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This research concentrates on the Unmanned Aircraft System (UAS) demand sites' hierarchical location–allocation problem in air route network design. With demand sites (locations where UAS operations are requested) organized and allocated according to the spatial hierarchy of UAS traffic flows, the hierarchical structure guarantees resource conservation and economies of scale through traffic consolidation. Therefore, in this paper, the UAS route network with a three-level hierarchy is developed under a multi-objective decision-making framework, where concerns about UAS transportation efficiency from the user side and construction efficiency from the supplier side are both simultaneously considered. Specifically, a bi-level Hybrid Simulated Annealing Genetic Algorithm (HSAGA) with global and local search combined is proposed to determine the optimal number, location, and allocation of hierarchical sites. Moreover, using the information of site closeness and UAS demand distribution, two problem-specific local search operators are designed to explore elite neighborhood regions instead of all the search space. A case study based on the simulated UAS travel demand data of the Beijing area in China was conducted to demonstrate the effectiveness of the proposed method, and the impact of critical parameter settings on the network layout was explored as well. Findings from this study will offer new insights for UAS traffic management in the future. [ABSTRACT FROM AUTHOR]

Periodically monitoring landslides is a key factor for supporting the realisation of hazard warning systems and risk reduction in the corresponding neighbourhood areas. Although satellite remote sensing solutions can be considered for low spatial resolution monitoring, this approach is still inappropriate for high spatial resolution investigations. Ground-based Radar Interferometry is also a widely used technique that allows for working at a proper spatial resolution, but it can often be an overbudget solution for most applications. Instead, photogrammetric surveys based on Unmanned Aerial System (UAS) imagery appear as a very interesting approach in terms of both spatial resolution and flexibility in temporally repeating the survey. Motivated by this observation, this work investigates the use of multi-temporal UAS surveys for landslide monitoring. To be more precise, Digital Image Correlation (DIC) has been applied to orthomosaics generated from different UAS photogrammetry surveys to compute the area's deformation map. Compared with a reference GNSS survey, the results obtained using NHAZCA IRIS software and an in-house DIC approach show a deformation estimation accuracy of approximately 0.1 m, a reasonable accuracy for landslides moving at moderate velocity. [ABSTRACT FROM AUTHOR]

ABSTRACTPeriodically monitoring landslides is a key factor for supporting the realisation of hazard warning systems and risk reduction in the corresponding neighbourhood areas. Although satellite remote sensing solutions can be considered for low spatial resolution monitoring, this approach is still inappropriate for high spatial resolution investigations. Ground-based Radar Interferometry is also a widely used technique that allows for working at a proper spatial resolution, but it can often be an overbudget solution for most applications. Instead, photogrammetric surveys based on Unmanned Aerial System (UAS) imagery appear as a very interesting approach in terms of both spatial resolution and flexibility in temporally repeating the survey. Motivated by this observation, this work investigates the use of multi-temporal UAS surveys for landslide monitoring. To be more precise, Digital Image Correlation (DIC) has been applied to orthomosaics generated from different UAS photogrammetry surveys to compute the area’s deformation map. Compared with a reference GNSS survey, the results obtained using NHAZCA IRIS software and an in-house DIC approach show a deformation estimation accuracy of approximately 0.1 m, a reasonable accuracy for landslides moving at moderate velocity.

This paper addresses the problem of Unmanned Aircraft System Traffic Management (UTM), which refers to an air transportation system wherein unmanned aircraft systems (UAS) are used to deliver small payloads on-demand by safely maneuvering in the low-altitude known urban infrastructure inhabited by dynamic air traffic. A multi-stage framework based on numerical potential fields is proposed for establishing UTM in the non-segregated airspace. Numerical potential fields are adopted for global path planning as they provide optimum stand-off clearance from obstacles in the environment. The proposed global path planning approach is augmented with a prediction-based zone determination algorithm to minimize high-density air traffic encounters by the UAS. In the event of low-density traffic in the vicinity of the UAS, the UAS neighborhood is sectorized into octants. An octant-based local collision avoidance strategy generates force fields that divert the UAS into a vacant octant. Extensive simulations have been carried out to show the efficacy of the proposed method in dense urban settings. [ABSTRACT FROM AUTHOR]

With the launch of Landsat-9 on 27 September 2021, Landsat continues its fifty-year continuity mission of providing users with calibrated Earth observations. It has become a requirement that an underflight experiment be performed during commissioning to support sensor cross-calibration. In this most recent experiment, Landsat-9 flew under Landsat-8 for nearly three days with over 50% ground overlap, from 13 to 15 November 2021. To address the scarcity of reference data that are available to support calibration and validation early-on in the mission, a ground campaign was planned and executed by the Rochester Institute of Technology (RIT) on 14 November 2021 to provide full spectrum measurements for early mission comparisons. The primary experiment was conducted in the Outer Banks, North Carolina at Jockey's Ridge Sand Dunes. Full-spectrum ground-based measurements were acquired with calibrated reference equipment, while a novel Unmanned Aircraft System (UAS)-based platforms acquired hyperspectral visible and near-infrared (VNIR)/Short-wave infrared (SWIR) imagery data and coincident broadband cooled thermal infrared (TIR) imagery. Results of satellite/UAS/ground comparisons were an indicator, during the commissioning phase, that Landsat-9 is behaving consistently with Landsat-8, ground reference, and UAS measurements. In the thermal infrared, all measurements agree to be within 1 K over water and to within 2 K over sand, which represents the most challenging material for estimating surface temperature. For the surface reflectance product(s), Landsat-8 and -9 are in good agreement and only deviate slightly from ground reference in the SWIR bands; a deviation of 2% in the VNIR and 5–8% in the SWIR regime. Subsequent longer-term studies indicate that Landsat 9 continues to perform as expected. The behavior of Thermal Infrared Sensor-2 (TIRS-2) against reference is also shown for the first year of the mission to illustrate its consistent performance. [ABSTRACT FROM AUTHOR]

