High Depth Resolution Research Articles

Automated High-Resolution Bathymetry from Sentinel-1 SAR Images in Deeper Nearshore Coastal Waters in Eastern Florida

Synthetic aperture radar (SAR) imagers are active microwave sensors that could overcome many challenges of passive optical bathymetry inversion, yet their capacity to yield accurate high-resolution bathymetric mapping is not studied sufficiently. In this study, we evaluate the feasibility of applying fast Fourier transform (FFT) to SAR data in coastal nearshore bathymetry derivation in Florida’s coastal waters. The study aims to develop a robust SAR bathymetry inversion framework across extensive spatial scales to address the dearth of bathymetric data in deeper nearshore coastal regions. By leveraging the Sentinel-1 datasets as a rich source of training data, our method yields high-resolution and accurate depth extraction up to 80 m. A comprehensive workflow to determine both the wavelength and peak wave period is associated with the proposed automated model compilation. A novel contour geometry-based spectral analysis technique for wavelength retrieval is presented that enables an efficient and scalable SAR bathymetry model. Multi-date SAR images were used to assess the robustness of the proposed depth-retrieval model. An accuracy assessment against the GMRT data demonstrated the high efficacy of the proposed approach, achieving a coefficient of determination (R2) above 0.95, a root-mean-square error (RMSE) of 1.56–10.20 m, and relative errors of 3.56–11.08% in automatically extracting the underwater terrain at every 50 m interval. A sensitivity analysis was conducted to estimate the uncertainty associated with our method. Overall, this study highlights the potential of SAR technology to produce updated, cost-effective, and accurate bathymetry maps of high resolution and to fill bathymetric data gaps worldwide. The code and datasets are made publicly available.

An intercomparison of SEMARA high-resolution AOD and MODIS operational AODs

SEMARA is a high-resolution aerosol optical depth (AOD) retrieval algorithm that incorporates two algorithms, Simplified and Robust Surface Reflectance Estimation Method (SREM) for estimating surface reflectance and the Simplified Aerosol Retrieval Algorithm (SARA) for retrieving AOD. This study used SEMARA approach for retrieving AOD utilizing data obtained from Aqua-MODIS in the green channel. The algorithm had good agreement with AERONET data (R = 0.98). Also it was analyzed over a wide area containing five provinces in Iran. SEMARA AOD retrievals were compared with the operational MODIS aerosol retrieval algorithms, including Dark Target (DT), Deep Blue (DB), and Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD retrievals over different land covers and low to high aerosol loadings. The results show high consistency in SEMARA AOD retrievals with MAIAC AOD retrievals, with an average correlation coefficient of 0.61, while the correlation coefficient with DB and DT was 0.50 and 0.29, respectively. During high AOD loadings and over bright surfaces, the best agreement of SEMARA AOD and DT, DB, and MAIAC AOD was achieved with correlation coefficients of 0.52, 0.75, and 0.80, respectively. SEMARA AOD shows the best spatial coverage, averaging 80%, while MAIAC, DB, and DT had 54%, 32%, and 12% spatial coverage, respectively. This study concluded that SEMARA AOD can retrieve AOD over various land covers and during low to high AOD loadings with acceptable results.

Carbon and silica fluxes during a declining North Atlantic spring bloom as part of the EXPORTS program

The goal of NASA's EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) project is to develop a predictive understanding of the fate of global ocean primary productivity and export of carbon from the surface to the deep ocean. Thorium-234 (234Th, t1/2 = 24.1 d) was used to measure sinking particle export from an anticyclonic eddy during the EXPORTS North Atlantic cruise (May 2021) at the Porcupine Abyssal Plain. The four-week sampling period was broken into three time periods (“epochs”) where 800 234Th seawater samples were collected from over 50 CTD casts with high depth resolution over the upper 500 m. Size-fractioned particulate samples were collected to determine particulate organic carbon (POC) and biogenic silica (bSi) to 234Th ratios using in situ McLane pumps. A 234Th non-steady state model shows an eddy center epoch average progression of increasing 234Th export (∼2800 ± 300 (Epoch 1; standard deviation) to 4500 ± 700 (Epoch 3) dpm m−2 d−1) out of the top 110 m of the water column over the course of the cruise (29 d). This translates into an epoch average progression of ∼11 ± 1 to 14 ± 2 mmol C m−2 d−1 of sinking POC flux, and ∼ 3 ± 1 to 6 ± 1 mmol bSi m−2 d−1 of sinking bSi flux to deeper waters at 110 m. The overall efficiency of the biological carbon pump (amount of net primary production reaching 100 m below the euphotic zone) increases from ∼10% to ∼30% throughout the sampling period. The temporal trends discussed extensively in this paper show that POC and bSi export increase during diatom bloom evolution.

