Search Results (2,097)

This review synthesizes the current knowledge of the various natural and human-caused processes that influence the evolution of sandy beaches and explores ways to improve predictions. Short-term storm-driven dynamics have been extensively studied, but long-term changes remain poorly understood due to a limited grasp of non-wave drivers, outdated topo-bathymetric (land–sea continuum digital elevation model) data, and an absence of systematic uncertainty assessments. In this study, we classify and analyze the various drivers of beach change, including meteorological, oceanographic, geological, biological, and human influences, and we highlight their interactions across spatial and temporal scales. We place special emphasis on the role of remote sensing, detailing the capacities and limitations of optical, radar, lidar, unmanned aerial vehicle (UAV), video systems and satellite Earth observation for monitoring shoreline change, nearshore bathymetry (or seafloor), sediment dynamics, and ecosystem drivers. A case study from the Langue de Barbarie in Senegal, West Africa, illustrates the integration of in situ measurements, satellite observations, and modeling to identify local forcing factors. Based on this synthesis, we propose a structured framework for quantifying uncertainty that encompasses data, parameter, structural, and scenario uncertainties. We also outline ways to dynamically update nearshore bathymetry to improve predictive ability. Finally, we identify key challenges and opportunities for future coastal forecasting and emphasize the need for multi-sensor integration, hybrid modeling approaches, and holistic classifications that move beyond wave-only paradigms. Full article

Rice is one of the most important staple crops in China, and the rapid and accurate extraction of rice planting areas plays a crucial role in the agricultural management and food security assessment. However, the existing rice field identification methods faced the significant challenges in mountainous regions due to the severe cloud contamination, insufficient utilization of multi-dimensional features, and limited classification accuracy. This study presented a novel rice field identification method based on the Graph Convolutional Networks (GCN) that effectively integrated multi-source remote sensing data tailored for the complex mountainous terrain. A coarse-to-fine cloud removal strategy was developed by fusing the synthetic aperture radar (SAR) imagery with temporally adjacent optical remote sensing imagery, achieving high cloud removal accuracy, thereby providing reliable and clear optical data for the subsequent rice mapping. A comprehensive multi-feature library comprising spectral, texture, polarization, and terrain attributes was constructed and optimized via a stepwise selection process. Furthermore, the 19 key features were established to enhance the classification performance. The proposed method achieved an overall accuracy of 98.3% for the rice field identification in Huoshan County of the Dabie Mountains, and a 96.8% consistency compared to statistical yearbook data. The ablation experiments demonstrated that incorporating terrain features substantially improved the rice field identification accuracy under the complex topographic conditions. The comparative evaluations against support vector machine (SVM), random forest (RF), and U-Net models confirmed the superiority of the proposed method in terms of accuracy, local performance, terrain adaptability, training sample requirement, and computational cost, and demonstrated its effectiveness and applicability for the high-precision rice field distribution mapping in mountainous environments. Full article

Low-Dimensional Nanohybrids (LDNHs) have emerged as potent multifunctional platforms for neurosensing and neuromodulation, providing elevated spatial-temporal precision, versatility, and biocompatibility. This review examines the intersection of LDNHs with artificial intelligence, brain–computer interfaces (BCIs), and closed-loop neurotechnologies, highlighting their transformative potential in personalized neuro-nano-medicine. Utilizing stimuli-responsive characteristics, optical, thermal, magnetic, and electrochemical LDNHs provide real-time feedback-controlled manipulation of brain circuits. Their pliable and adaptable structures surpass the constraints of inflexible bioelectronics, improving the neuronal interface and reducing tissue damage. We also examined their use in less invasive neurological diagnostics, targeted therapy, and adaptive intervention systems. This review delineates recent breakthroughs, integration methodologies, and fundamental mechanisms, while addressing significant challenges such as long-term biocompatibility, deep-tissue accessibility, and scalable manufacturing. A strategic plan is provided to direct future research toward clinical use. Ultimately, LDNHs signify a transformative advancement in intelligent, tailored, and closed-loop neurotechnologies, integrating materials science, neurology, and artificial intelligence to facilitate the next era of precision medicine. Full article