6G networks can support global, ubiquitous and seamless connectivity through the convergence of terrestrial and non-terrestrial networks (NTNs). Unlike terrestrial scenarios, NTNs pose unique challenges including propagation characteristics, latency and mobility, owing to the operations in spaceborne and airborne platforms. To overcome all these technical hurdles, this survey paper presents the use of artificial intelligence (AI) techniques in learning and adapting to the complex NTN environments. We begin by providing an overview of NTNs in the context of 6G, highlighting the potential security and privacy issues. Next, we review the existing AI methods adopted for 6G NTN optimization, starting from machine learning (ML), through deep learning (DL) to deep reinforcement learning (DRL). All these AI techniques have paved the way towards more intelligent network planning, resource allocation (RA), and interference management. Furthermore, we discuss the challenges and opportunities in AI-powered NTN for 6G networks. Finally, we conclude by providing insights and recommendations on the key enabling technologies for future AI-powered 6G NTNs.

This article focuses on the mapping and rate of spread (ROS) measurement of grass fires using near infrared (NIR) images acquired by a small fixed-wing unmanned aircraft system (UAS) operating at low altitudes. A new method is proposed for spatiotemporal representation of grass fire evolution using time labeled UAS NIR orthomosaics stitched from aerial images collected at varying time stamps over different regions of fire. Furthermore, a novel NIR intensity variance thresholding method is proposed for accurate identification and delineation of grass fire fronts based on the obtained NIR mosaics in digital numbers. The proposed methods are demonstrated and validated using UAS NIR imagery acquired over a prescribed tallgrass fire in Kansas (around 13 ha.). Three NIR short time-series orthomosaics are generated at a time interval of about 2 min with a spatial registration accuracy of 1.45 m (RMSE). The mean ROS for head, flank, and back tallgrass fires are measured to be 0.28, 0.1, and 0.025 m/s.

Copyright of Aeronautical Science Bulletins / Repüléstudományi Közlemények is the property of National University of Public Service and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This analysis examined systemic causes of Uncrewed Air Vehicle (UAV) accidents identifying operator, environmental, supervisory, and organisational factors through the use of the Human Factors Analysis and Classification System (HFACS). HFACS is a system-based analysis method for investigating the causal factors associated with accidents and incidents and has previously been used to reliably and systematically identify active and latent failures associated with both military and general aviation accidents. Whilst HFACS has previously been applied to UAV accidents, the last known application was conducted in 2014. Using reports retrieved from nine accident investigation organisations' databases, causal factors were coded against unsafe acts, preconditions, and failures at the supervisory, organisational, and environmental levels. Causal factors were assessed on 77 medium or large UAV mishaps/accidents that occurred over a 12-year period up to 2024. 42 mishap reports were deemed to involve a human factor as a causal factor. A large proportion of the mishaps contained factors attributed to Decision Errors at level 1 (Unsafe Acts) which was found to be associated with both the Technological Environment and Adverse Mental State at level 2 (Pre-conditions). Causal factors were identified at each of the other 3 levels (Supervisory, Organisational and External) with a number of emergent associations between causal factors. These data provide support for the identification and development of interventions aimed at improving the safety of organisations and advice of regulators for Uncrewed Air Systems. [ABSTRACT FROM AUTHOR]

Recent studies have been focusing on unmanned aircraft systems (UASs) to inspect safety issues in the construction industry. A UAS can monitor a broad range in real time and identify unsafe situations and objects at the jobsite. The related studies mostly focus on technological development, and there are few studies investigating potential performance that can be obtained by implementing UASs in the construction domain. Hence, the main objective of this research is to evaluate the potential of UAS-based construction safety inspection. To achieve the goal, this study developed a system dynamic (SD) model, and scenario analysis was conducted. When compared to the existing methods, the use of a UAS resulted in improved safety inspection performance, reduced possibility of incidents, reduced worker fatigue, and reduced amount of delayed work. The results of this research verified that UAS-based safety inspections can be more effective than existing methods. The results of this study can contribute to the understanding of UAS-based construction safety inspection technologies and the potential of the technology.

The exponential growth of unmanned aerial vehicles (UAVs) or drones in recent years has raised concerns about their safe operation, especially in beyond-line-of-sight (BLOS) scenarios. Existing unmanned aircraft system traffic management (UTM) heavily relies on commercial communication networks, which may become ineffective if network infrastructures are damaged or disabled. For this challenge, we propose a novel approach that leverages vehicle-to-vehicle (V2V) communications to enhance UAV safety and efficiency in UAV operations. In this study, we present a UAV information collection and sharing system named Drone Mapper®, enabled by V2V communications, so that UAVs can share their locations with each another as well as with the ground operation station. Additionally, we introduce an autonomous flight coordination control system (AFCCS) that augments UAV safety operations by providing two essential functionalities: UAV collision avoidance and UAV formation flight, both of which work based on V2V communications. To evaluate the performance of the developed AFCCS, we conducted comprehensive field experiments focusing on UAV collision avoidance and formation flight. The experimental results demonstrate the effectiveness of the proposed system and show seamless operations among multiple UAVs.

