High-resolution Field Research Articles

Monitoring mangroves with multi-sensor Earth Observation data sets: from one-shot historical cartography to online and near-real-time monitoring tools

Abstract. This study outlines advancements in mangrove monitoring using Earth Observation (EO) technologies, highlighting a transition from historical cartography to modern, near-real-time monitoring systems. Mangroves, critical for biodiversity, climate regulation, and coastal protection, face threats from climate change and human activities. The need for accurate and recurrent mapping tools is emphasized, addressing the limitations of current global datasets like the Global Mangrove Watch (GMW) by incorporating high-resolution data and field measurements. The French National Research Institute for Sustainable Development (IRD) has pioneered mangrove monitoring since the 1990s, with key methodologies including the use of Sentinel-2 and Pleiades satellite imagery for high-resolution mapping, texture-based analysis, and the incorporation of LiDAR data for accurate biomass estimation. Historical cartography is contrasted with contemporary monitoring efforts, focusing on the integration of multi-source data and the importance of localized ground-truthing. This approach enhances the capabilities of earth observation for carbon stock assessments and supports informed decision-making in conservation and climate policy. The work also highlights the need for further integration of local knowledge and advanced EO sensor data to refine carbon sequestration models and improve mangrove management strategies.

Lagrangian analysis of submesoscale flows from sparse data using Gaussian Process Regression for field reconstruction

Lagrangian analyses of oceanic flows provide insight into the various transport pathways in the ocean. This analysis typically relies on a dense set of trajectories that can be computed using high-resolution velocity fields, which are often not available during field experiments. Instruments like drifters and floats are often employed to overcome the limitations imposed by satellite- and radar-based velocity fields, to understand the transport pathways in the ocean. However, the sparsity in available drifter-trajectory data proves prohibitive to obtaining a comprehensive map of the Lagrangian characteristics of the underlying flow. To circumvent these issues, we use Gaussian Process Regression (GPR) to obtain velocity fields from sparse drifter data to generate synthetic trajectories and subsequently estimate two Lagrangian metrics, FTLE and dilation rate. A detailed error analysis is performed for drifter clusters deployed within various dynamical regions in the analytic Bickley jet system. The uncertainties in velocity reconstruction obtained from the GPR method, averaged along particle trajectories, locate Lagrangian confidence regions that are applicable both to synthetic trajectories and the dilation rate field. A sensitivity analysis reveals the role played by factors such as the spatial sampling density and temporal resolution of the drifter data, as well as the effect of position uncertainty as a result of GPS inaccuracy. The method is then applied to the drifter data from the Lagrangian Submesoscale Experiment in 2016 to locate convergent filaments. The results present a marked improvement over direct estimation of area-averaged dilation rates using drifter clusters.

Enhancing high-resolution reconstruction of flow fields using physics-informed diffusion model with probability flow sampling

The application of artificial intelligence (AI) technology in fluid dynamics is becoming increasingly prevalent, particularly in accelerating the solution of partial differential equations and predicting complex flow fields. Researchers have extensively explored deep learning algorithms for flow field super-resolution reconstruction. However, purely data-driven deep learning models in this domain face numerous challenges. These include susceptibility to variations in data distribution during model training and a lack of physical and mathematical interpretability in the predictions. These issues significantly impact the effectiveness of the models in practical applications, especially when input data exhibit irregular distributions and noise. In recent years, the rapid development of generative artificial intelligence and physics-informed deep learning algorithms has created significant opportunities for complex physical simulations. This paper proposes a novel approach that combines diffusion models with physical constraint information. By integrating physical equation constraints into the training process of diffusion models, this method achieves high-fidelity flow field reconstruction from low-resolution inputs. Thus, it not only leverages the advantages of diffusion models but also enhances the interpretability of the models. Experimental results demonstrate that, compared to traditional methods, our approach excels in generating high-resolution flow fields with enhanced detail and physical consistency. This advancement provides new insights into developing more accurate and generalized flow field reconstruction models.

