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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]

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]

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]

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]

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]

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]

Information on a crop's three-dimensional (3D) structure is important for plant phenotyping and precision agriculture (PA). Currently, light detection and ranging (LiDAR) has been proven to be the most effective tool for crop 3D characterization in constrained, e.g., indoor environments, using terrestrial laser scanners (TLSs). In recent years, affordable laser scanners onboard unmanned aerial systems (UASs) have been available for commercial applications. UAS laser scanners (ULSs) have recently been introduced, and their operational procedures are not well investigated particularly in an agricultural context for multi-temporal point clouds. To acquire seamless quality point clouds, ULS operational parameter assessment, e.g., flight altitude, pulse repetition rate (PRR), and the number of return laser echoes, becomes a non-trivial concern. This article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis. Several pre-designed ULS flights were conducted over an experimental research site in Fargo, North Dakota, USA, on three dates. The flight altitudes varied from 50 m to 60 m above ground level (AGL) along with scanning modes, e.g., repetitive/non-repetitive, frequency modes 160/250 kHz, return echo modes (1n), (2n), and (3n), were assessed over diverse crop environments, e.g., dry corn, green corn, sunflower, soybean, and sugar beet, near to harvest yet with changing phenological stages. Our results showed that the return echo mode (2n) captures the canopy height better than the (1n) and (3n) modes, whereas (1n) provides the highest canopy penetration at 250 kHz compared with 160 kHz. Overall, the multi-temporal CHM heights were well correlated with the in situ height measurements with an R2 (0.99–1.00) and root mean square error (RMSE) of (0.04–0.09) m. Among all the crops, the multi-temporal CHM of the soybeans showed the lowest height correlation with the R2 (0.59–0.75) and RMSE (0.05–0.07) m. We showed that the weaker height correlation for the soybeans occurred due to the selective height underestimation of short crops influenced by crop phonologies. The results explained that the return echo mode, PRR, flight altitude, and multi-temporal CHM analysis were unable to completely decipher the ULS operational practices and phenological impact on acquired point clouds. For the first time in an agricultural context, we investigated and showed that crop phenology has a meaningful impact on acquired multi-temporal ULS point clouds compared with ULS operational practices revealed by WF analyses. Nonetheless, the present study established a state-of-the-art benchmark framework for ULS operational parameter optimization and 3D crop characterization using ULS multi-temporal simulated WF datasets. [ABSTRACT FROM AUTHOR]

The application of lidar data to assist with forest inventory is common around the world. However, the determination of tree species is still somewhat elusive. Lidar data collected using UAS (uncrewed aircraft systems) platforms offer high density point cloud data for areas from a few to several hundred hectares. General point cloud metrics computed using these data captured differences in the crown structure that proved useful for species classification. For our study, we manually adjusted plot and tree locations to align field trees and UAS lidar point data and computed common descriptive metrics using a small cylindrical sample of points designed to capture the top three meters and leader of each tree. These metrics were used to train a random forest classifier to differentiate between two conifer species, Douglas fir and western hemlock, common in the Pacific Northwest region of the United States. Our UAS lidar data had a single swath pulse density of 90 pulses/m2 and an aggregate pulse density of 556 pulses/m2. We trained classification models using both height and intensity metrics, height metrics alone, intensity metrics alone, and a small subset of five metrics, and achieved overall accuracies of 91.8%, 88.7%, 78.6%, and 91.5%, respectively. Overall, we showed that UAS lidar data captured morphological differences between the upper crowns of our two target species and produced a classification model that could be applied over large areas. [ABSTRACT FROM AUTHOR]

Unmanned Aircraft Systems (UASs) have become increasingly important assets for naval and maritime law enforcement operations. In response to the ever-increasing demand for autonomous systems, this paper presents a design, development, integration, and flight testing of an unmanned rotorcraft system capable of operating with a moving ship. The system was developed initially by playing back ship motion data in a simulation environment. Ship motion prediction, relative guidance, operator control interface, and landing flight management system were developed and tested against simulated sea state motion. The system was then flight tested using a 15-kg electric helicopter operating off a US Naval Academy’s yard patrol craft. The craft was instrumented with IMU and GPS for the craft’s own state estimation. The craft was also used as a moving-base station for Real-time Kinematic (RTK) relative positioning. The flight operation was done in the Chesapeake Bay. Autonomous capabilities including takeoff, landing, station-keeping, and maneuvering relative to a moving ship were successfully demonstrated. [ABSTRACT FROM AUTHOR]

Miniaturised hyperspectral cameras are becoming more easily accessible and smaller, enabling efficient monitoring of agricultural crops using unoccupied aerial systems (UAS). This study's objectives were to develop and assess the performance of UAS-based hyperspectral cameras in the estimation of quantity and quality parameters of grass sward, including the fresh and dry matter yield, the nitrogen concentration (Ncont) in dry matter (DM), the digestibility of organic matter in DM (the D-value), neutral detergent fibre (NDF), and water-soluble carbohydrates (WSC). Next-generation hyperspectral cameras in visible-near-infrared (VNIR, 400–1000 nm; 224 bands) and shortwave-infrared (SWIR; 900–1700 nm; 224 bands) spectral ranges were used, and they were compared with commonly used RGB and VNIR multispectral cameras. The implemented machine-learning framework identified the most informative predictors of various parameters, and estimation models were then built using a random forest (RF) algorithm for each camera and its combinations. The results indicated accurate estimations; the best normalised root-mean-square errors (NRMSE) were 8.40% for the quantity parameters, and the best NRMSEs for the quality parameters were 7.44% for Ncont, 1% for D-value, 1.24% for NDF, and 12.02% for WSC. The hyperspectral datasets provided the best results, whereas the worst accuracies were obtained using the crop height model and RGB data. The integration of the VNIR and SWIR hyperspectral cameras generally provided the highest accuracies. This study showed for the first time the performance of novel SWIR range hyperspectral UAS cameras in agricultural application. [ABSTRACT FROM AUTHOR]

