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Effective waterfowl management relies on the collection of relevant demographic data to inform land-management decisions; however, some types of data are difficult to obtain. For waterfowl, brood surveys are difficult to conduct because wetland habitats often obscure ducklings from being visually assessed. Here, we used Unoccupied Aerial Systems (UAS) to assess what wetland habitat characteristics influenced brood abundance in Suisun Marsh, California, USA. Using a thermal-imaging camera, we surveyed 17 wetland units that encompassed 332ha of flooded area on the premises of seven waterfowl hunting clubs during the waterfowl breeding season. Additionally, using a combination of multi-spectral imagery collected from the UAS flights and LiDAR data from the previous year, we mapped habitat composition within each unit to relate to brood observation counts. From June 3–7, 2019, we identified 113 individual broods comprising 827 ducklings. We found a positive relationship between the number of broods observed and the proportion of the unit that was flooded. We also found a positive relationship between the number of broods observed and the area of effective habitat—a metric of flooded habitat within two times the 95th-percentile Euclidean distance that all broods were observed from any vegetated cover. Brood surveys using UAS could complement the traditional Breeding Population Survey and provide local managers with fine-scale and timely information about shifts in brood abundance in the region.

Estimating and monitoring plant population size is fundamental for ecological research, as well as conservation and restoration programs. High‐resolution imagery has potential to facilitate such estimation and monitoring. However, remotely sensed estimates typically have higher uncertainty than field measurements, risking biased inference on population status.We present a model that accounts for false negative (missed plants) and false positive (misclassified or double‐counted plants) error in counts from high‐resolution imagery via integration with ground data. We apply it to estimate the abundance of a foundational shrub species in post‐wildfire landscapes in the western United States. In these landscapes, plant recruitment is crucial for ecological recovery but locally patchy, motivating the use of spatially extensive measurements from unoccupied aerial systems (UAS). Integrating >16 ha of UAS imagery with >700 georeferenced field plots, we fit our model to generate insights into the prevalence and drivers of observation errors associated with classification algorithms used to distinguish individual plants, relationships between abundance and landscape context, and to generate spatially explicit maps of shrub abundance.Raw counts of plant abundance in high‐resolution imagery resulted in substantial false negative and false positive observation errors. The probability of detecting (p) adult plants (≥0.25 m tall) varied between sites within 0.52 < p̂adult < 0.82, whereas the detection of smaller plants (<0.25 m) was lower, 0.03 < p̂small < 0.3. On average, we estimate that 19% of all detected plants were false positive errors, which varied spatially in relation to topographic predictors. Abundance declined toward the interior of previous wildfires and was positively associated with terrain roughness.Our study demonstrates that integrated models accounting for imperfect detection improve estimates of plant population abundance derived from inherently imperfect UAS imagery. We believe such models will further improve inference on plant population dynamics—relevant to restoration, wildlife habitat and related objectives—and echo previous calls for remote sensing applications to better differentiate between ecological and observational processes. [ABSTRACT FROM AUTHOR]

Traditional methods for quantifying the internal temperature of marine mammals require handling live animals, which is not practical for free-swimming baleen whales. Developing a less invasive, more repeatable method would significantly improve our understanding of whale health and thermal physiology. Infrared thermography (IRT) devices compatible with remotely piloted aircraft systems (RPAS) have facilitated qualitative assessments of heat signatures from marine mammals at sea, but absolute temperatures derived using this approach are rare. The goal of this study was to develop a precise empirical method for estimating intranasal temperatures of baleen whales using RPAS-based IRT. We conducted controlled field experiments and flights over North Atlantic right whales (Eubalaena glacialis, NARWs) to develop and test the methodology. Two approaches were evaluated to estimate intranasal temperatures from IRT sensor intensities: a three-point empirical line regression calibrated per flight using known-temperature objects and a generalized linear model incorporating environmental variables. Controlled field experiments demonstrated that the former approach had a median bias of −0.6°C (interquartile range: 1.5ºC), while the latter approach had unexplained negative proportional bias with increasing true temperature of the target object. After accounting for bias, the former approach yielded an average intranasal temperature of 26.9 ± 1.7°C for 21 unique NARWs. The anatomy of the mysticete upper respiratory tract and physiological heat conservation strategies may explain why estimates were low compared to internal temperatures measured from baleen whales using other techniques (30–39ºC). Variability within whales was less than ± 2°C, supporting the use of these methods to monitor the health of individuals over time. However, variability among whales was greater (up to 7ºC). Improvements in our understanding of whale physiology and respiratory mechanics and advancements in RPAS-based IRT calibrations could make this technology more reliable for assessing individual body temperatures and monitoring populations in the future. [ABSTRACT FROM AUTHOR]

Uncrewed aerial system (UAS) structure from motion (SfM) monitoring strategies for individual trees has rapidly expanded in the early 21st century. It has become common for studies to report accuracies for individual tree heights and DBH, along with stand density metrics. This study evaluates individual tree detection and stand basal area accuracy and precision in five ponderosa pine sites against the range of SfM parameters in the Agisoft Metashape, Pix4DMapper, and OpenDroneMap algorithms. The study is designed to frame UAS-SfM individual tree monitoring accuracy in the context of data processing and storage demands as a function of SfM algorithm parameter levels. Results show that when SfM algorithms are properly tuned, differences between software types are negligible, with Metashape providing a median F-score improvement over OpenDroneMap of 0.02 and PIX4DMapper of 0.06. However, tree extraction performance varied greatly across algorithm parameters, with the greatest extraction rates typically coming from parameters causing increased density in dense point clouds and minimal point cloud filtering. Transferring UAS-SfM forest monitoring into management will require tradeoffs between accuracy and efficiency. Our analysis shows that a one-step reduction in dense point cloud quality saves 77–86% in point cloud processing time without decreasing tree extraction (F-score) or basal area precision using Metashape and PIX4DMapper but the same parameter change for OpenDroneMap caused a ~5% loss in precision. Providing reproducible processing strategies is a vital step in successfully transferring these technologies into usage as management tools. [ABSTRACT FROM AUTHOR]

Plant counting is a critical aspect of crop management, providing farmers with valuable insights into seed germination success and within-field variation in crop population density, both of which are key indicators of crop yield and quality. Recent advancements in Unmanned Aerial System (UAS) technology, coupled with deep learning techniques, have facilitated the development of automated plant counting methods. Various computer vision models based on UAS images are available for detecting and classifying crop plants. However, their accuracy relies largely on the availability of substantial manually labeled training datasets. The objective of this study was to develop a robust corn counting model by developing and integrating an automatic image annotation framework. This study used high-spatial-resolution images collected with a DJI Mavic Pro 2 at the V2–V4 growth stage of corn plants from a field in Wooster, Ohio. The automated image annotation process involved extracting corn rows and applying image enhancement techniques to automatically annotate images as either corn or non-corn, resulting in 80% accuracy in identifying corn plants. The accuracy of corn stand identification was further improved by training four deep learning (DL) models, including InceptionV3, VGG16, VGG19, and Vision Transformer (ViT), with annotated images across various datasets. Notably, VGG16 outperformed the other three models, achieving an F1 score of 0.955. When the corn counts were compared to ground truth data across five test regions, VGG achieved an R2 of 0.94 and an RMSE of 9.95. The integration of an automated image annotation process into the training of the DL models provided notable benefits in terms of model scaling and consistency. The developed framework can efficiently manage large-scale data generation, streamlining the process for the rapid development and deployment of corn counting DL models. [ABSTRACT FROM AUTHOR]

