Search Results (184)

Accurate mapping of tea plantations is crucial for agricultural management and economic planning, yet it poses a significant challenge due to the complex and variable nature of tea cultivation landscapes. This study presents a high-precision approach to mapping tea plantations in Anji County, Zhejiang Province, China, utilizing multi-source remote sensing data and advanced deep learning models. We employed a combination of Sentinel-2 optical imagery, Sentinel-1 synthetic aperture radar imagery, and digital elevation models to capture the rich spatial, spectral, and temporal characteristics of tea plantations. Three deep learning models, namely U-Net, SE-UNet, and Swin-UNet, were constructed and trained for the semantic segmentation of tea plantations. Cross-validation and point-based accuracy assessment methods were used to evaluate the performance of the models. The results demonstrated that the Swin-UNet model, a transformer-based approach capturing long-range dependencies and global context for superior feature extraction, outperformed the others, achieving an overall accuracy of 0.993 and an F1-score of 0.977 when using multi-temporal Sentinel-2 data. The integration of Sentinel-1 data with optical data slightly improved the classification accuracy, particularly in areas affected by cloud cover, highlighting the complementary nature of Sentinel-1 imagery for all-weather monitoring. The study also analyzed the influence of terrain factors, such as elevation, slope, and aspect, on the accuracy of tea plantation mapping. It was found that tea plantations at higher altitudes or on north-facing slopes exhibited higher classification accuracy, and that accuracy improves with increasing slope, likely due to simpler land cover types and tea’s preference for shade. The findings of this research not only provide valuable insights into the precision mapping of tea plantations but also contribute to the broader application of deep learning in remote sensing for agricultural monitoring. Full article

In the rapidly evolving domain of Unmanned Aerial Vehicles (UAVs), precise altitude estimation remains a significant challenge, particularly for lightweight UAVs. This research presents an innovative approach to enhance altitude estimation accuracy for UAVs weighing under 2 kg without cameras, utilizing advanced AI Deep Learning algorithms. The primary novelty of this study lies in its unique integration of unsupervised and supervised learning techniques. By synergistically combining K-Means Clustering with a multiple-input deep learning regression-based model (DL-KMA), we have achieved substantial improvements in altitude estimation accuracy. This methodology represents a significant advancement over conventional approaches in UAV technology. Our experimental design involved comprehensive field data collection across two distinct altitude environments, employing a high-precision Digital Laser Distance Meter as the reference standard (Class II). This rigorous approach facilitated a thorough evaluation of our model’s performance across varied terrains, ensuring robust and reliable results. The outcomes of our study are particularly noteworthy, with the model demonstrating remarkably low Mean Squared Error (MSE) values across all data clusters, ranging from 0.011 to 0.072. These results not only indicate significant improvements over traditional methods, but also establish a new benchmark in UAVs altitude estimation accuracy. A key innovation in our approach is the elimination of costly additional hardware such as Light Detection and Ranging (LiDAR), offering a cost-effective, software-based solution. This advancement has broad implications, enhancing the accessibility of advanced UAVs technology and expanding its potential applications across diverse sectors including precision agriculture, urban planning, and emergency response. This research represents a significant contribution to the integration of AI and UAVs technology, potentially unlocking new possibilities in UAVs applications. By enhancing the capabilities of lightweight UAVs, we are not merely improving a technical aspect, but revolutionizing the potential applications of UAVs across industries. Our work sets the stage for safer, more reliable, and precise UAVs operations, marking a pivotal moment in the evolution of aerial technology in an increasingly UAV-dependent world. Full article