The study focuses on the integrated use of multiscale and multisensor remote sensing techniques and big data analysis for the identification of buried archaeological remains or areas of potential archaeological interest. Satellite multispectral data (at very high and high resolution), drone based visible, multispectral, and thermal imagery, and geophysical prospecting (gradiometer) were used. The ancient city of Metaponto was chosen as case study, as it was a very important city in the formative panorama of Italian Magna Graecia and it also is one of the most important and best preserved archaeological sites in southern Italy. The analysis of remote sensing data from different sensors, with different resolutions, and referable to different physical parameters, allowed to deepen archaeological knowledge on a landscape scale, as well as on a site scale, going from the analysis of traces of the ancient landscape (e.g. palaeo-channels, canalisation system, main roads), to the discovery of small features (e.g. secondary roads, houses, facilities). [ABSTRACT FROM AUTHOR]

Wood-boring insect pests pose a significant threat to orchards, potentially leading to tree mortality. In the initial stages of infestation, no visible symptoms are apparent, but as infestations progress, rapid and widespread symptoms emerge, resulting in accelerated tree decline. Therefore, the timely detection of early wood-boring insect symptoms is critical for effective pest control, necessitating advanced methods such as remote sensing. In this study, remote sensing is utilized to identify the early symptoms of peach flatheaded root borer (PFRB) infestation in trees. A multispectral sensor attached to a UAV captures aerial imagery data from stone fruit and pome fruit orchards. These data undergo processing in photogrammetric and GIS programs, where NDVI, NDRE, and the tree crown area are computed. On-site observations confirm PFRB infestations. Various machine-learning models, including logistic regression (LR), artificial neural network (NN), random forest (RF), and extreme gradient boosting (XGBoost), are compared using mean NDVI values, mean NDRE values, crown area, mean temperature, and mean relative humidity. Mean NDVI values emerge as the most crucial factor for predicting PFRB infestation across all machine-learning models. The XGBoost model proves the most effective, achieving an accuracy of 0.85, with marginal variations from the other tested models. [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]

We are currently experiencing an unprecedented moment in forest restoration, where monitoring activities in recovery areas play a vital role in problem identification and method adaptation. However, it is crucial to shift away from expensive traditional methodologies and develop technologies that enable large‐scale monitoring using more accessible and cost‐effective tools. In an effort to provide an evidence‐based perspective, we conducted a systematic review of how remotely piloted aircraft systems equipped with various sensors have been employed for forest monitoring. We consulted three databases and included 53 articles in our review. The results revealed a trend toward research in tropical ecosystems, with forest structure being the most frequently assessed attribute, and canopy height being the most widely measured structural indicator. Red‐green‐blue sensors were commonly used, both individually and in combination with others, and there was a noticeable shift toward the use of light detection and ranging. Data validation primarily relied on forest inventory methods, often involving comparisons of outputs from different sensors and the use of artificial intelligence algorithms. Despite the wide range of studies utilizing sensor‐equipped drones to analyze forest attributes, there is a notable scarcity of research specifically addressing the application of these technologies in forest restoration monitoring. Filling this research gap is essential, as employing techniques that enable large‐scale monitoring, such as aerial photogrammetry and remote sensing, aligns with current trends and contributes to global commitments to environmental restoration and conservation. [ABSTRACT FROM AUTHOR]

Recently, sensors deployed on unpiloted aerial systems (UAS) have provided snow depth estimates with high spatial resolution over watershed scales. While light detection and ranging (LiDAR) produces precise snow depth estimates for areas without vegetation cover, there has generally been poorer precision in forested areas. At a constant flight speed, the poorest precision within forests is observed beneath tree canopies that retain foliage into or through winter. The precision of lidar-derived elevation products is improved by increasing the sample size of ground returns but doing so reduces the spatial coverage of a mission due to limitations of battery power. We address the influence of flight speed on ground return density for baseline and snow-covered conditions and the subsequent effect on precision of snow depth estimates across a mixed landscape, while evaluating trade-offs between precision and bias. Prior to and following a snow event in December 2020, UAS flights were conducted at four different flight speeds over a region consisting of three contrasting land types: (1) open field, (2) deciduous forest, (3) conifer forest. For all cover types, we observed significant improvements in precision as flight speeds were reduced to 2 m s−1, as well as increases in the area over which a 2 cm snow depth precision was achieved. On the other hand, snow depth estimate differences were minimized at baseline flight speeds of 2 m s−1 and 4 m s−1 and snow-on flight speeds of 6 m s−1 over open fields and between 2 and 4 m s−1 over forest areas. Here, with consideration to precision and estimate bias within each cover type, we make recommendations for ideal flight speeds based on survey ground conditions and vegetation cover. [ABSTRACT FROM AUTHOR]