Day and night continuous high-resolution shallow-water depth detection with single-photon underwater lidar.

Single-photon lidar has emerged as a strong technology for bathymetric measurements. However, its heightened sensitivity additionally makes it susceptible to solar radiation noise, particularly in the green light wavelength where solar radiation is strong, posing challenges for its daytime operation. To address this issue, a single-photon underwater lidar system is proposed and demonstrated. This scheme has these features. 1) Underwater applications not only mitigate the impact of the air-water interface on laser transmission but also significantly attenuate solar radiation reaching the lidar due to the absorption and scattering properties of water. 2) The telescope is designed with a small aperture and narrow field of view to significantly suppress solar radiation. 3) A combination of a narrowband laser and narrowband filter technique is effectively employed to minimize residual solar radiation, thus enabling continuous bathymetric observation capabilities during both day and night. 4) After acquiring the backscattered signal from the bottom, a water depth extraction algorithm utilizing bi-Gaussian fitting is proposed. To demonstrate the robustness of the lidar and the effectiveness of the algorithm, the underwater single-photon lidar system is deployed on a ship to conduct cruise surveys of two bays in the nearshore area, as well as a full-day stationary observation experiment. The lidar measurements are highly consistent with the synchronized sonar observations. The full-day stationary observation experiment showcased its capability to deliver continuous measurements throughout the day and night. These results demonstrate the potential of the system in various applications, including high-precision underwater terrain mapping, obstacle avoidance for underwater platforms, and underwater target imaging.

Technical note: Determination of proton linear energy transfer from the integral depth dose.

Proton linear energy transfer (LET) is associated with the relative biological effectiveness of radiation on tissues. Monte Carlo (MC) simulations have been known to be the preferred method to calculate LET. Detectors have also been built to measure LET, but they need to be calibrated with MC simulations. To propose and test a MC-free method for determining LET from the measured integral depth dose (LFI) of the protons of interest. LFI consists of three steps: (1) IDD measurements, (2) extraction of energy spectrum (ES) from the IDD, and (3) LET determination from the extracted ES and the stopping power of each energy. To validate the accuracy of the extraction of ES, we use Gaussian ES to synthesize IDD, extract ES from the synthesized IDD, and then compare the original (ground truth) and extracted ES. LETs calculated from the original and extracted ES are also compared. To obtain the LET of protons of interest, we measure IDDs by a large-area plane-parallel ionization chamber in water. Finally, TOPAS MC is employed to simulate IDDs, ES, and LETs. From the simulated IDD, the extracted ES and LET are compared with the simulations from TOPAS MC. From the synthesized IDDs, the LETs agreed excellently when the peak energies ≥10 and 1.25 MeV with depth resolutions 0.1and 0.01mm, respectively. For energy<1.25 MeV, even higher depth resolution than 0.01mm is required. From the MC simulated IDDs, our track-averaged LET excellently agreed with MC simulation, but not the LETd. Our LETd was smaller than MC simulated LETd in the shallow region but larger in the distal Bragg peak region. LET can be accurately determined from the IDD. This method can be used in the clinic to commission or validate LETs from other measurement methods or a treatment planning system.

Monocular Depth Estimation for Glass Walls with Context: A New Dataset and Method.

