Search Results (530)

Accurate mapping of marine aquaculture areas is critical for environmental management and sustainable development for marine ecosystem protection and sustainable resource utilization. However, remote sensing imagery based on single-sensor modalities has inherent limitations when extracting aquaculture zones in complex marine environments. To address this challenge, we constructed a multi-modal dataset from five Chinese coastal regions using cloud detection methods and developed Multi-modal Spatial–Frequency Adaptive Fusion Network (MSAFNet) for optical-radar data fusion. MSAFNet employs a dual-path architecture utilizing a Multi-scale Dual-path Feature Module (MDFM) that combines CNN and Transformer capabilities to extract multi-scale features. Additionally, it implements a Dynamic Frequency Domain Adaptive Fusion Module (DFAFM) to achieve deep integration of multi-modal features in both spatial and frequency domains, effectively leveraging the complementary advantages of different sensor data. Results demonstrate that MSAFNet achieves 76.93% mean intersection over union (mIoU), 86.96% mean F1 score (mF1), and 93.26% mean Kappa coefficient (mKappa) in extracting floating raft aquaculture (FRA) and cage aquaculture (CA), significantly outperforming existing methods. Applied to China’s coastal waters, the model generated 2020 nearshore aquaculture distribution maps, demonstrating its generalization capability and practical value in complex marine environments. This approach provides reliable technical support for marine resource management and ecological monitoring. Full article

Recovery from disasters is the complex process requiring coordinated measures to restore infrastructure, services and quality of life. While remote sensing is a well-established means for damage assessment, so far very few studies have shown how satellite imagery can be used by technical officers of affected countries to provide crucial, up-to-date information to monitor the reconstruction progress and natural restoration. To address this gap, the present study proposes a multi-temporal observatory method relying on GIS, change detection techniques and open and free multi-sensor satellite imagery to generate thematic maps documenting, over time, the impact and recovery from hydrological disasters such as hurricanes, tropical storms and induced flooding. The demonstration is carried out with regard to Hurricane Matthew, which struck Haiti in October 2016 and triggered a humanitarian crisis in the Sud and Grand’Anse regions. Synthetic Aperture Radar (SAR) amplitude change detection techniques were applied to pre-, cross- and post-disaster Sentinel-1 image pairs from August 2016 to September 2020, while optical Sentinel-2 images were used for verification and land cover classification. With regard to inundated areas, the analysis allowed us to determine the needed time for water recession and rural plain areas to be reclaimed for agricultural exploitation. With regard to buildings, the cities of Jérémie and Les Cayes were not only the most impacted areas, but also were those where most reconstruction efforts were made. However, some instances of new settlements located in at-risk zones, and thus being susceptible to future hurricanes, were found. This result suggests that the thematic maps can support policy-makers and regulators in reducing risk and making the reconstruction more resilient. Finally, to evaluate the replicability of the proposed method, an example at a country-scale is discussed with regard to the June 2023 flooding event. Full article

Understanding how solvents influence the luminescence behavior of Cu(I) complexes is crucial for designing advanced optical sensors. This study reports the synthesis, structures and photophysical investigation of an 8-hydroxyquinoline-functionalized 1H-imidazo[4,5-f][1,10]phenanthroline ligand, ipqH₂, and its four Cu(I) complexes with diphosphine co-ligands. Photoluminescence studies demonstrated distinct solvent-dependent excited-state mechanisms. In DMSO/alcohol mixtures, free ipqH₂ exhibited excited-state proton transfer (ESPT) and enol-keto tautomerization, producing dual emission at about 447 and 560 nm, while the complexes resisted ESPT due to hydrogen bond blocking by PF₆⁻ anions and Cu(I) coordination. In DMSO/H₂O, aggregation-caused quenching (ACQ) and high-energy O–H vibrational quenching dominated, but complexes 1 and 2 showed a significant red-shifted emission (569–574 nm) with high water content due to solvent-stabilized intra-ligand charge transfer and metal-to-ligand charge transfer ((IL+ML)CT) states. In DMSO/DMF, hydrogen bond competition and solvation-shell reorganization led to distinct responses: complexes 1 and 3, with flexible bis[(2-diphenylphosphino)phenyl]ether (POP) ligands, displayed peak splitting and (IL + ML)CT redshift emission (501 ⟶ 530 nm), whereas complexes 2 and 4, with rigid 9,9-dimethyl-4,5-bis(diphenylphosphino)-9H-xanthene (xantphos), showed weaker responses. The flexibility of the diphosphine ligand dictated DMF sensitivity, while the coordination, the hydrogen bonds between PF₆⁻ anions and ipqH₂, and water solubility governed the alcohol/water responses. This work elucidates the multifaceted solvent-responsive mechanisms in Cu(I) complexes, facilitating the design of solvent-discriminative luminescent sensors. Full article

