Remotely Piloted Aircraft Systems Research Articles

METHODOLOGY FOR AVOCADO (Persea americana Mill.) ORCHARD EVALUATION USING DIFFERENT MEASUREMENT TECHNOLOGIES

Avocado crop (Persea americana Mill.) is of great commercial importance due to its high profitability. However, it is being affected by various diseases and pests that affect yield and reduce fruit quality. The aim of this research was to develop methodologies for the evaluation of avocado plantations using different non-destructive technologies for rapid phenotyping and early detection of the incidence of diseases or damage due to stress in the stem. A plot of 0.7 ha. was evaluated, with a total of 44 individuals using Field-Map technology (dasometric and morphological characterization), RGB-multispectral images from Remotely Piloted Aircraft System (RPAS) (rapid phenotyping), while 15 individuals were evaluated using tomography (assessment of the internal state of the stem). The results with tomography indicated that there is a tree with wood rot of 14% with a lower acoustic speed with respect to the other trees evaluated. A high correlation was observed between the dasometric variables (r-Pearson from 0.63 to 0.98) estimated with Field-Map [crown base height, crown projection (m2 ) and total height] and with RPAS (height, perimeter and area). The vegetation indices do not have a direct correlation with the dasometric variables; five of the indices have a high contribution to variability except for the Normalized Difference Red Edge (NDRE). It can be concluded that the technologies used in this study would help to perform evaluations with a greater number of reliable and precise data with respect to the information obtained in a traditional way, while they can be replicated in commercial plots or research studies of different perennial crops, generating useful information for management decisions and crop evaluation.

Remotely Piloted Aircraft Systems to Identify Pests and Diseases in Forest Species: The global state of the art and future challenges

Remotely piloted aircraft systems (RPASs) have versatility of use and allow the attainment of images with high spatial resolution and low acquisition cost. Forestry researchers have a growing interest in developing research related to the use of images obtained by RPASs aimed at monitoring forest health.

On‐site geometric calibration of RPAS mounted sensors for SfM photogrammetric geomorphological surveys

AbstractThe application of structure from motion (SfM) photogrammetry for digital elevation model (DEM) and orthophoto generation from visible imagery enjoys ever‐growing popularity in geomorphological research. Photogrammetry experts, however, urge that a rigorous approach is a prerequisite for reliable results—a requirement that may conflict with real‐world survey. We present a method that unites the two disciplines, using the example of a challenging SfM photogrammetric survey at a Scottish river. Using simultaneous geometric pre‐calibration of a multi‐sensor remotely piloted aircraft system (RPAS), the method facilitates time‐efficient topography mapping and the integration of other wavelengths to create orthophotos providing additional surface information. The approach utilizes an on‐site 3D structure—for example, a building, as calibration object, by extracting coordinates of natural features from lidar scans and sensor imagery. We assess the workflow with specialized calibration software (VMS) and widely applied commercial SfM photogrammetric software (AM), using a DJI Phantom optical and a Workswell thermal sensor. We achieved calibration accuracies below one‐third (optical) and one‐quarter (thermal) of a pixel. Subsequently, we transfer the sensor parameters to pre‐calibrate the SfM application and compare the results to a self‐calibrated workflow. In a systematic experiment using the optical river survey dataset, we assess the effectiveness of pre‐calibration, oblique imagery, scale variation and masking to mitigate systematic DEM errors. Opposing trends show between the calibration strategies. Decreasing network complexity (i.e., flying heights/view angles) improves pre‐calibrated but compromises self‐calibrated scenarios. Pre‐calibrating (VMS) imagery from a single height (30 m nadir) yielded the best results. This finding could have implications for geomorphological surveys, in which single‐scale datasets are widespread practice, despite the literature's urge towards more complex imaging networks. The self‐calibrated results legitimise this insistence: The same dataset resulted in pronounced dome‐shaped DEM distortion, indicating systematic errors, whereas additional flying heights and angles significantly improved the results.