Meteorological factors, specifically wind direction and magnitude, influence the dispersion of atmospheric pollutants due to road traffic by affecting their spatial and temporal distribution. In this study, we are interested in the effect of the evolution of horizontal wind components, i.e., in the plane perpendicular to the altitude axis. A two-dimensional numerical model for solving the coupled traffic flow/pollution problem, whose pollutants are generated by vehicles, is developed. The numerical solution of this model is computed via an algorithm combining the characteristics method for temporal discretization with the finite-element method for spatial discretization. The numerical model is validated through a sensitivity study on the diffusion coefficient of road traffic and its impact on traffic density. The distribution of pollutant concentration, computed based on a source generated by traffic density, is presented for a single direction and different magnitudes of the wind velocity (stationary, Gaussian, linearly increasing and decreasing, sudden change over time), taking into account the stretching and tilting of plumes and patterns. The temporal evolution of pollutant concentration at various relevant locations in the domain is studied for two wind velocities (stationary and sudden change). Three regimes were observed for transport pollution depending on time and velocity: nonlinear growth, saturation, and decrease. Full article

A novel method to derive surface temperatures from aerial thermography is proposed. Its theoretical foundation, details regarding the implementation, relevant sensitivities, and its application on a day and night survey are presented here. The method differs from existing approaches particularly in two aspects: first, a three-dimensional sampling approach is used to determine the reflected thermal radiation component. Different surface classes based on hyperspectral classification with specific properties regarding the reflection and emission of thermal radiation are considered in this sampling process. Second, the method relies on a detailed, altitude-dependent, directionally and spectrally resolved modelling of the atmospheric radiation transfer and considers the spectral sensitivity of the sensor used. In order to accurately consider atmospheric influences, the atmosphere is modelled as a function of altitude regarding temperature, pressure and greenhouse gas concentrations. The atmospheric profiles are generated specifically for the time of the survey based on measurements, meteorological forecasts and generic models. The method was initially developed for application in urban contexts, as it is able to capture the pronounced three-dimensional character of such environments. However, due to the detailed consideration of elevation and atmospheric conditions, the method is also valuable for the analysis of rural areas. The included case studies covering two thermographic surveys of city area of Graz during daytime and nighttime demonstrate the capabilities and feasibility of the method. In relation to the detected brightness temperatures apparent to the sensor, the determined surface temperatures vary considerably and generally cover an increased temperature range. The two processed surface temperature maps of the city area of Graz are finally used to validate the method based on available temperature recordings. Full article

This paper analyzes the quantitative quality parameters of a mobile communication network in a controlled drone logistic use-case scenario. Based on the analysis of standards and recommendations, the values of key performance indicators (KPIs) are set. As the main network-impacting parameters, reference signal received power (RSRP), reference signal received quality (RSRQ), and signal to interference and noise ratio (SINR) were selected. Uplink (UL), downlink (DL), and ping parameters were chosen as the secondary ones, as they indicate the quality of the link depending on primary parameters. The analysis is based on experimental measurements performed using a Latvian mobile operator’s “LMT” JSC infrastructure in a real-life scenario. To evaluate the altitude impact on the selected network parameters, the measurements were performed using a drone as transport for the following altitude values: 40, 60, 90, and 110 m. Network parameter measurements were implemented in automatic mode, allowing switching between LTE4–LTE2 standards, providing the opportunity for more complex analysis. Based on the analysis made, the recommendations for the future mobile networks employed in controlled drone flights should correspond to the following KPI and their values: −100 dBm for RSRP, −16 dB for RSRQ, −5 dB for SINR, 4096 kbps for downlink, 4096 kbps for uplink, and 50 ms for ping. Lastly, recommendations for a network coverage digital twin (DT) model with integrated KPIs are also provided. Full article

Meteorological prediction is crucial for various sectors, including agriculture, navigation, daily life, disaster prevention, and scientific research. However, traditional numerical weather prediction (NWP) models are constrained by their high computational resource requirements, while the accuracy of deep learning models remains suboptimal. In response to these challenges, we propose a novel deep learning-based model, the Spatiotemporal Fusion Model (STFM), designed to enhance the accuracy of meteorological predictions. Our model leverages Fifth-Generation ECMWF Reanalysis (ERA5) data and introduces two key components: a spatiotemporal encoder module and a spatiotemporal fusion module. The spatiotemporal encoder integrates the strengths of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), effectively capturing both spatial and temporal dependencies. Meanwhile, the spatiotemporal fusion module employs a dual attention mechanism, decomposing spatial attention into global static attention and channel dynamic attention. This approach ensures comprehensive extraction of spatial features from meteorological data. The combination of these modules significantly improves prediction performance. Experimental results demonstrate that STFM excels in extracting spatiotemporal features from reanalysis data, yielding predictions that closely align with observed values. In comparative studies, STFM outperformed other models, achieving a 7% improvement in ground and high-altitude temperature predictions, a 5% enhancement in the prediction of the u/v components of 10 m wind speed, and an increase in the accuracy of potential height and relative humidity predictions by 3% and 1%, respectively. This enhanced performance highlights STFM’s potential to advance the accuracy and reliability of meteorological forecasting. Full article

