Search Results (759)

This study focuses on the key challenges in detecting and estimating motion parameters of ground maneuvering targets for airborne radar sensors. The complex unknown motion states of the ground maneuvering target, including velocity, acceleration, and jerk, result in range migrations (RMs) and Doppler frequency migrations (DFMs). These effects severely degrade the long-time coherent accumulation performance of the airborne radar, thereby limiting the reliable detection and precise parameter estimation of maneuvering targets. To address this issue, a new detection and motion parameter estimation method based on the range frequency reversal transform (RFRT) and searching Lv’s distribution (SLVD), i.e., RFRT-SLVD, is proposed. Specifically, the third-order RM (TRM) and quadratic DFM (QDFM) are considered. The proposed method operates as follows: First, RMs are eliminated simultaneously via the RFRT operation, which multiplies the echo by its reversed data in the range frequency and slow-time domains, leveraging the symmetric equal-interval sampling property of the range frequency. Subsequently, a phase compensation function (PCF) related to the jerk is constructed to compensate the QDFM. Finally, the LVD is performed to remove residual DFMs and achieve effective signal energy accumulation. Additionally, the case of a fast-moving target with Doppler ambiguity is analyzed, and a method for estimating three motion parameters is provided. A key advantage of the proposed technique is its ability to directly compensate the RMs without requiring prior knowledge of the maneuvering target, while also avoiding the blind speed sidelobe (BSSL) effect. In comparison with existing algorithms, RFRT-SLVD achieves a balanced trade-off between parameter estimation performance and computational efficiency. Numerical analyses and experiments are conducted to validate the method, assessing its detection capability for ground maneuvering targets, Doppler ambiguity resolution in parameter estimation, computational complexity, and method applicability in multi-target scenarios. Full article

In this work, different implementation strategies for a Time-Domain (TD) focusing procedure applied to airborne Synthetic Aperture Radar (SAR) raw data are presented, with the key objective of quantitatively assessing their computing time. In particular, two methodological approaches are proposed: a pixel-wise strategy, which processes each image pixel independently, and a matrix-wise strategy, which handles data blocks collectively. Both strategies are further extended to parallel execution frameworks to exploit multi-threading and multi-node capabilities. The presented analysis is conducted within the context of the airborne SAR infrastructure developed at the Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council (CNR) in Naples, Italy. This infrastructure integrates an airborne SAR sensor and a high-performance Information Technology (IT) platform well-tailored to the parallel processing of huge amounts of data. Experimental results indicate an advantage of the pixel-wise strategy over the matrix-wise counterpart in terms of computing time. Furthermore, the adoption of parallel processing techniques yields substantial speedups, highlighting its relevance for time-critical SAR applications. These findings are particularly relevant in operational scenarios that demand a rapid data turnaround, such as near-real-time airborne monitoring in emergency response contexts. Full article

Over the past two decades, Light Detection and Ranging (LiDAR) technology has evolved from early National Aeronautics and Space Administration (NASA)-led airborne laser altimetry into commercially mature systems that now underpin vegetation remote sensing across scales. Continuous advancements in laser engineering, signal processing, and complementary technologies—such as Inertial Measurement Units (IMU) and Global Navigation Satellite Systems (GNSS)—have yielded compact, cost-effective, and highly sophisticated LiDAR sensors. Concurrently, innovations in carrier platforms, including uncrewed aerial systems (UAS), mobile laser scanning (MLS), Simultaneous Localization and Mapping (SLAM) frameworks, have expanded LiDAR’s observational capacity from plot- to global-scale applications in forestry, precision agriculture, ecological monitoring, Above Ground Biomass (AGB) modeling, and wildfire science. This review synthesizes LiDAR’s cross-domain capabilities for the following: (a) quantifying vegetation structure, function, and compositional dynamics; (b) recent sensor developments encompassing ALS discrete-return ${(ALS}_D)$, and ALS full-waveform $\left({ALS}_{FW}\right)$, photon-counting LiDAR (PCL), emerging multispectral LiDAR (MSL), and hyperspectral LiDAR (HSL) systems; and (c) state-of-the-art data processing and fusion workflows integrating optical and radar datasets. The synthesis demonstrates that many LiDAR-derived vegetation metrics are inherently transferable across domains when interpreted within a unified structural framework. The review further highlights the growing role of artificial-intelligence (AI)-driven approaches for segmentation, classification, and multitemporal analysis, enabling scalable assessments of vegetation dynamics at unprecedented spatial and temporal extents. By consolidating historical developments, current methodological advances, and emerging research directions, this review establishes a comprehensive state-of-the-art perspective on LiDAR’s transformative role and future potential in monitoring and modeling Earth’s vegetated ecosystems. Full article

