Search Results (109)

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

Few studies have examined Hybrid Aquatic–Aerial Vehicles (HAAVs), autonomous vehicles designed to operate in both air and water, especially those that are aircraft-launched and recovered, with a variable-sweep design to free dive into a body of water and breach under buoyant and propulsive force to re-achieve flight. The novel design research examines the viability of a recoverable sonar-search child aircraft for maritime patrol, one which can overcome the prohibitive sea state limitations of all current HAAV designs in the research literature. This paper reports on the analysis from computational fluid dynamic (CFD) simulations of such an HAAV diving into static seawater at low speeds due to the reverse thrust of two retractable electric-ducted fans (EDFs) and its subsequent breach back into flight initially using a fast buoyancy engine developed for deep-sea research vessels. The HAAV model entered the water column at speeds around 10 ms⁻¹ and exited at 5 ms⁻¹ under various buoyancy cases, normal to the surface. Results revealed that impact force magnitudes varied with entry speed and were more acute according to vehicle mass, while a sufficient portion of the fuselage was able to clear typical wave heights during its breach for its EDF propulsors and wings to protract unhindered. Examining the medium transition dynamics of such a novel HAAV has provided insight into the structural, propulsive, buoyancy, and control requirements for future conceptual design iterations. Research is now focused on validating these unperturbed CFD dive and breach cases with pool experiments before then parametrically and numerically examining the effects of realistic ocean sea states. 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

The demand for spatially explicit and comprehensive forest attribute data has continued to increase. Light detection and ranging (LiDAR) remote sensing, which can measure three-dimensional (3D) forest attributes, plays a significant role. However, only a few studies have used uncrewed aerial vehicle (UAV)-LiDAR to extract the characteristics of the 3D structure of the forest understory. Therefore, this study proposes a method for classifying and mapping forest stratification and evaluating forest stratification changes using multitemporal UAV-LiDAR data. The study area is a forest of approximately 25 ha on the west side of the Expo Commemorative Park (Suita City, Osaka Prefecture, Japan). Three-dimensional point cloud models from two measurement periods during the leaf-fall season were used. Forest stratification was classified using time-series clustering of 2024 data. The classification of forest stratification and its spatial distribution effectively reflected the actual site conditions. By applying time-series clustering, the forest stratification was successfully classified using only UAV-LiDAR data. Changes in forest stratification were evaluated using data from 2022 to 2024. In areas where changes in forest stratification were evaluated as significant, evidence of tree felling was confirmed. In addition, changes in forest stratification were quantitatively evaluated. The proposed method uses only UAV-LiDAR, which is highly versatile; thus, it is expected to apply to various forests. The results of this study are expected to deepen our ecological understanding of forests and contribute to forest monitoring and management. Full article

(1) Background: The harvest index is important for measuring the correlation between grain yield and aboveground biomass. However, the harvest index can only be measured after a mature harvest. If it can be obtained in advance during the growth period, it will promote research on high harvest indices and variety breeding; (2) Methods: In this study, we proposed a method to predict the harvest index during the rice growth period based on uncrewed aerial vehicle (UAV) remote sensing technology. UAV obtained visible light and multispectral images of different varieties, and the data such as digital surface elevation, visible light reflectance, and multispectral reflectance were extracted after processing for correlation analysis. Additionally, characteristic variables significantly correlated with the harvest index were screened out; (3) Results: The results showed that TCARI (correlation coefficient −0.82), GRVI (correlation coefficient −0.74), MTCI (correlation coefficient 0.83), and TO (correlation coefficient −0.72) had a strong correlation with the harvest index. Based on the above characteristics, this study used a variety of machine learning algorithms to construct a harvest index prediction model. The results showed that the Stacking model performed best in predicting the harvest index (R² reached 0.88) and had a high prediction accuracy. (4) Conclusions: Therefore, the harvest index can be accurately predicted during rice growth through UAV remote sensing images and machine learning technology. This study provides a new technical means for screening high harvest index in rice breeding, provides an important reference for crop management and variety improvement in precision agriculture, and has high application potential. 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