Given the growing threat posed by the widespread availability of unmanned aircraft systems (UASs), which can be utilised for various unlawful activities, the need for a standardised method to evaluate the effectiveness of systems capable of detecting, tracking, and identifying (DTI) these devices has become increasingly urgent. This article draws upon research conducted under the European project COURAGEOUS, where 260 existing drone detection systems were analysed, and a methodology was developed for assessing the suitability of C-UASs in relation to specific threat scenarios. The article provides an overview of the most commonly employed technologies in C-UASs, such as radars, visible light cameras, thermal imaging cameras, laser range finders (lidars), and acoustic sensors. It explores the advantages and limitations of each technology, highlighting their reliance on different physical principles, and also briefly touches upon the legal implications associated with their deployment. The article presents the research framework and provides a structural description, alongside the functional and performance requirements, as well as the defined metrics. Furthermore, the methodology for testing the usability and effectiveness of individual C-UAS technologies in addressing specific threat scenarios is elaborated. Lastly, the article offers a concise list of prospective research directions concerning the analysis and evaluation of these technologies. [ABSTRACT FROM AUTHOR]

This study assesses the potential of using multispectral images collected by an unmanned aerial system (UAS) on machine learning (ML) frameworks to estimate cereal rye (Secale cereal L.) biomass. Multispectral images and ground-truth cereal rye biomass data were collected from 15 farmers' fields up to three times between March and May in northwest Ohio. Images were processed to derive 13 vegetation indices (VIs). Out of 13 VIs, six optimal sets of VIs, including excess green (ExG), normalized green red difference index (NGRDI), soil adjusted vegetation index (SAVI), blue green ratio (B_G_ratio), red-edge triangular vegetation index (RTVI), and normalized difference red-edge (NDRE) were selected using the variance inflation factor (VIF) based feature selection approach. Six regression models including a multiple linear regression (MLR), elastic net (ENET), multivariate adaptive regression splines (MARS), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB) were investigated for estimation of cereal rye biomass based on the VIs. For most of the models, the six selected VIs performed better than or similar to the full set of 13 VIs with R2 ranging from 0.24 to 0.59 and RMSE ranging from 83.13 to 91.89 g/m2 during 10-fold cross-validation. During independent accuracy assessment with the selected set of VIs, XGB exhibited the highest R2 (0.67) and lowest RMSE (83.13 g/m2) and MAE (48.13 g/m2) followed by RF and ENET. For all the models, the agreement between observed and predicted biomass was high for biomass less than or equal to 200 g/m2 but decreased for biomass greater than 200 g/m2. When field-collected structural features were integrated with the selected VIs, the models showed improved performance, with R2 and RMSE of the models reaching up to 0.82 and 61.67 g/m2 respectively. Among the six VIs, SAVI showed the strongest impact on the model prediction for the best-performing RF and XGB regression models. The findings of this study demonstrate the potential of precisely estimating and mapping cereal rye biomass based on UAS-captured multispectral images. Timely information on cover crop growth can facilitate numerous decision-making processes, including planning the planting operations, and management of nutrients, weeds, and soil moisture to improve agronomic and environmental outcomes. [ABSTRACT FROM AUTHOR]

Monitoring body length and body condition of individuals helps determine overall population health and assess adaptation to environmental changes. Aerial photogrammetry from drone‐based videos is a valuable method for obtaining body length and body condition measurements of cetaceans. However, the laborious manual processing of drone‐based videos to select frames to measure animals ultimately delays assessment of population health and hinders conservation actions. Here, we apply deep learning methods to expedite the processing of drone‐based videos to improve efficiency of obtaining important morphological measurements of whales. We develop two user‐friendly models to automatically (1) detect and output frames containing whales from drone‐based videos ("DeteX") and (2) extract body length and body condition measurements from input frames ("XtraX"). We use drone‐based videos of gray whales to compare manual versus automated measurements (n = 86). Our results show automated methods reduced processing times by one‐ninth, while achieving similar accuracy as manual measurements (mean coefficient of variation <5%). We also demonstrate how these methods are adaptable to other species and identify remaining challenges to help further improve automated measurements in the future. Importantly, these tools greatly speed up obtaining key morphological data while maintaining accuracy, which is critical for effectively monitoring population health. [ABSTRACT FROM AUTHOR]

The purpose of the present research is to develop and to demonstrate the design architecture of the AFTS (Automatic Flight and Transition System) for "Lift/Cruise Thrust" type VTOL (Vertical Takeoff and Landing) aircraft such as tiltwing, tilt-propeller, tilt-duct aircraft, etc. VTOL aircraft of this type, characterized by propulsor installations that reorient the thrust relative to the aircraft, offer advantages of a wide flight envelope. However, flight and stability characteristics significantly change depending on airspeed and configurations such as wing tilt angles or propulsor orientations. These make it difficult to operate VTOL aircraft safely and the AFTS, which comprises both autopilot and auto-transition algorithm, should be developed to provide the safe and effective means to control the aircraft over its entire flight envelope. In this research, the autopilot was designed to provide control commands that change control strategies and gains according to the configurations. Additionally, the auto-transition algorithm, which should be coordinated with the autopilot, was designed to select and change configurations for accelerated and decelerated flight. The proposed AFTS was installed to a "Lift/Cruise Thrust" type, QTW (Quad Tilt Wing) VTOL UAS (Unmanned Aircraft System), and fully automated operation capability was demonstrated through flight test both in calm and windy conditions. [ABSTRACT FROM AUTHOR]

The tiger longicorn beetle, Xylotrechus chinensis Chevrolat (Coleoptera: Cerambycidae), has posed a significant threat to mulberry trees in Greece since its invasion in 2017, which may be associated with global warming. Detection typically relies on observing adult emergence holes on the bark or dried branches, indicating severe damage. Addressing pest threats linked to global warming requires efficient, targeted solutions. Remote sensing provides valuable, swift information on vegetation health, and combining these data with machine learning techniques enables early detection of pest infestations. This study utilized airborne multispectral data to detect infestations by X. chinensis in mulberry trees. Variables such as mean NDVI, mean NDRE, mean EVI, and tree crown area were calculated and used in machine learning models, alongside data on adult emergence holes and temperature. Trees were classified into two categories, infested and healthy, based on X. chinensis infestation. Evaluated models included Random Forest, Decision Tree, Gradient Boosting, Multi-Layer Perceptron, K-Nearest Neighbors, and Naïve Bayes. Random Forest proved to be the most effective predictive model, achieving the highest scores in accuracy (0.86), precision (0.84), recall (0.81), and F-score (0.82), with Gradient Boosting performing slightly lower. This study highlights the potential of combining remote sensing and machine learning for early pest detection, promoting timely interventions, and reducing environmental impacts. [ABSTRACT FROM AUTHOR]

On a mounted laboratory stand, comparative tests were carried out on the effectiveness of spraying wheat plants with liquid using a multi-rotor drone. The study was undertaken with and without propeller rotations. The lack of rotations simulated spraying by a ground sprayer. The height of the drone's displacement above the plants was similar to that of the nozzles above the plants used when spraying with field sprayers, 0.5 m and 1.0 m. The speed of the drone movement was 0.57 and 1.0 m·s−1. The effects of the height and speed of the drone's movement and the impact of the airflow on the volume and uniformity of the liquid application on the plants were assessed. In addition, changes in the transverse distribution of liquid volume in the droplet stream and the transverse distribution of the air velocity in its stream were evaluated. The liquid was sprayed at a constant pressure of 0.2 MPa. The study's results show that the low height of the drone displacement not only had a strong effect on increasing the liquid volume applied to the plants but also improved the uniformity of application at plant levels. It was also noticed that, at a height of 0.5 m, there was a significant irregularity in the air stream under the drone. [ABSTRACT FROM AUTHOR]