Traditional monocular depth estimation assumes that all objects are reliably visible in the RGB color domain. However, this is not always the case as more and more buildings are decorated with transparent glass walls. This problem has not been explored due to the difficulties in annotating the depth levels of glass walls, as commercial depth sensors cannot provide correct feedbacks on transparent objects. Furthermore, estimating depths from transparent glass walls requires the aids of surrounding context, which has not been considered in prior works. To cope with this problem, we introduce the first Glass Walls Depth Dataset (GW-Depth dataset). We annotate the depth levels of transparent glass walls by propagating the context depth values within neighboring flat areas, and the glass segmentation mask and instance level line segments of glass edges are also provided. On the other hand, a tailored monocular depth estimation method is proposed to fully activate the glass wall contextual understanding. First, we propose to exploit the glass structure context by incorporating the structural prior knowledge embedded in glass boundary line segment detections. Furthermore, to make our method adaptive to scenes without structure context where the glass boundary is either absent in the image or too narrow to be recognized, we propose to derive a reflection context by utilizing the depth reliable points sampled according to the variance between two depth estimations from different resolutions. High-resolution depth is thus estimated by the weighted summation of depths by those reliable points. Extensive experiments are conducted to evaluate the effectiveness of the proposed dual context design. Superior performances of our method is also demonstrated by comparing with state-of-the-art methods. We present the first feasible solution for monocular depth estimation in the presence of glass walls, which can be widely adopted in autonomous navigation.

Estimation of high-resolution aerosol optical depth (AOD) from Landsat and Sentinel images using SEMARA model over selected locations in South Asia

The study investigates a high-resolution retrieval of AOD over complex surfaces and various aerosol regimes of South Asian countries such as China, Bangladesh and Nepal from Landsat 7/8/9 and Sentinel 2 images. To estimate AOD from these satellite images, unlike the conventional radiative transfer-based look-up-table (LUT) approach, this study uses SEMARA coupled model which combines a simplified and robust surface reflection estimation (SREM) model and a simplified aerosol retrieval algorithm (SARA). The SEMARA model uses top-of-atmospheric reflectance (TOA) and land surface reflectance (LSR), solar and sensors viewing geometries, including the in-situ AOD data from the Aerosol Robotic Network (AERONET) station to simulate AOD at 30 m and 10 m spatial resolutions. The results are compared with AERONET and MODIS-based AOD for the validation sites in the South Asian countries. A high degree of agreement with AERONET AOD is obtained over Beijing in China (R2 > 0.9) for all sensors, while it varies for the validation AERONET stations over Nepal and Bangladesh (R2 = 0.40–0.86). The accuracy metrices suggest a low uncertainty over China Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for Beijing: 0.07 and 0.13), while it remains higher for stations over Nepal and Bangladesh (MAE and RMSE: 0.33, and 0.41; 0.15–0.27, respectively). >85% of the modelled observations fall within expected error (EE) for China, while 20–75% of observations fall within EE for Nepal and Bangladesh. We have also obtained a proximity effect for the accuracy of the estimated AOD from the SEMARA model. The findings, in general, suggest that the complexity in surface cover and aerosol type produce a higher offset with respect to AERONET observations over Bangladesh and Nepal, and therefore further validation studies are needed for other parts of the globe to measure the consistency of the model.

DSRNet: Depth Super-Resolution Network guided by blurry depth and clear intensity edges

Although high resolution (HR) depth images are required in many applications such as virtual reality and autonomous navigation, their resolution and quality generated by consumer depth cameras fall short of the requirements. Existing depth upsampling methods focus on extracting multiscale features of HR color image to guide low resolution (LR) depth upsampling, thus causing blurry and inaccurate edges in depth. In this paper, we propose a depth super-resolution (SR) network guided by blurry depth and clear intensity edges, called DSRNet. DSRNet differentiates effective edges from a number of HR edges with the guidance of blurry depth and clear intensity edges. First, we perform global residual estimation based on an encoder–decoder architecture to extract edge structure from HR color image for depth SR. Then, we distinguish effective edges from HR edges in the decoder side with the guidance of LR depth upsampling. To maintain edges for depth SR, we use intensity edge guidance that extracts clear intensity edges from HR image. Finally, we use residual loss to generate accurate high frequency (HF) residual and reconstruct HR depth maps. Experimental results show that DSRNet successfully reconstructs depth edges in SR results as well as outperforms the state-of-the-art methods in terms of visual quality and quantitative measurements.11The proposed model with some test image pairs are available in https://github.com/lanhui-123/DSRNet.

Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 2: Snow processes and snow–canopy interactions

Abstract. Detailed information on seasonal snow cover and depth is essential to the understanding of snow processes, to operational forecasting, and as input for hydrological models. Recent advances in uncrewed or unmanned aircraft systems (UASs) and structure from motion (SfM) techniques have enabled low-cost monitoring of spatial snow depth distribution in resolutions of up to a few centimeters. Here, we study the spatiotemporal variability in snow depth and interactions between snow and vegetation in different subarctic landscapes consisting of a mosaic of conifer forest, mixed forest, transitional woodland/shrub, and peatland areas. To determine the spatiotemporal variability in snow depth, we used high-resolution (50 cm) snow depth maps generated from repeated UAS–SfM surveys in the winter of 2018/2019 and a snow-free bare-ground survey after snowmelt. Due to poor subcanopy penetration with the UAS–SfM method, tree masks were utilized to remove canopy areas and the area (36 cm) immediately next to the canopy before analysis. Snow depth maps were compared to the in situ snow course and a single-point continuous ultrasonic snow depth measurement. Based on the results, the difference between the UAS–SfM survey median snow depth and single-point measurement increased for all land cover types during the snow season, from +5 cm at the beginning of the accumulation to −16 cm in coniferous forests and −32 cm in peatland during the melt period. This highlights the poor representation of point measurements in selected locations even on the subcatchment scale. The high-resolution snow depth maps agreed well with the snow course measurement, but the spatial extent and resolution of maps were substantially higher. The snow depth range (5th–95th percentiles) within different land cover types increased from 17 to 42 cm in peatlands and from 33 to 49 cm in the coniferous forest from the beginning of the snow accumulation to the melt period. Both the median snow depth and its range were found to increase with canopy density; this increase was greatest in the conifer forest area, followed by mixed forest, transitional woodland/shrub, and open peatlands. Using the high-spatial-resolution data, we found a systematic increase (2–20 cm) and then a decline in snow depth near the canopy with increasing distance (from 1 to 2.5 m) of the peak value through the snow season. This study highlights the applicability of the UAS–SfM in high-resolution monitoring of snow depth in multiple land cover types and snow–vegetation interactions in subarctic and remote areas where field data are not available or where the available data are collected using classic point measurements or snow courses.

Focused ion beams from GaBiLi liquid metal alloy ion sources for nanofabrication and ion imaging

In this work, we present an overview of nanopatterning and imaging applications using newly developed workflows with focused ion beams (FIBs) produced with a GaBiLi liquid metal alloy ion source. The primary beam of this source type contains gallium, bismuth, and lithium as well as cluster ions which can be separated quickly using a Wien filter. Lithium ion milling has been applied to generate heptamer-arranged nanohole arrays in gold films with high resolution. Workflows for two-step bowtie nanofabrication using lithium and bismuth ions from the same source have been established. Furthermore, we present ion beam imaging results that were obtained with lithium ions on various sample materials. Combining the large sputter yield and high depth resolution of heavy bismuth ions with the high lateral imaging resolution of light lithium ions enables 3D nanoscale tomography using different ion species generated from the same source. Sample tilt is not required due to the top-down geometry of the FIB.

Depth super-resolution from explicit and implicit high-frequency features

Guided depth super-resolution aims at using a low-resolution depth map and an associated high-resolution RGB image to recover a high-resolution depth map. However, restoring precise and sharp edges near depth discontinuities and fine structures is still challenging for state-of-the-art methods. To alleviate this issue, we propose a novel multi-stage depth super-resolution network, which progressively reconstructs HR depth maps from explicit and implicit high-frequency information. We introduce an efficient transformer to obtain explicit high-frequency information. The shape bias and global context of the transformer allow our model to focus on high-frequency details between objects, i.e., depth discontinuities, rather than texture within objects. Furthermore, we project the input color images into the frequency domain for additional implicit high-frequency cues extraction. Finally, to incorporate the structural details, we develop a fusion strategy that combines depth features and high-frequency information in the multi-stage-scale framework. Exhaustive experiments on the main benchmarks show that our approach establishes a new state-of-the-art. Code will be publicly available at https://github.com/wudiqx106/DSR-EI.

Dust sources over the Arabian Peninsula

This study explores the characteristics of crucial dust sources and changes in their emissions over the Arabian Peninsula (AP) over the 2000–2022 period using high-resolution dust aerosol optical depth data from the Moderate Resolution Imagining Spectroradiometer (MODIS) aerosol measurements onboard Terra and Aqua platforms. The MODIS dust retrievals successfully unravel the hitherto-unexplored key dust source regions and spatial heterogeneity in dust emissions. Critically, MODIS-defined dust sources display a robust geomorphological signature. In Iraq, the Tigris and Euphrates River basins contain extensive dust sources; the Euphrates dust sources are stronger and more widespread. Localized dust sources are noticed over Syria. In the Kingdom of Saudi Arabia (KSA), the eastern province particularly facilitates extensive dust activity. Oman is the prominent dust source in the southern AP due to the presence of intruding sand dunes. Dust emissions in the Iraq and KSA regions exhibit a significant negative correlation with the Standardized Precipitation-Evapotranspiration Index, a drought index, establishing that the local droughts enhance the dust emissions in these regions. The recent sustained droughts from 2008 to 2013 caused a remarkable escalation in the dust emissions in these regions through the modification of land surface conditions.