This study proposes a low-cost and “proof-of-concept” methodology to obtain high spatial resolution soil moisture (SM) via processing reflected global positioning system (GPS) and a multispectral camera data acquired by small unmanned aircraft system (UAS) platforms. An SM estimation model is developed using a random forest (RF) machine-learning (ML) algorithm by combining features obtained from reflected GPS signals (collected by smartphones and commercial off-the-shelf receivers) in conjunction with ancillary vegetation indices from the multispectral camera data. The proposed ML algorithm uses in situ SM measurements acquired via SM probes as labels. A preliminary field experiment was conducted on 210 by 110 m (2.31 ha) crop fields (corn and cotton) in 2020 (from January to November, including crop planting through senescence time period) at Mississippi State University (MSU)’s the heavily instrumented North Farm to acquire data needed for the ML model to train and test. Our results showed that both fields could be covered by GPS reflectometry measurements with about 13 min of flight time at a 15-m altitude, and SM can be mapped with 5 × 5 m spatial resolution (corresponding to the elongated first Fresnel zone). The model is trained with and validated against eight in situ SM station datasets via tenfold and leave-one-probe-out cross-validation techniques. Overall, root-mean-square errors (RMSE) of 0.013 m $^{3}$ m$^{-3}$ volumetric SM and R-value of 0.95 [-] are obtained for tenfold cross validation. The proposed model reached an RMSE of 0.033 m$^{3}$ m$^{-3}$ and an R-value of 0.5 [-] in leave-one-probe-out cross validation. While having limited data, the results indicate that high-resolution SM measurement can be achieved with a low-cost GPS reflectometry system onboard a small UAS platform for use in precision agriculture applications.

Nowadays, there is an increase in the demand for optimized services in urban environments. However, ground transportation in big urban centers has been facing challenges for many years (e.g., resilience and congestion) and new paradigms have been proposed, such as the Urban Air Mobility (UAM) concept. UAM aims to enhance the urban transportation system using manned and unmanned aerial vehicles (i.e., Electric Vertical Takeoff and Landing - eVTOL - vehicles). Although UAM offers many benefits (e.g., cost reduction and increase in transportation capacity), many challenges need to be faced to enable safe and efficient operations. Furthermore, trajectory planning is challenging in the National Airspace System (NAS) and UAM operations due to several factors. Finally, new initiatives concerning UAM trajectory planning can be accelerated with the support of an automatic what-if platform capable of evaluating trajectories feasibility and efficiency. This research aims to propose a simulation platform for enabling trajectory evaluation in UAM operations. This platform, named Trajectory-Based Urban Air Mobility Simulator (TUS), focuses on simulating trajectories in the urban aerial environment. TUS enables users to test new UAM algorithms (e.g., flow management strategies, real-time evaluation of maneuvers effectiveness, airspace configurations) and simulate both manned and unmanned vehicles. Furthermore, TUS operation relies on a set of inputs and evaluates the trajectories generated from efficiency and safety perspectives. This process is performed using a Discrete Event Simulation (DES) approach. The experiments performed showed that TUS can perform a realistic evaluation of UAM trajectories and can be effortlessly used to simulate hundreds of scenarios. [ABSTRACT FROM AUTHOR]

With the launch of Landsat-9 on 27 September 2021, Landsat continues its fifty-year continuity mission of providing users with calibrated Earth observations. It has become a requirement that an underflight experiment be performed during commissioning to support sensor cross-calibration. In this most recent experiment, Landsat-9 flew under Landsat-8 for nearly three days with over 50% ground overlap, from 13 to 15 November 2021. To address the scarcity of reference data that are available to support calibration and validation early-on in the mission, a ground campaign was planned and executed by the Rochester Institute of Technology (RIT) on 14 November 2021 to provide full spectrum measurements for early mission comparisons. The primary experiment was conducted in the Outer Banks, North Carolina at Jockey’s Ridge Sand Dunes. Full-spectrum ground-based measurements were acquired with calibrated reference equipment, while a novel Unmanned Aircraft System (UAS)-based platforms acquired hyperspectral visible and near-infrared (VNIR)/Short-wave infrared (SWIR) imagery data and coincident broadband cooled thermal infrared (TIR) imagery. Results of satellite/UAS/ground comparisons were an indicator, during the commissioning phase, that Landsat-9 is behaving consistently with Landsat-8, ground reference, and UAS measurements. In the thermal infrared, all measurements agree to be within 1 K over water and to within 2 K over sand, which represents the most challenging material for estimating surface temperature. For the surface reflectance product(s), Landsat-8 and -9 are in good agreement and only deviate slightly from ground reference in the SWIR bands; a deviation of 2% in the VNIR and 5–8% in the SWIR regime. Subsequent longer-term studies indicate that Landsat 9 continues to perform as expected. The behavior of Thermal Infrared Sensor-2 (TIRS-2) against reference is also shown for the first year of the mission to illustrate its consistent performance.

In the context of the future urban mobility airspaces, U-space lays the foundation for unmanned aircrafts to be integrated with conventionally manned traffic to offer a multitude of services to citizens and businesses, such as urban public transport, goods movement, parcel delivery, etc. Given these extensive possibilities, it is estimated that thousands of aircrafts (or even more) from different owners will share common airspace volumes, and the probability that all of them will travel without colliding is extremely low if no means are put in place to prevent it. This paper aims to introduce the reader to the conflict management policy proposed in U-space and proposes a new version of the PCAN (Prediction-based Conflict-free Adaptive Navigation) algorithm to adapt to this policy. In particular, the new implementation of PCAN takes into account a more than likely classification of flights according to their priority. The evaluation of this priority-aware algorithm shows that it is still possible to avoid any conflict between the set of aircraft in flight, with an insignificant detriment to higher priority flights, while maintaining minimal detriment on average. [ABSTRACT FROM AUTHOR]

Fire metrics such as fire front location and rate of spread (ROS) are critical to understanding the behavior of prescribed fires and wildfires. This paper proposes a new method for prescribed grass fire evolution mapping and ROS measurement using multitemporal thermal orthomosaics collected by a small fixed-wing Unmanned Aircraft System (UAS) at low altitudes. The proposed method provides a low-cost, safe, and effective solution for active grass fire monitoring and fire metric measurement in areas that may be challenging for a typical rotor-wing UAS to cover due to endurance and size constraints. The proposed method is demonstrated using a prescribed grass fire data set collected by the KHawk fixed-wing UAS over a 13 ha. Kansas tallgrass prairie field on 8 October 2019. Repeat-pass thermal images collected by the KHawk UAS during about 10 min of the burning were grouped and processed to produce multitemporal orthomosaics with a spatial resolution of about 0 ˜.23 m and a horizontal position error of about 1.5 m. The resulting orthomosaics were further processed for fire front extraction and the measurement of fire front location and ROS. The head fire ROS of this grass burn was observed to be between 0.2 and 0.4 m s − 1 with a mean value of 0.27 m s − 1 . [ABSTRACT FROM AUTHOR]