Airborne particulate matter (PM) is a major environmental pollutant that accelerates skin aging, inflammation, and barrier impairment. Cannabidiol (CBD), a non-psychoactive phytocannabinoid derived from Cannabis sativa, has shown anti-inflammatory and cytoprotective effects, yet its role in protecting full-thickness human skin from pollution-induced damage remains unclear. In this study, human full-thickness ex vivo skin explants were topically exposed to PM (0.54 mg/cm²) and treated with CBD (6.4 mM) administered via the culture medium for 48 h. Proinflammatory mediators (interleukin-6, IL-6; matrix metalloproteinase-1, MMP-1; cyclooxygenase-2, COX-2), oxidative stress markers (reactive oxygen species, ROS; 8-hydroxy-2′-deoxyguanosine, 8-OHdG), the xenobiotic sensor aryl hydrocarbon receptor (AhR), extracellular matrix proteins (procollagen type I C-peptide, PIP; fibrillin), and the barrier protein filaggrin were quantified using ELISA and immunofluorescence. PM exposure triggered significant inflammation, oxidative stress, AhR induction, extracellular matrix degradation, and barrier disruption. CBD selectively counteracted these effects by reducing IL-6, MMP-1, COX-2, ROS, and 8-OHdG levels, downregulating AhR expression, and restoring PIP, fibrillin, and filaggrin expression. No measurable effects were observed in unstressed control tissues. These results demonstrate that CBD protects human skin from PM-induced molecular damage and supports its potential as a functional bioactive ingredient for anti-pollution applications. Full article

Eutrophication has emerged as a critical threat to water quality degradation and ecosystem health on a global scale, calling for prompt management actions. Remote sensing enables the monitoring of eutrophication by detected changes in ocean color caused by fluctuations in chlorophyll a (chl a). Although chl a is a crucial indicator of phytoplankton biomass and nutrient overloading, it reflects the outcome of eutrophication rather than its cause. Nutrients, the primary “drivers” of eutrophication, are essential indicators for predicting the potential phytoplankton growth in water bodies, allowing adoption of effective preventive measures. Long-term monitoring of nutrients combined with multiple water quality indicators using remotely sensed data could lead to a more precise assessment of the trophic state. Retrieving non-optically active constituents, such as nutrients and DO, remains challenging due to their weak optical characteristics and low signal-to-noise ratios. This work is an attempt to review the current progress in the retrieval of un-ionized ammonia (NH₃), ammonium (NH₄⁺), ammoniacal nitrogen (AN), nitrite (NO₂⁻), nitrate (NO₃⁻), dissolved inorganic nitrogen (DIN), phosphate (PO₄³⁻), dissolved inorganic phosphorus (DIP), silicate (SiO₂) and dissolved oxygen (DO) using remotely sensed data. Most studies refer to Case II highly nutrient-enriched water bodies. The commonly used spaceborne and airborne sensors, along with the selected spectral bands and band indices, per study area, are presented. There are two main model categories for predicting nutrient and DO concentration: empirical and artificial intelligence (AI). Comparative studies conducted in the same study area have shown that ML and NNs achieve higher prediction accuracy than empirical models under the same sample size. ML models often outperform NNs when training data are limited, as they are less prone to overfitting under small-sample conditions. The incorporation of a wider range of conditions (e.g., different trophic state, seasonality) into model training needs to be tested for model transferability. Full article