The widespread tree mortality caused by the European spruce bark beetle (Ips typographus L.) is a significant concern for Norway spruce-dominated (Picea abies H. Karst) forests in Europe and there is evidence of increases in the affected areas due to climate warming. Effective forest monitoring methods are urgently needed for providing timely data on tree health status for conducting forest management operations that aim to prepare and mitigate the damage caused by the beetle. Unoccupied aircraft systems (UASs) in combination with machine learning image analysis have emerged as a powerful tool for the fast-response monitoring of forest health. This research aims to assess the effectiveness of deep neural networks (DNNs) in identifying bark beetle infestations at the individual tree level from UAS images. The study compares the efficacy of RGB, multispectral (MS), and hyperspectral (HS) imaging, and evaluates various neural network structures for each image type. The findings reveal that MS and HS images perform better than RGB images. A 2D-3D-CNN model trained on HS images proves to be the best for detecting infested trees, with an F1-score of 0.759, while for dead and healthy trees, the F1-scores are 0.880 and 0.928, respectively. The study also demonstrates that the tested classifier networks outperform the state-of-the-art You Only Look Once (YOLO) classifier module, and that an effective analyzer can be implemented by integrating YOLO and the DNN classifier model. The current research provides a foundation for the further exploration of MS and HS imaging in detecting bark beetle disturbances in time, which can play a crucial role in forest management efforts to combat large-scale outbreaks. The study highlights the potential of remote sensing and machine learning in monitoring forest health and mitigating the impacts of biotic stresses. It also offers valuable insights into the effectiveness of DNNs in detecting bark beetle infestations using UAS-based remote sensing technology. [ABSTRACT FROM AUTHOR]

The availability of remote sensing imagery at high spatiotemporal resolutions presents the opportunity to monitor the surface motion of rock glaciers, a key constraint for characterizing the dynamics of their evolution. In this paper, we investigate four North American rock glaciers by automatically measuring their horizontal surface displacement using photogrammetric data acquired with crewed and uncrewed aircraft along with orbital spacecraft over monitoring periods of up to eight years. We estimate vertical surface changes on these rock glaciers with photogrammetrically generated digital elevation models (DEM) and digitized topographic maps. Uncertainty analysis shows that the imagery with the highest resolution and most precise positioning have the best performance when used with the automated change detection algorithm. This investigation produces gridded velocity fields over the entire surface area of each study site, from which we estimate the age of rock glacier formation using along-flow velocity integration. Though the age estimates vary, the ice within the modern extent of these landforms began flowing between 3000 and 7000 years before present, postdating the last glacial maximum. Surface elevation change maps indicate present-day thinning at the lower latitude/higher elevation sites in Wyoming, while the higher latitude/lower elevation sites in Alaska exhibit relatively stable surface elevations. [ABSTRACT FROM AUTHOR]

The aim of this study is to analyse problems related to thermal mapping obtained from thermal data acquired from unmanned aerial systems (UAS) equipped with thermal cameras. We focused on an accurate analysis of uncertainties introduced by the PIX4D Mapper software version 4.4.12 used to obtain the surface temperature maps of thermal images acquired using the UAS. To achieve this aim, we used artificial thermal references during the surveys, as well as natural hot targets, i.e., thermal anomalies in the Pisciarelli hydrothermal system in Campi Flegrei caldera (CFc). Artificial thermal targets, expressly created and designed for this goal, are a prototype here called "developed thermal target" (DTT) created by the drone laboratory at Istituto Nazionale di Geofisica e Vulcanologia—Osservatorio Vesuviano (INGV-OV). We show the results obtained through three surveys, and during the last two, thermal targets were positioned on land at different flight heights of the UAS. Different heights were also necessary to test the spatial resolution of the DTT with the used thermal camera as well as possible temperature differences between the raw images acquired via UAS with the thermal mapping obtained from the PIX4D Mapper software. In this work, we estimate the uncertainty that may be introduced by the mosaic procedure, and furthermore we find an attenuation of the measured temperatures introduced by the different distances between the thermal anomaly and sensor. These results appear to be of great importance for the subsequent calibration phase of the thermal maps, especially in cases where these methodologies are applied for the purposes of monitoring volcanic/geothermal areas. [ABSTRACT FROM AUTHOR]

The aim of this work was to elaborate a new methodology that can allow for the identification of the topsoil homogeneous area (tSHA) distribution along land parcels, supporting farmers in keeping low-cost, sustainable, and light logistic management of precision agriculture (PA) practices. This paper shows the assessment of tSHA variability over two production units (PUs), considering radiometric response (optical camera), physicochemical (texture, pH, electrical conductivity), and statistical and geostatistical data analysis. By using unmanned aircraft systems (UASs) and laboratory analysis, our results revealed that the integration between UAS-RGB and physicochemical data can improve the estimation accuracy of tSHA distribution. Firstly, the UAS-RGB dataset was used to isolate bare soil from the vegetative radiometric contribution. Secondly, starting from statistical approaches (correlation matrices), the highest correlation with UAS-RGB and physicochemical data was stated. Thirdly, by using a geostatistical approach (ordinary cokriging), the map representing the tSHA variability was finally obtained. [ABSTRACT FROM AUTHOR]

The technical condition of airport pavements not only has a significant impact on the performance of air operations in the ground movement area but also affects the comfort of aircraft movement on the airport pavement, which determines the safety of air operations. The requirements for airfield pavements are specified in defense standards, as well as in civil documents, e.g. (International Civil Aviation Organization), EASA (European Union Aviation Safety Agency), FAA (Federal Aviation Administration), where exploitation parameters in the field of airport pavement construction are defined. The article presents the methodology of the initial assessment of the technical condition of airport pavements in terms of existing damage, as well as anomalies of the existing gradeline, which may indicate inadequate evenness of the pavement. The assessment was made using an aerial photogrammetry kit and the test results were presented. This work focuses on the use of an aerial photogrammetry set, which made a flight over the runway of a military airport providing its full imaging. [ABSTRACT FROM AUTHOR]