Sex differences in large mammals with sexual dimorphism are important ecological and evolutionary issues and key factors for wildlife management. To examine the potential use of drone (unmanned aerial vehicle; UAV) observation using thermal infrared images for sex ratio monitoring of deer, we conducted UAV surveys at night in a sparse forest located on the distribution periphery of sika deer Cervus nippon and wild boar Sus scrofa local populations during summer and winter. Of the 163 thermal infrared images of large mammals detected, 132 (81.0%) and 16 (9.8%) were identified for deer and wild boar, respectively. In addition, velvet antlers of deer were visually recognized during summer, and 92% of the detected deer were antlered. This biased sex ratio would be a characteristic in the distribution periphery of local deer populations. Therefore, monitoring abundance and sex ratio using thermal infrared sensors on UAVs can improve deer management especially in the distribution periphery of local populations. [ABSTRACT FROM AUTHOR]

Urbanization, land use change, and agricultural activities continue to affect water quality standards at the urban–rural interface, such as the Boise River System located in Idaho, USA. This project demonstrates how the off-the-shelf unmanned aircraft system (UAS, also known as drone) equipped with other necessary hardware attachments can be used to monitor real-time water quality components, including pH, water temperature, electric conductivity (EC), and dissolved oxygen at open waterbodies. The proposed UAS-based hardware platform for water quality studies (UASWQP) appears a promising tool to advance environmental research activities, especially for impaired waterways (e.g., rivers, lakes, and reservoirs). The preliminary result shows that the proposed UASWQP effectively displays water quality components in real-time to the ThingSpeak Cloud web services, while an adequate water sample was also collected easily for further analysis at laboratory facilities, when needed. It is anticipated that UASWQP will be a useful tool to promote environmental stewardship by contributing to the water research communities in years to come.

Abstract Background Nowadays, automated phenotyping of plants is essential for precise and cost-effective improvement in the efficiency of crop genetics. In recent years, machine learning (ML) techniques have shown great success in the classification and modelling of crop parameters. In this research, we consider the capability of ML to perform grain yield prediction in soybeans by combining data from different optical sensors via RF (Random Forest) and XGBoost (eXtreme Gradient Boosting). During the 2018 growing season, a panel of 382 soybean recombinant inbred lines were evaluated in a yield trial at the Agronomy Center for Research and Education (ACRE) in West Lafayette (Indiana, USA). Images were acquired by the Parrot Sequoia Multispectral Sensor and the S.O.D.A. compact digital camera on board a senseFly eBee UAS (Unnamed Aircraft System) solution at R4 and early R5 growth stages. Next, a standard photogrammetric pipeline was carried out by SfM (Structure from Motion). Multispectral imagery serves to analyse the spectral response of the soybean end-member in 2D. In addition, RGB images were used to reconstruct the study area in 3D, evaluating the physiological growth dynamics per plot via height variations and crop volume estimations. As ground truth, destructive grain yield measurements were taken at the end of the growing season. Results Algorithms and feature extraction techniques were combined to develop a regression model to predict final yield from imagery, achieving an accuracy of over 90.72% by RF and 91.36% by XGBoost. Conclusions Results provide practical information for the selection of phenotypes for breeding coming from UAS data as a decision support tool, affording constant operational improvement and proactive management for high spatial precision.

The main goal of this research is to evaluate the Air Traffic Controller (ATCo) workload considering the Unmanned Aircraft System (UAS) integration into the National Airspace System (NAS) through fast-time simulations, and including futuristic scenarios in which the ATCo is familiar with manned and unmanned aircraft, i.e., different ATCo mindsets are considered. As these professionals play an essential role in optimizing the airspace operation, maintaining their workload at an acceptable level is essential. However, the integration of new technologies, such as UAS, may present an impact on safety levels from the workload perspective. In this context, the Technology Maturity Level (TML), which is a systematic metric/measurement system that supports assessments of the familiarity of a particular aircraft with ATCos, is proposed. The experiments showed that the integration of UAS into the NAS should be conducted gradually.

Recognizing the various and broad range of applications of unmanned aerial vehicles (UAVs) and unmanned aircraft systems (UAS) for personal, public and military applications, recent un-intentional malfunctions of uncontrollable UAVs or intentional attacks on them divert our attention and motivate us to devise a protection system, referred to as a counter UAV system (CUS). The CUS, also known as a counter-drone system, protects personal, commercial, public, and military facilities and areas from uncontrollable and belligerent UAVs by neutralizing or destroying them. This paper provides a comprehensive survey of the CUS to describe the key technologies of the CUS and provide sufficient information with wich to comprehend this system. The first part starts with an introduction of general UAVs and the concept of the CUS. In the second part, we provide an extensive survey of the CUS through a top-down approach: i) the platform of CUS including ground and sky platforms and related networks; ii) the architecture of the CUS consisting of sensing systems, command-and-control (C2) systems, and mitigation systems; and iii) the devices and functions with the sensors for detection-and-identification and localization-and-tracking actions and mitigators for neutralization. The last part is devoted to a survey of the CUS market with relevant challenges and future visions. From the CUS market survey, potential readers can identify the major players in a CUS industry and obtain information with which to develop the CUS industry. A broad understanding gained from the survey overall will assist with the design of a holistic CUS and inspire cross-domain research across physical layer designs in wireless communications, CUS network designs, control theory, mechanics, and computer science, to enhance counter UAV techniques further.