Estimate and Temporal Monitoring of Height and Diameter of the Canopy of Recently Transplanted Coffee by a Remotely Piloted Aircraft System

Digital agriculture is fundamental to potential improvements in the field by optimizing processes and providing intelligent decision making. This study aims to calculate the height and canopy diameter of recently transplanted coffee plants over three periods of crop development using aerial images, verify statistical differences between field measurements and aerial images, estimate linear equations between field data and aerial images, and monitor the temporal profile of the growth and development of the cultivar understudy in the field based on information extracted from aerial images through a Remotely Piloted Aircraft System (RPAS). The study area comprises a recently transplanted five-month-old Coffea arabica L. cultivar IAC J10 with information of height and crown diameter collected in the field and aerial images obtained by RPAS. As a result, it was possible to calculate the height and diameter of the canopy of coffee plants by aerial images obtained by RPAS. The linear estimation equation for height and crown diameter was determined with satisfactory results by coefficients R and R2 and performance metrics MAE, RMSE, and regression residuals, and it was possible to monitor the temporal profile of the height of the coffee cultivar in the field based on aerial images.

Integrated Fire Management as a Renewing Agent of Native Vegetation and Inhibitor of Invasive Plants in Vereda Habitats: Diagnosis by Remotely Piloted Aircraft Systems

The Cerrado biome is being gradually reduced. Remote sensing has been widely used to investigate spatio-temporal changes in the landscape, which are frequently limited to mapping with orbital sensors, while the Remotely Piloted Aircraft System (RPAS) proved to be advantageous in terms of spatial resolution and the application of advanced digital processing techniques. In this study, we investigated a vereda (humid area) of a conservation unit in the state of Mato Grosso, Brazil. Object-Based Image Analysis (OBIA) was applied to images obtained by RPAS to distinguish the phytophysiognomies of plant strata from the vereda and to diagnose the recovery of native and invasive vegetation after prescribed burning. The study was carried out in the following five stages: biomass collection; quality analysis of the land cover; phytosociological survey; collection of control points using a GNSS receiver (type L1/L2); and the capture of aerial images with an RGB camera coupled to a DJI Phantom 4 Pro, which was performed through overflights in three different periods. Object–Based Image Analysis was subsequently performed using the Nearest Neighbor classifier combined with Feature Space Optimization, obtaining classifications with accuracy and Kappa indexes greater than 80% and 0.80, respectively. The results of image processing allowed us to infer that fire acted as a renewing agent for native vegetation and as an inhibiting agent for invasive vegetation. The classification analyses combined with the phytosociological analysis allowed us to infer that the vereda is in the process of maturation. Therefore, the study demonstrated the potential of data obtained by RPAS for the diagnosis and analysis of vegetation dynamics in small wetlands submitted to Integrated Fire Management (IFM).

Performing low-altitude photogrammetric surveys, a comparative analysis of user-grade unmanned aircraft systems

This paper shows results of comparing performances of four unmanned aircraft systems (UAS) in terms of photogrammetric survey’s quality. This study aims to investigate what is the more suitable UAS for specific applications considering the required scale factor, such as for architectural, environmental, and restoration purposes. A series of photogrammetric surveys were conducted in a hilly area of about 5 ha using Phantom 4 Adv, Mavic 2 Pro, Mavic Air 2, and Mavic Mini 2. These unmanned aircrafts are commercial user–grade systems used mainly by private professionals. Several photogrammetric reconstructions were performed by varying essential parameters, such as flight altitude and cameras of remotely piloted aircraft systems (RPAS), applying structure-from-motion (SfM) algorithms to the images taken from the UAS. The surveys’ quality was analyzed by comparing the ground targets’ coordinates extrapolated from the point clouds to those measured on the field with indirect georeferencing through GNSS technology. Fifty targets were installed and arranged following a reasonably regular mesh. The boundary conditions were maintained the same for each flight mission, flight trajectories, and the ground control point distribution on the ground. For each survey made by each of the four UAS, altimetric and planimetric residuals were reported and compared. Average residuals from Phantom 4 Adv, about 15 mm, almost disappear compared to the other UASs; the discrepancy is one order of magnitude. With a regular grid geometry of ground targets, the Mavic Mini 2 led to an error average of about 5 cm. Remembering that the Mavic Mini 2 is an ultralight drone (does not require a pilot's license), it could significantly reduce cost compared to the other systems.