Acoustic field visualization and source localization via physics-informed learning of sparse data with adaptive sampling

Traditional methods for acoustic field visualization require considerable effort for capturing large amounts of acoustic data to achieve a high resolution field map, highly limiting their widespread use. In this study, we propose an approach for acoustic field visualization based on physics-informed neural networks (PINNs) by using a small amount of data, subsequently realizing accurate acoustic source localization. First, we present a PINN model integrated with an acoustic Helmholtz equation and adaptive sampling, the performance of which is testified via numerical simulations. The “no mesh” character of PINN enables achieving high resolution acoustic field visualization without requiring the capture of numerous data in advance. Furthermore, we experimentally validate the performance of the proposed method, which demonstrates that the acoustic sources can be precisely localized with sparse field data acquisition within a small area. This work would find potential applications in various acoustics, such as acoustic communication, biomedical imaging, and virtual reality.

Strong Subseasonal Variability of Oxic Methane Production Challenges Methane Budgeting in Freshwater Lakes.

Methane (CH4) accumulation in the well-oxygenated lake epilimnion enhances the diffusive atmospheric CH4 emission. Both lateral transport and in situ oxic methane production (OMP) have been suggested as potential sources. While the latter has been recently supported by increasing evidence, quantifying the exact contribution of OMP to atmospheric emissions remains challenging. Based on a large high-resolution field data set collected during 2019-2020 in the deep stratified Lake Stechlin and on three-dimensional hydrodynamic modeling, we improved existing CH4 budgets by resolving each component of the mass balance model at a seasonal scale and therefore better constrained the residual OMP. All terms in our model showed a large temporal variability at scales from intraday to seasonal, and the modeled OMP was most sensitive to the surface CH4 flux estimates. Future efforts are needed to reduce the uncertainties in estimating OMP rates using the mass balance approach by increasing the frequency of atmospheric CH4 flux measurements.

Hybrid Refractive and Diffractive Testing Method for Free-Form Convex Mirror in High-Resolution Remote-Sensing Cameras

The development of high-resolution and large field of view remote-sensing cameras is inextricably linked to the application of free-form mirrors. The free-form mirror offers higher design of freedom and is more effective at correcting aberrations in optical systems. The surface shape error of a free-form mirror directly affects the imaging quality of remote-sensing cameras. Consequently, a high-precision free-form mirror detection method is of paramount importance. For the convex free-form surface mirror with a large aperture, a hybrid refractive and diffractive testing method combining computer-generated holography (CGH) and spherical mirrors for high-precision null test is proposed in this paper. When comparing the effect of error and the detection sensitivity of different designs, the results showed that the influence of the system error is reduced by about 42% and the sensitivity is increased by more than 2.6 times. The proposed method can achieve higher testing accuracy and represents an effective and feasible approach for the surface shape detection method.

Shallow Reverse Moderate Earthquakes in the Weiyuan Shale Gas Field, Sichuan Basin, China, Related to Hydraulic Fracturing

Abstract The Weiyuan shale gas field in the stable southern Sichuan basin, China, has experienced increasing seismicity since systematic hydraulic fracturing (HF) operations in 2015. Three Mw≥4.4 shallow earthquakes occurred in the Weiyuan area between September 2019 and February 2020, yet their seismogenic faults, rupture models, and relationship with HF are unknown. In this study, we first obtain the high-resolution coseismic deformation fields of these three events and then invert their slip distribution. The result shows that all three events are shallow high-dip reverse events under the contractional Weiyuan anticline environment with peak slips of 158, 68, and 34 cm and at depths of 4, 3, and 1.6 km, respectively. The spatial relationship between seismogenic faults, horizontal wells, as well as geological data reveals that pore-pressure diffusion due to the HF may be the main mechanism of the 8 September 2019 and the 18 December 2019 events, whereas the 16 February 2020 event may be attributed to the poroelastic stress perturbation caused by the HF. Our study highlights that HF activities and regional geological characteristics jointly influence the properties of earthquakes in the Sichuan basin.

Double-Layer Metasurface Integrated with Micro-LED for Naked-Eye 3D Display.

