the framework by deploying vegetation indices to monitor spectral changes and perform supervised image classification for change detection. A mean overall accuracy of 89% was achieved; thus, Sentinel-2 imagery proved to be suitable for distinguishing stressed stands, bark-beetle-attacked canopies and wind-felled patches. The advantages of our methodology are its large-scale applicability to monitoring forest health and forest-cover changes and its usability to support the development of forest management strategies for dealing with massive bark beetle outbreaks.

In recent years digital sensors have been successfully integrated on board Unmanned Aerial Vehicles (UAV) to assess crop vigour, vegetation coverage, and to quantify the ‘greenness’ of foliage as indirect measurements of crop nitrogen status. The classical approach of precision agriculture has involved the use of multispectral sensors onboard UAV and the development of numerous vegetation indices associated with vegetation parameters, such as the mostly used Normalized Difference Vegetation Index (NDVI). However, the main negative issue when dealing with multi and hyper-spectral reflectance measuring tools is their high cost and complexity from the operational point of view. As a low-cost alternative, vegetation indices derived from Red Green Blue (RGB) cameras have been employed for remote-sensing assessment, providing data on different stress conditions and species. Digital images record information as amounts of RGB light emitted for each pixel of the image; however, the intensity of red and blue will often alter how green an image appears. To simplify the interpretation of digital colour data, recent studies have suggested converting RGB values to the more intuitive Hue, Saturation, and Brightness (HSB) colour spectrum, and then into a single measure of dark green colour, the Dark Green Color Index (DGCI). In this study, NDVI acquired by a ground-based handheld crop sensor and by a multispectral camera mounted on board a UAV has been compared with DGCI calculated from images taken with a commercial digital camera on board a UAV, trying to quantify the colour of turfgrass that had received different nitrogen (N) rates. The objectives of the trial were to study an affordable easy-to-use tool evaluating the relationship among NDVI, DGCI and leaf nitrogen content on turfgrass.