Search Results (26)

The Ieodo plume is a distinctive suspended sediment plume near the Ieodo Ocean Research Station (I-ORS), located in the middle of the northern East China Sea. Because the Ieodo plume exhibits multiple different spatial scales, this study conducted an integrated remote sensing observation using satellites and unmanned aerial vehicles (UAVs) to observe its development and dispersion. Sentinel-2 and Geostationary Ocean Color Imager-II (GOCI-II) data were used to determine the plume’s spatial characteristics, broad-scale behavior, hourly variability, and turbidity characteristics. Also, TPXO model outputs were employed to evaluate the relationship between plume occurrence and tides, together with satellite imagery. Plume was repeatedly observed near the top of the Ieodo Seamount, with an affected extent of 11.4 ± 3.2 km in the east–west direction and 14.3 ± 4.1 km in the north–south direction. Moreover, hourly variations observed using GOCI-II showed that the Ieodo plume rotated clockwise with shifting tidal currents, forming a counterclockwise curved band or a ring-shaped structure. Total suspended solids (TSSs) in the plume reached their maximum when the southward component of the TPXO tidal current was dominant. Based on UAV optical surveys at the I-ORS, fine-scale morphology at the early stage of plume development was revealed, and it was confirmed that the Ieodo plume can occur even when it is not detected by satellite imagery. Furthermore, the u- and v-velocity vectors of the propagating Ieodo plume were derived by applying large-scale particle image velocimetry (LSPIV) to geometrically corrected sequential UAV imagery obtained in I-ORS. Plume speed was greatest near the source during the initial stage (0.81 ± 0.30 m s⁻¹) and gradually decreased to 0.34 ± 0.29 m s⁻¹ over distance. Based on the results above, we propose that the Ieodo plume is primarily generated by a pressure reduction associated with tidally accelerated currents over topography, driven by the Bernoulli effect. This study shows that an integrated satellite and UAV observation framework can effectively monitor rapidly evolving suspended sediment plumes. It can further help improve our understanding of dynamically driven submesoscale marine events. Full article

Reliable, real-time river flow monitoring is essential for disaster prevention, but traditional in situ methods are costly and high-risk. Large-scale particle image velocimetry (LSPIV) offers a non-contact alternative, though its accuracy is often compromised by noise and non-water pixels, requiring intensive manual data processing. This study proposes an integrated framework for enhancing non-contact river surface velocity estimation by combining deep learning-based water surface segmentation with optimized LSPIV, using accessible smartphone imaging. The framework was tested on two urban rivers in Taichung, Taiwan. DeepLabV3+ was identified as the superior segmentation model based on MPA/PA and MIoU metrics. The DeepLabV3+-derived mask was integrated into the LSPIV workflow, which was optimized using a 32 × 32 pixels interrogation area (IA), reducing processing time by approximately 44%. By removing non-water pixels, the masked LSPIV yielded a 7% increase in mean surface velocity. This suggests that the inclusion of non-water elements diluted the average, underscoring their tendency to introduce a low-velocity bias in unmasked calculations. The overall validation showed mean absolute percentage errors below 6% compared to the radar velocimeter. Consequently, this integrated smartphone-based framework offers a cost-effective and precise solution for future large-scale deployment in urban flood monitoring and smart city hydrological management. Full article