Photoacoustic Spectral Response using Ultrasound and Interferometric Sensors: A Correlation Study for a High Bandwidth Real-Time Blood Vasculature Monitoring Application in a Chick-Embryo Chorioallantoic Membrane (CAM) Model.

Photoacoustic (PA) spectral response technique has shown good promise in efficient preclinical tissue diagnosis by depicting mechano-biological properties due to high spatial resolution and penetration depth. The conventional PA-based system is a pump-probe technique that utilizes neodymium-doped yttrium aluminum garnet pulsed laser as a pump and an ultrasound sensor as a probe. For biomedical studies, high-speed PA signals need to be acquired, requiring higher bandwidth ultrasound sensors. While the bandwidth increases, they exhibit a very low signal-to-noise ratio that inhibits acquiring PA signals of biomedical samples. An interferometer-based probe has recently been investigated as a potential ultrasound probe for obtaining PA signals as an alternative. This optical PA detection technique offers high sensitivity by combining low acoustic impedance with high electromechanical coupling. However, there is a lack of exploration of the same for real-time biomedical studies. This work shows the development of a homodyne Mach-Zehnder interferometer-based PA spectral response (PASR) followed by a correlation study between the conventional ultrasound sensor and the interferometer-based sensor. Further, this study demonstrates the capability of continuous monitoring of vascular growth and the effect of an antidrug (Cisplatin) on the vasculature tested on a chick-embryo chorioallantoic membrane model. PASR was able to monitor growth changes within one day, which was not possible with conventional methods. This opens up potential possibilities for using this technique in biomedical applications.

Research on Fruit Spatial Coordinate Positioning by Combining Improved YOLOv8s and Adaptive Multi-Resolution Model

Automated fruit-picking equipment has the potential to significantly enhance the efficiency of picking. Accurate detection and localization of fruits are particularly crucial in this regard. However, current methods rely on expensive tools such as depth cameras and LiDAR. This study proposes a low-cost method based on monocular images to achieve target detection and depth estimation. To improve the detection accuracy of targets, especially small targets, an advanced YOLOv8s detection algorithm is introduced. This approach utilizes the BiFormer block, an attention mechanism for dynamic query-aware sparsity, as the backbone feature extractor. It also adds a small-target-detection layer in the Neck and employs EIoU Loss as the loss function. Furthermore, a fused depth estimation method is proposed, which incorporates high-resolution, low-resolution, and local high-frequency depth estimation to obtain depth information with both high-frequency details and low-frequency structure. Finally, the spatial 3D coordinates of the fruit are obtained by fusing the planar coordinates and depth information. The experimental results with citrus as the target result in an improved YOLOv8s network mAP of 88.45% and a recognition accuracy of 94.7%. The recognition of citrus in a natural environment was improved by 2.7% compared to the original model. In the detection range of 30 cm~60 cm, the depth-estimation results (MAE, RSME) are 0.53 and 0.53. In the illumination intensity range of 1000 lx to 5000 lx, the average depth estimation results (MAE, RSME) are 0.49 and 0.64. In the simulated fruit-picking scenario, the success rates of grasping at 30 cm and 45 cm were 80.6% and 85.1%, respectively. The method has the advantage of high-resolution depth estimation without constraints of camera parameters and fruit size that monocular geometric and binocular localization do not have, providing a feasible and low-cost localization method for fruit automation equipment.