The flow field induced by multirotor drones is of high interest for atmospheric research, as it locally influences the atmosphere and therefore may have an impact on the sensors installed for atmospheric measurements. Further, on-board vibrations can cause significant interference with the measurement equipment. To investigate the near flow field, an approach combining measurements of pressure and temperature distribution in-flight and in a laboratory setup together with numerical simulations was applied. Existing high-frequency measurement equipment was piggybacked during the initial flight tests with a newly developed 25 kg quadcopter system in a low-cost early-stage-error approach to obtain initial data and experience. During the flights, high resolution sensors for measuring pressure, temperature, acceleration, and deformation were applied with different setups at different locations below one of the rotor planes, respectively, at one rotor arm, to determine the multicopter's influence on pressure and temperature measurements, to investigate rotor arm deformations, and to obtain data to compare with numerical simulations of this rotor setup. An external Schlieren-type measurement technique was tested to visualise the rotor vortices. The applied measurement techniques proved to be suitable for acquiring the state of the rotor-induced flow, but with some limitations. The comparison of measurements and simulations showed basic agreement and allowed for the identification of necessary adaptations for subsequent studies. The interaction of the rotor wakes with the rotor arms could be identified as the main source of the measured structural vibrations. The need for necessary improvements in the measurement setup, flight operation, and simulation setup is presented in detail. [ABSTRACT FROM AUTHOR]

Construction is a highly hazardous industry typified by several complex features in dynamic work environments that have the possibility of causing harm or ill health to construction workers. The constant monitoring of workers' unsafe behaviors and work conditions is considered not only a proactive but also an active method of removing safety and health hazards and preventing potential accidents on construction sites. The integration of sensor technologies and artificial intelligence for computer vision can be used to create a robust management strategy and enhance the analysis of safety and health data needed to generate insights and take action to protect workers on construction sites. This study presents the development and validation of a framework that implements the use of unmanned aerial systems (UASs) and deep learning (DL) for the collection and analysis of safety activity metrics for improving construction safety performance. The developed framework was validated using a pilot case study. Digital images of construction safety activities were collected on active construction sites using a UAS, and the performance of two different object detection deep-learning algorithms/models (Faster R-CNN and YOLOv3) for safety hardhat detection were compared. The dataset included 7041 preprocessed and augmented images with a 75/25 training and testing split. From the case study results, Faster R-CNN showed a higher precision of 93.1% than YOLOv3 (89.8%). The findings of this study show the impact and potential benefits of using UASs and DL in computer vision applications for managing safety and health on construction sites. [ABSTRACT FROM AUTHOR]

Controlling non-native plant invasions that reduce the quality of preferred wetland habitats is a challenge for many wetland managers. Herbicides may be used to control invasions, but it may be difficult to find effective application methods depending on the terrain. Manned aircraft cover large patches, but aerial use is limited by high costs, weather conditions, and overspray concerns. Ground applications target smaller patches, but their effectiveness may be limited by accessibility, labor costs, and applicator health concerns. Considering these difficulties, unmanned aerial systems (UAS) have emerged as a viable alternative for more effectively treating plant invasions. We tested the use of a specialized UAS to control invasive perennial pepperweed (Lepidium latifolium) in Suisun Marsh in northern California, USA. This "spray drone" flew at an altitude of 2–3 m, a speed of 24 kmph, and applied herbicide with a swath width of 6 m. We applied herbicide with the spray drone to treat small patches before they expanded. To delineate invasive patch boundaries, we first flew a survey drone with a 4K resolution camera to detect emerging plants with color imagery and conduct an initial classification analysis. We subsequently visited areas with suspected invasive patches based on the classification, and observers manually confirmed the presence of the invasive species. We then flew the spray drone on transects to treat the patches and examined the results with post-treatment survey drone imagery and plots along ground transects. In total, we sprayed 14 ha of Lepidium across eight sites and found that 87% of the Lepidium was discernibly affected by our herbicide treatment. Furthermore, we measured the overspray, which was substantially less than that reported for other aerial application methods such as helicopter-spraying, and our estimated operational costs were lower. Our results indicated that applying remote-sensing imagery for the identification of invasive species patches and the use of a spray drone for treatment may be an effective means of controlling invasive plants with high precision at a reasonable cost. In the near future, a unified UAS system that both identifies invasive species and then treats them in a single pass should be a promising goal for early detection and rapid response in wetland management. [ABSTRACT FROM AUTHOR]

The Archaic Acropolis of Europos (region of Central Macedonia, Greece) is an important archaeological site, which was inhabited since the 7th century BC up to the 4th century AD. In the lowland area, south of the acropolis and a short distance away, archaeologists speculate that the extensive ancient cemetery of the acropolis, which is a result of its long-term habitation, was located there. In an effort to locate marks that will support this view, WingtraOne GEN II, one of today's leading Uncrewed Aircraft Systems, was used to collect RGB and multispectral images. After the production of the necessary digital surface models and orthophotomosaics, index maps related to the crops were created. The total of the products allowed the visual identification of 123 marks, which are probably attributed to hitherto unknown covered ancient tombs. In addition, marks of unknown covered ditches of long length were found in the plain area, and on the outskirts of the acropolis marks of its possible wall and the base of one of its towers. [ABSTRACT FROM AUTHOR]