This work introduces a two-stage framework, named the Distorted underwater image Restoration Network (DR-Net), to address the complex degradation of underwater images caused by turbulence, water flow fluctuations, and optical properties of water. The first stage employs the Distortion Estimation Network (DE-Net), which leverages a fusion of Transformer and U-Net architectures to estimate distortion information from degraded images and focuses on image distortion recovery. Subsequently, the Image Restoration Generative Adversarial Network (IR-GAN) in the second stage utilizes this estimated distortion information to deblur images and regenerate lost details. Qualitative and quantitative evaluations on both synthetic and real-world image datasets demonstrate that DR-Net outperforms traditional methods and restoration strategies from different perspectives, showcasing its broader applicability and robustness. Our approach enables the restoration of underwater images from a single frame, which facilitates the acquisition of marine resources and enhances seabed exploration capabilities. 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

In recent years, evidence of gold mining during the Roman period has been found by archaeologists in the Cerdanya region (Girona, Catalonia). In this region, Les Guilleteres d’All has been described as a mining complex because of the erosive features observed in the landscape; surveys have identified hydraulic mining opencast structures named chantier-cirques and chantier-ravins. The latter are smaller, but both require a water reservoir, specifically a water retention facility, to supply water flow. One of these buried water reservoirs has been excavated, revealing an enlarged area with a dam constructed from stone blocks. Two pottery sherds were found within the sediment layers deposited on the bottom of the reservoir—one dated to the 1st–2nd c. AD and the other to the Bronze Age—indicating that the reservoir was filled during historical times and the nearby presence of settlements from these periods. Optically Stimulated Luminescence (OSL) dating was performed on two waterlain sediment layers deposited at the bottom deposited at the reservoir. The obtained ages, dating to 2nd–4th c. AD, correspond to the final phase or abandonment of mining activities. Hence, these ages provide the first evidence of mining activities in Les Guilleteres during Roman times. Full article

Digital holographic microscopes provide a microscopy solution with a resolution in the low-micrometre range that offers similar performance to optical microscopy, but as a relatively low-cost alternative. The most significant cost saving is due to the ability to reconstruct microscopic images from holograms using low-cost components without the need for an optical stack. The cost saving opens up the avenue towards a feasible solution for geographically distributed in situ microscopic sensing in rural areas for problems like air and water pollution monitoring. The most significant contributors to cost are the camera sensor module, the pinhole, and the processing platform. The latter two components are addressed, at least in part, in this work. We successfully manufactured sub-100 $\mu$m diameter pinholes using ultraviolet (UV) laser cutting with an LPKF printed circuit board (PCB) prototyping platform and present the low-cost micromachining method. The pinholes were utilised within a prototype field-programmable gate array (FPGA) demonstrator that successfully reconstructed the holographic images. The choice for the FPGA approach as the initial step, albeit more complex, lends itself towards the easier development of a dedicated reconstructed application-specific integrated circuit (ASIC) to ultimately drive the cost down even further. Full article

