Search Results (91)

This paper aims to analyze the performance of a tilt-rotor fixed-wing RPAS (Remotely Piloted Aircraft System) using a segmented approach, focusing on a nominal mission for SAR (Search and Rescue) applications. The study employs optimization techniques tailored to each segment to meet power consumption requirements, and the results highlight the accuracy of the physical characterization, which incorporates nonlinear propulsive and aerodynamic models derived from wind tunnel test campaigns. Critical segments for this nominal mission, such as the vertical take off or the transition from vertical to horizontal flight regimes, are addressed to fully understand the performance response of the aircraft. The proposed framework integrates experimental models into trajectory optimization procedures for each segment, enabling a realistic and modular analysis of energy use and aerodynamic performance. This approach provides valuable insights for both flight control design and future sizing iterations of convertible UAVs (Uncrewed Aerial Vehicles). Full article

Environmental assumptions, which are expectations about a system’s operating context, play a critical yet often underexplored role in the emergence of requirements technical debt (RTD). When these assumptions are incorrect, incomplete, or evolve over time, they can compromise the validity of system requirements and lead to costly rework in later stages of development. This paper investigates how environmental assumptions influence the identification of RTD through the analysis of a real-world case study in the domain of small uncrewed aerial systems (sUASs). A structured qualitative analysis of safety-related requirements and their associated assumptions was conducted to examine how deviations in these assumptions can introduce various forms of RTD. This work addresses a gap in the literature by explicitly focusing on the role of environmental assumptions in RTD identification. A classification framework is proposed, highlighting five distinct types of assumption-driven RTD. This framework serves as a foundation for supporting early detection of debt and improving the sustainability and resilience of software-intensive systems. Full article

Recent advancements in precision agriculture have been significantly bolstered by the Uncrewed Aerial Vehicles (UAVs) equipped with multispectral sensors. These systems are pivotal in transforming sensor-recorded Digital Number (DN) values into universal reflectance, crucial for ensuring data consistency irrespective of collection time, region, and illumination. This study, conducted across three regions in China using Sequoia and Phantom 4 Multispectral cameras, focused on examining the effects of radiometric correction on data consistency and accuracy, and developing a conversion model for data from these two sensors. Our findings revealed that radiometric correction substantially enhances data consistency in vegetated areas for both sensors, though its impact on non-vegetated areas is limited. Recalibrating reflectance for calibration plates significantly improved the consistency of band values and the accuracy of vegetation index calculations for both cameras. Decision tree and random forest models emerged as more effective for data conversion between the sensors, achieving R² values up to 0.91. Additionally, the P4M generally outperformed the Sequoia in accuracy, particularly with standard reflectance calibration. These insights emphasize the critical role of radiometric correction in UAV remote sensing for precision agriculture, underscoring the complexities of sensor data consistency and the potential for generalization of models across multi-sensor platforms. Full article

Congested airspace conflict resolution during terminal operations is a common air traffic management issue that may produce cascading delays. Vehicles needing emergency clearance to land, at either traditional airports or vertiports, would require others on approach to move out of the way and, in some instances, cause a wave of delay to propagate through all vehicles on approach. Specifically, uncrewed aerial systems utilizing near-maximum arrival rates would be greatly impacted when requested to move off their approach path and may interfere with others. Vertiports further complicate crowded approaches because vehicles can arrive from many different angles at the same time to maximize landing area usage. Traditional air traffic management techniques were studied for vertiport applications specific to high-capacity operations. This work investigated methods of uniformly re-directing vehicles on approach to a vertiport that would be impacted by an emergency vehicle to minimize or avoid cascading delays. A route of time-optimal Bézier curves as well as Dubins paths optimized for interception heading was generated and flown on as an alternate maneuver when an unaccounted-for emergency vehicle initiated a bypass of an air traffic fleet. A comparison to flight on a holding pattern showed that the Bézier and Dubins route improved delay times and mitigated a cascading delay effect. Full article