Facility inspections are mainly carried out through manual visual inspections. However, it is difficult to determine the extent of damages to facilities, and it depends on the subjective opinion of the manager in charge of the monitoring. Additionally, when inspectors inspect facilities that cannot be safely accessed, such as high-rise buildings, there are high risks of fatal accidents. For this reason, the construction industry conducts research into unmanned aircraft system (UAS)-based facility inspections. These studies have been focusing on developing the technologies or processes for using UAS in facility condition monitoring, ranging from infrastructure systems to commercial buildings. This study conducted extensive and synthetic reviews of the recent studies in UAS-based facility monitoring using a preferred reporting items for systematic reviews and meta-analysis (PRISMA) method. A total of 32 papers were selected and classified through the types of facilities and the technologies addressed in the studies. This paper analyzes the trends of recent studies by synthesizing the selected papers and consolidates the further directions of UAS applications and studies in facility monitoring domains.

Copyright of Studia Archaeologica Brunensia is the property of Masaryk University, Faculty of Arts and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of NASA's Unmanned Aircraft Systems (UAS) Integration in the National Airspace System project is to support the FAA with research that examines the risks involved with using UAS for commercial operations. The safety risk model described in this paper aims to provide such research and is derived from a NASA Concept of Operations (ConOps). A ConOps is a document that describes a specific commercial operation for an aircraft and presents the operation's logistics, its associated environment, plausible scenarios, safety and performance considerations of the operation, and a possible plan for the execution of the operation. The specific operation defined in the studied ConOps focuses on a UAS administering a 'targeted aerial application' of agricultural chemicals to small, pre-determined areas of crops in large fields. Applying UAS to agriculture in this manner is very realistic since it is a relatively minimal risk operation compared to other potential UAS commercial operations and presents significant value to the agricultural community. [ABSTRACT FROM AUTHOR]

Low-altitude flights of unmanned aircraft systems (UASs), such as drones, can cause substantial noise pollution in urban areas. This study aims to tackle the crucial issue of noise in UAS flight planning and analyze the feasibility of implementing low-noise urban flights at a micro level. We employ a virtual flight simulator to accurately model the UAS noise impact in realistic flights and optimize the flight path via a heuristic algorithm accordingly. The flight noise assessment incorporates a sound source modeling technique for practical UAS models and an efficient Gaussian beam tracing method for outdoor noise propagation in realistic environments. Case studies for flight simulations are conducted in a representative resident community and metropolitan area to evaluate the noise impact resulting from different flight paths. By comparing our approach with benchmark shortest-distance paths, we validate its effectiveness in significantly reducing en-route noise exposure, encompassing both instantaneous and continuous noise levels. This method holds great potential for contributing to the planning and management of urban air transport systems. It effectively mitigates noise impact, promoting quieter and more environmentally friendly UAS operations and sustainable urban transportation. [ABSTRACT FROM AUTHOR]

Abstract Background Iron deficiency chlorosis (IDC) is an abiotic stress in soybean [Glycine max (L.) Merr.] that causes significant yield reductions. Symptoms of IDC include interveinal chlorosis and stunting of the plant. While there are management practices that can overcome these drastic yield losses, the preferred way to manage IDC is growing tolerant soybean varieties. To develop varieties tolerant to IDC, breeders may easily phenotype up to thousands of candidate soybean lines every year for severity of symptoms related to IDC, a task traditionally done with a 1–5 visual rating scale. The visual rating scale is subjective and, because it is time consuming and laborious, can typically only be accomplished once or twice during a growing season. Results The goal of this study was to use an unmanned aircraft system (UAS) to improve field screening for tolerance to soybean IDC. During the summer of 2017, 3386 plots were visually scored for IDC stress on two different dates. In addition, images were captured with a DJI Inspire 1 platform equipped with a modified dual camera system which simultaneously captures digital red, green, blue images as well as red, green, near infrared (NIR) images. A pipeline was created for image capture, orthomosaic generation, processing, and analysis. Plant and soil classification was achieved using unsupervised classification resulting in 95% overall classification accuracy. Within the plant classified canopy, the green, yellow, and brown plant pixels were classified and used as features for random forest and neural network models. Overall, the random forest and neural network models achieved similar misclassification rates and classification accuracy, which ranged from 68 to 77% across rating dates. All 36 trials in the field were analyzed using a linear model for both visual score and UAS image-based scores on both dates. In 32 of the 36 tests on date 1 and 33 of 36 trials on date 2, the LSD associated with UAS image-based scores was lower than the LSD associated with visual scores, indicating the image-based scores provided more precise measurements of IDC severity. Conclusions Overall, the UAS was able to capture differences in IDC stress and may be used for evaluations of candidate breeding lines in a soybean breeding program. This system was both more efficient and precise than traditional scoring methods.

The key challenge to integrate the unmanned aircraft system (UAS) into airspace is to develop a means to sense and avoid (SAA) other aircrafts. The main function of the SAA is self-separation, i.e. remaining “well clear” of the other aircrafts. The separation thresholds must be quantitatively defined for the UAS to autonomously maintain self-separation. In this paper, the separation thresholds are defined quantitatively for the UAS in a dynamic airspace full of aircrafts that differ in motion state and performance. Then, a “sector-like” dynamic collision-free region (CFR) was set up around the UAS. The size of the CRF can be adjusted adaptively according to the relative motion states of the surrounding intruders, the performance of the UAS, and the altitude of the airspace. The simulation results show that the proposed adaptive separation thresholds adapted to the dynamic airspace environment better than the fixed separation thresholds recommended by Sense & Avoid Science and Research Panel (SARP), and controlled the missing and false alarm rates on low levels. This means our adaptive separation thresholds can effectively balance the safety and operation efficiency of the airspace.