Due to its conjugated structure, 1,4,6,8-tetrakis((E)-2-(thiophen-2-yl)vinyl)azulene (L) has a high potential for nonlinear optics and coloring. This compound was studied electrochemically using cyclic voltammetry, pulse differential voltammetry and rotating disk voltammetry in organic electrolytes. The main processes occurring during oxidation and reduction scans were highlighted and characterized. Density functional theory (DFT) calculations were conducted to assess the chemical reactivity of this compound. UV-Vis studies of L were performed in acetonitrile to establish the optical parameters in this solvent and its complexing power towards heavy metal (HM) ions. Chemically modified electrodes (CMEs) based on L were prepared by electrooxidation of L in organic electrolytes. To evaluate the electrochemical behavior of the CMEs, they were characterized with a ferrocene redox probe. They were also tested for the analysis of synthetic samples of heavy metal ions (HM): Cd(II), Pb(II), Cu(II) and Hg(II) by anodic stripping. Specific responses were obtained for Pb(II) and Cd(II) ions. The preparation conditions have an influence on the electrochemical responses. This study is relevant for the design and further development of advanced materials based on this azulene for the analysis of HMs in water samples. Electrochemical experiments and DFT calculations recommended L as a new ligand for modifying the electrode surface for the analysis of HMs. Full article

Methane emissions from freshwater reservoirs represent a globally relevant greenhouse gas source, which are estimated to range from 3% to 10% of all global anthropogenic methane emissions. However, there is high uncertainty in estimating reservoir emissions on local to global scales due to a combination of data paucity in key regions, particularly in the Southern Hemisphere, and challenges monitoring emission pathways. The key to improved spatially and temporally representative estimation of emission rates is to better understand the primary drivers of emission pathways, in particular, ebullition. We examine ebullition from 15 freshwater storages located in the Southern Hemisphere subtropical (South East Queensland) and temperate (Tasmania) regions using a combination of optical methane detection to develop the high-resolution mapping of ebullition zones and floating chamber incubation within ebullition zones to quantify areal emission rates. We demonstrate the equivalent water level, through air pressure or physical water level change, as a key driver of ebullition and examine the implications for spatially and temporally representative estimation of reservoir emissions. This study represents the largest broadscale ebullition survey undertaken across Australian temperate and subtropical reservoirs. The study findings are of broad relevance to scientists and corporate and government entities navigating the complexities of estimating greenhouse gas emissions from reservoirs and related policy instruments. Full article

For aquaculture systems, maximizing feed efficiency is a major challenge since it directly affects growth rates and economic sustainability. Feed is one of the largest costs in aquaculture, and feed waste is a significant environmental issue that requires effective management strategies. This paper suggests a novel approach for optimal fish feeding in pond aquaculture systems that integrates vision language models (VLMs), optical flow, and advanced image processing techniques to enhance feed management strategies. The system allows for the precise assessment of fish needs in connection to their feeding habits by integrating real-time data on biomass estimates and water quality conditions. By combining these data sources, the system makes informed decisions about when to activate automated feeders, optimizing feed distribution and cutting waste. A case study was conducted at a profit-driven tilapia farm where the system had been operational for over half a year. The results indicate significant improvements in feed conversion ratios (FCR) and a 28% reduction in feed waste. Our study found that, under controlled conditions, an average of 135 kg of feed was saved daily, resulting in a cost savings of approximately $1800 over the course of the study. The VLM-based fish feeding behavior recognition system proved effective in recognizing a range of feeding behaviors within a complex dataset in a series of tests conducted in a controlled pond aquaculture setting, with an F1-score of 0.95, accuracy of 92%, precision of 0.90, and recall of 0.85. Because it offers a scalable framework for enhancing aquaculture resource use and promoting sustainable practices, this study has significant implications. Our study demonstrates how combining language models and image processing could transform feeding practices, ultimately improving aquaculture’s environmental stewardship and profitability. Full article