Transportation agencies often rely on manual surveys to monitor seat belt compliance; however, these methods are limited by surveyor fatigue, reduced visibility due to tinted windows or low lighting, and restricted geographic coverage, making manual surveys prone to errors and unrepresentative of the broader driving population. This paper presents an automated seat belt detection system leveraging the YOLO11 neural network on video footage captured by a tethered uncrewed aerial vehicle (UAV). The objectives are to (1) develop a robust system for detecting seat belt use at stop-controlled intersections, (2) evaluate factors affecting detection accuracy, and (3) demonstrate the potential of UAV-based compliance monitoring. The model was tested in real-world scenarios at a single-lane and a complex multi-lane stop-controlled intersection in Iowa. Three studies examined key factors influencing detection accuracy: (i) seat belt–shirt color contrast, (ii) sunlight direction, and (iii) vehicle type. System performance was compared against manual video review and large language model (LLM)-assisted analysis, with assessments focused on accuracy, resource requirements, and computational efficiency. The model achieved a mean average precision (mAP) of 0.902, maintained high accuracy across the three studies, and outperformed manual methods in reliability and efficiency while offering a scalable, cost-effective alternative to LLM-based solutions. Full article

In response to the demand for the evaluation of the actual navigation performance (ANP) of rotorcraft logistics uncrewed aerial vehicle (UAV) navigation systems in urban scenarios, this paper proposes a method for evaluating the ANP of rotorcraft logistics UAVs based on the Improved Sparrow Search Algorithm (ISSA). Taking ANP as the optimization objective, an optimization model for the ANP of rotorcraft logistics UAVs is constructed. Based on the probability of the UAV’s actual position falling within the error circle, an initial population strategy based on probabilistic decision-making is designed, and an adaptive dynamic step size strategy and dynamic compression search strategy are proposed to improve the traditional Sparrow Search Algorithm (SSA), enhancing the algorithm’s ability of optimization and to escape local extremum. The contribution of this paper mainly includes constructing the ANP optimization model and designing the ISSA method. Experimental results show that the proposed method can effectively estimate ANP, achieve onboard performance monitoring and warning, and ensure the required navigation performance (RNP) and flight safety of UAVs. Full article

Growing awareness of the environmental cost of mining operations has led to increased research on monitoring and restoring legacy mine sites. Hyperspectral imaging (HSI) has emerged as a valuable tool in the mining life cycle, including post-mining environment. By detecting variations in crystal structure and physicochemical attributes on the surface of materials, HSI provides insights into site environmental and ecological conditions. Here, we explore the capabilities of drone-based HSI for mapping surface patterns related to contamination dispersal in a legacy uranium-rare earth element mine site. Hyperspectral data across the visible to near-infrared (VNIR) and short-wave infrared (SWIR) wavelength ranges (400–2500 nm) were collected over selected areas of the former Mary Kathleen mine site in Queensland, Australia. Analyses were performed using data-driven (Spectral Angle Mapper—SAM) and knowledge-based (Band Ratios—BRs) spectral processing techniques. SAM identifies contamination patterns and differentiates mineral compositions within visually similar areas. However, its accuracy is limited when mapping specific minerals, as most endmembers represent mineral groups or mixtures. BR highlights reactive surfaces and clay mixtures, reinforcing key patterns identified by SAM. The results indicate that drone-based HSI can capture and distinguish complex surface trends, demonstrating the technology’s potential to enhance the assessment and monitoring of environmental conditions at a mine site. Full article