Characterization of Recently Planted Coffee Cultivars from Vegetation Indices Obtained by a Remotely Piloted Aircraft System

Brazil is the main producer and exporter and the second-largest consumer of coffee in the world, and Remotely Piloted Aircraft Systems stands out as an efficient remote detection technique applied to the study and mapping of crops. The objective of this study was to characterize three recently planted cultivars of Coffea arabica L. The study area is in Minas Gerais, Brazil, with a coffee plantation of the initial age of 5 months. The temporal behavior was determined based on monthly mean values. The spectral profile was obtained with mean values of the last month of dry and rainy periods. The statistical differences were obtained based on the non-parametric test of multiple comparisons. The estimation of the exponential equation was obtained through the Spearman correlation coefficient of determination and root mean square error. It was concluded that the seasons influence the behavior and development of cultivars, and significant statistical differences were detected for the variables, except for the chlorophyll variable. Due to the proximity and overlap of the reflectance values, spectral bands were not used to individualize cultivars. A correlation between the vegetation indices and leaf area index was observed and the exponential regression equation was estimated for each cultivar under study.

DAAMSIM: A simulation framework for establishing detect and avoid SYSTEM requirements

Performance requirements for detect, alert, and avoid (DAA) systems for remotely piloted aircraft systems (RPAS) are under development by many regulatory agencies and standards bodies. A DAA system can be decomposed into three functions, “detect” — situational awareness; “alert” — determination of traffic that may be in conflict, evaluation of the de-conflicting flight path, and informing the pilot-in-command; and “avoid” — avoidance maneuver execution, and determination of “clear of conflict”. The “Detect” function of a DAA system depends on the sensor, target, and environment characteristics (e.g., signal-to-noise ratio of the target vs. background). The “alert” function depends on conflict prediction algorithms and human factors requirements. The “avoid” function depends on the RPAS maneuvering performance, airspace “rules”, and the size of the protection volume. The aforementioned factors impact the time required to calculate, and execute, an avoidance maneuver that will guarantee a prescribed miss distance, and dominate the “detect” requirements of a sensor. This paper describes DAAMSim: a publicly available modeling and simulation framework, developed by the National Research Council of Canada, to support the determination of DAA system requirements, and evaluation of DAA system performance. The framework described herein incorporates the functional components including various sensor, tracker, and avoid models; data replay; visualization tools; and offline metrics. Further, this paper presents sample results of the framework’s ability to determine DAA system requirements for various degrees of RPAS and intruder performance, and concludes with a description of future work activities.

TOWARDS STRATEGIC MANAGEMENT OF DRONE APPLICATION PROCESS AND REGULATION IN SOUTH AFRICA

This paper discusses strategies to improve the application process for drones and Part 101 of the regulations for the approval of applications. Drones have the potential to transform the world by doing things differently and improving technology in the future. The use of drones is growing fast, and the need for control is important for reasons of safety and privacy. A qualitative method of data collection and analysis was used in this study, using the South African Civil Aviation Authority (SACAA) as a case study. Telephonic interviews were conducted to collect data from participants and to understand the issues affecting the drone application process. Drone operators were interviewed to collect data on the drone application process and the regulations. The researcher discovered that there were issues affecting the existing drone application and approval processes, such as restrictive regulations, the inclusion of manned aircraft requirements, slow issuing of Remotely Piloted Aircraft Systems operators’ certificates, and the capital needed to enter the drone business. Recommendations are made to improve the drone application and approval processes.

Detecting the Spatial Variability of Seagrass Meadows and Their Consequences on Associated Macrofauna Benthic Activity Using Novel Drone Technology