Small unmanned aircraft systems (drones) have only been widely available for civilian application for less than 10 years. Despite the newness, drones are being used to support a broad range of public safety applications. The purpose of this study is to evaluate if law enforcement programs at two-year community colleges are providing adequate drone education. The course catalogs and websites of 255 community colleges in the Southeast United States were searched for drone-related keywords. Only 16% of the schools had at least one drone-related course available. A follow-up survey was sent to these schools' law enforcement program coordinators. The survey revealed that the program coordinators know that law enforcement is using drones, felt drones reduced injuries, and desired to expand their UAS curriculum. However, the survey also found that current faculty are unfamiliar with UAS regulations and have a general lack of professional development in this area. [ABSTRACT FROM AUTHOR]

Real-time (re-)planning is crucial for autonomous quadrotors to navigate in uncertain environments where obstacles may be detected and trajectory plans must be adjusted on-the-fly to avoid collision. In this paper, we present a control system design for autonomous quadrotors that has real-time re-planning capability, including the hardware pipeline for the hardware–software integration to realize the proposed real-time re-planning algorithm. The framework is based on a modified version of the PX4 Autopilot and a Raspberry Pi 5 companion computer. The planning algorithm utilizes minimum-snap trajectory generation, taking advantage of the differential flatness property of quadrotors, to realize computationally light, real-time re-planning using an onboard computer. We first verify the control system and the planning algorithm through simulation experiments, followed by implementing and demonstrating the system on hardware using a quadcopter. [ABSTRACT FROM AUTHOR]

China as the world’s prominent market for Unmanned Aircraft Systems (UAS) has just passed a new regulation on UAS. The new regulation is expected to form a new pattern of rule of law on UAS in China. With the need for harmonisation of laws internationally, this article highlights the three aspects out of China’s new UAS legislation against an international setting. [ABSTRACT FROM AUTHOR]

In recent years, thanks to the great diffusion of drone technology and the development of miniaturized sensors that can be connected to drones, in order to increase the sustainability of landfills and oil and gas facilities, interest in finding methane leaks and quantifying the relative flow has grown significantly. This operation requires the methane background concentration to be subtracted from the calculations. Therefore, in order to proceed with a right estimate of CH4 flows emitted, the possibility of correctly measuring or estimating the background level becomes crucial. The present work intends to illustrate the effects of different variables on the background-noise measurement in a drone-based system that uses a tunable diode laser absorption spectrometer (TDLAS). The methodology used is that of field testing; the data acquisition campaign consisted of the execution of 80 flights during which different flight variables (drone speed, flight altitude) were tested; the flights were repeated in different weather and climate conditions both during the same day and in different periods of the year. Different surfaces, similar to those found in landfill or natural gas sites, were also tested. In some of the field trials, a controlled methane release test was performed in order to verify how much the quantification of the methane flow can vary depending on the background level used. The results of the different field trials highlighted the best conditions under which to measure methane emissions with a TDLAS sensor in order to minimize the number of outliers: flight altitude not exceeding 15 m above ground level; the drone speed appears to have less impact on the results, however, it is optimal between 1 and 2 ms−1; a very sunny day produces much higher methane background levels than a cloudy one. The type of surface also significantly affects the measurement of background noise. Finally, tests conducted with a controlled methane release highlighted that different levels of background have a significant impact on the estimation of the methane flux emitted. [ABSTRACT FROM AUTHOR]

Coastal marsh ecosystems are changing and being lost at a rapid rate around the world. One of the fastest rates of land loss, specifically coastal marsh habitat, occurs in Louisiana on the Northern Gulf Coast of the USA. To address this issue, state and federal agencies have undertaken massive wetland restoration efforts to preserve and restore coastal marsh habitats in Louisiana. For these efforts to be successful in the long-term, it is critical to understand what methodologies and techniques result in resilient restoration projects. However, traditional methods to monitor restoration success rely on labor intensive field measurements that are often limited in scope, difficult to maintain, and underfunded. Recent technological developments with uncrewed aircraft systems (UASs) and image processing have substantially improved the ability of restoration practitioners to use off-the-shelf UASs and cameras to map projects. We present a streamlined method using a commercially available drone with a high-resolution red, green, blue (RGB) camera to assess the effects of wetland restoration and integrate more modern tools into evaluation approaches. We conducted drone flights at restored brackish marshes of various ages using a space for time substitution with the goal of understanding the long-term success of marsh restoration. We observed that created marshes had higher land to water ratios than natural marshes. This finding suggests that these restored areas were gaining and maintaining elevation after approximately 10 years. Our method shows that drone surveys offer low-cost, minimally invasive methods for evaluating restored wetlands and ultimately tell us more about ecosystem function through realistic site-level habitat configurations. [ABSTRACT FROM AUTHOR]

Abstract Antarctic moss beds are sensitive to climatic conditions, and both their survival and community composition are particularly influenced by the availability of liquid water over summer. As Antarctic regions increasingly face climate pressures (e.g., changing hydrology and heat waves), advancing capabilities to efficiently and non‐destructively monitor water content in moss communities becomes a key research priority. Because of the complexity induced by multiple micro‐climatic drivers and its fragility, tracking the evolution and responses of moss bed moisture requires monitoring methods that are non‐intrusive, efficient, and spatially significant, such as the use of unoccupied aerial systems (UAS). In this study, we combine a multi‐species drying laboratory experiment with short‐wave infrared (SWIR) spectroscopy analyses to first develop a Random Forest regression Model (RFM) capable of predicting Antarctic moss turf water content (~5% error). The RFM was then applied to UAS‐borne SWIR imaging data (900–1700 nm,

Carbon dioxide is a key gas to monitor at volcanoes because its concentration and isotopic signature can indicate changes to magma supply and degassing behavior prior to eruptions, yet carbon isotopic fluctuations at volcanic summits are not well constrained. Here we present δ13C results measured from plume samples collected at Stromboli volcano, Italy, by Uncrewed Aerial Systems (UAS). We found contrasting volcanic δ13C signatures in 2018 during quiescence (−0.36 ± 0.59‰) versus 10 days before the 3 July 2019 paroxysm (−5.01 ± 0.56‰). Prior to the eruption, an influx of CO2‐rich magma began degassing at deep levels (∼100 MPa) in an open‐system fashion, causing strong isotopic fractionation and maintaining high CO2/St ratios in the gas. This influx occurred between 10 days and several months prior to the event, meaning that isotopic changes in the gas could be detected weeks to months before unrest. Plain Language Summary: Volcanoes produce gases which change composition depending on how active the volcano is. One of these gases, carbon dioxide, is known to change in proportion to other gases before an eruption occurs, but little is known about how the isotopes of carbon change leading up to an eruption. Using drones to reach the gaseous plume of Stromboli volcano, Italy, we have captured carbon dioxide both during an inactive phase in 2018 and during the lead‐up to a highly explosive eruption called a paroxysm in 2019. There is a stark difference in the carbon isotopes measured 10 days before the 3 July 2019 paroxysm as opposed to those measured in 2018. This is caused by the arrival of CO2‐rich magma which progressively degassed, leading to lighter carbon isotopes in the residual magma over time. This process could have started anywhere from 10 days to several months before the paroxysm. This provides a warning signal which can be detected weeks to months before an active period begins. Key Points: Rapid collection of volcanic plume CO2 enabled by Uncrewed Aerial SystemsA carbon isotopic anomaly was present two weeks prior to the Stromboli 2019 paroxysmHigh CO2 concentrations, elevated CO2/St, and light δ13C‐CO2 may precede paroxysms on timescales of months to weeks [ABSTRACT FROM AUTHOR]

