Search Results (5,503)

Culturable airborne microorganisms significantly impact air quality and human health in urban forest land. Their concentrations serve as key air quality indicators. Over a year, this study analyzed airborne microorganisms in six forest stands within the Zhuyu Bay Scenic Area, Yangzhou, Jiangsu Province, China, to assess concentration characteristics and seasonal variations. Results showed that bacterial concentrations peaked in spring and summer, while fungal concentrations were highest in March. Microbial levels remained elevated from April to June, with variations among forest stands. A correlation analysis linked humidity, temperature, negative ion concentration, particulate matter 2.5 (PM2.5), and air pressure to microorganism fluctuations. To further explore the impact mechanism of urban microclimate on air microorganism concentrations, this study confirmed a strong positive influence of climatic factors on microorganism concentrations, particularly temperature and humidity. In conclusion, this study identifies seasonal patterns and microclimate interactions that affect airborne microorganism concentrations in urban forests. Findings contribute to ecosystem assessment, urban ecological planning, and climate improvement strategies, supporting informed decision-making. Full article

In the air-to-air UAV target detection tasks, the existing algorithms suffer from low precision, low recall and high dependence on device processing power, which makes it difficult to detect UAV small targets efficiently. To solve the above problems, this paper proposes an high-precision model, ATA-YOLOv8. In this paper, we analyze the problem of UAV small target detection from the perspective of the efficient receptive field. The proposed model is evaluated using two air-to-air UAV image datasets, MOT-FLY and Det-Fly, and compared with YOLOv8n and other SOTA algorithms. The experimental results show that the mAP50 of ATA-YOLOv8 is 94.9% and 96.4% on the MOT-FLY and Det-Fly datasets, respectively, which are 25% and 5.9% higher than the mAP of YOLOv8n, while maintaining a model size of 5.1 MB. The methods in this paper improve the accuracy of UAV target detection in air-to-air scenarios. The proposed model’s small size, fast speed and high accuracy make it possible for real-time air-to-air UAV detection on edge-computing devices. Full article

The development of unmanned aerial spraying systems (UASSs) has significantly transformed pest and disease control methods of crop plants. Precisely adjusting pesticide application rates based on the target conditions is an effective method to improve pesticide use efficiency. In orchard spraying, the structural characteristics of the canopy are crucial for guiding the pesticide application system to adjust spraying parameters. This study selected mango trees as the research sample and evaluated the differences between UAV aerial photography with a Structure from Motion (SfM) algorithm and airborne LiDAR in the results of extracting canopy parameters. The maximum canopy height, canopy projection area, and canopy volume parameters were extracted from the canopy height model of SfM (${CHM}_{SfM}$) and the canopy height model of LiDAR (${CHM}_{LiDAR}$) by grids with the same width as the planting rows (5.0 m) and 14 different heights (0.2 m, 0.3 m, 0.4 m, 0.5 m, 0.6 m, 0.8 m, 1.0 m, 2.0 m, 3.0 m, 4.0 m, 5.0 m, 6.0 m, 8.0 m, and 10.0 m), respectively. Linear regression equations were used to fit the canopy parameters obtained from different sensors. The correlation was evaluated using $R^2$ and $rRMSE$, and a t-test (α = 0.05) was employed to assess the significance of the differences. The results show that as the grid height increases, the $R^2$ values for the maximum canopy height, projection area, and canopy volume extracted from ${CHM}_{SfM}$ and ${CHM}_{LiDAR}$ increase, while the $rRMSE$ values decrease. When the grid height is 10.0 m, the $R^2$ for the maximum canopy height extracted from the two models is $92.85\%$, with an $rRMSE$ of $0.0563$. For the canopy projection area, the $R^2$ is 97.83%, with an $rRMSE$ of 0.01, and for the canopy volume, the $R^2$ is 98.35%, with an $rRMSE$ of 0.0337. When the grid height exceeds 1.0 m, the t-test results for the three parameters are all greater than 0.05, accepting the hypothesis that there is no significant difference in the canopy parameters obtained by the two sensors. Additionally, using the coordinates $x_0$ of the intersection of the linear regression equation and $y=x$ as a reference, ${CHM}_{SfM}$ tends to overestimate lower canopy maximum height and projection area, and underestimate higher canopy maximum height and projection area compared to ${CHM}_{LiDAR}$. This to some extent reflects that the surface of ${CHM}_{SfM}$ is smoother. This study demonstrates the effectiveness of extracting canopy parameters to guide UASS systems for variable-rate spraying based on UAV oblique photography combined with the SfM algorithm. Full article