Seagrass meadows are undergoing significant decline locally and globally from human and climatic impacts. Seagrass decline also impacts seagrass-dependent macrofauna benthic activity, interrupts their vital linkage with adjacent habitats, and creates broader degradation through the ecosystem. Seagrass variability (gain and loss) is a driver of marine species diversity. Still, our understanding of macrofauna benthic activity distribution and their response to seagrass variability from remotely sensed drone imagery is limited. Hence, it is critical to develop fine-scale seasonal change detection techniques appropriate to the scale of variability that will apply to dynamic marine environments. Therefore, this research tested the performance of the VIS and VIS+NIR sensors from proximal low altitude remotely piloted aircraft system (RPAS) to detect fine-scale seasonal seagrass variability using spectral indices and a supervised machine learning classification technique. Furthermore, this research also attempted to identify and quantify macrofauna benthic activity from their feeding burrows and their response to seagrass variability. The results from VIS (visible spectrum) and VIS+NIR (visible and near-infrared spectrum) sensors produced a 90–98% classification accuracy. This accuracy established that the spectral indices were fundamental in this study to identify and classify seagrass density. The other important finding revealed that seagrass-associated macrofauna benthic activity showed increased or decreased abundance and distribution with seasonal seagrass variability from drone high spatial resolution orthomosaics. These results are important for seagrass conservation because managers can quickly detect fine-scale seasonal changes and take mitigation actions before the decline of this keystone species affects the entire ecosystem. Moreover, proximal low-altitude, remotely sensed time-series seasonal data provided valuable contributions for documenting spatial ecological seasonal change in this dynamic marine environment.

Exploring RPAS potentiality using a RGB camera to understand short term variation on sandy beaches

Remotely Piloted Aircraft Systems (RPAS) is a low-cost and useful alternative to track spatial-temporal variations in a rapidly changing environment. However, there is a lack of studies on beach geomorphology characterization in association with visible spectrum reflectance potentialities. The objective of this study was to embrace this technology for coastal change detection. For this purpose, the data set samples are covering the winter season of 2019. The materials used were digital orthophotos, digital terrain models, and geomorphological parameters, such as the Relative Tide Range (RTR) and Omega (Ω). The goals are to (i) classify the beach morphodynamics; (ii) identify the berms, cusps, and rip current channels; (iii) extract RGB indexes to detect the shoreline variability; (iv) assess beach profiles and (v) evaluate the volume of sediments. The study area, Paiva beach, is located in the Northeast of Brazil, which presents significant morphodynamic variations. The results indicate three morphodynamic stages, namely: Rhythmic Bar and Beach (RBB); Transverse Bar and Rip (TBR), and Low Tide Terrace (LTT). The wave climate for the region averaged Dir of 120°, Tp of 9 s, and Hs of 1.8 m. Between June and July, the shoreline had the most significant advance with 51% of the beach extension. On the other hand, between July and August, the beach registered a considerable 88% retreat. The profiles showed a maximum variation of 1.5 m vertically on the berm, a common characteristic of winter profiles, detected in August. For the sediment volume, July and August showed erosion processes with sediment loss of 12,508.58 m3 in the south sector and accretion of 23,052.97 m3 in the north. The planimetric and altimetric mean square error were 2.7 and 4.5 cm, respectively, showing the advantage of high spatial resolution data for morphodynamic classification, and combining it to identify vegetation indexes to detect shoreline variability.

Accuracy Assessment of Cultural Heritage Models Extracting 3D Point Cloud Geometric Features with RPAS SfM-MVS and TLS Techniques

A proper classification of 3D point clouds allows fully exploiting data potentiality in assessing and preserving cultural heritage. Point cloud classification workflow is commonly based on the selection and extraction of respective geometric features. Although several research activities have investigated the impact of geometric features on classification outcomes accuracy, only a few works focused on their accuracy and reliability. This paper investigates the accuracy of 3D point cloud geometric features through a statistical analysis based on their corresponding eigenvalues and covariance with the aim of exploiting their effectiveness for cultural heritage classification. The proposed approach was separately applied on two high-quality 3D point clouds of the All Saints’ Monastery of Cuti (Bari, Southern Italy), generated using two competing survey techniques: Remotely Piloted Aircraft System (RPAS) Structure from Motion (SfM) and Multi View Stereo (MVS) techniques and Terrestrial Laser Scanner (TLS). Point cloud compatibility was guaranteed through re-alignment and co-registration of data. The geometric features accuracy obtained by adopting the RPAS digital photogrammetric and TLS models was consequently analyzed and presented. Lastly, a discussion on convergences and divergences of these results is also provided.