The key to the integration of unmanned aircrafts in the national airspace is to prevent them from colliding with the other traffics in the airspace. However, it is a great challenge to generate a safe, stable and robust collision-free path for unmanned aircraft system (UAS), a.k.a. unmanned aircraft vehicle (UAV), in real time, if the airspace is highly dynamics and heterogenous (i.e. thronged with aircrafts with different motion states). Based on the dynamic artificial potential field (DAPF) algorithm, this paper provides advisories on how to generate a real-time reactive collision-free path for unmanned aircraft vehicles flying in a dynamic airspace, aiming to ensure the flight safety and minimize the impact on surrounding traffic. Firstly, the safety distance was defined as a variable threshold, which scaled adaptively according to the relative motion states of the surrounding obstacles and the performance of the own UAV. Moreover, the forces of the potential field were improved, such that their magnitudes could be adjusted automatically according to the threat levels of the surrounding obstacles. The threat level of an obstacle depends on the relative position, speed and flight trend between the UAV and the obstacle. In addition, the repulsive force along the relative position of the traditional artificial potential field (APF) was retained, and a steering force was added to change the flight direction of the UAV, aiming to speed up the collision avoidance. Furthermore, the attractive force was modified to help the UAV return to the planned path quickly and stably. After that, the capacity were determined to ensure the feasible and practical path planning for the UAV. Finally, the proposed UAV path-planning method was proved effective, safe, stable and adaptive through simulations.

Excessive streambank erosion is a significant source of fine sediments and associated nutrients in many river systems as well as poses risk to infrastructure. Geomorphic change detection using high-resolution topographic data is a useful method for monitoring the extent of bank erosion along river corridors. Recent advances in an unmanned aircraft system (UAS) and structure from motion (SfM) photogrammetry techniques allow acquisition of high-resolution topographic data, which are the methods used in this study. To evaluate the effectiveness of UAS-based photogrammetry for monitoring bank erosion, a fixed-wing UAS was deployed to survey 20 km of river corridors in central Vermont, in the northeastern United States multiple times over a two-year period. Digital elevation models (DEMs) and DEMs of difference allowed quantification of volumetric changes along selected portions of the survey area where notable erosion occurred. Results showed that UAS was capable of collecting high-quality topographic data at fine resolutions even along vegetated river corridors provided that the surveys were conducted in early spring, after snowmelt but prior to summer vegetation growth. Longer term estimates of streambank movements using the UAS showed good comparison to previously collected airborne lidar surveys and allowed reliable quantification of significant geomorphic changes along rivers.

Literature reviews can provide evidence of the scientific results obtained from preliminary studies. However, in a secondary review, information regarding current limitations and future research directions that are necessary for continuous improvement in a field of study can remain scattered. To extract in-depth information on unmanned aircraft system (UAS) applications, particularly in civil engineering areas, including construction, infrastructure operation, and environmental areas, an evidence-based tertiary review was conducted. A systematic tertiary review using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) method was performed based on 33 secondary review articles published between 2011 and 2019. The results documented a total of seven limitations and four future research directions. This paper highlights the need for further studies addressing human and UAS interaction concepts, potential risk analysis in UAS flights, and objective performance assessment. This review indicates that increased levels of autonomy, safety, and efficiency of UAS applications are highly recommended to enhance the capability of UAS applications. This work contributes to providing the comprehensive argument on UAS applications in the civil engineering field, and to documenting future directions of UAS research for both practitioners and academics working in the area. [ABSTRACT FROM AUTHOR]

Airport inspections are essential to ensuring the safety of aircraft operation for the general public. The current, manual, approach to inspection consumes a significant amount of time. Unmanned aircraft systems (UASs) are often employed to improve the efficiency of visual inspections of various infrastructure facilities. However, only a few studies have documented their use in the context of airport inspections. This study proposes a framework for inspecting runway design codes (RDCs) for airfields that relies on mosaic imagery. Scale-invariant feature transform and best bin first algorithms were integrated to generate accurate UAS-based mosaic imagery for airports. A fixed-wing UAS platform was deployed to capture aerial images of an airport testbed provided by the DOT. The validation results showed that the framework had a high enough level of accuracy to measure pixel-based distances for runway design code (RDC) items that were comparable to the results of manual airport inspections. Across all RDC items in this study, an average error rate of 4.664% between the two types of inspection was documented. The lowest level of error (1.520%) was recorded for the distance between the runway centerline and aircraft parking area, and the highest (11.200%) corresponded to taxiway width. This study contributes to a better understanding of UAS-based airport inspection applications and strengthens and broadens the utility of UASs in visual inspections of civil infrastructure systems. [ABSTRACT FROM AUTHOR]

Long-range, high-altitude Unoccupied Aerial System (UAS) operations now enable in-situ measurements of volcanic gas chemistry at globally-significant active volcanoes. However, the extreme environments encountered within volcanic plumes present significant challenges for both air frame development and in-flight control. As part of a multi-disciplinary field deployment in May 2019, we flew fixed wing UAS Beyond Visual Line of Sight (BVLOS) over Manam volcano, Papua New Guinea, to measure real-time gas concentrations within the volcanic plume. By integrating aerial gas measurements with ground- and satellite-based sensors, our aim was to collect data that would constrain the emission rate of environmentally-important volcanic gases, such as carbon dioxide, whilst providing critical insight into the state of the subsurface volcanic system. Here, we present a detailed analysis of three BVLOS flights into the plume of Manam volcano and discuss the challenges involved in operating in highly turbulent volcanic plumes. Specifically, we report a detailed description of the system, including ground and air components, and flight plans. We present logged flight data for two successful flights to evaluate the aircraft performance under the atmospheric conditions experienced during plume traverses. Further, by reconstructing the sequence of events that led to the failure of the third flight, we identify a number of lessons learned and propose appropriate recommendations to reduce risk in future flight operations.