Accurate estimation of river surface velocity is essential for hydrological monitoring and flood management. However, conventional Large-Scale Particle Image Velocimetry (LSPIV) is often affected by errors arising from inaccurate Region of Interest (ROI) delineation and interference from floating objects or vessels. To overcome these limitations, this study integrates LSPIV with two deep learning models, SegNet and YOLOv8, to enable automated ROI segmentation and vessel detection. SegNet performs real-time identification of water body regions, while YOLOv8 detects and removes vessel intrusions within the ROI, thereby enhancing the precision of velocity estimation. Six field experiments were conducted to assess the performance of the proposed system. The deep learning-enhanced LSPIV achieved Root Mean Square Error (RMSE) values ranging from 0.048 to 0.11 m/s and Normalized RMSE (NRMSE) values between 3.53% and 10.34%, with coefficients of determination (R²) exceeding 0.895 when compared with Acoustic Doppler Current Profiler (ADCP) measurements. SegNet-based ROI segmentation reduced RMSE by up to 0.046 m/s andNRMSE by up to 3.44%, and improved R² by up to 0.012, while image enhancement further improved segmentation accuracy under varying illumination conditions. Moreover, YOLOv8 successfully detected all vessel intrusions observed in this study, thereby reducing the discrepancies between LSPIV and ADCP-derived velocities from 0.032–0.345 m/s to 0.022–0.314 m/s. Overall, the integration of LSPIV with SegNet and YOLOv8 establishes a highly automated and accurate framework for river surface velocity estimation, demonstrating strong potential for real-time hydrological monitoring and flood risk assessment. Full article

Quito (Ecuador) has a history of mudflow events from ravines that pose significant risks to its urban areas. Located close to the Pichincha Volcanic Complex, on 31 January 2022, the northwest and central parts of the city were hit by a mudflow triggered by unusual rainfall in the upper part of the drainage, with 28 fatalities and several properties affected. This research focuses on the affected area from collector overflow to the end, considering sedimentological characteristics and behavior through various urban elements. This study integrates the analysis of videos, images, and sediment deposits to understand the dynamics and impacts of the mudflow using a multidisciplinary approach. The methodology includes verifying multimedia materials using free software alongside the Large-Scale Particle Image Velocimetry (LSPIV) to estimate the kinematic parameters of the mudflow. The affected area, reaching a maximum distance of 3.2 km from the overflow point, was divided into four zones for a detailed analysis, each characterized by its impact level and sediment distribution. Results indicate significant variations in mudflow behavior across different urban areas, influenced by topographical and anthropogenic factors. Multimedia analysis provided insights into the mudflow’s velocity and evolution as it entered urban areas. The study also highlights the role of urban planning and infrastructure in modifying the mudflow’s distribution, particularly in the Northern and Southern Axes of its path, compared with a similar 1975 event, seven times larger than this. It also contributes to understanding urban mudflow events in Quito, offering valuable insights for disaster risk management in similar contexts. Full article

An innovative payload containing a sensitive mid-wave infrared camera was flown on an uncrewed aircraft system (UAS) to acquire thermal imagery along a reach of the Sacramento River, California, USA. The imagery was used as input for an ensemble particle image velocimetry (PIV) algorithm to produce near-continuous maps of surface flow velocity along a reach approximately 1 km in length. To assess the accuracy of PIV velocity estimates, in situ measurements of flow velocity were obtained with an acoustic Doppler current profiler (ADCP). ADCP measurements were collected along pre-planned cross-section lines within the area covered by the imagery. The PIV velocities showed good agreement with the depth-averaged velocity measured by the ADCP, with $R^2$ values ranging from 0.59–0.97 across eight transects. Velocity maps derived from the thermal image sequences acquired on consecutive days during a period of steady flow were compared. These maps showed consistent spatial patterns of velocity vector magnitude and orientation, indicating that the technique is repeatable and robust. PIV of thermal imagery can yield velocity estimates in situations where natural water-surface textures or tracers are either insufficient or absent in visible imagery. Future work could be directed toward defining optimal environmental conditions, as well as limitations for mapping flow velocities based on thermal images acquired via UAS. Full article

Large-scale particle image velocimetry (LSPIV) is a computer vision-based technique renowned for its precise and efficient measurement of river surface velocity. However, a crucial prerequisite for utilizing LSPIV involves camera calibration. Conventional techniques rely on ground control points, thus restricting their scope of application. This study introduced a near-field remote-sensing measurement system based on LSPIV, capable of accurately measuring river surface velocity sans reliance on ground control points. The system acquires gravity-acceleration data using a triaxial accelerometer and converts this data into a camera pose, thereby facilitating swift camera calibration. This study validates the system through method verification and field measurements. The method verification results indicate that the system’s method for retroactively deriving ground control-point coordinates achieves an accuracy exceeding 90%. Then, field measurements were performed five times to assess the surface velocity of the Datong River. These measured results were analyzed and compared with data collected from the radar wave velocity meter (RWCM) and the LS1206B velocity meter. Finally, a comprehensive sensitivity analysis of each parameter was conducted to identify those significantly impacting the river’s surface velocity. The findings revealed that this system achieved an accuracy exceeding 92% for all river surface velocities measured. Full article