The integrity of earth infrastructure, encompassing slopes, dams, pavements, and embankments, is fundamental to the functioning of transportation networks, energy systems, and urban development. However, these infrastructures are increasingly threatened by a range of natural and anthropogenic factors. Conventional monitoring techniques, including inclinometers and handheld instruments, often exhibit limitations in spatial coverage and operational efficiency, rendering them insufficient for comprehensive evaluation. In response, Uncrewed Aerial Vehicles (UAVs) and Electrical Resistivity Imaging (ERI) have emerged as pivotal technological advancements, offering high-resolution surface characterization and critical subsurface diagnostics, respectively. UAVs facilitate the detection of deformations and geomorphological dynamics, while ERI is instrumental in identifying zones of water saturation and geological structures, detecting groundwater, characterizing vadose zone hydrology, and assessing subsurface soil and rock properties and potential slip surfaces, among others. The integration of these technologies enables multidimensional monitoring capabilities, enhancing the ability to predict and mitigate infrastructure instabilities. This article focuses on recent advancements in the integration of UAVs and ERI through data fusion frameworks, which synthesize surface and subsurface data to support proactive monitoring and predictive analytics. Drawing on a synthesis of contemporary research, this study underscores the potential of these integrative approaches to advance early-warning systems and risk mitigation strategies for critical infrastructure. Furthermore, it identifies existing research gaps and proposes future directions for the development of robust, integrated monitoring methodologies. Full article

Rising sea levels, warming ocean temperatures, and other climate change impacts threaten the German North Sea coast, making monitoring of this system even more critical. This study reviews the potential of remote sensing for the German North Sea coast, analyzing 97 publications from 2000 to 2024. Publications fell into four main research topics: coastal morphology (33), water quality (34), ecology (22), and sediment (8). More than two-thirds of these papers (69%) used satellite platforms, whereas about one third (29%) used aircrafts and very few (4%) used uncrewed aerial vehicles (UAVs). Multispectral data were the most used data type in these studies (59%), followed by synthetic aperture radar data (SAR) (23%). Studies on intertidal topography were the most numerous overall, making up one-fifth (21%) of articles. Research gaps identified in this review include coastal morphology and ecology studies over large areas, especially at scales that align with administrative or management areas such as the German Wadden Sea National Parks. Additionally, few studies utilized free, publicly available high spatial resolution imagery, such as that from Sentinel-2 or newly available very high spatial resolution satellite imagery. This review finds that remote sensing plays a notable role in monitoring the German North Sea coast at local scales, but fewer studies investigated large areas at sub-annual temporal resolution, especially for coastal morphology and ecology topics. Earth Observation, however, has the potential to fill this gap and provide critical information about impacts of coastal hazards on this region. Full article

Tailings generated by mining account for the largest world-wide waste from industrial activities. As an element, copper is relatively uncommon, with low concentrations in sediments and waters, yet is very elevated around mining operations. On the Keweenaw Peninsula of Michigan, USA, jutting out into Lake Superior, 140 mines extracted native copper from the Portage Lake Volcanic Series, part of an intercontinental rift system. Between 1901 and 1932, two mills at Gay (Mohawk, Wolverine) sluiced 22.7 million metric tonnes (MMT) of copper-rich tailings (stamp sands) into Grand (Big) Traverse Bay. About 10 MMT formed a beach that has migrated 7 km from the original Gay pile to the Traverse River Seawall. Another 11 MMT are moving underwater along the coastal shelf, threatening Buffalo Reef, an important lake trout and whitefish breeding ground. Here we use remote sensing techniques to document geospatial environmental impacts and initial phases of remediation. Aerial photos, multiple ALS (crewed aeroplane) LiDAR/MSS surveys, and recent UAS (uncrewed aircraft system) overflights aid comprehensive mapping efforts. Because natural beach quartz and basalt stamp sands are silicates of similar size and density, percentage stamp sand determinations utilise microscopic procedures. Studies show that stamp sand beaches contrast greatly with natural sand beaches in physical, chemical, and biological characteristics. Dispersed stamp sand particles retain copper, and release toxic levels of dissolved concentrations. Moreover, copper leaching is elevated by exposure to high DOC and low pH waters, characteristic of riparian environments. Lab and field toxicity experiments, plus benthic sampling, all confirm serious impacts of tailings on aquatic organisms, supporting stamp sand removal. Not only should mining companies end coastal discharges, we advocate that they should adopt the UNEP “Global Tailings Management Standard for the Mining Industry”. Full article