Robotic platforms including Unmanned Aerial Vehicles (UAVs) require an accurate and reliable source of position information, especially in indoor environments where GNSS cannot be used. This is typically accomplished by using multiple independent position sensors. This paper presents a UAV position estimation mechanism based on a particle filter, that combines information from visual odometry cameras and visual detection of fiducial markers. The article proposes very compact, lightweight and robust method for indoor localization, that can run with high frequency on the UAV's onboard computer. The filter is implemented such that it can seamlessly handle sensor failures and disconnections. Moreover, the filter can be extended to include inputs from additional sensors. The implemented approach is validated on data from real-life UAV test flights, where average position error under 0.4 m was achieved. [ABSTRACT FROM AUTHOR]

In the context of increasing global food demand and the urgent need for production processes optimization, plant protection products play a key role in safeguarding crops from insects, pests, and fungi, responsible of plant diseases proliferation and yield losses. Despite the inaccurate distribution of conventional aerial spraying performed by airplanes and helicopters, Unmanned Aerial Spraying Systems (UASSs) offer low health risks and operational cost solutions, preserving crops and soil from physical damage. This study explores the impact of UASS flight height (2 m and 2.5 m above ground level), speed (1 m s−1 and 1.5 m s−1), and position (over the canopy and the inter-row) on vineyard aerial spraying efficiency by analysing Water Sensitive Papers droplet coverage, density, and Number Median Diameter using a MATLAB script. Flight position factor, more than others, influenced the application results. The specific configuration of 2 m altitude, 1.5 m s−1 cruising speed, and inter-row positioning yielded the best results in terms of canopy coverage, minimizing off-target and ground dispersion, and represented the best setting to facilitate droplets penetration, reaching the lowest parts generally more affected from disease. Further research is needed to assess UASS aerial PPP distribution effectiveness and environmental impact in agriculture, crucial for technology implementation, especially in countries where aerial treatments are not yet permitted. [ABSTRACT FROM AUTHOR]

Background: The expanding use of Uncrewed Aircraft System (UAS) technology in disaster response shows its immense potential to enhance emergency management. However, there is limited documentation on the challenges and data management procedures related to UAS operation. Aims: This manuscript documents and analyses the operational, technical, political, and social challenges encountered during the deployment of UAS, providing insights into the complexities of using these technologies in disaster situations. Methods: This manuscript documents and analyses the operational, technical, political, and social challenges encountered during the deployment of UAS, providing insights into the complexities of using these technologies in disaster situations. Key results: UAS technology plays a significant role in search and rescue, reconnaissance, mapping, and damage assessment, alongside notable challenges such as extreme flying conditions, data processing difficulties, and airspace authorisation complexities. Conclusions: The study concludes with the need for updated infrastructure standards, streamlined policies, and better coordination between technological advancements and political processes, emphasising the necessity for reform to enhance disaster response capabilities. Implications: The findings of this study inform future guidelines for the effective and safe use of UAS in disaster situations, advocating for a bridge between state-of-the-art UAS research and its practical application in emergency response. We document how Uncrewed Aircraft System (UAS) technology was used in the 2020 Labour Day wildfires in Oregon, USA. Information from a literature review, social media, and interviews with disaster responders were compiled. Qalitative analyses on the interview data synthesised typical UAS applications and highlighted challenges. [ABSTRACT FROM AUTHOR]

Chronic spontaneous urticaria (CSU) is an immunological disease that is depicted by high prevalence and eminent burden for patients and society that is attributable to the arbitrary nature of symptoms and inconsistent tools for assessment of activity and severity. Transglutaminase-2 (TG2) is a posttranslational enzyme that is pervasively expressed in many cells and tissue types including mast cells. It has various biological functions, and its role in allergic disorders has been highlighted and delineated through several postulated mechanisms. This case–control study aimed at determining the relationship between serum levels TG2 and severity of CSU. To the best of our knowledge, this is the first study in Egypt to determine the relationship between serum TG2 and severity of CSU. We enrolled 60 adult patients with confirmed diagnosis of CSU. According to urticaria activity score (UAS), patients were categorized into three groups [20 with mild disease; UAS = 0, 20 with moderate disease; UAS = 1–3, 20 with severe disease; UAS = 4–6]. Another 20 healthy individuals (age and gender matched) served as a control group. All patients were subjected to detailed medical history, clinical examination, complete blood count with differential, serum total IgE, CRP, ESR, TSH, ANA, liver and renal function tests. Serum level of TG2 was done by quantitative ELISA for all enrolled patients and controls. Serum TG2 is significantly higher in patients group compared to control group (P value < 0.001). Serum TG2 levels were significantly higher in patients with severe disease compared to patients with moderate or mild disease. This is illustrated by the significant positive correlation between serum TG2 and UAS (r 0.814 and P value 0.000). Moreover, serum TG2 accurately classified CSU patients into mild, moderate and severe subgroups: as regards differentiation between mild and moderate cases (sensitivity 70%, specificity 80%, PPV 77.8, NPV 72.7) and as for the differentiation between moderate and severe cases (sensitivity 95%, specificity 90%, PPV 90.5, NPV 94.7). Serum TG2 may have a pivotal role as a marker of severity in patients with CSU. [ABSTRACT FROM AUTHOR]

Hypoglossal nerve stimulation (HGNS) has emerged as a widespread and innovative treatment option for selected moderate-to-severe obstructive sleep apnea (OSA) patients who cannot be treated effectively with traditional treatment modalities. In this observational cohort study, the objective and subjective outcomes of Belgian OSA patients treated with HGNS therapy were analyzed at 6 and 12 months post-implantation. Thirty-nine patients implanted with a respiration-synchronized HGNS device at the Antwerp University Hospital w ere included in this study. Patients underwent baseline in-laboratory polysomnography and a follow-up sleep study 6 and 12 months post-implantation. Questionnaires on patient experience and daytime sleepiness were filled out and data on objective therapy usage were collected. All 39 patients completed the 6-month follow-up and 21 patients (54%) completed the 12-month follow-up. Median AHI decreased from 33.8 [26.1;45.0] to 10.2 [4.8;16.4] at the 6-month follow-up, and to 9.6 [4.1;16.4] at the 12-month follow-up (p < 0.001). The surgical success rate, according to the Sher20 criteria, was 80% and 76% at the 6- and 12-month follow-ups, respectively. Median ESS improved from 12.0 [7.0;18.0] at baseline to 6.0 [2.5;11.0] at 6 months (p < 0.001) and to 6.5 [2.8;11.5] at 12 months (p = 0.012). Objective therapy usage was 7.4 [6.6;8.0] and 7.0 [5.9:8.2] h/night at the 6- and 12-month follow-ups, respectively. A high overall clinical effectiveness of HGNS therapy, as shown by a mean disease alleviation of 58%, was demonstrated at 12 months post-implantation. Overall, HGNS therapy using respiration-synchronized neurostimulation of the XII cranial nerve resulted in a significant improvement in both objective and subjective OSA outcomes, with a high level of patient satisfaction and high treatment adherence. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of the 'Carol I' National Defence University / Buletinul Universitatii Nationale de Aparare 'Carol I' is the property of Carol I National Defence University 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.)