An integral part of daily dental practice is preparing and polishing placed composite restorations. When these procedures are performed, significant amounts of composite dust are released from the grinding material. This systematic review aims to enhance the existing body of knowledge, encourage further dialogue, and expand the understanding of composite dust and its related risks. Following inclusion and exclusion criteria, twelve studies were included. Several studies highlight that composite dust contains nanoparticles capable of deep lung penetration, posing significant health risks to both dental staff and patients. Inhalation of composite dust can lead to respiratory diseases such as pneumoconiosis. Studies have shown that water cooling during composite grinding reduces dust emissions but does not eliminate them completely. Researchers suggest that thermal degradation of the composite material, not just filler particles, may be the source of the nanoparticles. In vitro studies have shown the toxicity of composite dust to bronchial and gingival epithelial cells, especially at high concentrations. Further research is needed on the health effects of composite dust and the development of effective methods to protect staff and patients. Full article

The combination of unmanned aerial vehicles and atomic magnetometers can be used for detection applications such as mineral resource exploration, environmental protection, and earthquake monitoring, as well as the detection of sunken ships and unexploded ordnance. A dark-resonance atomic magnetometer offers the significant advantages of a fully optical probe and omnidirectional measurement with no dead zones, making it an ideal choice for airborne applications on unmanned aerial vehicles. Enhancing the sensitivity of such atomic magnetometers is an essential task. In this study, we sought to enhance the sensitivity of a dark-state resonance atomic magnetometer. Initially, through theoretical analysis, we compared the excitation effects of coherent population trapping (CPT) resonance on the D₁ and D₂ transitions of ¹³³Cs thermal vapor. The results indicate that excitation via the D₁ line yields an increase in resonance contrast and a reduction in linewidth when compared with excitation through the D₂ line, aligning with theoretical predictions. Subsequently, considering the impact of various quantum system parameters on sensitivity, as well as their interdependent characteristics, two experimental setups were developed for empirical investigation. One setup focused on parameter optimization experiments, where we compared the linewidth and contrast of CPT resonances excited by both D₁ and D₂ transitions; this led to an optimization of atomic cell size, buffer gas pressure, and operating temperature, resulting in an ideal parameter range. The second setup was employed to validate these optimized parameters using a coupled dark-state atom magnetometer experiment, achieving approximately a 10-fold improvement in sensitivity. Full article

Airborne LiDAR (ALS) data have been extensively utilized for aboveground biomass (AGB) estimation; however, the high acquisition costs make it challenging to attain wall-to-wall estimation across large regions. Some studies have leveraged ALS data as intermediate variables to amplify sample sizes, thereby reducing costs and enhancing sample representativeness and model accuracy, but the cost issue remains in larger-scale estimations. Satellite LiDAR data, offering a broader dataset that can be acquired quickly with lower costs, can serve as an alternative intermediate variable for sample expansion. In this study, we employed a three-stage up-scaling approach to estimate forest AGB and introduced a method for quantifying estimation uncertainty. Based on the established three-stage general-hierarchical-model-based estimation inference (3sGHMB), an RK-3sGHMB inference method is proposed to make use of the regression-kriging (RK) method, and then it is compared with conventional model-based inference (CMB), general hierarchical model-based inference (GHMB), and improved general hierarchical model-based inference (RK-GHMB) to estimate forest AGB and uncertainty at both the pixel and forest farm levels. This study was carried out by integrating plot data, sampled ALS data, wall-to-wall Sentinel-2A data, and airborne P-SAR data. The results show that the accuracy of CMB ($R_{adj}^2$ = 0.37, RMSE = 33.95 t/ha, EA = 63.28%) is lower than that of GHMB ($R_{adj}^2$ = 0.38, RMSE = 33.72 t/ha, EA = 63.53%), while it is higher than that of 3sGHMB ($R_{adj}^2$ = 0.27, RMSE = 36.58 t/ha, EA = 60.43%). Notably, RK-GHMB ($R_{adj}^2$ = 0.60, RMSE= 27.07 t/ha, EA = 70.72%) and RK-3sGHMB ($R_{adj}^2$ = 0.55, RMSE = 28.55 t/ha, EA = 69.13%) demonstrate significant accuracy enhancements compared to GHMB and 3sGHMB. For population AGB estimation, the precision of the proposed RK-3sGHMB (p = 94.44%) is the highest, providing that there are sufficient sample sizes in the third stage, followed by RK-GHMB (p = 93.32%) with sufficient sample sizes in the second stage, GHMB (p = 90.88%), 3sGHMB (p = 88.91%), and CMB (p = 87.96%). Further analysis reveals that the three-stage model, considering spatial correlation at the third stage, can improve estimation accuracy, but the prerequisite is that the sample size in the third stage must be sufficient. For large-scale estimation, the RK-3sGHMB model proposed herein offers certain advantages. Full article