Thermal refuges in streams are essential for the survival of coldwater fish species such as Redband trout (Oncorhynchus mykiss) in landscapes with stressful or lethal stream temperatures. We utilized an uncrewed aerial system (UAS) mounted with thermal and natural color sensors to conduct hourly flights over a 24 h period in the desert stream Little Jacks Creek during late summer when temperatures were near seasonal maximums and streamflow was near seasonal minimums. We used fine-resolution imagery to map stream temperatures and characterize how our thermal sensor exhibits variability across a diel period in an environment where thermal sensor viability had not yet been assessed. Thermal imagery from 3 out of 24 flights showed no significant differences when compared to true water temperatures from in-stream temperature loggers, which appeared to be highly dependent on atmospheric conditions. The thermal imagery (range of $9.17$ to $21.04 °$C) consistently underestimated HOBO logger stream temperatures (range of $13.6$ to $17.1 °$C) during cooler, nighttime flights and overestimated temperatures during hotter, afternoon hours, resulting in a global RMSE of 2.12 $°$C. Between-flight RMSE values ranged from 0.53$°$C to 4.00$°$C, within the error range of the thermal sensor. The thermal data support existing findings of optimal hours for flying UAS thermal surveys and showed specific patterns in TIR sensor accuracy that were dependent on the time of flight. This study yields valuable lessons for future stream temperature data collection in environments with highly variable temperatures, aiding in the calibration of thermal sensors on UAS missions. Furthermore, our results provide insights into environmental stressors such as increased stream temperatures, which is vital for conservation efforts for organisms that rely on coldwater refuges within desert streams. Full article

Plant growth analysis provides insight regarding the variation behind yield differences in tested genotypes for plant breeders, but adopting this application solely for traditional plant phenotyping remains challenging. Here, we propose a procedure of using uncrewed aerial systems (UAS) to obtain successive phenotype data for growth analysis. The objectives of this study were to obtain high-temporal UAS-based phenotype data for growth analysis and investigate the correlation between the UAS-based phenotype and biomass yield. Seven different energy cane genotypes were grown in a random complete block design with four replications. Twenty-six UAS flight missions were flown throughout the growing season, and canopy cover (CC) and canopy height (CH) measurements were extracted. A five-parameter logistic (5PL) function was fitted through these temporal measurements of CC and CH. The first- and second-order derivatives of this function were calculated to obtain several growth parameters, which were then used to assess the growth of different genotypes with respect to weed competitiveness and biomass yield traits. The results show that CC and CH growth rates significantly differed among genotypes. TH16-16 was outstanding for its ground cover growth; therefore, it was identified as a weed-competitive genotype. Furthermore, TH16-22 had a higher CH maximum growth rate per day, yielding a higher biomass compared to other genotypes. The CH-based multi-temporal data as well as the growth parameters had a better relationship with biomass yield. This study highlights the application of UAS-based high-throughput phenotyping (HTP), along with growth analysis, for assisting plant breeders in decision-making. Full article