Aerial and remote sensing archaeology are tools for identifying marks on images of archaeological remains covered by soil. In other words, they are archaeological prospection tools that fall into the category of non-destructive research methods. In this paper, a short review of these valuable research tools is carried out, presenting the way marks appear, and also the categories of aerial and remote sensing archaeology, depending on the medium of storage of images and the type of platform of transfer of sensors. The timing of the emergence of each category is determined and examples of relevant surveys are presented. Focusing on the use of an Unmanned Aircraft System (UAS) with a multispectral sensor (aerial remote sensing archaeology), their partial utilization is revealed for collecting images in areas outside of the visible spectrum, aiming at the identification of covered archaeological remains. To this end, examples of the use of UAS with different sensors are presented and indexes that have been used so far in respective applications are gathered. Aerial remote sensing archaeology took place in two areas of particular interest in Northern Greece. UAS WingtraOne GEN II was used to collect multispectral images. In both study areas, soil and vegetation or crop marks were detected, possibly covered archaeological remains, initially in RGB orthophotomosaics and then more clearly in the index maps, such as the normalized difference vegetation index, simple ratio, brightness index, second brightness index, and anthocyanin reflectance index 2b. [ABSTRACT FROM AUTHOR]

Highlights RPAAS drift study with 12 replications at fine spatial resolution (0.5 m). RPAAS produced more drift than a tractor-mounted boom sprayer up to 10 m off field. Data confirmed use of an extra coarse nozzle best mitigated spray drift. Swath offset proved to be a BMP for RPAAS drift mitigation and regulatory consideration. The phenomenal growth of remotely piloted aerial application systems (RPAASs) in recent years has raised questions about their impact on off-target movement of crop protection products. Few quality field data currently exist to improve the understanding of the fundamental factors that govern spray drift and deposition for RPAASs and to compare such data with conventional ground application platforms. A field study was conducted to compare spray deposition and downwind drift from a 10-L RPAAS with a conventional tractor-mounted boom sprayer. Applications were made with two different agricultural spray nozzles, which produced medium and extra coarse spray droplet spectra. Spray drift and deposition were measured up to 100 m downwind, and airborne drift was determined up to 50 m downwind at heights of 0.2 and 2 m. Regardless of the delivery platforms, overall spray drift was greater for the medium nozzle than that for the extra coarse nozzle, indicating that the applied droplet size was a critical factor governing spray deposition. The RPAAS had significantly higher ground spray deposition than the tractor-mounted boom sprayer for both the medium and extra coarse nozzle treatments out to 10 and 6 m downwind without swath offset, respectively. With swath offset, these distances were reduced to 4.5 and 2.7 m downwind, respectively. Utilizing swath offset to account for displacement of the swath caused by wind-driven off-target movement could be effective in reducing drift for the RPAAS platform, particularly for the medium nozzle treatment. The use of swath offset with careful consideration of wind speed proved to be a best management practice for mitigating spray drift for RPAAS applications and is recommended for consideration for ongoing regulation development on spray drift characterizations. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging systems frequently rely on spectral rather than spatial resolving power for identifying objects within a scene. A hyperspectral imaging system's response to point targets under flight conditions provides a novel technique for extracting system-level radiometric performance that is comparable to spatially unresolved objects.The system-level analysis not only provides a method for verifying radiometric calibration during flight but also allows for the exploration of the impacts on small target radiometry, post orthorectification. Standard Lambertian panels do not provide similar insight due to the insensitivity of orthorectification over a uniform area. In this paper, we utilize a fixed mounted hyperspectral imaging system (radiometrically calibrated) to assess eight individual point targets over 18 drone flight overpasses. Of the 144 total observations, only 18.1% or 26 instances are estimated to be within the uncertainty of the predicted entrance aperture-reaching radiance signal. For completeness, the repeatability of Lambertian and point targets are compared over the 18 overpasses, where the effects of orthorectification drastically impact the radiometric estimate of point targets. The unique characteristic that point targets offer, being both a known spatial and radiometric source, is that they are the only field-deployable method for understanding the small target radiometric performance of drone-based hyperspectral imaging systems. [ABSTRACT FROM AUTHOR]

Context: Dynamic feedbacks between physical structure and ecological function drive ecosystem productivity, resilience, and biodiversity maintenance. Detailed maps of canopy structure enable comprehensive evaluations of structure–function relationships. However, these relationships are scale-dependent, and identifying relevant spatial scales to link structure to function remains challenging. Objectives: We identified optimal scales to relate structure heterogeneity to ecological resistance, measured as the impacts of wildfire on canopy structure, and ecological resilience, measured as native shrub recruitment. We further investigated whether structural heterogeneity can aid spatial predictions of shrub recruitment. Methods: Using high-resolution imagery from unoccupied aerial systems (UAS), we mapped structural heterogeneity across ten semi-arid landscapes, undergoing a disturbance-mediated regime shift from native shrubland to dominance by invasive annual grasses. We then applied wavelet analysis to decompose structural heterogeneity into discrete scales and related these scales to ecological metrics of resilience and resistance. Results: We found strong indicators of scale dependence in the tested relationships. Wildfire effects were most prominent at a single scale of structural heterogeneity (2.34 m), while the abundance of shrub recruits was sensitive to structural heterogeneity at a range of scales, from 0.07 – 2.34 m. Structural heterogeneity enabled out-of-site predictions of shrub recruitment (R2 = 0.55). The best-performing predictive model included structural heterogeneity metrics across multiple scales. Conclusions: Our results demonstrate that identifying structure–function relationships requires analyses that explicitly account for spatial scale. As high-resolution imagery enables spatially extensive maps of canopy heterogeneity, models for scale dependence will aid our understanding of resilience mechanisms in imperiled arid ecosystems. [ABSTRACT FROM AUTHOR]

This paper aims to discuss in detail the legal aspects of drone use in Poland and demonstrate the need to establish a legal act (Drone Code) that would comprehensively regulate the principles of safe drone use, whether aerial, land or sea, as well as to show the importance of creating a public administration body competent for drone-related issues, or perhaps even an entirely new international organization to safeguard the legal and ethical use of drones. [ABSTRACT FROM AUTHOR]

Civilian drones are becoming more functionally independent from human involvement which sets them on a path towards “autonomous” status. When defining “autonomy,” the European Union (EU) regulations, among other jurisdictions, employ an all-or-nothing approach, according to which a drone is either able to operate fully autonomously or not at all. This dichotomous approach disregards the various levels of drone autonomy and fails to capture the complexity of civilian drone operation. Within the EU, this has regulatory implications, such as regulatory lag, hindrance in better safety regulation, and incoherence with the Union’s regulatory approach towards Artificial Intelligence (AI). This article argues that understanding autonomy as a spectrum, rather than in a dichotomous way, would be more coherent with the technical functioning of drone and would avoid potential regulatory problems caused by the current dichotomous approach. In delineating this spectral approach, this article (1) analyses manifestations of autonomy in drone operations, (2) delineates efforts in the technical literatures and drone standardization to conceptualize “autonomy”, and (3) explores definitional attempts for autonomy made in three other technologies: self-driving cars, autonomous weapon systems, and autonomous maritime ships. [ABSTRACT FROM AUTHOR]

Copyright of e & i Elektrotechnik und Informationstechnik is the property of Springer Nature 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.)