The accurate measurement of river velocity is essential due to its multifaceted significance. In response to this demand, remote measurement techniques have emerged, including large-scale particle image velocimetry (LSPIV), which can be implemented through cameras or unmanned aerial vehicles (UAVs). This study conducted water surface velocity measurements in the Xihu River, situated in Miaoli County, Taiwan. These measurements were subjected to analysis using five distinct algorithms (PIVlab, Fudaa-LSPIV, OpenPIV, KLT-IV, and STIV) and were compared with surface velocity radar (SVR) results. In the quest for identifying the optimal parameter configuration, it was found that an IA size of 32 pixels × 32 pixels, an image acquisition frequency of 12 frames per second (fps), and a pixel size of 20.5 mm/pixel consistently yielded the lowest values for mean error (ME) and root mean squared error (RMSE) in the performance of Fudaa-LSPIV. Among these algorithms, Fudaa-LSPIV consistently demonstrated the lowest mean error (ME) and root mean squared error (RMSE) values. Additionally, it exhibited the highest coefficient of determination (R² = 0.8053). Subsequent investigations employing Fudaa-LSPIV delved into the impact of various water surface velocity calculation parameters. These experiments revealed that alterations in the size of the interrogation area (IA), image acquisition frequency, and pixel size significantly influenced water surface velocity. This parameter set was subsequently employed in an experiment exploring the incorporation of artificial particles in image velocimetry analysis. The results indicated that the introduction of artificial particles had a discernible impact on the calculation of surface water velocity. Inclusion of these artificial particles enhanced the capability of Fudaa-LSPIV to detect patterns on the water surface. Full article

The accuracy of discharge measurements derived from surface velocities are highly dependent on the accuracy of conversions from surface velocity ${\mathrm{u}}_{\mathrm{s}}$ to depth-averaged velocity $\mathrm{U}$. This conversion factor is typically known as the ‘velocity coefficient’, ‘velocity index’, ‘calibration factor’, ‘alpha coefficient’, or simply ‘alpha’, where $\alpha ={\mathrm{U}/\mathrm{u}}_{\mathrm{s}}$. At some field sites detailed in situ measurements can be made to calculate alpha, while in other situations (such as rapid response flood measurements) alpha must be estimated. This paper provides a review of existing methods for estimating alpha and presents a workflow for selecting the appropriate method, based on available data. Approaches to estimating alpha include: reference discharge and surface velocimetry measurements; extrapolated ADCP velocity profiles; log law profiles; power law profiles; site characteristics; and default assumed values. Additional methods for estimating alpha that require further development or validation are also described. This paper then summarises methods for accounting for spatial and temporal heterogeneity in alpha, such as ‘stage to alpha rating curves’, ‘site alpha vs. local alpha’, and ‘the divided channel method’. Remaining challenges for the accurate estimation of alpha are discussed, as well as future directions that will help to address these challenges. Although significant work remains to improve the estimation of alpha (notably to address surface wind effects and velocity dip), the methods covered in this paper could provide a substantial accuracy improvement over selecting the ‘default value’ of 0.857 for alpha for every discharge measurement. Full article

Space-time image velocimetry (STIV) technology has achieved good performance in river surface-flow velocity measurement, but the application in a field environment is affected by bad weather or lighting conditions, which causes large measurement errors. To improve the measurement accuracy and robustness of STIV, we combined STIV with deep learning. Additionally, considering the light weight of the neural network model, we adopted MobileNetV2 and improved its classification accuracy. We name this method MobileNet-STIV. We also constructed a sample-enhanced mixed dataset for the first time, with 180 classes of images and 100 images per class to train our model, which resulted in a good performance. Compared to the current meter measurement results, the absolute error of the mean velocity was 0.02, the absolute error of the flow discharge was 1.71, the relative error of the mean velocity was 1.27%, and the relative error of the flow discharge was 1.15% in the comparative experiment. In the generalization performance experiment, the absolute error of the mean velocity was 0.03, the absolute error of the flow discharge was 0.27, the relative error of the mean velocity was 6.38%, and the relative error of the flow discharge was 5.92%. The results of both experiments demonstrate that our method is more accurate than the conventional STIV and large-scale particle image velocimetry (LSPIV). Full article