Naked-eye 3D micro-LED display combines the characteristics of 3D display with the advantages of micro-LED. However, the 3D micro-LED display is still at the conceptual stage, limited by its intrinsic emission properties of large divergence angle and non-coherence, as well as difficulties in achieving large viewing angles with high luminous efficiency. In this work, we propose a double-layer metasurface film integrating functions of collimation with multiple deflections, constituting a micro-LED naked-eye 3D display system. The system is characterized through numerical simulations using the 3D finite-difference time-domain method. The simulation results show that the double-layer metasurface film restricts 90% of the emitted light of the micro-LED to the vicinity of the 0° angle, improving its spatial coherence. Subsequently, a large-angle, low-crosstalk outgoing from -45° to 45° is achieved, while providing a deflection efficiency of over 80% and a pixel density of up to 605. We believe this design provides a feasible approach for realizing naked-eye 3D micro-LED displays with a large field of view, low crosstalk, and high resolution.

High-resolution 7T fMRI reveals the visual zone of the human claustrum

Abstract The claustrum is a thin grey matter structure located between the insular cortex and the putamen. The function of the claustrum is largely unknown with diverse hypotheses ranging from multisensory integration and consciousness to attention and cognitive control. Much research on the function of the claustrum relies on invasive techniques in animal models, as the claustrum’s uniquely thin shape makes it difficult to image non-invasively in human subjects. In the current proof-of-concept study, we used high-resolution ultra-high field (7 Tesla) functional magnetic resonance imaging (fMRI) to measure activity in the human claustrum during the processing of naturalistic stimuli. We presented short video clips as visual only, auditory only or audiovisual conditions while participants performed a central fixation task. We found distinct visual responses in both the left and the right claustrum at a consistent spatial location across participants, hemispheres and sessions. We also found deactivations in response to auditory stimulation. These deactivations were confined to the right claustrum and did not overlap with visual activity. The deactivation in response to auditory stimulation demonstrates the complexity of claustrum’s functional organization and suggests functional differentiation within the claustrum. This is the first study to demonstrate sensory-specific effects within the human claustrum. It opens the possibility for studying the claustrum’s role in higher-level aspects of sensory processing in humans.

Layer-specific BOLD effects in gradient and spin-echo acquisitions in somatosensory cortex.

Previous studies have shown varied BOLD signals with gradient echo (GE) across cortical depth. To interpret these variations, and understand the effects of vascular geometry and size, the magnitudes and layer distributions of GE and spin-echo (SE) BOLD functional MRI signals were compared in the somatosensory cortex of squirrel monkeys during tactile stimulation and in a resting state at high spatial resolution and high field. A block-design stimulation was used to identify tactile-evoked activation signals in somatosensory Areas 3b and 1. Layer-specific connectivities were calculated using resting-state data. Signal power spectra were compared by depth and pulse sequence. The measured ratios of transverse relaxation rate changes were compared with Anderson and Weiss's model. SE signals showed a 26% lower percentage signal change during tactile stimulation compared with GE, along with a slower time course. SE signals remained consistent but weaker in lower layers, whereas GE signals decreased with cortical depth. This pattern extended to resting-state power spectra. Resting-state functional connectivity indicated larger connectivity between the top layers of Area 3b and Area 1 for GE, with minimal changes for SE. Comparisons with theory suggest vessel diameters ranging from 19.4 to 9 microns are responsible for BOLD effects across cortical layers at 9.4 T. These results provide further evidence that at high field, SE BOLD signals are relatively free of contributions from sources other than microvascular changes in response to neural activity, whereas GE signals, even in the superficial layers, are not dominated by very large vessels.

Upscaling transport in heterogeneous media featuring local-scale dispersion: Flow channeling, macro-retardation and parameter prediction