Influence of flight hours on spectral behavior of rice (Oryza Sativa L.) and soybean (Glycine Max L.) cultivars in images obtained by remotely piloted aircraft system

The research aimed to evaluate the spectral behavior of remotely piloted aircraft system (RPAS) images taken of agricultural crops during the day to answer the following hypotheses: (1) there are no significant differences between spectral reflectance information obtained through RPAS images and data from spectroradiometry; (2) the survey time can be defined as a limiting factor in obtaining accurate reflectance data; and (3) it is possible to characterize homogeneous observation windows throughout the day. With regular intervals of 1 h, 12 surveys were made in rice and soybean cultivars, using a Sequoia® camera, embedded in RPAS, and the FieldSpec®3 spectroradiometer, to validate the image spectra. Normalized Difference Vegetation Index (NDVI), Optimized Soil Adjusted Vegetation Index (OSAVI), and Transformed Chlorophyll Absorption Reflectance Index (TCARI) derived from aerial images were subjected to Kruskal–Wallis and hierarchical cluster tests to define homogeneous observation times. The Sequoia® system made it possible to obtain reflectance data that met the validation criteria, even operating under varying interaction factors during the day, so the flight schedule did not result in loss of information about the vegetation. The hierarchical cluster analysis allowed the detection of temporal patterns of NDVI, OSAVI, and TCARI throughout the day. For rice and soybeans, three more homogeneous observation windows were characterized. In terms of temporal pattern of NDVI, OSAVI, and TCARI, the Kruskal–Wallis analysis was quite sensitive to the inherent variation of the crops throughout the day, so a hypothesis of homogeneous monitoring windows input throughout the day could not be proven.

The Role of UAS–GIS in Digital Era Governance. A Systematic Literature Review

UAS (Unmanned Aircraft Systems) technologies, also known as UAV (Unmanned Aerial Vehicle), drones, or Remotely Piloted Aircraft System (RPAS) and GIS (Geographic Information System) are recognised for the value of the results that can be achieved by their combined use. However, their use and the results achieved are rarely framed within the context of Digital Era Governance (DEG), an undertaking that would significantly reduce the capabilities of knowledge transfer from the academic and/or private environment to the public domain. The purpose of this study was to highlight, by a bibliometric analysis, the areas of proposed use of this team of tools and the extent to which these can enter the sphere of interest of public administrations, especially local ones. From a methodological point of view, based on the 439 articles filtered from the Web of Science database where UAS/UAV and GIS technologies were used, several bibliometric analyses have emerged. VOSviewer and R (Bibliometrix tool) were used to conduct the bibliometric analyses. Most scientific publications that used UAV technology as a working tool have predominant applicability in photogrammetry, while GIS applications are found in publications dedicated to image processing, landslides, and cultural and archaeological heritage. We point out that from the point of view of international cooperation, at the level of institutions or countries, certain international organisations from the USA, China, and the central and northern European states have a high interest in this topic, and a low cooperation between academia and public administration is exhibited. The conclusion is represented by the apparent lack of framing of the results of UAS–GIS technologies usage into wider and more topical contexts, such as digital era governance, and also a reduced applicability of the research results.

Climate-Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite-Based Deep-Learning Method for Estimating Pasture Yield

The aim of this research is to expand recent developments in the mapping of pasture yield with remotely piloted aircraft systems to that of satellite-borne imagery. To date, spatially explicit and accurate information of the pasture resource base is needed for improved climate-adapted livestock rangeland grazing. This study developed deep learning predictive models of pasture yield, as total standing dry matter in tonnes per hectare (TSDM (tha−1)), from field measurements and both remotely piloted aircraft systems and satellite imagery. Repeated remotely piloted aircraft system structure measurements derived from structure from motion photogrammetry provided measures of pasture biomass from many overlapping high-resolution images. These measurements were taken throughout a growing season and were modelled with persistent photosynthetic pasture responses from various Planet Dove high spatial resolution satellite image-derived vegetation indices. Pasture height modelling as an input to the modelling of yield was assessed against terrestrial laser scanning and reported correlation coefficients (R2) from 0.3 to 0.8 for both a coastal grassland and inland woodland pasture. Accuracy of the predictive modelling from both the remotely piloted aircraft system and the Planet Dove satellite image estimates of pasture yield ranged from 0.8 to 1.8 TSDM (tha−1). These results indicated that the practical application of repeated remotely piloted aircraft system derived measures of pasture yield can, with some limitations, be scaled-up to satellite-borne imagery to provide more temporally and spatially explicit measures of the pasture resource base.