There is a growing interest in high-altitude platform stations (HAPSs) as potential telecommunication infrastructures in the stratosphere, providing direct communication services to ground-based smartphones. Enhanced coverage and capacity can be realized in HAPSs by adopting multicell configurations. To improve the communication quality, previous studies have investigated methods based on search algorithms, such as genetic algorithms (GAs), which dynamically optimize antenna parameters. However, these methods face hurdles in swiftly adapting to sudden distribution shifts from natural disasters or major events due to their high computational requirements. Moreover, they do not utilize the previous optimization results, which require calculations each time. This study introduces a novel optimization approach based on a neural network (NN) model that is trained on GA solutions. The simple model is easy to implement and allows for instantaneous adaptation to unexpected distribution changes. However, the NN faces the difficulty of capturing the dependencies among neighboring cells. To address the problem, a classifier chain (CC), which chains multiple classifiers to learn output relationships, is integrated into the NN. However, the performance of the CC depends on the output sequence. Therefore, we employ an ensemble approach to integrate the CCs with different sequences and select the best solution. The results of simulations based on distributions in Japan indicate that the proposed method achieves a total throughput whose cumulative distribution function (CDF) is close to that obtained by the GA solutions. In addition, the results show that the proposed method is more time-efficient than GA in terms of the total time required to optimize each user distribution. Full article

The study of the physical properties of the topside ionosphere is fundamental to investigating the energy balance of the ionosphere and developing accurate models to predict relevant phenomena, which are often at the root of Space Weather effects in the near-Earth environment. One of the most important physical parameters characterising the ionospheric medium is electrical conductivity, which is crucial for the onset and amplification of ionospheric currents and for calculating the power density dissipated by such currents. We characterise, for the first time, electrical conductivity in the direction perpendicular to the geomagnetic field, namely Pedersen and Hall conductivities, in the topside ionosphere at an altitude of about 450 km. For this purpose, we use eight years of in situ simultaneous measurements of electron density, electron temperature and geomagnetic field strength acquired by the Swarm A satellite. We present global statistical maps of perpendicular electrical conductivity and study their variations depending on magnetic latitude and local time, seasons, and solar activity. Our findings indicate that the most prominent features of perpendicular electrical conductivity are located at low latitudes and are probably driven by the complex dynamics of the Equatorial Ionisation Anomaly. At higher latitudes, perpendicular conductivity is a few orders of magnitude lower than that at low latitudes. Nevertheless, conductivity features are modulated by solar activity and seasonal variations at all latitudes. Full article

This study presents the hygroscopic properties of aerosols from the Arctic free troposphere by means of contemporary lidar and radiosonde observations only. It investigates the period from the Arctic Haze in spring towards the summer season in 2021. Therefore, a one-parameter growth curve model is applied to lidar data from the Koldewey Aerosol Raman Lidar (AWIPEV in Ny-Ålesund, Svalbard) and simultaneous radiosonde measurements. Hygroscopic growth depends on different factors like aerosol diameter and chemical composition. To detangle this dependency, three trends in hygroscopicity are additionally investigated by classifying the aerosol first by its dry color ratio, and then by its season and altitude. Generally, we found a complex altitude dependence with the least hygroscopic particles in the middle of the troposphere. The most hygroscopic aerosol is located in the upper free troposphere. A hypothesis based on prior lifting of the particles is given. The expected trend with aerosol diameter is not observed, which draws attention to the complex dependence of hygroscopic growth on geographical region and altitude, and to the development of backscatter with the aerosol size itself. In a seasonal overview, two different modes of stronger or weaker hygroscopic particles are additionally observed. Furthermore, two special days are discussed using the Mie theory. They show, on the one hand, the complexity of analyzing hygroscopic growth by means of lidar data, but on the other hand, they demonstrate that it is in fact measurable with this approach. For these two case studies, we calculated that the aerosol effective radius increased from $0.16\mathsf{\mu }\mathrm{m}$ (dry) to $0.18\mathsf{\mu }\mathrm{m}$ (wet) and from $0.28\mathsf{\mu }\mathrm{m}$ to $0.32\mathsf{\mu }\mathrm{m}$ for the second case. Full article