Polar vegetation is a critical component of global biodiversity and ecosystem health but is vulnerable to climate change and environmental disturbances. Analysing the spatial distribution, regional variations, and temporal dynamics of this vegetation is essential for implementing conservation efforts in these unique environments. However, polar regions pose distinct challenges for remote sensing, including sparse vegetation, extreme weather, and frequent cloud cover. Advances in remote sensing technologies, including satellite platforms, uncrewed aerial vehicles (UAVs), and sensor fusion techniques, have improved vegetation monitoring capabilities. This review explores applications—including land cover mapping, vegetation health assessment, biomass estimation, and temporal monitoring—and the methods developed to address these needs. We also examine the role of spatial, spectral, and temporal resolution in improving monitoring accuracy and addressing polar-specific challenges. Sensors such as Red, Green, and Blue (RGB), multispectral, hyperspectral, Synthetic Aperture Radar (SAR), light detection and ranging (LiDAR), and thermal, as well as UAV and satellite platforms, are analysed for their roles in low-stature polar vegetation monitoring. We highlight the potential of sensor fusion and advanced machine learning techniques in overcoming traditional barriers, offering a path forward for enhanced monitoring. This paper highlights how advances in remote sensing enhance polar vegetation research and inform adaptive management strategies. 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

Forest ecosystems play a pivotal role in the global carbon cycle and climate change mitigation. Forest aboveground biomass (AGB), a critical indicator of carbon storage and sequestration capacity, has garnered significant attention in ecological research. Recently, uncrewed aerial vehicle-borne laser scanning (ULS) technology has emerged as a promising tool for rapidly acquiring three-dimensional spatial information on AGB and vegetation carbon storage. This study evaluates the applicability and accuracy of UAV-LiDAR technology in estimating the spatiotemporal dynamics of AGB and vegetation carbon storage in Robinia pseudoacacia (R. pseudoacacia) plantations in the gully regions of the Loess Plateau, China. At the sample plot scale, optimal parameters for individual tree segmentation (ITS) based on the canopy height model (CHM) were determined, and segmentation accuracy was validated. The results showed root mean square error (RMSE) values of 13.17 trees (25.16%) for tree count, 0.40 m (3.57%) for average tree height (AH), and 320.88 kg (16.94%) for AGB. The regression model, which links sample plot AGB with AH and tree count, generated AGB estimates that closely matched the observed AGB values. At the watershed scale, ULS data were used to estimate the AGB and vegetation carbon storage of R. pseudoacacia plantations in the Caijiachuan watershed. The analysis revealed a total of 68,992 trees, with a total carbon storage of 2890.34 Mg and a carbon density of 62.46 Mg ha⁻¹. Low-density forest areas (<1500 trees ha⁻¹) dominated the landscape, accounting for 94.38% of the tree count, 82.62% of the area, and 92.46% of the carbon storage. Analysis of tree-ring data revealed significant variation in the onset of growth decline across different density classes of plantations aged 0–30 years, with higher-density stands exhibiting delayed growth decline compared to lower-density stands. Compared to traditional methods based on diameter at breast height (DBH), carbon storage assessments demonstrated superior accuracy and scientific validity. This study underscores the feasibility and potential of ULS technology for AGB and carbon storage estimation in regions with complex terrain, such as the Loess Plateau. It highlights the importance of accounting for topographic factors to enhance estimation accuracy. The findings provide valuable data support for density management and high-quality development of R. pseudoacacia plantations in the Caijiachuan watershed and present an efficient approach for precise forest carbon sink accounting. Full article