UAS and smartphone integration at wildfire management in Aotearoa New Zealand

Background: From 2016, wildfire emergency response used Remotely Piloted Aircraft Systems (RPAS) also known as Uninhabited or Unmanned Aerial Vehicles (UAVs) and Systems (UAS) or "drones" (hereafter UAS), smartphones and smartphone applications (apps) on-site, for the first time at scale in Aotearoa New Zealand (hereafter New Zealand). This study outlines the deployment and use of this new technology in monitoring at wildfires in New Zealand from 2016, and the conveyance of fire response information to operational personnel. Methods: A quantitative and qualitative questionnaire, and semi-structured interviews were used to gather feedback on the use of this emerging technology from wildfire management personnel. The results were analysed to determine perception change over time, using retrospective analysis. The issues presented, and the uptake by fire management and personnel for the incorporation of such technology at wildfires in New Zealand are discussed. Findings: The integration of UAS and visual, infrared/infrared-thermal (IR/TIR) sensors has been used at over ten wildfire management response incidents throughout New Zealand since 2016. The quantitative perception of use and benefit of information technology in wildfire management response improved from the initial viewpoints, from indifferent to strongly supportive, and supportive to strongly supportive for UAS and smartphone use, respectively. Qualitative analysis showed that both positive views on the new technology increased, and indifferent and negative views diminished substantially following exposure to its operational integration into wildfire management. Conclusions: The use of technology such as UAS has gained support and currently offers the potential to increase safety and reduce suppression and mop-up costs. A reduction in the time taken for hotspot detection and management, combined with the ability to redeploy heavy-lift aircraft away from such tasks would lead to efficiencies in cost and resource utilisation. UAS as platforms for remote-sensing devices (such as cameras and laser scanners), and smartphone apps are now considered important tools for deployment at New Zealand wildfires by operational and Incident Management personnel. The adoption of any new systems or technology requires flexibility, especially in terms of management support, in which regular information, training and instruction should be considered crucial.

Training of Remotely Piloted Aircraft Operators for Border Surveillance in Ukraine based on the European Union Standards

The study reveals the theoretical and practical approaches to building of an all-European training strategy for remotely piloted aircraft systems (RPAS) operators to increase their interoperability in the European Union countries and Ukraine. Nowadays, the border guard agencies both in EU and Ukraine have developed their national courses and training programs related to training of RPAS operators, however, the proposed in this study common approach is based on the Sectoral Qualification Framework in the field of border protection and Common Core Curriculum for border guards in EU and is aimed at expanding the use of small RPAS for a specific task of protecting the border sectors of various types (land, sea, air) in accordance with the Concept of European Integrated Border Management. The research was carried out in the frames of the Grant Program of the European Agency for Border and Coast Protection Frontex "The use of RPAS for border protection - tactics, guidelines, best practices". The study covers the analysis of the theory, market solutions, takes into account practical border protection experience, which resulted in creation of a common curricula for operators of RPAS in the specific sphere – surveillance of the state borders. The developed training course for RPAS operators provides an opportunity to train external crews of unmanned aerial vehicles, which will best meet the objectives and peculiar needs of the state border protection agencies of the European Union and Ukraine. The readiness of future RPAS operators to perform professional duties on border surveillance was diagnosed according to cognitive (knowledge formation) and operational (skills and abilities formation) criteria. Comparison of the values of these criteria before and after the formative stage of the experiment shows that the experimental measures for the application of best European practices in the training of RPAS operators have positively affected the quality of their training. The results of the study will help strengthen the state border security within the European context of border guarding by means of increasing the level of interoperability of RPAS operators in the EU countries and Ukraine.