Antarctic moss beds are sensitive to climatic conditions, and both their survival and community composition are particularly influenced by the availability of liquid water over summer. As Antarctic regions increasingly face climate pressures (e.g., changing hydrology and heat waves), advancing capabilities to efficiently and non‐destructively monitor water content in moss communities becomes a key research priority. Because of the complexity induced by multiple micro‐climatic drivers and its fragility, tracking the evolution and responses of moss bed moisture requires monitoring methods that are non‐intrusive, efficient, and spatially significant, such as the use of unoccupied aerial systems (UAS). In this study, we combine a multi‐species drying laboratory experiment with short‐wave infrared (SWIR) spectroscopy analyses to first develop a Random Forest regression Model (RFM) capable of predicting Antarctic moss turf water content (~5% error). The RFM was then applied to UAS‐borne SWIR imaging data (900–1700 nm, <16 nm spectral resolution) of the moss beds at high spatial resolution (2 cm) across three sites in the vicinity of Casey Station, Antarctica. The sites differed in terrain, snow cover, and moisture availability to evaluate method capabilities under different conditions. Optimum RFM parameters and input variables (spectral indices and reflectance spectra) were determined. Maps of moss moisture were validated via acquiring moss spectra and water content (using sponges inserted into the moss turf) collected in situ, for which an exponential correlation (R2 = 0.72) was reported. RFM further allowed investigation of the influential spectral variables to model water content in moss and associated spectral water absorption features. We demonstrated that UAS‐borne SWIR imaging is a promising new tool to map and quantify water content in Antarctic moss beds. Hyperspectral mapping facilitates the exploration of the spatial variability of moss health and enables the creation of a baseline against which changes in these moss communities can be measured. [ABSTRACT FROM AUTHOR]

This paper presents the method and results of surveying a dispersed radioactive field using an unmanned air vehicle (UAV) and an unmanned ground vehicle (UGV). A 35 GBq of 140 ${}^{140}$La material was distributed in a specific geometric l‐polygon pattern measuring a 120 m × $\times $ 20 m longitudinal rectangle and an 80 m × $\times $ 10 m transverse rectangle. Two methods were used to determine the amount and distribution of the lanthanum over the polygon including 20 plywood coupons distributed over the area and a UGV equipped with a Kromek GR1® driving over the area. The aerial survey was conducted using an unmanned aerial vehicle UAV carrying the Kromek GR1® while flying a traditional grid pattern and circular pattern at different elevations and speeds over the area. The data collected by the UAV were further postprocessed using N‐Visage, a 3D radiation modeling method developed by Createc, to create a model of the ground activity. This model was compared with the reference data collected on the ground by the UGV and was found to be in agreement with 11%. [ABSTRACT FROM AUTHOR]

Accurately detecting nitrogen (N) deficiency and determining the need for additional N fertilizer is a key challenge to achieving precise N management in many crops, including rice (Oryza sativa L.). Many remotely sensed vegetation indices (VIs) have shown promise in this regard; however, it is not well-known if VIs measured from different sensors can be used interchangeably. The objective of this study was to quantitatively test and compare the ability of VIs measured from an aerial and proximal sensor to predict the crop yield response to top-dress N fertilizer in rice. Nitrogen fertilizer response trials were established across two years (six site-years) throughout the Sacramento Valley rice-growing region of California. At panicle initiation (PI), unmanned aircraft system (UAS) Normalized Difference Red-Edge Index (NDREUAS) and GreenSeeker (GS) Normalized Difference Vegetation Index (NDVIGS) were measured and expressed as a sufficiency index (SI) (VI of N treatment divided by VI of adjacent N-enriched area). Following reflectance measurements, each plot was split into subplots with and without top-dress N fertilizer. All metrics evaluated in this study indicated that both NDREUAS and NDVIGS performed similarly with respect to predicting the rice yield response to top-dress N at PI. Utilizing SI measurements prior to top-dress N fertilizer application resulted in a 113% and 69% increase (for NDREUAS and NDVIGS, respectively) in the precision of the rice yield response differentiation compared to the effect of applying top-dress N without SI information considered. When the SI measured via NDREUAS and NDVIGS at PI was ≤0.97 and 0.96, top-dress N applications resulted in a significant (p < 0.05) increase in crop yield of 0.19 and 0.21 Mg ha-1, respectively. These results indicate that both aerial NDREUAS and proximal NDVIGS have the potential to accurately predict the rice yield response to PI top-dress N fertilizer in this system and could serve as the basis for developing a decision support tool for farmers that could potentially inform better N management and improve N use efficiency.

This paper investigates the response of mid-latitude montane peatlands to climate warming, focusing on changes occurring in a montane peat bog during a drought period. Unmanned Aerial Systems (UAS) equipped with multispectral and thermal sensors were used for high-resolution monitoring to analyze qualitative changes within the peat bog and their spatial distribution. The study was conducted in the Rokytka mountain peat bog in Šumava National Park, Czech Republic, which is one of the largest mountain peat bog complexes in Central Europe. Monitoring took place during the 2019 vegetation season, coinciding with the peak of the 2015–2019 drought. The recurrent UAS imaging campaigns were complemented by continuous hydrological and hydropedological monitoring and in-situ calibration measurements. The findings revealed diverging responses of montane peatlands to climate change across different functional zones of the peat bog. UAS thermal mapping identified distinct land surface temperature variations across various vegetation categories under different conditions. Notably, ponds and waterlogged areas displayed a stabilizing effect on land surface temperature variability, though they exhibited different absolute temperatures. In contrast, shallow waterlogged areas exhibited surface temperatures akin to dry open peat areas. Multispectral UAS monitoring demonstrated significant transitions among the peat bog zones in response to heat and drought propagation. The most pronounced changes occurred in shallow waterlogged areas, which shrank notably from 22.8% to 4.5%, while bare peat expanded from 26.8% to 45.5% during the 2019 drought season. High-resolution thermal and multispectral monitoring has revealed the scope and magnitude of the intra-peatland responses to drought and heat waves and serves as a sensible indicator of environmental changes of peatlands. It has disclosed a large cumulative effect of change in an environment composed of highly heterogeneous and subtle structures. The results highlighted the effectiveness of UAS monitoring in understanding the extent of change in montane peatlands as a fragile environment exposed to the effects of climate change.

TUnmanned Aerial Systems (UAS), despite their relative novelty, are already an integral component of the general aviation structure, and, based on a number of complex and sophisticated solutions, including those related to artificial intelligence, are part of the spectrum of high technologies. UAS find application not only in the sphere of commerce, but also in the realisation of specific tasks of public administration, such as territory management, healthcare, emergency prevention and elimination, ensuring environmental safety and law and order, nature management. The purpose of the study, the results of which are presented in this publication, is to assess the current state and potential of the use of UAS in these areas based on a review of literature sources on the practice of their application in order to solve the tasks of public administration in different countries of the world. The main conclusion drawn from the analysis and characterising the general scientific novelty of the study is that, despite a significant number of specific examples, the breadth of UAS use range can be characterised as imaginary. The main and the only really significant application is various types of aerial survey in the interests of state control and supervision bodies. In the course of the work, a number of promising directions for the use of UAS have been identified. It is also noted that their wider implementation in the practice of public administration is hindered by the lack and/or imperfection of the relevant legal framework, typical for most legislative systems of the world, and postulates the need to actively address these issues in relation to the classification and certification of UAS, requirements for their operators and software, the procedure and rules of operation, the integration of unmanned aircraft (UAS) into the existing air traffic management system.