Many issues arise from the recession of sea cliffs, including threats to coastal communities and infrastructure. The best proxy to study cliff instability processes is the cliff face evolution. Unfortunately, due to its verticality, this proxy is difficult to observe and measure. This study proposed and compared three remote sensing methods based on structure-from-motion (SfM) photogrammetry or stereorestitution: boat-based SfM photogrammetry with smartphones, unmanned aerial system (UAS) or unmanned aerial vehicle (UAV) photogrammetry with centimetric positioning and Pléiades tri-stereo imagery. An inter-comparison showed that the mean distance between the point clouds produced by the different methods was about 2 m. The satellite approach had the advantage of covering greater distances. The SfM photogrammetry approach from a boat allowed for a better reconstruction of the cliff foot (especially in the case of overhangs). However, over long distances, significant geometric distortions affected the method. The UAS with centimetric positioning offered a good compromise, but flight autonomy limited the extent of the monitored area. SfM photogrammetry from a boat can be used as an initial estimate for risk management services following a localized emergency. For long-term monitoring of the coastline and its evolution, satellite photogrammetry is recommended. [ABSTRACT FROM AUTHOR]

Spectrally derived bathymetry (SDB) algorithms are rapidly gaining in acceptance and widespread use for nearshore bathymetric mapping. In the past, refraction correction could generally be ignored in SDB, due to the relatively small fields of view (FOVs) of satellite sensors, and the fact that such corrections were typically small in relation to the uncertainties in the output bathymetry. However, the validity of ignoring refraction correction in SDB is now called into question, due to the ever-improving accuracies of SDB, the desire to use the data in nautical charting workflows, and the application of SDB algorithms to airborne cameras with wide FOVs. This study tests the hypothesis that refraction correction leads to a statistically significant improvement in the accuracy of SDB using uncrewed aircraft system (UAS) imagery. A straightforward procedure for SDB refraction correction, implemented as a modification to the well-known Stumpf algorithm, is presented and applied to imagery collected from a commercially available UAS in two study sites in the Florida Keys, U.S.A. The results show that the refraction correction produces a statistically significant improvement in accuracy, with a reduction in bias of 46–75%, a reduction in RMSE of 3–11 cm, and error distributions closer to Gaussian. [ABSTRACT FROM AUTHOR]

Knowledge about the spatial distribution of seagrasses is essential for coastal conservation efforts. Imagery obtained from unoccupied aerial systems (UAS) has the potential to provide such knowledge. Classifier choice and hyperparameter settings are, however, often based on time-consuming trial-and-error procedures. The presented study has therefore investigated the performance of five machine learning algorithms, i.e., Bayes, Decision Trees (DT), Random Trees (RT), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM) when used for the object-based classification of submerged seagrasses from UAS-derived imagery. The influence of hyperparameter tuning and training sample size on the classification accuracy was tested on images obtained from different altitudes during different environmental conditions. The Bayes classifier performed well (94% OA) on images obtained during favorable environmental conditions. The DT and RT classifier performed better on low-altitude images (93% and 94% OA, respectively). The kNN classifier was outperformed on all occasions, while still producing OA between 89% and 95% in five out of eight scenarios. The SVM classifier was most sensitive to hyperparameter tuning with OAs ranging between 18% and 97%; however, it achieved the highest OAs most often. The findings of this study will help to choose the appropriate classifier and optimize related hyperparameter settings. [ABSTRACT FROM AUTHOR]

Machine learning is frequently combined with imagery acquired from uncrewed aircraft systems (UASs) to detect invasive plants. Having prior knowledge of which machine learning algorithm will produce the most accurate results is difficult. This study examines the efficacy of a voting-based ensemble classifier to identify invasive Phragmites australis from three-band (red, green, blue; RGB) and five-band (red, green, blue, red edge, near-infrared; multispectral; MS) UAS imagery acquired over multiple Minnesota wetlands. A Random Forest, histogram-based gradient-boosting classification tree, and two artificial neural networks were used within the voting-based ensemble classifier. Classifications from the RGB and multispectral imagery were compared across validation sites both with and without post-processing from an object-based image analysis (OBIA) workflow (post-machine learning OBIA rule set; post-ML OBIA rule set). Results from this study suggest that a voting-based ensemble classifier can accurately identify invasive Phragmites australis from RGB and multispectral imagery. Accuracies greater than 80% were attained by the voting-based ensemble classifier for both the RGB and multispectral imagery. The highest accuracy, 91%, was achieved when using the multispectral imagery, a canopy height model, and a post-ML OBIA rule set. The study emphasizes the need for further research regarding the accurate identification of Phragmites australis at low stem densities. [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. [ABSTRACT FROM AUTHOR]

Mangrove forests provide valuable ecosystem services to coastal communities across tropical and subtropical regions. Current anthropogenic stressors threaten these ecosystems and urge researchers to create improved monitoring methods for better environmental management. Recent efforts that have focused on automatically quantifying the above-ground biomass using image analysis have found some success on high resolution imagery of mangrove forests that have sparse vegetation. In this study, we focus on stands of mangrove forests with dense vegetation consisting of the endemic Pelliciera rhizophorae and the more widespread Rhizophora mangle mangrove species located in the remote Utría National Park in the Colombian Pacific coast. Our developed workflow used consumer-grade Unoccupied Aerial System (UAS) imagery of the mangrove forests, from which large orthophoto mosaics and digital surface models are built. We apply convolutional neural networks (CNNs) for instance segmentation to accurately delineate (33% instance average precision) individual tree canopies for the Pelliciera rhizophorae species. We also apply CNNs for semantic segmentation to accurately identify (97% precision and 87% recall) the area coverage of the Rhizophora mangle mangrove tree species as well as the area coverage of surrounding mud and water land-cover classes. We provide a novel algorithm for merging predicted instance segmentation tiles of trees to recover tree shapes and sizes in overlapping border regions of tiles. Using the automatically segmented ground areas we interpolate their height from the digital surface model to generate a digital elevation model, significantly reducing the effort for ground pixel selection. Finally, we calculate a canopy height model from the digital surface and elevation models and combine it with the inventory of Pelliciera rhizophorae trees to derive the height of each individual mangrove tree. The resulting inventory of a mangrove forest, with individual P. rhizophorae tree height information, as well as crown shape and size descriptions, enables the use of allometric equations to calculate important monitoring metrics, such as above-ground biomass and carbon stocks. [ABSTRACT FROM AUTHOR]