Background: Urban air pollution, particularly fine particulate matter (PM2.5 and PM10), poses health risks, including damage to the ocular surface. This pilot study (BIKE-EYE) aimed to assess ocular exposure to airborne pollutants during bicycle commuting and to evaluate particle presence in human tear fluid. Methods: Fifteen healthy volunteers wore portable sensors measuring PM2.5 and PM10 during daily bike commutes over six months. Exposure was calculated as time-weighted integrals over the ten days preceding an ophthalmologic exam assessing conjunctival hyperemia, epithelial damage, tear film quality, and meibomian gland function. Ocular symptoms were assessed via the Ocular Surface Disease Index (OSDI). Tear samples were analyzed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). Results: Higher pollutant exposure was significantly associated with conjunctival hyperemia and corneal epithelial damage, while temperature and humidity showed no effect. OSDI scores moderately correlated with PM levels. SEM/EDS analysis confirmed airborne particles in post-exposure tear samples, including carbonaceous material, aluminosilicates, iron, and sulfur compounds. Conclusions: Ocular surface alterations and conjunctival hyperemia were significantly associated with air pollution exposure, while subjective symptoms showed weaker trends. The detection of particulate matter in human tear fluid supports the use of the ocular surface as a sensitive, non-invasive tool for biomonitoring. These findings highlight its potential role in early warning systems for pollution-related health effects, with implications for public health surveillance and urban planning. Full article

The use of unmanned aerial vehicles (UAVs) as mobile sensor platforms has grown significantly in recent years, including applications where drones emulate radar targets or serve as dynamic measurement systems. This paper presents a novel approach to time-synchronized UAV trajectory planning for radar environment simulation. The proposed method considers a UAV equipped with a software-defined radio (SDR) capable of reproducing the radar signature of a simulated airborne object, e.g., a high-maneuverability or high-speed aerial platform. The UAV must follow a spatial trajectory that replicates the viewing geometry—specifically, the observation angles—of the reference target as seen from a ground-based radar. The problem is formulated within a factor graph framework, enabling joint optimization of the UAV trajectory, observation geometry, and temporal synchronization constraints. While factor graphs have been extensively used in robotics and sensor fusion, their application to trajectory planning under temporal and sensing constraints remains largely unexplored. The proposed approach enables unified optimization over space and time, ensuring that the UAV reproduces the target motion as perceived by the radar, both geometrically and with appropriate signal timing. Full article

To address the lack of reliable test data for evaluating star sensor performance in dynamic airborne environments, this paper presents a high-fidelity star map simulation method for all-time three-Field of View (FOV) star sensors. A comprehensive simulation framework integrating stellar radiation, atmospheric transmission, and detector noise models was developed to accurately model star trailing effects under dynamic conditions. First, a stellar position calculation model incorporating atmospheric refraction correction and platform motion parameters was established through coordinate transformations between the Geocentric Celestial Reference System (GCRS) and FOV coordinate system. Next, a complete energy transfer chain was constructed by combining star catalog data, atmospheric radiative properties, and detector noise characteristics. Finally, a quantitative evaluation system was introduced, employing metrics such as signal-to-noise ratio (SNR), total grayscale value (G_total), grayscale concentration index (GCI), and dynamic star displacement (DSD). Field experiments at 2388 m altitude (100.23°E, 26.86°N) demonstrated the average relative error of all evaluation metrics below 9% for static conditions and approximately 8% for dynamic scenarios between simulated and real star maps. The method effectively reproduces stellar radiation, atmospheric noise, and dynamic degradation, providing reliable simulation conditions for airborne star sensor testing and star trailing restoration algorithm development. Full article