In free space optical (FSO) communications, signals are affected by turbulence when transmitted through the atmosphere. Fluctuations in intensity caused by atmospheric turbulence lead to an increase in the bit error rate of FSO systems. Deep learning (DL), as a current research hotspot, offers a promising approach to improve the accuracy of signal detection. In this paper, we propose a signal detection method based on a bidirectional gated recurrent unit (BiGRU) neural network for FSO communications. The proposed detection method considers the temporal correlation of received signals due to the properties of the BiGRU neural network, which is not available in existing detection methods based on DL. In addition, the proposed detection method does not require channel state information (CSI) for channel estimation, unlike maximum likelihood (ML) detection technology with perfect CSI. Numerical results demonstrate that the proposed BiGRU-based detector achieves significant improvements in bit error rate (BER) performance compared with a multilayer perceptron (MLP)-based detector. Specifically, under weak turbulence conditions, the BiGRU-based detector achieves an approximate 2 dB signal-to-noise ratio (SNR) gain at a target BER of ${10}^{-6}$ compared to the MLP-based detector. Under moderate turbulence conditions, it achieves an approximate 6 dB SNR gain at the same target BER of ${10}^{-6}$. Under strong turbulence conditions, the proposed detector obtains a 6 dB SNR gain at a target BER of ${10}^{-4}$. Additionally, it outperforms conventional methods by more than one order of magnitude in BER under the same turbulence and SNR conditions. Full article

Background: Optic nerve sheath diameter (ONSD) and its ratio to eyeball transverse diameter (ETD; ONSD/ETD) are potential markers for elevated intracranial pressure in comatose survivors of out-of-hospital cardiac arrest (OHCA). However, their prognostic accuracy remains uncertain. We compared their predictive value via compted tomography (CT)and magnetic resonance imaging (MRI) before and after targeted temperature management (TTM) in OHCA survivors. Methods: This retrospective study included adult comatose OHCA survivors who underwent TTM and serial brain imaging. ONSD and ONSD/ETD ratios were measured on brain CT and MRI at two predefined time-points: within 6 h (pre-TTM) and at 72–96 h (post-TTM) after return of spontaneous circulation. Intra-rater reliability was assessed using intraclass correlation coefficients (ICC). Poor neurological outcome was defined as a Cerebral Performance Category score of 3–5 at 6 months. Prognostic performance was evaluated using area under the receiver operating characteristic curve (AUC). Results: Among 136 patients, 78 (57%) had poor neurological outcomes. Only ONSD (5.12 vs. 5.37 mm) and ONSD/ETD ratio (0.22 vs. 0.23) measured on post-TTM MRI were significantly higher in the poor outcome group. These results depicted modest predictive performance (AUC, 0.67 and 0.65, respectively), whereas all CT-based and early MRI measurements had AUC < 0.60. Intra-rater reliability for ONSD and ETD was higher on CT (ICC: up to 0.93) than on MRI (ICC: 0.73–0.80). Conclusions: Delayed MRI-based ONSD and ONSD/ETD showed statistically significant but modest prognostic value, with limited clinical applicability as a stand-alone tool. These findings underscore the relevance of measurement timing, supporting ONSD as an adjunctive, rather than definitive, tool in multimodal prognostication. Full article

The study presents a spectral assessment and analysis of various data and methods for snow cover analysis in different ranges of the electromagnetic spectrum through a differentiated approach applied to the territory of Bansko, Pirin Mountain. The aim of the presented research is to assess the effectiveness and accuracy of satellite observations together with field (in situ) measurements and to create a model of an integrated methodology. To achieve this goal, several indices, such as land surface temperature (LST), optical indices, Tasseled Cap Transformation (TCT) with wetness component (TCW), High-Resolution (HR) imagery, and Synthetic Aperture Radar (SAR) measurements, were analyzed. The results of the analysis proved that combining satellite and field data through a mobile thermal camera provides an accurate and comprehensive picture of snow conditions in high mountain regions for powder, hard-packed and wet snow. As the most important, there is the verification and validation of the results through the so-called regression analysis of the different data types, through which multiple correlations (over 10) were established, both in data from Sentinel 1SAR, Sentinel 2MSI, Sentinel 3 SLSTR, and PlanetScope. The results showed the effectiveness of optical indices for hard and fresh snow and radar and LST data for wet snow. The results can be used to improve snow surveys, event prediction (e.g., avalanches), and the interpretation of spectral analysis of snow. The study does not aim to perform a temporal analysis; all satellite data is from the temporal period 30 December 2024–5 January 2025. Full article