Many theoretical treatments of transport in heterogeneous Darcy flows consider advection only. When local-scale dispersion is neglected, flux weighting persists over time; mean Lagrangian and Eulerian flow velocity distributions relate simply to each other and to the variance of the underlying hydraulic conductivity field. Local-scale dispersion complicates this relationship, potentially causing initially flux-weighted solute to experience lower-velocity regions as well as Taylor-type macrodispersion due to transverse solute movement between adjacent streamlines. To investigate the interplay of local-scale dispersion with conductivity log-variance, correlation length, and anisotropy, we perform a Monte Carlo study of flow and advective-dispersive transport in spatially-periodic 2D Darcy flows in large-scale, high-resolution multivariate Gaussian random conductivity fields. We observe flow channeling at all heterogeneity levels and quantify its extent. We find evidence for substantial effective retardation in the upscaled system, associated with increased flow channeling, and observe limited Taylor-type macrodispersion, which we physically explain. A quasi-constant Lagrangian velocity is achieved within a short distance of release, allowing usage of a simplified continuous-time random walk (CTRW) model we previously proposed in which the transition time distribution is understood as a temporal mapping of unit time in an equivalent system with no flow heterogeneity. The numerical data set is modeled with such a CTRW; we show how dimensionless parameters defining the CTRW transition time distribution are predicted by dimensionless heterogeneity statistics and provide empirical equations for this purpose.

Leukocyte detection based on convolutional neural network fusion with color Fourier ptychographic microscopy

Abstract In the field of optical microscopic imaging, color Fourier Ptychographic Microscopy (FPM) technology has attracted much attention due to its advantages of large field of view, high resolution, and quantitative phase imaging. In this paper, a color FPM fusion algorithm based on deep learning is proposed in combination with Convolutional Neural Networks (CNN) and applied to leukocyte detection. Firstly, this paper introduces a fusion model of a convolutional neural network based on the traditional color FPM imaging method and fuses low-resolution color images and high-resolution grayscale images through a multilayer convolutional network. This method improves the quality of reconstructed images while reducing the reconstruction time. Secondly, this paper constructs a leukocyte detection dataset by using an improved color FPM reconstruction algorithm and builds a leukocyte detection system based on the YOLOv7 architecture. This paper shows that combining convolutional neural networks with color FPM technology can provide higher-quality reconstructed images in medical imaging and cell detection, which provides strong technical support for digital pathology and medical diagnosis.

Private sensors and crowdsourced rainfall data: Accuracy and potential for modelling pluvial flooding in urban areas of Oslo, Norway

Cloudbursts and extreme rainstorms pose a growing threat to urban areas. Accurate rainfall data is essential for predicting inundations and urban flooding. Private weather stations are becoming increasingly common, and their spatial distribution roughly follows population density. This makes them a valuable source of crowdsourced data for high-resolution rainfall fields in urban areas. We evaluated the performance of private rain gauges in two recent pluvial flood events in Oslo. We also explored the potential use of private rain gauge data in inundation models. Our results indicate that private sensors have excellent rain detection capabilities, but they tend to underestimate the reference value on average by approximately 25%. However, bias-corrected crowdsourced rainfall data can produce significantly more accurate inundation maps than those generated from official rain gauges, if compared with maps resulting from bias-corrected weather radar. Overall, our study highlights the potential of utilizing crowdsourced rainfall data from private sensors for accurately representing pluvial flooding in urban areas. These findings have significant implications for improving flood prediction and mitigation strategies in vulnerable urban settings.

Multi-scale assessment of high-resolution reanalysis precipitation fields over Italy

This study focuses on the validation of high-resolution regional reanalyses to understand their effectiveness in reproducing precipitation patterns over Italy, a climate change hotspot characterized by coastal sea-land interaction and complex orography. Nine reanalysis products were evaluated, with the ECMWF global reanalysis ERA5 serving as a benchmark. These included both European (COSMO-REA6, CERRA) and Italy-specific (BOLAM, MERIDA, MERIDA-HRES, MOLOCH, SPHERA, VHR-REA_IT) datasets, using different models and parametrizations. The inter-comparison involved determining the effective resolution of daily precipitation fields using wavelet techniques and assessing intense precipitation statistics through frequency distributions. In-situ observations and observational gridded datasets were used to independently validate reanalysis precipitation fields. The capability of reanalyses to depict daily precipitation patterns was assessed, highlighting a maximum radius of precipitation misplacement of about 15 km, with notably lower skills during summer. An overall overestimation of precipitation was identified in the reanalysis climatological fields over the Po Valley and the Alps, whereas multiple products showed an underestimation of precipitations across the North-West coast, the Apennines, and Southern Italy. Finally, a comparison with a time-consistent observational dataset (UniMi/ISAC-CNR) revealed a non-stable deviation from observations in the annual precipitation cumulate of the reanalysis products analyzed. This should be taken into account when interpreting precipitation trends over Italy.