As a core component of MEMS LiDAR, the 2D MEMS mirror, with high-precision optical angle detection, is a key technology for radar scanning and imaging. Existing piezoresistive detection schemes of mirrors suffer from high fabrication complexity, high temperature sensitivity, and a limited accuracy of only 0.08°, failing to meet the requirements for vehicular and airborne scanning applications. This study focuses on a two-dimensional electromagnetic MEMS mirror. Based on the reflection principles of geometric optics, angle detection schemes with photodiode (PD) arrays are analyzed. A novel four-quadrant optical measurement sensor featuring a 16-PD array is proposed. This design replaces conventional large-area PDs with a compact PD array, effectively mitigating nonlinearity and low accuracy issues caused by oversized PD trenches and edge dimensions. High-precision detection of the mirror’s deflection angle is achieved by measuring the current variations induced by the reflected spot position on the PDs in each quadrant. The experimental results demonstrate that the 16-PD array optical angle sensor achieves an accuracy between 0.03° and 0.036° over a detection range of ±8°. Full article

The Leaf Area Index (LAI) is a key vegetation biophysical variable extensively studied using various remote sensing platforms and applications. Most studies focused on retrieving LAI using remote sensing data have primarily applied visible to shortwave infrared (0.3–2.5 µm) data. While we have previously retrieved LAI using thermal infrared (TIR 2.5–14 µm) hyperspectral data under controlled laboratory conditions, this study aims to evaluate the reliability of our earlier findings using in situ and airborne TIR hyperspectral data. In this study, 36 plots, each 30 × 30 m in size, were randomly selected in the Bavarian Forest National Park in southeastern Germany. The EUFAR-TIR flight campaign, conducted on 6 July 2017, aligned with field data collection using an AISA Owl TIR hyperspectral sensor at 3 m spatial resolution. Statistical univariate and multivariate approaches have been applied to predict LAI using emissivity data. The LAI was derived using six narrowband indices, computed from all possible combinations of wavebands between 8 µm and 12.3 µm, via partial least squares regression (PLSR) and artificial neural network (ANN) models, applying the Levenberg–Marquardt and Scaled Conjugate Gradient algorithms. The results indicated that compared to LAI estimation under controlled conditions, TIR narrowband indices demonstrated poor performance in estimating in situ LAI (R² = 0.28 and RMSE_CV = 0.02). Instead, it was observed that the PLSR model unexpectedly achieved higher prediction accuracy (R² = 0.86 and RMSE_CV = 0.36) in retrieving LAI compared to the ANN approach using the Levenberg–Marquardt algorithm (R² = 0.56, RMSE_CV = 0.71); however, it was outperformed by the Scaled Conjugate Gradient algorithm (R² = 0.83, RMSE_CV = 0.18). The results revealed that wavebands located at 8.1 µm, 9.1 µm, 9.85–9.95 µm, and 9.99–10.27 µm are equally effective in predicting LAI, regardless of sensor or measurement/environmental conditions. Our findings have important implications for upscaling LAI predictions, as the identified wavebands are effective across varying conditions and align with the capabilities of upcoming thermal satellite missions such as Landsat Next and Copernicus LSTM. Full article

The long-term reliability of catalytic gas sensors is strongly influenced by changes in the chemical state and cleanliness of the catalyst surface. In this work, we investigate the surface composition and stability of the platinum (Pt) nanoparticle catalytic layer in Gas Metal-Oxide-Semiconductor (GMOS) sensors under varying environmental conditions. Using X-ray Photoelectron Spectroscopy (XPS) and High-Resolution (HR) XPS, we compared fresh, aged samples, thermally treated samples, and samples stored with or without a mechanical filter. The results show that prolonged ambient storage leads to the accumulation of adventitious carbon and nitrogen-containing species, as well as partial oxidation of platinum, which reduces the number of active metallic Pt sites. Thermal treatment at 300 °C for 30 min restores metallic Pt exposure by removing surface contaminants and narrowing the Pt 4f peaks. However, recontamination occurs during subsequent storage, with significant differences depending on surface protection. Sensors equipped with a mechanical filter exhibited obvious Pt metallic peaks in HR-XPS analysis, with lower carbon and nitrogen levels, compared to unprotected samples. These findings demonstrate that while heating refreshes catalytic activity, long-term stability requires complementary filtration to prevent re-adsorption of airborne species. The combined approach of heating and filtration is thus essential to ensure reliable performance of GMOS sensors for indoor and outdoor air quality monitoring. Full article