Soil salinization poses a severe threat to agricultural sustainability in the Yellow River Delta, where conventional spectral indices are limited by vegetation interference and seasonal dynamics in coastal saline-alkali landscapes. To address this, we developed an inversion framework integrating spectral indices and vegetation temporal features, combining multi-temporal Sentinel-2 optical data (January 2024–March 2025), Sentinel-1 SAR data, and terrain covariates. The framework employs Savitzky–Golay (SG) filtering to extract vegetation temporal indices—including NDVI temporal extremum and principal component features, capturing salt stress response mechanisms beyond single-temporal spectral indices. Based on 119 field samples and Variable Importance in Projection (VIP) feature selection, three ensemble models (XGBoost, CatBoost, LightGBM) were constructed under two strategies: single spectral features versus fused spectral and vegetation temporal features. The key results demonstrate the following: (1) The LightGBM model with fused features achieved optimal validation accuracy (R² = 0.77, RMSE = 0.26 g/kg), outperforming single-feature models by 13% in R². (2) SHAP analysis identified vegetation-related factors as key predictors, revealing a negative correlation between peak biomass and salinity accumulation, and the summer crop growth process affects soil salinization in the following spring. (3) The fused strategy reduced overestimation in low-salinity zones, enhanced model robustness, and significantly improved spatial gradient continuity. This study confirms that vegetation phenological features effectively mitigate agricultural interference (e.g., tillage-induced signal noise) and achieve high-resolution salinity mapping in areas where traditional spectral indices fail. The multi-temporal integration framework provides a replicable methodology for monitoring coastal salinization under complex land cover conditions. Full article

Biomass burning in the Amazon region, especially during the dry season, generates aerosol dispersion events across the southern part of the continent, with impacts observable thousands of kilometers from the emission source. This study presents a long-range aerosol transport case from September 2024, in which smoke aerosols from forest fires in the central Amazon reached southeastern and southern Brazil, affecting the air quality in distant areas such as São Paulo and São Martinho. The event was simulated using the Weather Research and Forecasting model with Chemistry (WRF-Chem), configured with the MOZCART chemical mechanism, combined with MERRA-2 reanalysis data and by using the 3BEM biomass burning emission inventory. Satellite datasets from MODIS and MERRA-2 reanalysis were used to evaluate the model’s performance. The results indicate that the South American Low-Level Jet (SALLJ) played a key role in transporting carbonaceous aerosols over long distances. The model successfully captured the spatial and temporal evolution of the aerosol plume and its impacts, although it tended to underestimate aerosol optical depth (AOD) values compared with satellite observations. This study highlights the WRF-Chem’s capability to simulate extreme smoke transport events in South America and supports its potential application in forecasting and air quality assessments. Full article

Cloud cover can delay landslide detection in optical satellite imagery for weeks, complicating disaster response. Synthetic Aperture Radar (SAR) backscatter imagery, which is widely used for monitoring floods and avalanches, remains underutilised for landslide detection due to a limited understanding of landslide signatures in SAR data. We developed a conceptual model of landslide expression in SAR backscatter (σ°) change images through iterative investigation of over 1000 landslides across 30 diverse study areas. Using multi-temporal composites and dense time series Sentinel-1 C-band SAR data, we identified characteristic patterns linked to land cover, terrain, and landslide material. The results showed either increased or decreased backscatter depending on environmental conditions, with reduced visibility in urban or mixed vegetation areas. Detection was also hindered by geometric distortions and snow cover. The diversity of landslide expression illustrates the need to consider local variability and multi-track (ascending and descending) satellite data in designing representative training datasets for automated detection models. The conceptual model was applied to three recent disaster events using the first post-event Sentinel-1 image, successfully identifying previously unknown landslides before optical imagery became available in two cases. This study provides a theoretical foundation for interpreting landslides in SAR imagery and demonstrates its utility for rapid landslide detection. The findings support further exploration of rapid landslides in SAR backscatter data and future development of automated detection models, offering a valuable tool for disaster response. Full article