The widely adaptable capabilities of artificial intelligence, in particular deep learning and computer vision have led to significant research output regarding flame and smoke detection. The composition of flame and smoke, also described as fire behavior, can be considerably different depending on factors like weather, fuels, and the specific landscape fire is being observed on. The ability to detect definable classes of fire behavior using computer vision has not been explored and could be helpful given it often dictates how firefighters respond to fire situations. To test whether types of fire behavior could be reliably classified, we collected and labeled a unique unmanned aerial system (UAS) image dataset of fire behavior classifications to be trained and validated using You Only Look Once (YOLO) detection models. Our 960 labeled images were sourced from over 21 h of UAS video collected during prescribed fire operations covering a large region of Texas and Louisiana, United States. National Wildfire Coordinating Group (NWCG) fire behavior observations and descriptions served as a reference for determining fire behavior classes during labeling. YOLOv8 models were trained on NWCG Rank 1–3 fire behavior descriptions in grassland, shrubland, forested, and combined fire regimes within our study area. Models were first trained and validated on classifying isolated image objects of fire behavior, and then separately trained to locate and segment fire behavior classifications in UAS images. Models trained to classify isolated image objects of fire behavior consistently performed at a mAP of 0.808 or higher, with combined fire regimes producing the best results (mAP = 0.897). Most segmentation models performed relatively poorly, except for the forest regime model at a box (locate) and mask (segment) mAP of 0.59 and 0.611, respectively. Our results indicate that classifying fire behavior with computer vision is possible in different fire regimes and fuel models, whereas locating and segmenting fire behavior types around background information is relatively difficult. However, it may be a manageable task with enough data, and when models are developed for a specific fire regime. With an increasing number of destructive wildfires and new challenges confronting fire managers, identifying how new technologies can quickly assess wildfire situations can assist wildfire responder awareness. Our conclusion is that levels of abstraction deeper than just detection of smoke or flame are possible using computer vision and could make even more detailed aerial fire monitoring possible using a UAS.

Remote-sensing methods are being used to study a growing number of issues in the San Francisco Estuary, such as (1) detecting the optical properties of chlorophyll-a concentrations and dissolved organic matter to assess productivity and the nature of carbon inputs, (2) creating historical records of invasive aquatic vegetation expansion through space and time, (3) identifying origins and expansions of invasions, and (4) supporting models of greenhouse-gas sequestration by expanding restoration projects. Technological capabilities of remote sensing have likewise expanded to include a wide array of opportunities: from boat-mounted sensors, human-operated low-flying planes, and aerial drones, to freely accessible satellite imagery. Growing interest in coordinating these monitoring methods in the name of collaboration and cost-efficiency has led to the creation of diverse expert teams such as the Remote Imagery Collaborative, and monitoring frameworks such as the Interagency Ecological Program Aquatic Vegetation Monitoring Framework and Wetland Regional Monitoring Program. This paper explores the emerging technologies and applications of various methods for studying primary producers, with an emphasis on remote sensing.

To address the issue of estimating soil moisture at a hyper-resolution scale, a methodology referred to as Precision Irrigation Soil Moisture Mapper (PrISMM), that includes three key components, is developed: high-resolution remotely sensed optical and thermal data, surface energy balance modeling, and site-specific soil analysis. An Unmanned Aerial Vehicle/System (UAV or UAS) collects high-resolution multispectral imagery in the Dallas–Fort Worth metropolitan study area. Orthomosaics are converted to thermal inertia estimates in a spatially distributed format using the remotely sensed data combined with a set of surface energy balance modeling equations. Using thermal and physical properties of soil gained from site-specific soil analysis, thermal inertia estimates were further converted from thermal inertia to daily volumetric soil water content (VSWC) with a horizonal resolution of 8.6 cm. A ground truthing dataset of measured VSWC values taken from a Time Domain Reflectometer was compared with model results, producing a reasonable correlation with an average coefficient of determination of (R2) = 0.79, an average root mean square error (RMSE) = 0.0408, and mean absolute error (MAE) = 0.0308. This study highlights a practical approach of estimating VSWC for irrigation purposes while providing superior spatio-temporal coverage over in situ methods. The authors envision that PrISMM can be implemented in water usage management by relating VSWC with weather forecasts and evapotranspiration rates to develop time-based spatially distributed irrigation management plans. [ABSTRACT FROM AUTHOR]

Copyright of Fire Ecology is the property of Springer Nature 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.)

Thanks to the development of geodetic methods and equipment, there has been a transition from conventional methods to modern technologies, which can efficiently and accurately acquire a large amount of data in a short time without the need for direct contact with the measured object. Combined technologies such as Structure from Motion (SfM), Multi-View Stereo (MVS) photogrammetry using Unmanned Aerial Systems (UAS), and terrestrial laser scanning (TLS) are often used for monitoring geohazards and documenting objects in quarries to obtain detailed and accurate information about their condition and changes. This article deals with the analysis of point clouds obtained with different settings in terms of average absolute point distance, average point density, and time range for surveying and office work. The numerical and graphical results of the research lead to conclusions for scientific and practical applications for activities in the mining industry. [ABSTRACT FROM AUTHOR]

Spongy moth (Lymantria dispar dispar) has caused considerable damage to oak trees across eastern deciduous forests. Forest management, post-outbreak, is resource intensive and typically focused on ecosystem restoration or resource loss mitigation. Some local forest managers and government partners are exploring developing technologies such as Unpiloted Aerial Systems (UASs, UAVs, or drones) to enhance their ability to gather reliable fine-scale information. However, with limited resources and the complexity of investing in hardware, software, and technical expertise, the decision to adopt UAS technologies has raised questions on their effectiveness. The objective of this study was to evaluate the abilities of two UAS surveying approaches for classifying the health of individual oak trees following a spongy moth outbreak. Combinations of two UAS multispectral sensors and two Structure from Motion (SfM)-based software are compared. The results indicate that the overall classification accuracy differed by as much as 3.8% between the hardware and software configurations. Additionally, the class-specific accuracy for 'Declining Oaks' differed by 5–10% (producer's and user's accuracies). The processing experience between open-source and commercial SfM software was also documented and demonstrated a 25-to-75-fold increase in processing duration. These results point out major considerations of time and software accessibility when selecting between hardware and software options for fine-scale forest mapping. Based on these findings, future stakeholders can decide between cost, practicality, technical complexity, and effectiveness. [ABSTRACT FROM AUTHOR]

Analysis of a series of thermal mappings obtained by UAS flights on quiescent volcanoes requires some special techniques to be performed. The main challenge is represented by the difficulty of separating hot and cold pixels in areas where their temperatures are quite similar. This task is indeed much simpler, for example, for lava flows where the temperature differences between the hot lava and the cold soil is rather big. This paper shows various software developed in order to perform this extraction and calculate the trends over time of both the average temperature and the heat flux from the soil. This prototypal implementation used thermal flights performed over a time span of a few years on an area in the Campi Flegrei caldera in southern Italy. Standard image manipulation techniques were used to segmentate and clusterise each thermal mapping in order to reduce the thermal anomalies to some sets of simpler features characterised by their fundamental parameters. The temporal trends of some physical parameters (temperature, heat flux, etc.) were extracted from these sets, and we found interesting results necessary for correlations and for ongoing research with other parameters. [ABSTRACT FROM AUTHOR]