The accurate monitoring of short-term bathymetric changes in shallow waters is essential for effective coastal management and planning. Machine Learning (ML) applied to Unmanned Aerial Vehicle (UAV)-based multispectral imagery offers a rapid and cost-effective solution for bathymetric surveys. However, models based solely on multispectral imagery are inherently limited by confounding factors such as shadow effects, poor water quality, and complex seafloor textures, which obscure the spectral–depth relationship, particularly in heterogeneous coastal environments. To address these issues, we developed a hybrid bathymetric inversion model that integrates digital surface model (DSM) data—providing high-resolution topographic information—with ML applied to UAV-based multispectral imagery. The model training was supported by multibeam sonar measurements collected from an Unmanned Surface Vehicle (USV), ensuring high accuracy and adaptability to diverse underwater terrains. The study area, located around Lazarus Island, Singapore, encompasses a sandy beach slope transitioning into seagrass meadows, coral reef communities, and a fine-sediment seabed. Incorporating DSM-derived topographic information substantially improved prediction accuracy and correlation, particularly in complex environments. Compared with linear and bio-optical models, the proposed approach achieved accuracy improvements exceeding 20% in shallow-water regions, with performance reaching an R² > 0.93. The results highlighted the effectiveness of DSM integration in disentangling spectral ambiguities caused by environmental variability and improving bathymetric prediction accuracy. By combining UAV-based remote sensing with the ML model, this study presents a scalable and high-precision approach for bathymetric mapping in complex shallow-water environments, thereby enhancing the reliability of UAV-based surveys and supporting the broader application of ML in coastal monitoring and management. Full article

As an important traffic and transportation roadway, tunnel engineering is widely used in important fields such as highways, railways, water conservancy, subways and mining. It is limited by complex geological conditions, harsh construction environments and poor robustness of the monitoring system. If the construction process and monitoring method are not properly designed, it will often directly induce disasters such as tunnel deformation, collapse, leakage and rockburst. This seriously threatens the safety of tunnel construction and operation and the protection of the regional ecological environment. Therefore, based on distributed fiber optic sensing technology, the full–cycle spatiotemporally continuous sensing information of the tunnel structure is obtained in real time. Accordingly, the health status of the tunnel is dynamically grasped, which is of great significance to ensure the intrinsic safety of the whole life cycle for the tunnel project. Firstly, this manuscript systematically sorts out the development and evolution process of the theory and technology of structural health monitoring in tunnel engineering. The scope of application, advantages and disadvantages of mainstream tunnel engineering monitoring equipment and main optical fiber technology are compared and analyzed from the two dimensions of equipment and technology. This provides a new path for clarifying the key points and difficulties of tunnel engineering monitoring. Secondly, the mechanism of action of four typical optical fiber sensing technologies and their application in tunnel engineering are introduced in detail. On this basis, a spatiotemporal continuous perception method for tunnel engineering based on DFOS is proposed. It provides new ideas for safety monitoring and early warning of tunnel engineering structures throughout the life cycle. Finally, a high–speed rail tunnel in northern China is used as the research object to carry out tunnel structure health monitoring. The dynamic changes in the average strain of the tunnel section measurement points during the pouring and curing period and the backfilling period are compared. The force deformation characteristics of different positions of tunnels in different periods have been mastered. Accordingly, scientific guidance is provided for the dynamic adjustment of tunnel engineering construction plans and disaster emergency prevention and control. At the same time, in view of the development and upgrading of new sensors, large models and support processes, an innovative tunnel engineering monitoring method integrating “acoustic, optical and electromagnetic” model is proposed, combining with various machine learning algorithms to train the long–term monitoring data of tunnel engineering. Based on this, a risk assessment model for potential hazards in tunnel engineering is developed. Thus, the potential and disaster effects of future disasters in tunnel engineering are predicted, and the level of disaster prevention, mitigation and relief of tunnel engineering is continuously improved. Full article

Whispering-gallery-mode (WGM) microresonators are amongst the most promising optical sensors for detecting bio-chemical targets. A number of laser interrogation methods have been proposed and demonstrated over the last decade, based on scattering and absorption losses or resonance splitting and shift, harnessing the high-quality factor and ultra-small volume of WGMs. Actually, regardless of the sensitivity enhancement, their practical sensing operation may be hampered by the complexity of coupling devices as well as the signalprocessing required to extract the WGM response. Here, we use a silica microsphere immersed in an aqueous environment and efficiently excite optical WGMs with a free-space visible laser, thus collecting the relevant information from the transmitted and back-scattered light without any optical coupler, fiber, or waveguide. We show that a 640-nm diode laser, actively frequency-locked on resonance, provides real-time, fast sensing of dielectric nanoparticles approaching the surface with direct analog readout. Thanks to our illumination scheme, the sensor can be kept in water and operate for days without degradation or loss of sensitivity. Diverse noise contributions are carefully considered and quantified in our system, showing a minimum detectable particle size below 1 nm essentially limited by the residual laser microcavity jitter. Further analysis reveals that the inherent laserfrequency instability in the short, -mid-term operation regime sets an ultimate bound of 0.3 nm. Based on this work, we envisage the possibility to extend our method in view of developing new viable approaches for detection of nanoplastics in natural water without resorting to complex chemical laboratory methods. Full article