Precise aboveground biomass (AGB) estimation of forests is crucial for sustainable carbon management and ecological monitoring. Traditional methods, such as destructive sampling, field measurements of Diameter at Breast Height with height (DBH and H), and optical remote sensing imagery, often fall short in capturing detailed spatial heterogeneity in AGB estimation and are labor-intensive. Recent advancements in remote sensing technologies, predominantly Light Detection and Ranging (LiDAR), offer potential improvements in accurate AGB estimation and ecological monitoring. Nonetheless, there is limited research on the combined use of UAS (Uncrewed Aerial System) and Backpack-LiDAR technologies for detailed forest biomass. Thus, our study aimed to estimate AGB at the plot level for Picea crassifolia forests in eastern Qinghai, China, by integrating UAS-LiDAR and Backpack-LiDAR data. The Comparative Shortest Path (CSP) algorithm was employed to segment the point clouds from the Backpack-LiDAR, detect seed points and calculate the DBH of individual trees. After that, using these initial seed point files, we segmented the individual trees from the UAS-LiDAR data by employing the Point Cloud Segmentation (PCS) method and measured individual tree heights, which enabled the calculation of the observed/measured AGB across three specific areas. Furthermore, advanced regression models, such as Random Forest (RF), Multiple Linear Regression (MLR), and Support Vector Regression (SVR), are used to estimate AGB using integrated data from both sources (UAS and Backpack-LiDAR). Our results show that: (1) Backpack-LiDAR extracted DBH compared to field extracted DBH shows about (R² = 0.88, RMSE = 0.04 m) whereas UAS-LiDAR extracted height achieved the accuracy (R² = 0.91, RMSE = 1.68 m), which verifies the reliability of the abstracted DBH and height obtained from the LiDAR data. (2) Individual Tree Segmentation (ITS) using a seed file of X and Y coordinates from Backpack to UAS-LiDAR, attaining a total accuracy F-score of 0.96. (3) Using the allometric equation, we obtained AGB ranges from 9.95–409 (Mg/ha). (4) The RF model demonstrated superior accuracy with a coefficient of determination (R²) of 89%, a relative Root Mean Square Error (rRMSE) of 29.34%, and a Root Mean Square Error (RMSE) of 33.92 Mg/ha compared to the MLR and SVR models in AGB prediction. (5) The combination of Backpack-LiDAR and UAS-LiDAR enhanced the ITS accuracy for the AGB estimation of forests. This work highlights the potential of integrating LiDAR technologies to advance ecological monitoring, which can be very important for climate change mitigation and sustainable environmental management in forest monitoring practices. Full article

Woody debris (WD) is an important element in forest ecosystems. It provides critical habitats for plants, animals, and insects. It is also a source of fuel contributing to fire propagation and sometimes leads to catastrophic wildfires. WD inventory is usually conducted through field surveys using transects and sample plots. Light Detection and Ranging (LiDAR) point clouds are emerging as a valuable source for the development of comprehensive WD detection strategies. Results from previous LiDAR-based WD detection approaches are promising. However, there is no general strategy for handling point clouds acquired by different platforms with varying characteristics such as the pulse repetition rate and sensor-to-object distance in natural forests. This research proposes a general and adaptive morphological WD detection strategy that requires only a few intuitive thresholds, making it suitable for multi-platform LiDAR datasets in both plantation and natural forests. The conceptual basis of the strategy is that WD LiDAR points exhibit non-planar characteristics and a distinct intensity and comprise clusters that exceed a minimum size. The developed strategy was tested using leaf-off point clouds acquired by Geiger-mode airborne, uncrewed aerial vehicle (UAV), and backpack LiDAR systems. The results show that using the intensity data did not provide a noticeable improvement in the WD detection results. Quantitatively, the approach achieved an average recall of 0.83, indicating a low rate of omission errors. Datasets with a higher point density (i.e., from UAV and backpack LiDAR) showed better performance. As for the precision evaluation metric, it ranged from 0.40 to 0.85. The precision depends on commission errors introduced by bushes and undergrowth. Full article

Vegetation characteristics significantly influence the impact of wildfires on individual building structures, and these effects can be systematically analyzed using heat transfer modelling software. Close-range light detection and ranging (LiDAR) data obtained from uncrewed aerial systems (UASs) capture detailed vegetation morphology; however, the integration of dense vegetation and merged canopies into three-dimensional (3D) models for fire modelling software poses significant challenges. This study proposes a method for integrating the UAS–LiDAR-derived geometric features of vegetation components—such as bark, wooden core, and foliage—into heat transfer models. The data were collected from the natural woodland surrounding an elevated building in Samford, Queensland, Australia. Aboveground biomass (AGB) was estimated for 21 trees utilizing three 3D tree reconstruction tools, with validation against biomass allometric equations (BAEs) derived from field measurements. The most accurate reconstruction tool produced a tree mesh utilized for modelling vegetation geometry. A proof of concept was established with Eucalyptus siderophloia, incorporating vegetation data into heat transfer models. This non-destructive framework leverages available technologies to create reliable 3D tree reconstructions of complex vegetation in wildland–urban interfaces (WUIs). It facilitates realistic wildfire risk assessments by providing accurate heat flux estimations, which are critical for evaluating building safety during fire events, while addressing the limitations associated with direct measurements. Full article