Solar proton events (SPEs) affect the Earth’s atmosphere, causing additional ionization in the high-latitude mesosphere and stratosphere. Ionization rates from such solar proton events maximize in the stratosphere, but the formation of ozone-depleting nitrogen and hydrogen oxides begins at mesospheric altitudes. The destruction of mesospheric ozone is associated with protons with energies of about 10 MeV and higher and will strongly depend on the intensity of the flux of these particles. Most studies investigating the impact of SPEs on the characteristics of the middle atmosphere have been based on either simulations or reanalysis datasets, and some studies have used satellite observations to validate model results. We study the impact of SPEs on cold-season ozone loss in both the northern and southern hemispheres using Aura MLS mesospheric ozone measurements over the 2004 to 2024 period. Here, we show how strongly SPEs can deplete polar mesospheric ozone in different hemispheres and attempt to evaluate this dependence on the intensity of solar proton events. We found that moderate SPEs consisting of protons with an energy of more than 10 MeV and a flux intensity of more than 100 pfu destroy mesospheric ozone in the northern hemisphere up to 47% and in the southern hemisphere up to 33%. For both hemispheres, the peak of winter ozone loss was observed at about 76 km. In the northern hemisphere, maximum winter ozone loss was observed on the second day after a solar proton event, but in the southern hemisphere, winter ozone depletion was already detected on the first day. In the southern hemisphere, mesospheric ozone concentrations return to pre-event levels on the ninth day after a solar proton event, but in the northern hemisphere, even on the tenth day after a solar proton event, the mesospheric ozone layer may not be fully recovered. The strong SPEs with a proton flux intensity of more than 1000 pfu lead to a maximum winter ozone loss of up to 85% in the northern hemisphere, and in the southern hemisphere winter, ozone loss reaches 73%. Full article

This study investigates the effect of oil viscosity on pollutant emissions and fuel consumption of an internal combustion engine (ICE) at high altitudes using a response surface methodology (RSM). A Chevrolet Corsa Evolution 1.5 SOHC gasoline engine was used in Cuenca, Ecuador (2560 m above sea level), testing three lubricating oils with kinematic viscosities of 9.66, 14.08, and 18.5 mm²/s, measured at a temperature of 100 °C under various engine speeds and loads. Key findings include the following: hydrocarbon (HC) emissions were minimized from 150.22 ppm at the maximum load to 7.25 ppm with low viscosity and load; carbon dioxide (CO₂) emissions peaked at 15.2% vol with high viscosity and load; carbon monoxide (CO) ranged from 0.04% to 3.74% depending on viscosity and load; nitrogen oxides (NOx) were significantly influenced by viscosity, RPM, and load, indicating a need for model refinement; and fuel consumption was significantly affected by load and viscosity. RSM-based optimization identified optimal operational conditions with a viscosity of 13 mm²/s, 1473 rpm, and a load of 78%, resulting in 52.35 ppm of HC, 13.97% vol of CO₂, 1.2% vol of CO, 0 ppm of NOx, and a fuel consumption of 6.66 L/h. These conditions demonstrate the ability to adjust operational variables to maximize fuel efficiency and minimize emissions. This study underscores the critical role of optimizing lubricant viscosity and operational conditions to mitigate environmental impact and enhance engine performance in high-altitude environments. Full article