Free-space optical (FSO) communication systems face significant challenges from atmospheric turbulence, which induces time-correlated fading and burst errors that critically affect link reliability. This paper presents a comprehensive end-to-end CCSDS O3K simulation platform with detailed atmospheric channel and pointing error modeling, enabling realistic performance evaluation. The atmospheric channel model follows ITU-R P.1622-1 recommendations and incorporates amplitude scintillation with temporal correlation using Ornstein–Uhlenbeck processes, while the pointing error model captures beam misalignment effects inherent in satellite optical links. Through extensive Monte Carlo simulations, we investigate the impact of coherence time, and interleaving depth on system performance. Results show that deeper interleaving significantly improves reliability under realistic channel conditions, providing valuable design guidance for CCSDS-compliant optical communication systems. This study does not propose new algorithms or protocols; rather, it delivers the first end-to-end CCSDS-compliant simulation framework under realistically modeled turbulence and pointing errors. Accordingly, the results offer meaningful reference value and practical benchmarks for inter-satellite optical communication research and system design. Full article

Aerosols from natural and anthropogenic sources exert significant yet highly variable influences on the Earth’s radiative balance characterized by pronounced spatial and temporal heterogeneity. Accurate quantification of these effects is crucial for enhancing climate projections and informing effective mitigation strategies. In this study, we evaluated the performance of three widely used aerosol optical depth (AOD) datasets—MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2), MODIS Aqua, and MODIS Terra—by comparing them against ground-based AERONET observations from ten stations located within the dust belt region. Statistical assessments included coefficient of determination (R²), correlation coefficient (R), Index of Agreement (IOA), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Relative Mean Bias (RMB), and standard deviation (SD). The results indicate that MERRA-2 showed the highest agreement (R = 0.76), followed by MODIS Aqua (R = 0.75) and MODIS Terra (R = 0.73). Seasonal and annual AOD climatology maps revealed comparable spatial patterns across datasets, although MODIS Terra consistently reported slightly higher AOD values. These findings provide a robust assessment and reanalysis of satellite AOD products over arid regions, offering critical guidance for aerosol modeling, data assimilation, and climate impact studies. Full article

Coral reefs exposed to chronically turbid conditions challenge conventional assumptions about the optical environments required for reef persistence and productivity. This study investigates the utility of light absorption coefficients as indicators of optical water quality in Varadero Reef, an extreme coral ecosystem located in Cartagena Bay, Colombia. Field campaigns were conducted across three seasons (rainy, dry, and transitional) along a transect from fluvial to marine influence. Absorption coefficients at 440 nm were derived for particulate (a_p(440)) and chromophoric dissolved organic matter (a_CDOM(440)) to assess their contribution to underwater light attenuation. Average values across seasons show that a_p(440) reached 0.466 m⁻¹ in the rainy season (September 2021), 0.285 m⁻¹ in the dry season (February 2022), and 0.944 m⁻¹ in the transitional rainy season (June 2022). Meanwhile, mean a_CDOM(440) values were 0.368, 0.111, and 0.552 m⁻¹, respectively. These coefficients reflect the dominant influence of particulate absorption under turbid conditions and increasing a_CDOM(440) relevance during lower turbidity periods. Mean Secchi Disk Depth (ZSD) ranged from 0.6 m in the rainy season to 3.0 m in the dry season, aligning with variations in Kd PAR, which averaged 2.63 m⁻¹, 1.13 m⁻¹, and 1.08 m⁻¹ for the three campaigns. Chlorophyll-a concentrations at 1 m depth also varied significantly, with average values of 2.3, 2.7, and 6.2 μg L⁻¹, indicating phytoplankton biomass peaks associated with seasonal freshwater inputs. While particulate absorption limits light penetration, CDOM plays a potentially photoprotective role by attenuating UV radiation. The observed variability in these optical constituents reflects complex hydrodynamic and environmental gradients, providing insight into the mechanisms that sustain coral functionality under suboptimal light conditions. The absorption-based approach applied here, using standardized spectrophotometric methods, proved to be a reliable and reproducible tool for characterizing the spatial and temporal variability of IOPs. We propose integrating these indicators into monitoring frameworks as cost-effective, component-resolving tool for evaluating light regimes and ecological resilience in optically dynamic coastal systems. Full article