Large-scale particle image velocimetry (LSPIV) provides a cost-effective, rapid, and secure monitoring tool for streamflow measurements. However, surveys of ground control points (GCPs) might affect the camera parameters through the solution of collinearity equations and then impose uncertainty on the measurement results. In this paper, we explore and present an uncertainty analysis for image-based streamflow measurements with the main focus on the ground control points. The study area was Yufeng Creek, which is upstream of the Shimen Reservoir in Northern Taiwan. A monitoring system with dual cameras was set up on the platform of a gauge station to measure the surface velocity. To evaluate the feasibility and accuracy of image-based LSPIV, a comparison with the conventional measurement using a flow meter was conducted. Furthermore, the degree of uncertainty in LSPIV streamflow measurements influenced by the ground control points was quantified using Monte Carlo simulation (MCS). Different operations (with survey times from one to nine) and standard errors (30 mm, 10 mm, and 3 mm) during GCP measurements were considered. Overall, the impacts in the case of single GCP measurement are apparent, i.e., a shifted and wider confidence interval. This uncertainty can be alleviated if the coordinates of the control points are measured and averaged with three repetitions. In terms of the standard errors, the degrees of uncertainty (i.e., normalized confidence intervals) in the streamflow measurement were 20.7%, 12.8%, and 10.7%. Given a smaller SE in GCPs, less uncertain estimations of the river surface velocity and streamflow from LSPIV could be obtained. Full article

The frequency of droughts and floods is increasing due to the extreme climate. Therefore, water resource planning, allocation, and disaster prevention have become increasingly important. One of the most important kinds of hydrological data in water resources planning and management is discharge. The general way to measure the water depth and discharge is to use the Acoustic Doppler Current Profiler (ADCP), a semi-intrusive instrument. This method would involve many human resources and pose severe hazards by floods and extreme events. In recent years, it has become mainstream to measure hydrological data with nonintrusive methods such as the Large-Scale Particle Image Velocimetry (LSPIV), which is used to measure the surface velocity of rivers and estimate the discharge. However, the unknown water depth is an obstacle for this technique. In this study, a method combined with LSPIV to estimate the bathymetry was proposed. The experiments combining the LSPIV technique and the continuity equation to obtain the bed elevation were conducted in a 27 m long and 1 m wide flume. The flow conditions in the experiments were ensured to be within uniform and subcritical flow, and thermoplastic rubber particles were used as the tracking particles for the velocity measurement. The two-dimensional bathymetry was estimated from the depth-averaged velocity and the continuity equation with the leapfrog scheme in a predefined grid under the constraints of Courant–Friedrichs–Lewy (CFL). The LSPIV results were verified using Acoustic Doppler Velocimetry (ADV) measurements, and the bed elevation data of this study were verified using conventional point gauge measurements. The results indicate that the proposed method effectively estimated the variation of the bed elevation, especially in the shallow water level, with an average accuracy of 90.8%. The experimental results also showed that it is feasible to combine the nonintrusive imaging technique with the numerical calculation in solving the water depth and bed elevation. Full article