Along with the development and improvement of measuring technologies and techniques in recent times, new methods have appeared to model and monitor the behavior of land and constructions over time. The main purpose of this research was to develop a new methodology to model and monitor large buildings in a non-invasive way. The methods proposed in this research are non-destructive and can be used to monitor the behavior of buildings over time. A method of comparing point clouds obtained using terrestrial laser scanning combined with aerial photogrammetric methods was used in this study. The advantages and disadvantages of using non-destructive measurement techniques over the classic methods were also analyzed. With a building located in the University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca campus as a case study and with the help of the proposed methods, the deformations over time of the facades of that building were determined. As one of the main conclusions of this case study, it can be stated that the proposed methods are adequate to model and monitor the behavior of constructions over time, ensuring a satisfactory degree of precision and accuracy. The methodology can be successfully applied to other similar projects. [ABSTRACT FROM AUTHOR]

Rapid slope instabilities (i.e., rockfalls) involving highway networks in mountainous areas pose a threat to facilities, settlements and life, thus representing a challenge for asset management plans. To identify different morphological expressions of degradation processes that lead to rock mass destabilization, we combined satellite and uncrewed aircraft system (UAS)-based products over two study sites along the State Highway 133 sector near Paonia Reservoir, Colorado (USA). Along with a PS-InSAR analysis covering the 2017–2021 interval, a high-resolution dataset composed of optical, thermal and multi-spectral imagery was systematically acquired during two UAS surveys in September 2021 and June 2022. After a pre-processing step including georeferencing and orthorectification, the final products were processed through object-based multispectral classification and change detection analysis for highlighting moisture or lithological variations and for identifying areas more susceptible to deterioration and detachments at the small and micro-scale. The PS-InSAR analysis, on the other hand, provided multi-temporal information at the catchment scale and assisted in understanding the large-scale morpho-evolution of the displacements. This synergic combination offered a multiscale perspective of the superimposed imprints of denudation and mass-wasting processes occurring on the study site, leading to the detection of evidence and/or early precursors of rock collapses, and effectively supporting asset management maintenance practices. [ABSTRACT FROM AUTHOR]

Understanding the population health status of long‐lived and slow‐reproducing species is critical for their management. However, it can take decades with traditional monitoring techniques to detect population‐level changes in demographic parameters. Early detection of the effects of environmental and anthropogenic stressors on vital rates would aid in forecasting changes in population dynamics and therefore inform management efforts. Changes in vital rates strongly correlate with deviations in population growth, highlighting the need for novel approaches that can provide early warning signs of population decline (e.g., changes in age structure). We tested a novel and frequentist approach, using Unoccupied Aerial System (UAS) photogrammetry, to assess the population age structure of small delphinids. First, we measured the precision and accuracy of UAS photogrammetry in estimating total body length (TL) of trained bottlenose dolphins (Tursiops truncatus). Using a log‐transformed linear model, we estimated TL using the blowhole to dorsal fin distance (BHDF) for surfacing animals. To test the performance of UAS photogrammetry to age‐classify individuals, we then used length measurements from a 35‐year dataset from a free‐ranging bottlenose dolphin community to simulate UAS estimates of BHDF and TL. We tested five age classifiers and determined where young individuals (<10 years) were assigned when misclassified. Finally, we tested whether UAS‐simulated BHDF only or the associated TL estimates provided better classifications. TL of surfacing dolphins was overestimated by 3.3% ±3.1% based on UAS‐estimated BHDF. Our age classifiers performed best in predicting age‐class when using broader and fewer (two and three) age‐class bins with ~80% and ~72% assignment performance, respectively. Overall, 72.5%–93% of the individuals were correctly classified within 2 years of their actual age‐class bin. Similar classification performances were obtained using both proxies. UAS photogrammetry is a non‐invasive, inexpensive, and effective method to estimate TL and age‐class of free‐swimming dolphins. UAS photogrammetry can facilitate the detection of early signs of population changes, which can provide important insights for timely management decisions. [ABSTRACT FROM AUTHOR]

Mapping volcanic flow deposits can be achieved by considering backscattering characteristics as a metric of surface roughness. In this study, we developed an approach to extract a measure of surface roughness from dual-band airborne Synthetic Aperture Radar (ASAR) backscattering data to characterize and map various volcanic flow deposits—namely, debris avalanches, lahars, lava flows, and pyroclastic density currents. We employed ASAR and Indian Space Research Organization (ISRO) airborne SAR datasets, from a joint project (ASAR-ISRO), acquired in December 2019 at 2 m spatial resolution, to assess the role and importance of incorporating dual-band data, i.e., L-band and S-band, into surface roughness models. Additionally, we derived and analyzed surface roughness from a digital surface model (DSM) generated from unoccupied aircraft systems (UAS) acquisitions using Structure from Motion (SfM) photogrammetry techniques. These UAS-derived surface roughness outputs served as meter-scale calibration products to validate the radar roughness data over targeted areas. Herein, we applied our method to a region in the United States over the Mount St. Helens volcano in the Cascade Range of Washington state. Our results showed that dual-band systems can be utilized to characterize different types of volcanic deposits and range of terrain roughness. Importantly, we found that a combination of radar wavelengths (i.e., 9 and 24 cm), in tandem with high-spatial-resolution backscatter measurements, yields improved surface roughness maps, compared to single-band, satellite-based approaches at coarser resolution. The L-band (24 cm) can effectively differentiate small, medium, and large-scale structures, namely, blocks/boulders from fine-grained lahar deposits and hummocks from debris avalanche deposits. Additionally, variation in the roughness estimates of lahar and debris avalanche deposits can be identified and quantified individually. In contrast, the S-band (9 cm) can distinguish different soil moisture conditions across variable terrain; for example, identify wet active channels. In principle, this dual-band approach can also be employed with time series of various other SAR data of higher coherence (such as satellite SAR), using different wavelengths and polarizations, encompassing a wider range of surface roughness, and ultimately enabling additional applications at other volcanoes worldwide and even beyond volcanology. [ABSTRACT FROM AUTHOR]