Durum wheat (Triticum durum Desf.), among the herbaceous crops, is one of the most extensively grown in the Mediterranean area due to its fundamental role in supporting typical food productions like bread, pasta, and couscous. Among the environmental and technical aspects, nitrogen (N) fertilization is crucial to shaping plant development and that of kernels by also affecting their protein concentration. Today, new techniques for monitoring fields using uncrewed aerial vehicles (UAVs) can detect crop multispectral (MS) responses, while advanced machine learning (ML) models can enable accurate predictions. However, to date, there is still little research related to the prediction of the N nutritional status and its effects on the productivity of durum wheat grown in the Mediterranean environment through the application of these techniques. The present research aimed to monitor the MS responses of two different wheat varieties, one ancient (Timilia) and one modern (Ciclope), grown under three different N fertilization regimens (0, 60, and 120 kg N ha⁻¹), and to estimate their quantitative and qualitative production (i.e., grain yield and protein concentration) through the Pearson’s correlations and five different ML approaches. The results showed the difficulty of obtaining good predictive results with Pearson’s correlation for both varieties of data merged together and for the Timilia variety. In contrast, for Ciclope, several vegetation indices (VIs) (i.e., CVI, GNDRE, and SR_RE) performed well (r-value > 0.7) in estimating both productive parameters. The implementation of ML approaches, particularly random forest (RF) regression, neural network (NN), and support vector machine (SVM), overcame the limitations of correlation in estimating the grain yield (R² > 0.6, RMSE = 0.56 t ha⁻¹, MAE = 0.43 t ha⁻¹) and protein (R² > 0.7, RMSE = 1.2%, MAE 0.47%) in Timilia, whereas for Ciclope, the RF approach outperformed the other predictive methods (R² = 0.79, RMSE = 0.56 t ha⁻¹, MAE = 0.44 t ha⁻¹). Full article

Salt marsh ecosystems play a critical role in coastal protection, carbon sequestration, and biodiversity preservation. However, they are increasingly threatened by climate change and anthropogenic activities, necessitating precise vegetation mapping for effective conservation. This study investigated the effectiveness of spectral features and machine learning models in separating typical salt marsh vegetation types in the Yellow River Delta using uncrewed aerial vehicle (UAV)-derived multispectral imagery. The results revealed that the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Optimized Soil Adjusted Vegetation Index (OSAVI) were pivotal in differentiating vegetation types, compared with spectral reflectance at individual bands. Among the evaluated models, U-Net achieved the highest overall accuracy (94.05%), followed by SegNet (93.26%). However, the U-Net model produced overly distinct and abrupt boundaries between vegetation types, lacking the natural transitions found in real vegetation distributions. In contrast, the SegNet model excelled in boundary handling, better capturing the natural transitions between vegetation types. Both deep learning models outperformed Random Forest (83.74%) and Extreme Gradient Boosting (83.34%). This study highlights the advantages of deep learning models for precise salt marsh vegetation mapping and their potential in ecological monitoring and conservation efforts. 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

Wave attenuation by natural coastal features is recognised as a soft engineering approach to shoreline protection from storm surges and destructive waves. The effectiveness of wave energy dissipation is determined, in part, by vegetation structure, extent, and distribution. Mangroves line ca. 15% of the world’s coastlines, primarily in tropical and subtropical regions but also extending into temperate climates, where mangroves are shorter and multi-stemmed. Using wave loggers deployed across mangrove and non-mangrove shorelines, we studied the wave attenuating capacity and the drag coefficient (C_D) of temperate Avicennia marina mangrove forests of varying structure in Western Port, Australia. The structure of the vegetation obstructing the flow path was represented along each transect in a three-dimensional point cloud derived from overlapping uncrewed aerial vehicle (UAV) images and structure-from-motion (SfM) algorithms. The wave attenuation coefficient (b) calculated from a fitted exponential decay model at the vegetated sites was on average 0.011 m⁻¹ relative to only 0.009 m⁻¹ at the unvegetated site. We calculated a C_D for this forest type that ranged between 2.7 and 4.9, which is within the range of other pencil-rooted species such as Sonneratia sp. but significantly lower than prop-rooted species such as Rhizophora spp. Wave attenuation efficiency significantly decreased with increasing water depth, highlighting the dominance of near-bed friction on attenuation in this forest type. The UAV-derived point cloud did not describe the vegetation (especially near-bed) in sufficient detail to accurately depict the obstacles. We found that a temperate mangrove greenbelt of just 100 m can decrease incoming wave heights by close to 70%, indicating that, similarly to tropical and subtropical forests, temperate mangroves significantly attenuate incoming wave energy under normal sea conditions. 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