Unmanned aerial vehicles (UAVs) are proving to be an important modern sensing platform that supplement the sensing capabilities from platforms such as satellites, aircraft, research vessels, moorings, and gliders. UAVs, like satellites and aircraft can provide a synoptic view of a relatively large area. However, the coarse resolution provided by satellites and the operational limitations of manned aircraft has motivated the development of unmanned systems. UAVs offer unparalleled flexibility of tasking; for example, low altitude flight and slow airspeed allow for the characterization of a wide variety of geophysical phenomena at the ocean surface and in the marine atmospheric boundary layer. Here, we present the development of cutting-edge payload instrumentation for UAVs that provides a new capability for ship-deployed operations to capture a unique, high resolution spatial and temporal variability of the changing air-sea interaction processes than was previously possible. The modular design of the base payload means that new instruments can be incorporated into new research proposals that may include new instruments for expanded use of the payloads as a long-term research facility. Additionally, we implement a novel capability for vertical take-off and landing (VTOL) from research vessels. This VTOL capability is safer and requires less logistical support than previous ship-deployed systems. The payloads developed include thermal infrared, visible broadband and hyperspectral, and near-infrared hyperspectral high-resolution imaging. Additional capabilities include quantification of the longwave and shortwave hemispheric radiation budget (up- and down-welling) as well as direct air-sea turbulent fluxes. Finally, a UAV-deployed dropsonde-microbuoy was developed in order to profile the temperature, pressure and humidity of the atmosphere and the temperature and salinity of the near-surface ocean. These technological advancements provide the next generation of instrumentation capability for UAVs. When deployed from research vessels, UAVs will provide a transformational science prism unequaled using 1-D data snapshots from ships or moorings alone.

The paper deals with the governance of Unmanned Aircraft Systems (UAS) in European law. Three different kinds of balance have been struck between multiple regulatory systems, in accordance with the sector of the governance of UAS which is taken into account. The first model regards the field of civil aviation law and its European Union (EU)'s regulation: the model looks like a traditional mix of top-down regulation and soft law. The second model concerns the EU general data protection law, the GDPR, which has set up a co-regulatory framework summed up with the principle of accountability also, but not only, in the field of drones. The third model of governance has been adopted by the EU through methods of legal experimentation and coordination mechanisms for UAS. The overall aim of the paper is to elucidate the ways in which such three models interact, insisting on differences and similarities with other technologies (e.g. self-driving cars), and further legal systems (e.g. the US). [ABSTRACT FROM AUTHOR]

This paper improves and implements a framework based on robustness analysis to aid in the certification of unmanned aircraft system (UAS) flight controllers. The approach first models the UAS using a linear fractional transformation on uncertainties and then conducts robustness analysis on the uncertain system via integral quadratic constraint (IQC) theory. By expressing the set of desired UAS flight paths with an uncertainty, the framework considers the analysis of the uncertain UAS flying about any level path whose radius of curvature is bounded. The approach is capable of analyzing trajectory-tracking and path-following controllers, and an accurate expression of UAS path-following dynamics in the presence of nonzero wind is derived. To demonstrate the versatility of this technique, we use IQC analysis to tune trajectory-tracking and path-following controllers that are designed via $\mathcal {H}_{2}$ or $\mathcal {H}_\infty $ synthesis methods. IQC analysis is also used to tune path-following PID controllers. By employing a nondeterministic simulation environment and conducting numerous flight tests, we see that the uncertain UAS framework reliably predicts loss of control, compares the robustness of different controllers, and provides tuned controllers that are sufficiently robust. Finally, this work demonstrates that signal IQCs have an important role in obtaining IQC analysis results that are less conservative and more consistent with observations from flight test data. [ABSTRACT FROM AUTHOR]

Current studies have focused on the exploration of the unmanned aircraft system (UAS) applications in the construction and infrastructure domains, such as progress monitoring, condition inspections, and particularly safety inspection. While their works have had significant contributions to highlighting the potential of the UAS technology, there are still gaps in the lack of operational considerations and workflows established to integrate the UAS into the current safety management program. To fill this gap, this study conducted field tests at commercial building construction worksite by adopting conceptually developed UAS flight protocol. A total of eight flights were performed to collect visual assets and provide for safety professionals at the jobsite with the insight of how this technology can be utilized in accordance with the developed workflow. A total of eight project personnel participated in the UAS flight test at their jobsite as well as post-flight interview to discuss how this technology can meet their safety management task goals. As the qualitative study, this paper narratively described the conceptual procedures consist of three different decision-making processes for ensuring the safe flight of the UAS: pre-flight, flight, and post-flight decision makings. Based on the potential users' perceptions obtained during the field testing, this presented study focused on three different operational considerations: involving project-based, UAS's capability and hardware specifications and UAS and project team-based aspects. The main goal of this study proposes a decision-driven workflow for UAS-integrated safety management that encourages sustainable and continuous improvement and documents the operational considerations related to this application. As the qualitative exploratory study, this research is limited to employ only one test-bed environment and the small number of interviewees. However, this study can contribute to a better understanding of the concerns, processes, and further recommendations to safely and effectively implement the UAS into the construction safety management program. [ABSTRACT FROM AUTHOR]

Background: Nowadays, automated phenotyping of plants is essential for precise and cost-effective improvement in the efficiency of crop genetics. In recent years, machine learning (ML) techniques have shown great success in the classification and modelling of crop parameters. In this research, we consider the capability of ML to perform grain yield prediction in soybeans by combining data from different optical sensors via RF (Random Forest) and XGBoost (eXtreme Gradient Boosting). During the 2018 growing season, a panel of 382 soybean recombinant inbred lines were evaluated in a yield trial at the Agronomy Center for Research and Education (ACRE) in West Lafayette (Indiana, USA). Images were acquired by the Parrot Sequoia Multispectral Sensor and the S.O.D.A. compact digital camera on board a senseFly eBee UAS (Unnamed Aircraft System) solution at R4 and early R5 growth stages. Next, a standard photogrammetric pipeline was carried out by SfM (Structure from Motion). Multispectral imagery serves to analyse the spectral response of the soybean end-member in 2D. In addition, RGB images were used to reconstruct the study area in 3D, evaluating the physiological growth dynamics per plot via height variations and crop volume estimations. As ground truth, destructive grain yield measurements were taken at the end of the growing season. Results: Algorithms and feature extraction techniques were combined to develop a regression model to predict final yield from imagery, achieving an accuracy of over 90.72% by RF and 91.36% by XGBoost. Conclusions: Results provide practical information for the selection of phenotypes for breeding coming from UAS data as a decision support tool, affording constant operational improvement and proactive management for high spatial precision. [ABSTRACT FROM AUTHOR]