The spatial structures of turbulent flow in the atmospheric boundary layer (ABL) are complex and diverse. Multi-point spatial correlation measurements can help improve our understanding of these structures and their statistics. In this context, we investigate Taylor's hypothesis and the statistics of spatial structures on the microscale. For the first time, simultaneous horizontally distributed wind measurements with a fleet of 20 quadrotor UAS (unmanned aerial systems) are realized. The measurements were taken at different heights and under different atmospheric conditions at the boundary layer field site in Falkenberg of the German Meteorological Service (DWD). A horizontal flight pattern has been specifically developed, consisting of measurements distributed along and lateral to the mean flow direction with separation distances of 5 ... 205 m. The validity of Taylor's hypothesis is studied by examining the cross-correlations of longitudinally distributed UAS and comparing them with the autocorrelations of single UAS. To assess the similarity of flow structures on different scales, the lateral and longitudinal coherence of the streamwise velocity component is examined. Two modeling approaches for the decay of coherence are compared. The experimental results are in good agreement with the model approaches for neutral atmospheric conditions, whereas in stable and convective ABL, the exponential approaches are not unconditionally valid. The validation results and the agreement with the literature on coherence in the ABL underline the potential of the UAS fleet for the purpose of spatial turbulence measurements. [ABSTRACT FROM AUTHOR]

Simple Summary: When an insect pest outbreak occurs, it is crucial to quickly assess the damage to manage the outbreak effectively. This study investigated a serious outbreak of the beet armyworm, Spodoptera exigua (Hübner) (Lepidoptera: Noctuidae), that occurred in soybean fields in South Korea. We conducted an aerial survey of 31 soybean blocks within the outbreak region using drones. The aerial images were analyzed to quantify soybean defoliation and to investigate the spatial patterns of the soybean damage by S. exigua. The results of this study showed that the aerial survey was an effective and rapid method for estimating the defoliation of soybeans caused by S. exigua. Moreover, it was found that the aerial survey followed by image analysis was more economical and required less time than a conventional ground survey, especially when the number of target soybean blocks subject to the survey was more than 15 blocks. Overall, the study demonstrated the effectiveness of using an autonomous drone and image analysis to conduct a low-cost aerial survey of soybean damage caused by S. exigua during its outbreak. Rapid assessment of crop damage is essential for successful management of insect pest outbreaks. In this study, we investigated the use of an unmanned aircraft system (UAS) and image analyses to assess an outbreak of the beet armyworm, Spodoptera exigua (Hübner) (Lepidoptera: Noctuidae), that occurred in soybean fields in South Korea. A rotary-wing UAS was deployed to obtain a series of aerial images over 31 soybean blocks. The images were stitched together to generate composite imagery, followed by image analyses to quantify soybean defoliation. An economic analysis was conducted to compare the cost of the aerial survey with that of a conventional ground survey. The results showed that the aerial survey precisely estimated the defoliation compared to the ground survey, with an estimated defoliation of 78.3% and a range of 22.4–99.8% in the 31 blocks. Moreover, the aerial survey followed by image analyses was found to be more economical than the conventional ground survey when the number of target soybean blocks subject to the survey was more than 15 blocks. Our study clearly demonstrated the effectiveness of using an autonomous UAS and image analysis to conduct a low-cost aerial survey of soybean damage caused by S. exigua outbreaks, which can inform decision-making for S. exigua management. [ABSTRACT FROM AUTHOR]

Image‐based acquisition and digital documentation of geometry and condition of railway bridges The continuous safety and performance of infrastructure structures is of particular importance for a modern society. The rail network becomes increasingly relevant in the context of the energy transition. Demands for resource efficiency in the construction sector will require significantly longer service lives for structures in the future. New digital maintenance strategies can make this goal achievable while reducing maintenance expenditures at the same time. This report focuses on new image‐based methods for condition assessment and digital modelling of the geometry of structures as well as damage characteristics. In particular, the permanent storage of highly accurate damage position and extent information allows the tracking of damage propagation as a basis for novel predictive maintenance strategies. Using the example of railway bridges, it is shown how high‐quality digital condition documentation is possible with the concept of digital twins. Photogrammetry is used to generate highly accurate geo‐referenced 3D building models from image data acquired by drones, for example, and AI processes are used to perform automated damage detection. The linking in a digital twin enables analyses and high‐quality documentation over the life cycle and should start with an initialization measurement on new structures. [ABSTRACT FROM AUTHOR]