Transcranial ultrasound neuromodulation facilitates isoflurane-induced general anesthesia recovery and improves cognition in mice

Delayed arousal and cognitive dysfunction are common, especially in older patients after general anesthesia (GA). Elevating central nervous system serotonin (5-HT) levels can promote recovery from GA and increase synaptic plasticity to improve cognition. Ultrasound neuromodulation has become a noninvasive physical intervention therapy with high spatial resolution and penetration depth, which can modulate neuronal excitability to treat psychiatric and neurodegenerative diseases. This study aims to use ultrasound to noninvasively modulate the brain 5-HT levels of mice to promote recovery from GA and improve cognition in mice. The dorsal raphe nucleus (DRN) of mice during GA was stimulated by the 1.1 MHz ultrasound with a negative pressure of 356 kPa, and the liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) method was used to measure the DRN 5-HT concentrations. The mice’s recovery time from GA was assessed, and the cognition was evaluated through spontaneous alternation Y-maze and novel object recognition (NOR) tests. After ultrasound stimulation, the mice’s DRN 5-HT levels were significantly increased (control: 554.0 ± 103.2 ng/g, anesthesia + US: 664.2 ± 84.1 ng/g, *p = 0.0389); the GA recovery time (return of the righting reflex (RORR) emergence latency time) of mice was significantly reduced (anesthesia: 331.6 ± 70 s, anesthesia + US: 223.2 ± 67.7 s, *p = 0.0215); the spontaneous rotation behavior score of mice was significantly increased (anesthesia: 59.46 ± 5.26 %, anesthesia + US: 68.55 ± 5.24 %; *p = 0.0126); the recognition index was significantly increased (anesthesia: 55.02 ± 6.23 %, anesthesia + US: 78.52 ± 12.21 %; ***p = 0.0009). This study indicates that ultrasound stimulation of DRN increases serotonin levels, accelerates recovery from anesthesia, and improves cognition, which could be an important strategy for treating delayed arousal, postoperative delirium, or even lasting cognitive dysfunction after GA.

Miniature Probe for Optomechanical Focus-Adjustable Optical-Resolution Photoacoustic Endoscopy

Photoacoustic microscopy (PAM) is a promising imaging modality because it is able to reveal optical absorption contrast in high resolution on the order of a micrometer. It can be applied in an endoscopic approach by implementing PAM into a miniature probe, termed photoacoustic endoscopy (PAE). Here we develop a miniature focus-adjustable PAE (FA-PAE) probe characterized by both high resolution (in micrometers) and large depth of focus (DOF) via a novel optomechanical design for focus adjustment. To realize high resolution and large DOF in a miniature probe, a 2-mm plano-convex lens is specially adopted, and the mechanical translation of a single-mode fiber is meticulously designed to allow the use of multi-focus image fusion (MIF) for extended DOF. Compared with existing PAE probes, our FA-PAE probe achieves high resolution of 3-5 μm within unprecedentedly large DOF of >3.2 mm, more than 27 times the DOF of the probe without performing focus adjustment for MIF. The superior performance is first demonstrated by imaging both phantoms and animals including mice and zebrafish in vivo by linear scanning. Further, in vivo endoscopic imaging of a rat's rectum by rotary scanning of the probe is conducted to showcase the capability of adjustable focus. Our work opens new perspectives for PAE biomedical applications.

Automated Visual Inspection of Defects in Transparent Display Layers Using Light-Field 3-D Imaging

Since a display panel comprises multiple layered components, defects may occur within different layers through manufacturing processes. Traditional visual inspection systems with a 2D camera cannot identify the occurrence location among layers. Several 3D imaging technologies, such as CT, TSOM, and MRI, suffer from slow performance and a large form factor. In this work, we propose a novel visual inspection method to detect defects on a display panel using light-field 3D imaging. Without powering the target display panel, we first acquire the high-resolution depth information of defects located inside the transparent layers. We then convert the depth information to the object coordinate system to estimate the physical locations of defects. We automatically classify the types of defects and their layer locations along the depth axis in multiple transparent layers of the display panel. Lastly, our experimental results validate that our method can successfully detect and classify various display defects.