Geostatistical Simulation of Daily Rainfall Fields—Performance Assessment for Extremes in West Africa

Abstract The spatial description of high-resolution extreme daily rainfall fields is challenging because of the high spatial and temporal variability of rainfall, particularly in tropical regions due to the stochastic nature of convective rainfall. Geostatistical simulations offer a solution to this problem. In this study, a stochastic geostatistical simulation technique based on the spectral turning bands method is presented for modeling daily rainfall extremes in the data-scarce tropical Ouémé River basin (Benin). This technique uses meta-Gaussian frameworks built on Gaussian random fields, which are transformed into realistic rainfall fields using statistical transfer functions. The simulation framework can be conditioned on point observations and is computationally efficient in generating multiple ensembles of extreme rainfall fields. The results of tests and evaluations for multiple extremes demonstrate the effectiveness of the simulation framework in modeling more realistic rainfall fields and capturing their variability. It successfully reproduces the empirical cumulative distribution function of the observation samples and outperforms classical interpolation techniques like ordinary kriging in terms of spatial continuity and rainfall variability. The study also addresses the challenge of dealing with uncertainty in data-poor areas and proposes a novel approach for determining the spatial correlation structure even with low station density, resulting in a performance boost of 9.5% compared to traditional techniques. Additionally, we present a low-skill reference simulation method to facilitate a comprehensive comparison of the geostatistical simulation approaches. The simulations generated have the potential to provide valuable inputs for hydrological modeling.

Range-dynamical low-rank split-step Fourier method for the parabolic wave equation.

Numerical solutions to the parabolic wave equation are plagued by the curse of dimensionality coupled with the Nyquist criterion. As a remedy, a new range-dynamical low-rank split-step Fourier method is developed. The integration scheme scales sub-linearly with the number of classical degrees of freedom in the transverse directions. It is orders of magnitude faster than the classic full-rank split-step Fourier algorithm and saves copious amounts of storage space. This enables numerical solutions of the parabolic wave equation at higher frequencies and on larger domains, and simulations may be performed on laptops rather than high-performance computing clusters. Using a rank-adaptive scheme to optimize the low-rank equations further ensures the approximate solution is highly accurate and efficient. The methodology and algorithms are demonstrated on realistic high-resolution data-assimilative ocean fields in Massachusetts Bay for two three-dimensional acoustic configurations with different source locations and frequencies. The acoustic pressure, transmission loss, and phase solutions are analyzed in the two geometries with seamounts and canyons across and along Stellwagen Bank. The convergence with the rank of the subspace and the properties of the rank-adaptive scheme are demonstrated, and all results are successfully compared with those of the full-rank method when feasible.

Coral Community Composition Linked to Hypoxia Exposure.

Tropical reef ecosystems are strongly influenced by the composition of coral species, but the factors influencing coral diversity and distributions are not fully understood. Here we demonstrate that large variations in the relative abundance of three major coral species across adjacent Caribbean reef sites are strongly related to their different low O2 tolerances. In laboratory experiments designed to mimic reef conditions, the cumulative effect of repeated nightly low O2 drove coral bleaching and mortality, with limited modulation by temperature. After four nights of repeated low O2, species responses also varied widely, from > 50% bleaching in Acropora cervicornis to no discernable sensitivity of Porites furcata. A simple metric of hypoxic pressure that combines these experimentally derived species sensitivities with high-resolution field data accurately predicts the observed relative abundance of species across three reefs. Only the well-oxygenated reef supported the framework-building hypoxia-sensitive Acropora cervicornis, while the hypoxia-tolerant weedy species Porites furcata was dominant on the most frequently O2-deplete reef. Physiological exclusion of acroporids from these O2-deplete reefs underscores the need for hypoxia management to reduce extirpation risk.