Anthracnose on Illicium verum Hook.f (I. verum) significantly affects the yield and quality of I. verum, and timely detection methods are urgently needed for early control. However, early warning is difficult due to two major challenges, including the sparse availability of optical remote sensing observations due to frequent cloud and rain interference, and the weak spectral responses caused by infestation during early stages. In this article, a framework for early warning of anthracnose on I. verum that combines high-frequency environmental (meteorological and topographical) data and Sentinel-2 remote sensing time-series data, along with a Time-Aware Long Short-Term Memory (T-LSTM) network incorporating an attentional mechanism (At-T-LSTM) was proposed. First, all available environmental and remote sensing data during the study period were analyzed to characterize the early anthracnose outbreaks, and sensitive features were selected as the algorithm input. On this basis, to address the issue of unequal temporal lengths between environmental and remote sensing time series, the At-T-LSTM model incorporates a time-aware mechanism to capture intra-feature temporal dependencies, while a Self-Attention layer is used to quantify inter-feature interaction weights, enabling effective multi-source features time-series fusion. The results show that the proposed framework achieves a spatial accuracy (F1-score) of 0.86 and a temporal accuracy of 83% in early-stage detection, demonstrating high reliability. By integrating remote sensing features with environmental drivers, this approach enables multi-feature collaborative modeling for the risk assessment and monitoring of I. verum anthracnose. It effectively mitigates the impact of sparse observations and significantly improves the accuracy of early warnings. Full article

The optically pumped magnetometer, OPM-MEG, has the potential to replace the traditional low-temperature superconducting quantum interference device, SQUID-MEG. Event-related spectral perturbations (ERSPs) can be used to examine the temporal- and frequency-domain characteristics of a signal. In this paper, a finger-tapping movement paradigm based on auditory cues is adopted, and OPM-MEG is used to measure the functional signals of the brain. The event-related spectral perturbation values of the right and left hands of right-handed people were calculated and compared. The results showed that there was a significant difference in the event-related spectral perturbations between the right and left hands of right-handed people. In summary, OPM-MEG has the ability to measure the event-related spectral perturbations of the brain during finger movements and verify the asymmetry of motor skills. Full article

Riparian vegetation plays a critical role in maintaining ecosystem function, ensuring drainage capacity, and enhancing disaster prevention and mitigation. However, existing ground-based survey methods are limited in both spatial coverage and temporal resolution, which increases the difficulty of meeting the growing demand for rapid, dynamic, and fine-scale monitoring of riverine vegetation. To address this challenge, this study proposes a remote sensing approach that integrates Sentinel-1 synthetic aperture radar imagery with Sentinel-2 optical data. A composite vegetation index was developed by combining the normalized difference vegetation index and synthetic aperture radar backscatter coefficients, thereby enabling the joint characterization of horizontal and vertical vegetation activity. The method was first tested in the Kuji River Basin in Japan and subsequently validated across eight representative river systems nationwide using 16 sets of satellite images acquired between 2016 and 2023. The results demonstrate that the proposed method achieves an average geometric correction error of less than three pixels and yields a spatial distribution of the composite index that closely aligns with the actual vegetation conditions. Moreover, the difference rate between sparse and dense vegetation exceeded 90% across all rivers, indicating a strong discriminative capability and temporal sensitivity. Overall, this method is well-suited for the multiregional and multitemporal monitoring of riparian vegetation and offers a reliable quantitative tool for water environment management and ecological assessment. Full article