Extraction of discontinuity sets of rocky slopes using iPhone-12 derived 3DPC and comparison to TLS and SfM datasets

Characterisation of a rock mass requires data from the intact rock along with the discontinuities. Assuming that the discontinuities are planar, its characterisation requires its number and orientation. This leads to the analysis of the normal spacing, the persistence and the roughness, among others. The geometrical analysis of the surface enables the calculation of the parameters to characterise the discontinuities, and the use of digital datasets enhance them. Remote sensing techniques, such as the Terrestrial Laser Scanning (TLS) instruments of Structure from Motion (SfM) technique, provide 3D point clouds that enable the geometrical analysis. The scientific community has been testing both techniques since the 2000s, and companies are introducing their use in their workflows. However, the cost of the TLS instrument could still be a barrier to its use to most scholars. Because of this, the community shows a growing interest in Remotely Piloted Aircraft Systems (RPAS) equipped with digital cameras or in smartphones equipped with high-quality cameras to capture digital datasets of rocky slopes. The SfM workflow processes the captured images, reconstructing the rocky slope through a 3D point cloud and textured meshes. Although previous studies show that the SfM-derived point clouds present less quality than TLS-derived datasets in terms of accuracy, the use of SfM is still of interest because of its cost. In 2020 Apple launched the Iphone-12 device, which is equipped with a LiDAR sensor that is not used to capture the surface coordinates but to enhance the photo’s quality. Since then, the community has developed several applications to reconstruct 3D surfaces using this device. This leads to consider this device as an intermediate option between the TLS and SfM to characterise rocky slopes and their discontinuities. In this communication we explore the digitalisation of a rocky slope via TLS instruments, SfM technique and using the Iphone-12 device. It comprises a 26 meter high mechanically excavated rocky slope in Cretaceous marlstones and limestones. To capture the surface, we used three configurations, and we found that to scan ground surface the distance device-surface had to be less than 3 meters. The discontinuities are characterised using the three sources of information using the DSE software. The results show a promising match compared to the TLS or SfM. This evidences that these devices will soon be widely employed for evaluating rocky slopes.

Application of RPAS to disaster risk reduction in Brazil: application in the analysis of urban floods

Geotechnologies have significant potential for application in socio-environmental analysis coupled to disaster risk reduction. Equipment and applications are available that are supported by scientific computing, promoting advances in the acquisition and processing of remote sensing data. Among these are two types: (i) the associated equipment to technology LiDAR (light detection and ranging) and (ii) remotely piloted aircraft systems (RPAS) with platforms of remote sensors. Recently, an growing number of studies has been observed that have the potential for applications in the sensors equipped in RPAS for environmental studies, especially those that evaluate the impacts of natural disasters. In this context, the aim of this research is to demonstrate the possibilities of RPAS applications in the collection of data of interest in the management of natural disasters. Also associated with this task is the prospect of implementing some techniques of scientific computing necessary for the implementation of applications. With these activities, we seek to contribute to the advancement of the employment of RPAS in managing and preventing the risk of natural disasters.

Multi-temporal modeling of road-induced overland flow alterations in a terraced landscape characterized by shallow landslides

The presence of roads in high steep agricultural systems is often linked with landslides occurrence. This research aims to model multi-temporal overland flow dynamics in a shallow landslides-prone terraced landscape (northern Italy).The combined use of Remotely Piloted Aircraft Systems (RPAS) and photogrammetric techniques (e.g., Structure from Motion-SfM) allowed to elaborate multi-temporal high-resolution Digital Elevation Models (DEMs). Hydrological analyses of water flow's depth alterations due to the road presence were carried out adopting the SIMulated Water Erosion model (SIMWE), focusing on different scenarios considering the presence of the road and assuming its absence through a specific DEM smoothing procedure. The possibility to perform multi-temporal hydrological simulations at a hillslope scale so as to analyse the role played by the road in overland flows alteration is still a challenge to be investigated. Results proved the role played by the road in water flows change above the two observed shallow landslides, with respective maximum water depth values equal to 0.18 m and 0.14 m. On the contrary, no-road simulations not revealed significant water flows deviations towards landslides, with water depth values around 0 m, underlining that the absence of the road would avoid relevant changes in water flow paths toward the collapsed surfaces. This work could be a solid starting point for analyse road impact on runoff dynamics and hillslopes stability also at a wider scale, as well as for planning efficient mitigation intervention so as to reduce the occurrence of similar future scenarios.