Bridge decks need to be routinely inspected to ensure their serviceability, capacity, and safety under current traffic conditions. Traditionally, bridge deck inspection is performed on the ground by having inspectors either visually inspect surface conditions or interpret the acoustic feedback from hammer sounding or chain dragging to determine subsurface conditions. These traditional methods have many limitations, including but not limited to, expensive, labour-intensive, time-consuming, subjective, can exhibit a high degree of variability, requiring specialized staff on a regular basis, and unsafe. Recent advancements in remote sensing, especially small-uncrewed aircraft systems (S-UAS) based airborne imaging techniques and advanced image analysis techniques, have shown promise in improving current bridge deck inspection practices by providing an above-ground inspection method. This research explored the utility of S-UAS-based airborne imaging techniques and image processing techniques to develop a complete aerial data acquisition and analysis system to accurately detect and assess bridge deck wearing surface distresses in a timely and cost-effective manner. As part of the research project, a robust tool was also developed with the aim of being able to detect, extract, and map bridge deck wearing surface distresses with an adequate degree of accuracy while maximizing the ability to assist bridge inspectors with varying expertise. Research results revealed that the developed tool is able to effectively detect and map bridge deck wearing surface distresses at a high accuracy. [ABSTRACT FROM AUTHOR]

Pushbroom hyperspectral imaging (HSI) systems intrinsically measure our surroundings by leveraging 1D spatial imaging, where each pixel contains a unique spectrum of the observed materials. Spatial misregistration is an important property of HSI systems because it defines the spectral integrity of spatial pixels and requires characterization. The IEEE P4001 Standards Association committee has defined laboratory-based methods to test the ultimate limit of HSI systems but negates any impacts from mounting and flying the instruments on airborne platforms such as unmanned aerial vehicles (UAV's) or drones. Our study was designed to demonstrate a novel vicarious technique using convex mirrors to bridge the gap between laboratory and field-based HSI performance testing with a focus on extracting hyperspectral spatial misregistration. A fast and simple extraction technique is proposed for estimating the sampled Point Spread Function's width, along with keystone, as a function of wavelength for understanding the key contributors to hyperspectral spatial misregistration. With the ease of deploying convex mirrors, off-axis spatial misregistration is assessed and compared with on-axis behavior, where the best performance is often observed. In addition, convex mirrors provide an easy methodology to exploit ortho-rectification errors related to fixed pushbroom HSI systems, which we will show. The techniques discussed in this study are not limited to drone-based systems but can be easily applied to other airborne or satellite-based systems. [ABSTRACT FROM AUTHOR]

Three-dimensional modeling and non-invasive diagnosis are fundamental prerequisites for planning reliable assessments of the seismic vulnerability, renovation and conservation of heritage buildings. In the case of multi-layered, huge and complex ancient constructions, various problems can be encountered in the early design phases of interventions, among which there is often a lack of an adequate building documentation. Such issues include drawings that are without a metric scale, not detailed, not updated or not reflecting the real situation. In addition, the fragility of these constructions requires an accurate census of every sign of deterioration in order to prepare an ad hoc intervention for the site. As an example, in this paper, the results of a survey regarding the Patriarchal Basilica of Santa Maria Assunta (Aquileia, Italy) are reported. The basilica has a rich history of about two thousand years. in which each era has marked the actual architectural layout with its own culture and art. The result is an intricate association of complementary and/or interdependent elements that make the building very complex. Given the need to obtain accurate documentation, a realistic representation and a simulation of the criticalities of the structure, which previously did not exist or were not sufficiently accurate, a multi-methodological and multi-scale diagnosis was performed. In detail, the ground-penetrating radar (GPR) technique was applied to verify the presence of structures still buried under some internal surfaces, and a topographic survey, terrestrial laser scanning (TLS), and structure for motion (SfM) aerial and terrestrial photogrammetry were integrated for the detailed survey of the entire internal and external macro-structure. The resulting outcome provided the comprehensive information needed for preparing projects for the preservation, management and restoration of the basilica and the buildings connected to it. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles, known as UAVs, are controlled by the autopilot system remotely and autonomously. It is a component of Unmanned Aerial System (UAS) which contains a UAV, a Ground Control System (GCS), and Air link. They are used vastly in all applications; however, over a period of time, a number of security flaws have surfaced in UASs. While considerable research has been undertaken to propose secure solutions for UAS, the prior work on the subject fails to consider a holistic treatment of the security issues. Thereby, keeping in view the lack of a structured approach for UAS security, we have proposed an ISO 18028 standard-based framework for defining security architecture of UAS. ISO standard provides generic guidelines for the security architecture of a network; yet, the same has been extended in this work to propose a holistic security architecture for UASs, which effectively mitigates all the associated risks. Furthermore, the architecture has been used to evaluate two case studies: Commercial UAS based on Cube and Commercial UAS based on DJI A3. The appraisal undertaken in the case studies indicated a number of security limitations in the considered commercial solutions, thus leading to corresponding recommendations for security enhancements. [ABSTRACT FROM AUTHOR]

Traditional methods on crack inspection for large infrastructures require a number of structural health inspection devices and instruments. They usually use the signal changes caused by physical deformations from cracks to detect the cracks, which is time-consuming and cost-ineffective. In this work, we propose a novel real-time crack inspection system based on unmanned aerial vehicles for real-world applications. The proposed system successfully detects and classifies various types of cracks. It can accurately find the crack positions in the world coordinate system. Our detector is based on an improved YOLOv4 with an attention module, which produces 90.02% mean average precision (mAP) and outperforms the YOLOv4-original by 5.23% in terms of mAP. The proposed system is low-cost and lightweight. Moreover, it is not restricted by navigation trajectories. The experimental results demonstrate the robustness and effectiveness of our system in real-world crack inspection tasks. [ABSTRACT FROM AUTHOR]