Formation and evolution of thermokarst landslides in the Qinghai-Tibet Plateau, China

Thermokarst landslide (TL) activity in the Qinghai-Tibet Plateau (QTP) is intensifying due to climate warming-induced permafrost degradation. However, the mechanisms driving landslide formation and evolution remain poorly understood. This study investigates the spatial distribution, annual frequency, and monthly dynamics of TLs along the Qinghai-Tibet engineering corridor (QTEC), in conjunction with in-situ temperature and rainfall observations, to elucidate the interplay between warming, permafrost degradation, and landslide activity. Through the analysis of high-resolution satellite imagery and field surveys, we identified 1298 landslides along the QTEC between 2016 and 2022, with an additional 386 landslides recorded in a typical landslide-prone sub-area. In 2016, 621 new active-layer detachments (ALDs) were identified, 1.3 times the total historical record. This surge aligned with unprecedented mean annual and August temperatures. The ALDs emerged primarily between late August and early September, coinciding with maximum thaw depth. From 2016 to 2022, 97.8 % of these ALDs evolved into retrogressive thaw slumps (RTSs), identified as active landslides. Landslides typically occur in alpine meadows at moderate altitudes and on gentle northward slopes. The thick ice layer near the permafrost table serves as the material basis for ALD occurrence. Abnormally high temperature significantly increased the active layer thickness (ALT), resulting in melting of the ice layer and formation of a thawed interlayer, which was the direct causing factor for ALD. By altering the local material, micro-topography, and thermal conditions, ALD activity significantly increases RTS susceptibility. Understanding the mechanisms of ALD formation and evolution into RTS provides a theoretical foundation for infrastructure development and disaster mitigation in extreme environments.

Development and Design Validation of an Inflow-Settling Chamber for Turbomachinery Test-Benches

At Leibniz University of Hannover, Germany, a new turbomachinery test facility has been built over the last few years. A major part of this facility is a new 6 MW compressor station, which is connected to a large piping system, both designed and built by AERZEN. This system provides air supply to several wind tunnel and turbomachinery test rigs, e.g., axial turbines and axial compressors. These test rigs are designed to conduct high-quality aerodynamic, aeroelastic, and aeroacoustic measurements to increase physical understanding of steady and unsteady effects in turbomachines. One primary purpose of these investigations is the validation of aerodynamic and aeroacoustic numerical methods. To provide precise boundary conditions for the validation process, extremely high homogeneity of the inflow to the investigated experimental setup is imminent. Thus, customized settling chambers have been developed using analytical and numerical design methods. The authors have chosen to follow basic aerodynamic design steps, using analytical assumptions for the inlet section, the “mixing” area of a settling chamber, and the outlet nozzle in combination with state-of-the-art numerical investigations. In early 2020, the first settling chamber was brought into operation for the acceptance tests. In order to collect high-resolution flow field data during the tests, Leibniz University and AERZEN have designed a unique measurement device for robust and fast in-line flow field measurements. For this measurement device, total pressure and total-temperature rake probes, as well as traversing multi-hole probes, have been used in combination to receive high-resolution flow field data at the outlet section of the settling chamber. The paper provides information about the design process of the settling chamber, the developed measurement device, and measurement data gained from the acceptance tests.

Pyramidal dislocation driven martensitic nucleation: A step toward consilience of deformation scenario in fcc materials (II)

Dislocation glides and their interactions with other defects are central to our understanding of deformation mechanism enhancing plasticity and damage tolerance. Martensitic transformation (MT) is a fascinating phenomenon overcoming strength-elongation trade-off and thus tailoring structure-property architecture. However, dislocation plasticity of Fe-based hexagonal close-packed (hcp) martensite is still challenging due to its metastability, further complicated by controversies surrounding pyramidal dislocation even in pure hcp metals. To resolve the uncertainties, we address the pyramidal-dislocation-driven plasticity in Fe-based hcp martensite by employing in-situ transmission electron microscopy (TEM) and high-resolution (HR) annular bright field (ABF) imaging together with dislocation-contrast analyses. We show that the activation and dissociation of pyramidal dislocation govern the initial stage of plastic deformation, while subsequent cross-slip by the cooperative motion of dissociated pyramidal dislocations plays a key role in nucleation of new hcp martensite. By incorporating current dislocation model into previous models coupled with stacking-fault-energy concept, we propose a synthesized deformation scenario that offers a comprehensive perspective on plasticity and transformability.