The accuracy of river velocity measurements plays an important role in the effective management of water resources. Various methods have been developed to measure river velocity. Currently, image-based techniques provide a promising approach to avoid physical contact with targeted water bodies by researchers. In this study, measured surface velocities collected under low flow and high flow conditions in the Houlong River, Taiwan, using large-scale particle image velocimetry (LSPIV) captured by an unmanned aerial vehicle (UAV) and a terrestrial fixed station were analyzed and compared. Under low flow conditions, the mean absolute errors of the measured surface velocities using LSPIV from a UAV with shooting heights of 9, 12, and 15 m fell within 0.055 ± 0.015 m/s, which was lower than that obtained using LSPIV on video recorded from a terrestrial fixed station (i.e., 0.34 m/s). The mean absolute errors obtained using LSPIV derived from UAV aerial photography at a flight height of 12 m without seeding particles and with different seeding particle densities were slightly different, and fell within the range of 0.095 ± 0.025 m/s. Under high flow conditions, the mean absolute errors associated with using LSPIV derived from terrestrial fixed photography and LSPIV derived from a UAV with flight heights of 32, 62, and 112 m were 0.46 m/s and 0.49 m/s, 0.27 m, and 0.97 m/s, respectively. A UAV flight height of 62 m yielded the best measured surface velocity result. Moreover, we also demonstrated that the optimal appropriate interrogation area and image acquisition time interval using LSPIV with a UAV were 16 × 16 pixels and 1/8 s, respectively. These two parameters should be carefully adopted to accurately measure the surface velocity of rivers. Full article

Recent advances in image-based methods for environmental monitoring are opening new frontiers for remote streamflow measurements in natural environments. Such techniques offer numerous advantages compared to traditional approaches. Despite the wide availability of cost-effective devices and software for image processing, these techniques are still rarely systematically implemented in practical applications, probably due to the lack of consistent operational protocols for both phases of images acquisition and processing. In this work, the optimal experimental setup for LSPIV based flow velocity measurements under different conditions is explored using the software PIVlab, investigating performance and sensitivity to some key factors. Different synthetic image sequences, reproducing a river flow with a realistic velocity profile and uniformly distributed floating tracers, are generated under controlled conditions. Different parametric scenarios are created considering diverse combinations of flow velocity, tracer size, seeding density, and environmental conditions. Multiple replications per scenario are processed, using descriptive statistics to characterize errors in PIVlab estimates. Simulations highlight the crucial role of some parameters (e.g., seeding density) and demonstrate how appropriate video duration, frame-rate and parameters setting in relation to the hydraulic conditions can efficiently counterbalance many of the typical operative issues (i.e., scarce tracer concentration) and improve algorithms performance. Full article

This paper presents the findings of a flume study using large-scale particle velocimetry (LSPIV) to estimate the top-width of the vena contracta formed by an approach open-channel flow entering a contraction of the channel. LSPIV is an image-based method that non-invasively measures two-dimensional instantaneous free-surface velocities of water flow using video equipment. The experiments investigated the requisite dimensions of two essential LSPIV components—search area and interrogation area– to establish the optimum range of these components for use in LSPIV application to contractions of open-channel flows. Of practical concern (e.g., bridge hydraulics) is flow contraction and contraction scour that can occur in the vena contracta region. The study showed that optimum values for the search area (SA) and interrogation area (IA) were 10 and 60 pixels, respectively. Also, the study produced a curve indicating a trend for vena-contracta width narrowing with a variable ratio of approach-channel and contracted-channel widths and varying bed shear stress of approach flow. Full article

Image velocimetry is a popular remote sensing method mainly because of the very modest cost of the necessary equipment. However, image velocimetry methods employ parameters that require high expertise to select appropriate values in order to obtain accurate surface flow velocity estimations. This introduces considerations regarding the subjectivity introduced in the definition of the parameter values and its impact on the estimated surface velocity. Alternatively, a statistical approach can be employed instead of directly selecting a value for each image velocimetry parameter. First, probability distribution should be defined for each model parameter, and then Monte Carlo simulations should be employed. In this paper, we demonstrate how this statistical approach can be used to simultaneously produce the confidence intervals of the estimated surface velocity, reduce the uncertainty of some parameters (more specifically, the size of the interrogation area), and reduce the subjectivity. Since image velocimetry algorithms are CPU-intensive, an alternative random number generator that allows obtaining the confidence intervals with a limited number of iterations is suggested. The case study indicated that if the statistical approach is applied diligently, one can achieve the previously mentioned threefold objective. Full article