Unmanned aircraft system (UAS)-mounted spectroradiometers offer a new capability to measure spectral reflectance and solar-induced chlorophyll fluorescence at detailed canopy scales. This capability offers potential for upscaling and comparison with airborne and space-borne observations [e.g., the upcoming European Space Agency (ESA) Fluorescence Explorer (FLEX) satellite mission]. In this respect, the accurate spatial characterization and georeferencing of the UAS acquisition footprints are essential to unravel the origin and spatial variability of optical signals acquired within the extent of airborne/satellite pixels. In this article, we present and validate a novel algorithm to georeference the footprint extent of a nonimaging spectroradiometer mounted on a multirotor UAS platform. We used information about the spectroradiometer position and orientation during flight and about topography of observed terrain to calculate the footprint geolocation. In a recursive process, the field of view (FOV) of the spectroradiometer projected on the ground. Multiple FOV ground projections retrieved during a spectroradiometer reading (i.e., a single integration time) were aggregated to calculate the footprint extent. The spatial accuracy of the footprint geolocation was validated by applying the georeferencing algorithm on checkpoint pixels of image acquired with a comounted digital camera. Geolocations of the checkpoint pixels, which served as a proxy for the spectroradiometer footprint, were successfully compared with surveyed ground checkpoints. Finally, the spectral and radiometric quality of UAS-acquired reflectance signatures was compared with ground-measured reflectance of four natural targets (three different types of grass and a bare soil), and a strong agreement was observed. [ABSTRACT FROM AUTHOR]

Gully erosion is a major land management challenge globally and a particularly important issue in dry tropical savanna rangelands tributary to the Great Barrier Reef, Australia. This study investigated linkages between hillslope hydrological connectivity pathways and gully geomorphic change in the Burdekin River Basin. High‐resolution (0.1 m) topographic and land cover data derived from low‐cost aerial (via unmanned aircraft system) structure‐from‐motion with multiview stereo photogrammetry (SfM) were used to map fine‐scale connectivity patterns and quantify headcut retreat at the hillslope scale (~150,000 m2). Very high resolution (0.01 m) topographic models derived from ground‐based (via handheld digital camera) SfM were used to quantify the morphology and geomorphic change of several gully arms (300–700 m2) between 2016 and 2018. Median linear, areal, and volumetric headcut (n = 21) retreat rates were 0.2 m, 0.8 m2, and 0.3 m3 yr−1, respectively. At all study sites, the points where modelled hydrological flow lines intersected gullies corresponded to observed geomorphic change, enabling spatially explicit identification of gully extension pathways as a result of overland flow. Application of an index of connectivity demarcated parts of the hillslope most connected to the gully network. Bare areas, roads, and cattle trails were identified as important runoff source areas and hydrological conduits driving gully extension. Ground‐based SfM accurately reconstructed complex morphologic features including undercuts, overhangs, rills, and flutes, providing insights into within‐channel erosion processes. This study contributes to an improved understanding and modelling of hydrogeomorphic drivers of gully erosion in degraded savanna rangelands, ultimately benefiting gully management. [ABSTRACT FROM AUTHOR]

Excessive streambank erosion is a significant source of fine sediments and associated nutrients in many river systems as well as poses risk to infrastructure. Geomorphic change detection using high-resolution topographic data is a useful method for monitoring the extent of bank erosion along river corridors. Recent advances in an unmanned aircraft system (UAS) and structure from motion (SfM) photogrammetry techniques allow acquisition of high-resolution topographic data, which are the methods used in this study. To evaluate the effectiveness of UAS-based photogrammetry for monitoring bank erosion, a fixed-wing UAS was deployed to survey 20 km of river corridors in central Vermont, in the northeastern United States multiple times over a two-year period. Digital elevation models (DEMs) and DEMs of difference allowed quantification of volumetric changes along selected portions of the survey area where notable erosion occurred. Results showed that UAS was capable of collecting high-quality topographic data at fine resolutions even along vegetated river corridors provided that the surveys were conducted in early spring, after snowmelt but prior to summer vegetation growth. Longer term estimates of streambank movements using the UAS showed good comparison to previously collected airborne lidar surveys and allowed reliable quantification of significant geomorphic changes along rivers. [ABSTRACT FROM AUTHOR]

Current studies have addressed the significant potential of technology integration in construction and infrastructure environments; however, they have not sufficiently explored the human aspects of this integration, such as users' cognitive capabilities and task performance. Unmanned aircraft systems (UAS) can enhance work performance for various purposes. However, the abilities required of UAS operators have not been addressed, even though the topic is critical for safe and effective operations. The main goals of this research were to evaluate human performance, identify the relationship between performance and experience, and describe the resulting implications and future research possibilities. To achieve these goals, practitioners were recruited from a state highway agency to conduct UAS construction site inspections. This study measured both task and cognitive performances during the experiments. A thematic analysis was conducted to discern patterns within the data set collected during the experiment. The main contribution of this work is a better understanding of the relationship between human performance and UAS uses. In addition, this study highlights opportunities for future UAS studies in construction and infrastructure environments. [ABSTRACT FROM AUTHOR]