Advancements in aviation technology have made intelligent navigation systems essential for improving flight safety and efficiency, particularly in low-visibility conditions. Radar and GPS systems face limitations in bad weather, making visible–infrared sensor fusion a promising alternative. This study proposes a salient object detection (SOD) method that integrates visible and infrared sensors for robust airport runway detection in complex environments. We introduce a large-scale visible–infrared runway dataset (RDD5000) and develop a SOD algorithm capable of detecting salient targets from unaligned visible and infrared images. To enable real-time processing, we design a lightweight dual-modal fusion network (DCFNet) with an independent–shared encoder and a cross-layer attention mechanism to enhance feature extraction and fusion. Experimental results show that the MobileNetV2-based lightweight version achieves 155 FPS on a single GPU, significantly outperforming previous methods such as DCNet (4.878 FPS) and SACNet (27 FPS), making it suitable for real-time deployment on airborne systems. This work offers a novel and efficient solution for intelligent navigation in aviation. Full article

Due to the development of spectral remote sensing imaging technology, hyperspectral data in different spectral ranges, such as visible and near-infrared, short-wave infrared, etc., can be acquired simultaneously. Data fusion between these nonoverlapping spectral ranges’ hyperspectral data has become an urgent task. Most existing hyperspectral data fusion methods focus on two types of hyperspectral data with overlapping spectral ranges, requiring spectral response functions as a necessary condition, which is not applicable to this task. To address this issue, we propose the combined spectral unmixing fusion (CSUF) method, an unsupervised method with certain physical significance. It effectively solves the problem of hyperspectral data fusion with nonoverlapping spectral ranges through the two hyperspectral data point spread function estimation and combined spectral unmixing. Experiments on airborne datasets and HJ-2 satellite data show that, compared with various leading methods, our method achieves the best performance in terms of reference evaluation indicators such as the PSNR and SAM, as well as the non-reference evaluation indicator the QNR. Furthermore, we deeply analyze the spectral response relationship and the impact of the ratio of spectral bands between the fused data on the fusion effect, providing references for future research. Full article

This paper presents a novel methodology to generate Digital Elevation Models (DEMs) in flat areas, incorporating river channels from relatively coarse initial data. The technique primarily utilizes filtered dense point clouds derived from SfM-MVS (Structure from Motion-Multi-View Stereo) photogrammetry of available crewed aerial imagery datasets. The methodology operates under the assumption that the aerial survey was carried out during low-flow or drought conditions so that the dry (or almost dry) riverbed is detected, although in an imprecise way. Direct interpolation of the detected elevation points yields unacceptable river channel bottom profiles (often exhibiting unrealistic artifacts) and even distorts the floodplain. In our Fluvial Domain Method, channel bottoms are represented like “highways”, perhaps overlooking their (unknown) detailed morphology but gaining in general topographic consistency. For instance, we observed an 11.7% discrepancy in the river channel long profile (with respect to the measured cross-sections) and a 0.38 m RMSE in the floodplain (with respect to the GNSS-RTK measurements). Unlike conventional methods that utilize active sensors (satellite and airborne LiDAR) or classic topographic surveys—each with precision, cost, or labor limitations—the proposed approach offers a more accessible, cost-effective, and flexible solution that is particularly well suited to cases with scarce base information and financial resources. However, the method’s performance is inherently limited by the quality of input data and the simplification of complex channel morphologies; it is most suitable for cases where high-resolution geomorphological detail is not critical or where direct data acquisition is not feasible. The resulting DEM, incorporating a generalized channel representation, is well suited for flood hazard modeling. A case study of the Ranchería river delta in the Northern Colombian Caribbean demonstrates the methodology. Full article

Camera localization approaches often degrade in challenging environments characterized by illumination variations and significant viewpoint changes, presenting critical limitations for fixed-wing aircraft landing applications. To address these challenges, we propose LandNet—a novel absolute camera pose estimation network specifically designed for airborne scenarios. Our framework processes images from forward-looking aircraft cameras to directly predict 6-DoF camera poses, subsequently enabling aircraft pose determination through rigid transformation. As a first step, we design two encoders from Transformer and CNNs to capture complementary spatial–temporal features. Furthermore, a novel Feature Interactive Block (FIB) is employed to fully utilize spatial clues from the CNN encoder and temporal clues from the Transformer encoder. We also introduce a novel Attentional Convtrans Fusion Block (ACFB) to fuse the feature maps from encoder and transformer encoder, which can enhance the image representations to promote the accuracy of the camera pose. Finally, two Multi-Layer Perceptron (MLP) heads are applied to estimate 6-DOF of camera position and orientation, respectively. Thus the estimated position and orientation of our LandNet can be further used to acquire the pose and orientation of the aircraft through the rigid connection between the airborne camera and the aircraft. The experimental results from simulation and real flight data demonstrate the effectiveness of our proposed method. 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