This paper presents a Kriging interpolation method for constructing a database of atmospheric refractivity in Korea. To collect as much data as possible for the interpolation, meteorological data from 120 regions of Korea, including both land and sea areas, are examined. Then, the normalized atmospheric refractivity N_n is calculated for an altitude of 0 m, because the refractivity tends to vary depending on the altitude of the observation site. In addition, the optimal variogram model to obtain the spatial correlation required for the Kriging method is investigated. The estimation accuracy of the Kriging interpolation is compared with that of the inverse distance weighting (IDW) and the bi-linear methods. The average and maximum estimation errors when using the Kriging method are 0.24 and 1.32, respectively. The result demonstrates that the Kriging method is more suitable for the interpolation of atmospheric refractivity in Korea than the conventional methods. Full article

The dependence of ozone content in the polar stratosphere upon different phases of the quasi-biennial oscillation (QBO) of the zonal wind and the El Niño–Southern Oscillation (ENSO) during winter was studied. The monthly (from November to January) mean residual meridional circulation (RMC) was calculated for four different combinations of the main phases of ENSO and QBO using MERRA2 reanalysis data. It has been demonstrated that the QBO phase manifests itself in different vertical distributions of ozone in the equatorial stratosphere, as well as in strengthening/weakening of the secondary meridional circulation in the tropics. The enhancement of the RMC from the tropical to the polar stratosphere is stronger at altitudes where ozone is higher in the tropics under El Niño conditions. The RMC modification and intensification are observed from ozone-depleted areas under La Niña conditions. A “cumulative” effect is observed by February under La Niña conditions and the easterly QBO, which is expressed in the lowest ozone content in the polar stratosphere. The numerical experiments carried out using the Middle and Upper Atmosphere Model (MUAM) confirmed tendencies in changes in the meridional transport detected from the reanalysis data for different combinations of QBO and ENSO. Full article

Wildlife conservationists have historically depended on manual methods for the identification and tracking of avian species, to monitor population dynamics and discern potential threats. Nonetheless, many of these techniques present inherent challenges and time constraints. With the advancement in computer vision techniques, automated bird detection and recognition have become possible. This study aimed to further advance the task of detecting wild birds using computer vision methods with drone footage, as well as entirely automating the process of detection and tracking. However, detecting objects from drone footage presents a significant challenge, due to the elevated altitudes, as well as the dynamic movement of both the drone and the birds. In this study, we developed and introduce a state-of-the-art model titled ORACLE (optimized rigorous advanced cutting-edge model for leveraging protection to ecosystems). ORACLE aims to facilitate robust communication across multiple models, with the goal of data retrieval, rigorously using various computer vision techniques such as object detection and multi-object tracking (MOT). The results of ORACLE’s vision models were evaluated at 91.89% mAP at 50% IoU. Full article

The rapid and accurate detection of road cracks is of great significance for road health monitoring, but currently, this work is mainly completed through manual site surveys. Low-altitude UAV remote sensing can provide images with a centimeter-level or even subcentimeter-level ground resolution, which provides a new, efficient, and economical approach for rapid crack detection. Nevertheless, crack detection networks face challenges such as edge blurring and misidentification due to the heterogeneity of road cracks and the complexity of the background. To address these issues, we proposed a real-time edge reconstruction crack detection network (ERNet) that adopted multi-level information aggregation to reconstruct crack edges and improve the accuracy of segmentation between the target and the background. To capture global dependencies across spatial and channel levels, we proposed an efficient bilateral decomposed convolutional attention module (BDAM) that combined depth-separable convolution and dilated convolution to capture global dependencies across the spatial and channel levels. To enhance the accuracy of crack detection, we used a coordinate-based fusion module that integrated spatial, semantic, and edge reconstruction information. In addition, we proposed an automatic measurement of crack information for extracting the crack trunk and its corresponding length and width. The experimental results demonstrated that our network achieved the best balance between accuracy and inference speed compared to six established models. Full article