The protection of first responders from radioactive contamination with alpha emitters that may result from a radiological accident is of great complexity due to the short range of alpha particles in the air of a few centimeters. To overcome this issue, for the first time, a system mounted on a UAS for the near-real-time remote measurement of alpha particles has been developed, tested, and calibrated. The new system, based on an optical system adapted to be installed on a UAS in order to measure the UV-C fluorescence emitted by alpha particles in the air, has been tested and calibrated, carried out in the laboratory and in field experiments using UV-C LEDs and 241Am sources. In experimental flights, the probability of detecting a point source was determined to be approximately 60%. In the case of a surface extended source, a detection efficiency per unit surface activity of 10 counts per second per MBq cm−2 was calculated. A background count rate of UV-C of around 26 ± 28 s−1 for an integration time of 0.1 s was measured during flights, which led to a decision threshold surface activity of 5 MBq cm−2. [ABSTRACT FROM AUTHOR]

Early disease management following the onset of disease symptoms is crucial for controlling their spread. Heterogenous collaboration between unmanned aerial systems (UAS) and unmanned ground vehicles (UGV) for field scouting and disease diagnosis is a potential approach for developing automated disease management solutions. However, automation of crop-specific disease identification requires the use of above and below-canopy sensors and properly trained deep learning (DL) models. This research proposes to develop a novel disease management system for diagnosing corn diseases from above and below the canopy by collaboratively using edge devices mounted on UAS and UGV, respectively. Three separate datasets were acquired using UAS above the canopy, UGV below the canopy, and handheld imaging platforms within diseased corn fields. DL-based image classification models were first trained for identifying common corn diseases under field conditions, resulting in up to 95.04% testing accuracy using the DenseNet169 architecture. After creating bounding box annotations for disease images, You Only Look Once (YOLO)v7 DL-based object detection models were trained to identify diseases from each platform separately. Trained YOLOv7 models resulted in the highest mAP@IoU=0.5 of 37.6%, 46.4%, and 72.2% for locating and identifying diseases above the canopy using UAS, below the canopy using UGV, and handheld sensors, respectively. A client/server architecture was developed to establish communication between the UAS, UGV, and Google Spreadsheets via Wi-Fi communication protocol. The coordinates of diseased regions and distinct disease types were recorded using the client/server architecture on Google Spreadsheets. A web application utilized the data from the Google Spreadsheet to help users diagnose diseases in real time and provide recommendations for implementing appropriate disease management practices. This study reports findings of independently operated UAS and UGV that can potentially offer disease spread from below and over the corn canopy by combining the information. [ABSTRACT FROM AUTHOR]

The availability of multi-rotor UAVs with lifting capacities of several kilograms allows for a new paradigm in atmospheric measurement techniques, i.e., the integration of research-grade sonic anemometers for airborne turbulence measurements. With their ability to hover and move very slowly, this approach yields unrevealed flexibility compared to mast-based sonic anemometers for a wide range of boundary layer investigations that require an accurate characterization of the turbulent flow. For an optimized sensor placement, potential disturbances by the propeller-induced flow (PIF) must be considered. The PIF characterization can be done by CFD simulations, which, however, require validation. For this purpose, we conducted an experiment to map the PIF below a multi-rotor drone using a mobile array of five sonic anemometers. To achieve measurements in a controlled environment, the drone was mounted inside a hall at a 90° angle to its usual flying orientation, thus leading to the development of a horizontal downwash, which is not subject to a pronounced ground effect. The resulting dataset maps the PIF parallel to the rotor plane from two rotor diameters, beneath, to 10 D, and perpendicular to the rotor plane from the center line of the downwash to a distance of 3 D. This measurement strategy resulted in a detailed three-dimensional picture of the downwash below the drone in high spatial resolution. The experimental results show that the PIF quickly decreases with increasing distance from the centerline of the downwash in the direction perpendicular to the rotor plane. At a distance of 1 D from the centerline, the PIF reduced to less than 4 ms−1 within the first 5 D beneath the drone, and no conclusive disturbance was measured at 2 D out from the centerline. A PIF greater than 4 ms−1 was still observed along the center of the downwash at a distance of 10 D for both throttle settings tested (35% and 45%). Within the first 4 D under the rotor plane, flow convergence towards the center of the downwash was measured before changing to diverging, causing the downwash to expand. This coincides with the transition from the four individual downwash cores into a single one. The turbulent velocity fluctuations within the downwash were found to be largest towards the edges, where the shear between the PIF and the stagnant surrounding air is the largest. [ABSTRACT FROM AUTHOR]

The inspection of power transmission and distribution systems is performed visually by foot patrolling and helicopter methods. These methods have various disadvantages such as time-consuming, high operating cost, safety issues, and improper results. To overcome this issue, power utility companies are searching for the alternatives like Unmanned Aerial Systems (UAS) or drones. UAS are safe, cost effective, and requires less time for power transmission line inspection compared to the regular methods. In this manuscript, a decisive flight path planning for UAS to visually inspect a power transmission line and towers is explained. The objective of this paper is to maximize the performance of three functions such as coverage of transmission tower, quality of captured image, and flight time. Second, proposing an automated inspection strategy for UAS to follow the overhead power transmission lines. These objectives are achieved by formulating a cost function, to convert the path planning into a safe operation for UAS. The results of Particle Swarm Optimization (PSO) and Simulated Annealing (SA) are compared to find the best path for UAS. The experimental finding shows that PSO algorithm has higher efficiency and effectiveness compared to the SA algorithm and benefits the UAS with a safe flight path for the inspection of power transmission towers. [ABSTRACT FROM AUTHOR]

Within the framework of the European Union's Horizon 2020 research and innovation program, one of the main goals of the Labyrinth project was to develop and test the Conflict Management services of a U-space-based Unmanned Traffic Management (UTM) system. The U-space concept of operations (ConOps) provides a high-level description of the architecture, requirements and functionalities of these systems, but the implementer has a certain degree of freedom in aspects like the techniques used or some policies and procedures. The current document describes some of those implementation decisions. The prototype included part of the services defined by the ConOps, namely e-identification, Tracking, Geo-awareness, Drone Aeronautical Information Management, Geo-fence Provision, Operation Plan Preparation/Optimization, Operation Plan Processing, Strategic Conflict Resolution, Tactical Conflict Resolution, Emergency Management, Monitoring, Traffic Information and Legal Recording. Moreover, a Web app interface was developed for the operator/pilot. The system was tested in simulations and real visual line of sight (VLOS) and beyond VLOS (BVLOS) flights, with both vertical take-off and landing (VTOL) and fixed-wing platforms, while assisting final users interested in incorporating drones to support their tasks. The development and testing of the environment provided lessons at different levels: functionalities, compatibility, procedures, information, usability, ground control station (GCS) integration and aircrew roles. [ABSTRACT FROM AUTHOR]

Abstract Carbon dioxide is a key gas to monitor at volcanoes because its concentration and isotopic signature can indicate changes to magma supply and degassing behavior prior to eruptions, yet carbon isotopic fluctuations at volcanic summits are not well constrained. Here we present δ13C results measured from plume samples collected at Stromboli volcano, Italy, by Uncrewed Aerial Systems (UAS). We found contrasting volcanic δ13C signatures in 2018 during quiescence (−0.36 ± 0.59‰) versus 10 days before the 3 July 2019 paroxysm (−5.01 ± 0.56‰). Prior to the eruption, an influx of CO2‐rich magma began degassing at deep levels (∼100 MPa) in an open‐system fashion, causing strong isotopic fractionation and maintaining high CO2/St ratios in the gas. This influx occurred between 10 days and several months prior to the event, meaning that isotopic changes in the gas could be detected weeks to months before unrest.