Laser wireless power transfer (LWPT) offers a transformative approach to wireless energy transmission, addressing critical limitations in unmanned aerial vehicles (UAVs) such as battery energy limitation. However, challenges like beam divergence, non-uniform irradiation, and alignment instability limit its practical application. Here, we present a lightweight air-floating metalens platform to overcome these barriers. This innovative lens focuses laser beams near the photovoltaic receiver with an energy distribution uniformity across a single spot at the focal plane that is 50 times greater than that of a conventional Gaussian beam spot, achieving a multi-spot energy distribution uniformity of up to 99% theoretically. Experimentally, we achieved 75% uniformity using a metalens sample. Simultaneously, our system maintains superior beam quality within a dynamic range of 4 m and enhances charging efficiency by 1.5 times. Our research provides a robust technical solution to improve UAV endurance, enabling efficient, long-range wireless power transfer and opening broader technological implications. Full article

Pollen forecasting models are helpful tools to predict environmental processes and allergenic risk events. Parietaria belongs to the Urticaceae family, and due to its high-level pollen production, is responsible for many cases of severe pollinosis reactions. This research aims to develop different machine learning models such as the random forest—RF, support vector machine—SVM, and artificial neural network—ANN models, to predict Parietaria pollen concentrations in the atmosphere of northwest Spain using 24 years of data from 1999 to 2022. The results obtained show an increase in the duration and intensity of the Parietaria main pollen season in the Mediterranean region (Ourense). Machine learning models exhibited their capacity to forecast Parietaria pollen concentrations at one, two, and three days ahead. The best selected models presented high correlation coefficients between 0.713 and 0.859, with root mean squared errors between 5.55 and 7.66 pollen grains·m⁻³ for the testing phase. The models developed could be improved by increasing the number of years, studying other hyperparameter ranges, or analyzing different data distributions. Full article

In recent years, a growing demand for alternative energy sources, including solar energy, has been observed. This article presents a methodology for assessing the solar potential of buildings using images from Unmanned Aerial Vehicles (UAVs) and point clouds from airborne LIDAR. The proposed method includes the following stages: DSM generation, extraction of building footprints, determination of roof parameters, map solar energy generation, removing of the areas that are not suitable for the installation solar systems, calculation of power per each building, conversion of solar irradiance into energy, and mapping the potential for solar power generation. This paper describes also the Detecting Photovoltaic Panels algorithm with the use of deep learning techniques. The proposed algorithm enabled assessing the efficiency of photovoltaic panels and comparing the results of maps of the solar potential of buildings, as well as identifying the areas that require optimization. The results of the analysis, which had been conducted in the test areas in the village and on the campus of the university, confirmed the usefulness of the above proposed methods. The analysis provides that the UAV image data enable generation of solar potential maps with higher accuracy (MAE = 8.5 MWh) than LIDAR data (MAE = 10.5 MWh). Full article

The nitrogen nutrition index (NNI) is a significant agronomic statistic used to assess the nitrogen nutrition status of crops. The use of remote sensing to invert it is crucial for accurately diagnosing and managing nitrogen nutrition in crops during critical periods. This study utilizes the UHD185 airborne hyperspectral imager and the ASD Field Spec3 portable spectrometer to acquire hyperspectral remote sensing data and agronomic parameters of the winter wheat canopy during the nodulation and flowering stages. The objective is to estimate the NNI of winter wheat through a winter wheat nitrogen gradient experiment conducted in Leling, Shandong Province. The ASD spectral reflectance data of the winter wheat canopy were selected as the reference standard and compared with the UHD185 hyperspectral data obtained from an unmanned aerial vehicle (UAV). The comparison focused on analyzing the trends in the spectral curve changes and the spectral correlation between the two datasets. The findings indicated a strong agreement between the UHD185 hyperspectral data and the spectral data obtained by ASD in the range of 450–830 nm. A spectrum index was developed to estimate the nitrogen nutritional index utilizing the bands within this range. The linear model, based on the first-order derivative ratio spectral index (RSI) (FD₆₆₆, FD₈₂₆), demonstrated the highest accuracy in estimating the nitrogen nutrient index in winter wheat. The model yielded R² values of 0.85 and 0.75, respectively, and may be represented by the equation y = −2.0655x + 0.156. The results serve as a benchmark for future utilization of the UHD185 hyperspectral data in estimating agronomic characteristics of winter wheat. Full article