Onboard the MetOp satellite series, Infrared Atmospheric Sounding Interferometer (IASI) is a Fourier Transform spectrometer based on the Michelson interferometer. IASI acquires interferograms, which are processed to provide high-resolution atmospheric emission spectra. These spectra enable the derivation of temperature and humidity profiles, among other parameters, with exceptional spectral resolution. In this study, we evaluate a novel, rapid retrieval approach in the interferogram domain, aiming for near-real-time (NRT) analysis of large spectral datasets anticipated from next-generation tropospheric sounders, such as MTG-IRS. The Partially Sampled Interferogram (PSI) method, applied to trace gas retrievals from IASI, has been sparsely explored. However, previous studies suggest its potential for high-accuracy retrievals of specific gases, including CO, CO₂, CH₄, and N₂O at the resolution of a single IASI footprint. This article presents the results of a study based on retrieval in the interferogram domain. Furthermore, the optical pathway differences sensitive to the parameters of interest are studied. Interferograms are generated using a fast Fourier transform on synthetic IASI spectra. Finally, the relationship to the total column of carbon monoxide is explored using three different algorithms—from the most intuitive to a complex neural network approach. These algorithms serve as a proof of concept for interferogram classification and rapid predictions of surface temperature, as well as the abundances of H₂O and CO. IASI spectra simulations were performed using the LATMOS Atmospheric Retrieval Algorithm (LARA), a robust and validated radiative transfer model based on least squares estimation. The climatological library TIGR was employed to generate IASI interferograms from LARA spectra. TIGR includes 2311 atmospheric scenarios, each characterized by temperature, water vapor, and ozone concentration profiles across a pressure grid from the surface to the top of the atmosphere. Our study focuses on CO, a critical trace gas for understanding air quality and climate forcing, which displays a characteristic absorption pattern in the 2050–2350 ${\mathrm{cm}}^{-1}$ wavenumber range. Additionally, the study explores the potential of correlating interferogram characteristics with surface temperature and H₂O content, aiming to enhance the accuracy of CO column retrievals. Starting with intuitive retrieval algorithms, we progressively increased complexity, culminating in a neural network-based algorithm. The results of the NN study demonstrate the feasibility of fast interferogram-domain retrievals, paving the way for operational applications. Full article

We present a dual-mode optical sensing strategy for selective and sensitive detection of sulfide ions (S²⁻), employing copper-anchored nitrogen-doped graphene quantum dots (Cu@N-GQDs) as bifunctional nanozymes. The Cu@N-GQDs were synthesized via citric acid pyrolysis in the presence of ammonium hydroxide (serving as both nitrogen source and reductant) and copper chloride, leading to uniform incorporation of copper oxide species onto the N-GQD surface. The resulting nanohybrids exhibit two synergistic functionalities: intrinsic fluorescence comparable to pristine N-GQDs, and significantly enhanced peroxidase-like catalytic activity attributed to the anchored copper species. Upon interaction with sulfide ions, the system undergoes a dual-optical response: (i) fluorescence quenching via Cu-S complexation, and (ii) inhibition of peroxidase-like activity due to the deactivation of Cu catalytic centers via the interaction with S²⁻. This dual-signal strategy enables sensitive quantification of S²⁻, achieving detection limits of 0.5 µM (fluorescence) and 3.5 µM (colorimetry). The sensor demonstrates excellent selectivity over competing substances and high reliability and precision in real tap water samples. These findings highlight the potential of Cu@N-GQDs as robust, bifunctional, and field-deployable nanozyme probes for environmental and biomedical sulfide ion monitoring. Full article