The growing threat of airborne biological agents necessitates rapid, sensitive, and portable detection systems to mitigate risks to public health and national security. We present a comprehensive overview of biosensor technologies developed for airborne biothreat detection, with a focus on aptamer-based electrochemical sensors. These sensors offer key advantages in portability, chemical stability, and adaptability for multiplexed detection in field settings. The urgency for real-time surveillance tools capable of identifying viral, bacterial, and toxin-based agents is discussed, particularly in the context of biodefense. Aerosolized particle capture strategies are reviewed, focusing on microfluidics for micron-sized particles and condensation-based systems for submicron-sized particles, which are preferred for their small-volume operation and seamless integration with biosensors. Key biosensor components are described, including recognition elements—such as aptamers—and transduction mechanisms like electrochemical impedance spectroscopy. EIS is highlighted for its label-free, miniaturizable, and real-time readout capabilities, making it well-suited for portable biosensors. Advances in sensing strategies for both viral and bacterial targets are explored, featuring innovations in nanoporous membrane platforms, nanomaterials, and multiplexed assay formats. Recent developments demonstrate improved sensitivity through nanopore-based signal amplification and enhanced selectivity using engineered aptamer libraries. The review concludes by addressing current limitations, including environmental stability, system integration, and the need for validation with complex real-world samples. Future directions point toward the development of fully integrated, field-deployable biosensing platforms that combine effective aerosol capture with robust and selective biosensing technologies. Full article

The use of Uncrewed Aerial Systems (UASs) for remote sensing applications has increased significantly in recent years due to their low cost, operational flexibility, and rapid advancements in sensor technologies. In many cases, UAS platforms are considered viable alternatives to conventional satellite and crewed airborne platforms, offering very high spatial, spectral, and temporal resolution data. However, the radiometric quality of UAS-acquired data has not received equivalent attention, particularly with respect to absolute calibration. In this study, we (1) evaluate the absolute radiometric performance of two commonly used UAS sensors: the Headwall Nano-Hyperspec hyperspectral sensor and the MicaSense RedEdge-MX Dual Camera multispectral system; (2) assess the effectiveness of the Empirical Line Method (ELM) in improving the radiometric accuracy of reflectance products generated by these sensors; and (3) investigate the influence of calibration target characteristics—including size, material type, reflectance intensity, and quantity—on the performance of ELM for UAS data. A field campaign was conducted jointly by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the USGS National Uncrewed Systems Office (NUSO) from 15 to 18 July 2023, at the USGS EROS Ground Validation Radiometer (GVR) site in Sioux Falls, South Dakota, USA, over a 160 m × 160 m vegetated area. Absolute calibration accuracy was evaluated by comparing UAS sensor-derived reflectance to in situ measurements of the site. Results indicate that the Headwall Nano-Hyperspec and MicaSense sensors underestimated reflectance by approximately 0.05 and 0.015 reflectance units, respectively. While the MicaSense sensor demonstrated better inherent radiometric accuracy, it exhibited saturation over bright targets due to limitations in its automatic gain and exposure settings. Application of the ELM using just two calibration targets reduced discrepancies to within 0.005 reflectance units. Reflectance products generated using various target materials—such as felt, melamine, or commercially available validation targets—showed comparable agreement with in situ measurements when used with the Nano-Hyperspec sensor. Furthermore, increasing the number of calibration targets beyond two did not yield measurable improvements in calibration accuracy. At a flight altitude of 200 ft above ground level (AGL), a target size of 0.6 m × 0.6 m or larger was sufficient to provide pure pixels for ELM implementation, whereas smaller targets (e.g., 0.3 m × 0.3 m) posed challenges in isolating pure pixels. Overall, the standard manufacturer-recommended calibration procedures were insufficient for achieving high radiometric accuracy with the tested sensors, which may restrict their applicability in scenarios requiring greater accuracy and precision. The use of the ELM significantly improved data quality, enhancing the reliability and applicability of UAS-based remote sensing in contexts requiring high precision and accuracy. Full article