A novel multi-strategy hydrological feature extraction (MHFE) method to improve urban waterlogging risk prediction, a case study of Fuzhou City in China

Reliable hydrological data ensure the precision of the urban waterlogging simulation. To reduce the simulation error caused by insufficient basic data, a multi-strategy method (MHFE) for extracting hydrological features is proposed, which includes land use/land cover (LULC) extraction (LE) and digital elevation model (DEM) reconstruction (DR). First, the high-resolution remote image, satellite DEM, precipitation, flood points and depth, and planned LULC were collected. Second, the buildings, roads, and other areas of the satellite image were segmented using the U-Net model, and the LULC data with drainage features were extracted by combining the segmentation result with the planned LULC and drainage data. Then, the terrain features of the road were enhanced to construct high-precision DEM based on the fusion of multi-source data, such as elevation points, LULC, and satellite DEM. Finally, the waterlogging model was implemented under different return periods of rainfalls and typhoon rainfall to obtain the waterlogging distribution and water depth. The simulation results were compared with historical waterlogging event data and water depth observations. The results indicated that the proposed method significantly improved the accuracy of the simulation. In terms of identifying the waterlogging points, the average F1 score increased by 0.36, 0.20, and 0.07 compared to the raw model and the single LE and DR methods, respectively. In terms of water depth simulation, the average Nash-Sutcliffe efficiency (NSE) was increased from −0.24 to 0.86, with DR and LE contributing to 79.1 % and 20.9 %, respectively. The principal contribution and novelty of this paper is to explore the generic method that enhance the hydrological data, and the findings of this study improved the performance of urban waterlogging simulation.

Comparing sputter rates, depth resolution, and ion yields for different gas cluster ion beams (GCIB): A practical guide to choosing the best GCIB for every application

Molecular gas species for gas cluster ion beams (GCIBs), such as carbon dioxide and water, were examined with a range of beam energies and cluster sizes to compare with the “universal relation” of the sputter yield, Y, per cluster atom against incident beam energy, E, per cluster atom of Arn cluster beam using Irganox 1010. In this work, we compare Arn, (CO2)n, and (H2O)n gas clusters to the universal equations for Arn clusters. To discuss molecular gas species for GCIBs, energy per nucleon (E/N) needs to replace energy per atom. We monitored sputter rate, depth resolution, and secondary ion yield as a function of the beam parameters: gas species, beam energy, and cluster size. (H2O)n GCIB shows reduced sputter rates and improved depth resolution with high sensitivity compared to Arn and (CO2)n GCIBs. These initial results indicate the potential to achieve high-depth resolution with high sensitivity and suggest that (H2O)n cluster ion beam has the potential to play a significant role in surface analysis techniques with organic materials. Results also show that no single set of conditions will provide the “best gas cluster ion beam” for all applications. However, it is possible to choose a set of conditions that will be more or less optimal depending on the experimental goals, such as maximizing the sputter rate, depth resolution, and molecular ion yield. In this work, we recommend the following three guidelines for GCIB users to set their own conditions: (1) to maximize the sputter rate, select a smaller cluster (higher E/N), but be aware that this will increase fragmentation and reduce molecular ion yield; (2) to maximize the depth resolution, select a larger cluster (lower E/N), and use (H2O)n GCIB, if possible; and (3) to maximize the molecular ion signal, use the highest beam energy available, and select a cluster with 0.15–0.25 eV/nucleon for Ar and (CO2)n GCIBs or around 0.1 eV/nucleon if using (H2O)n GCIB. These results are valid for XPS, SIMS, and any technique that utilizes GCIBs.

Reproductive behavior of Atlantic halibut (Hippoglossus hippoglossus) interpreted from electronic tags.

Reproductive timing, location, and behavior are important characteristics that determine marine population dynamics, structure, and resilience to threats, including fishing and climate change. It is challenging to evaluate factors driving variability in these reproductive traits in wild fishes because of the difficulty observing individuals in their natural environments. In the present study, we used high-resolution depth, temperature, and acceleration time series recorded by pop-up satellite archival tags to (1) identify and characterize patterns in depth and acceleration that may be indicative of spawning events in large Atlantic halibut (Hippoglossus hippoglossus), and (2) estimate the effects of individual traits (body size and sex) and environmental factors (location and temperature) on spawning time and frequency. Unique rapid rises observed in the winter depth profiles were interpreted as spawning events. The initiation of the first presumed spawning rise was negatively correlated to water temperature experienced during the prespawning season, suggesting that currently increasing water temperature in the Gulf of St. Lawrence may induce phenological change in halibut spawning time. The number of rises of batch-spawning females was unrelated to female body size. The present study demonstrates how electronic tagging can be used for in-depth characterization of timing, location, and behaviors associated with spawning in a large flatfish species. Such information can inform spatiotemporal management and conservation measures aiming to protect species from directed fishing and by-catch during spawning.