Accurate remote sensing scene classification is essential for applications such as environmental monitoring and disaster management. In real-world scenarios, particularly during emergency response and disaster relief operations, acquiring nadir-view satellite images is often infeasible due to cloud cover, satellite scheduling constraints, or dynamic scene conditions. Instead, off-nadir images are frequently captured and can provide enhanced spatial understanding through angular perspectives. However, remote sensing scene classification has primarily relied on nadir-view satellite or airborne imagery, leaving off-nadir perspectives largely unexplored. This study addresses this gap by introducing Off-nadir-Scene10, the first controlled and comprehensive benchmark dataset specifically designed for off-nadir satellite image scene classification. The Off-nadir-Scene10 dataset contains 5200 images across 10 common scene categories captured at 26 different off-nadir angles. All images were collected under controlled single-day conditions, ensuring that viewing geometry was the sole variable and effectively minimizing confounding factors such as illumination, atmospheric conditions, seasonal changes, and sensor characteristics. To effectively leverage abundant nadir imagery for advancing off-nadir scene classification, we propose an angle-aware active domain adaptation method that incorporates geometric considerations into sample selection and model adaptation processes. The method strategically selects informative off-nadir samples while transferring discriminative knowledge from nadir to off-nadir domains. The experimental results show that the method achieves consistent accuracy improvements across three different training ratios: 20%, 50%, and 80%. The comprehensive angular impact analysis reveals that models trained on larger off-nadir angles generalize better to smaller angles than vice versa, indicating that exposure to stronger geometric distortions promotes the learning of view-invariant features. This asymmetric transferability primarily stems from geometric perspective effects, as temporal, atmospheric, and sensor-related variations were rigorously minimized through controlled single-day image acquisition. Category-specific analysis demonstrates that angle-sensitive classes, such as sparse residential areas, benefit significantly from off-nadir viewing observations. This study provides a controlled foundation and practical guidance for developing robust, geometry-aware off-nadir scene classification systems. Full article

Forest height is a critical parameter for understanding ecosystem functions, assessing carbon stocks, and supporting sustainable forest management. Its accurate measurement is essential for climate change mitigation and understanding the global carbon cycle. While traditional methods like field surveys and airborne LiDAR provide accurate measurements, their high costs and limited spatial coverage make them impractical for the large-scale, dynamic monitoring required for effective sustainability initiatives. This research presents a multi-source remote sensing fusion approach to tackle this problem. For regional forest height inversion, it includes Sentinel-1 SAR, Sentinel-2 multispectral images, ICESat-2 lidar, and SRTM DEM data. Sentinel-1 + ICESat-2 + SRTM, Sentinel-2 + ICESat-2 + SRTM, and Sentinel-1 + Sentinel-2 + ICESat-2 + SRTM were the three data combination methods built using Shangri-La Second-class Category Resource Survey data as ground truth. An accuracy assessment was performed using three machine learning models: Light Gradient Boosting (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Based on the results, the ideal configuration using the LightGBM model and the following sensors: Sentinel-1, Sentinel-2, ICESat-2, and SRTM yields a correlation coefficient of 0.72, an $RMSE$ of 5.52 m, and an $MAE$ of 4.08 m. The XGBoost model obtained $r$ = 0.716, $RMSE$ = 5.55 m, and $MAE$ = 4.10 m using the same data combination as the Random Forest model, which produced $r$ = 0.706, $RMSE$ = 5.63 m, and $MAE$ = 4.16 m. The multi-source comprehensive fusion technique produced the greatest results; however, including either Sentinel-1 or Sentinel-2 enhances model performance, according to comparisons across multiple data combinations. This work presents an efficient technological strategy for monitoring forest height in complex terrains, thereby providing a scalable and robust methodological reference for supporting sustainable forest management and large-scale ecological assessment. The proposed multi-source spatiotemporal fusion framework, coupled with systematic model evaluation, demonstrates significant potential for operational applications, especially in regions with limited LiDAR coverage. Full article