scholarly journals Inferring Surface Flow Velocities in Sediment-Laden Alaskan Rivers from Optical Image Sequences Acquired from a Helicopter

2020 ◽  
Vol 12 (8) ◽  
pp. 1282 ◽  
Author(s):  
Carl J. Legleiter ◽  
Paul J. Kinzel
Keyword(s):  
Remote Sensing ◽  
Field Measurements ◽  
Video Camera ◽  
Image Sequences ◽  
Surface Flow ◽  
Optical Image ◽  
Frame Rate ◽  
Digital Mapping ◽  
Lower Frame ◽  
Flow Velocities

The remote, inaccessible location of many rivers in Alaska creates a compelling need for remote sensing approaches to streamflow monitoring. Motivated by this objective, we evaluated the potential to infer flow velocities from optical image sequences acquired from a helicopter deployed above two large, sediment-laden rivers. Rather than artificial seeding, we used an ensemble correlation particle image velocimetry (PIV) algorithm to track the movement of boil vortices that upwell suspended sediment and produce a visible contrast at the water surface. This study introduced a general, modular workflow for image preparation (stabilization and geo-referencing), preprocessing (filtering and contrast enhancement), analysis (PIV), and postprocessing (scaling PIV output and assessing accuracy via comparison to field measurements). Applying this method to images acquired with a digital mapping camera and an inexpensive video camera highlighted the importance of image enhancement and the need to resample the data to an appropriate, coarser pixel size and a lower frame rate. We also developed a Parameter Optimization for PIV (POP) framework to guide selection of the interrogation area (IA) and frame rate for a particular application. POP results indicated that the performance of the PIV algorithm was highly robust and that relatively large IAs (64–320 pixels) and modest frame rates (0.5–2 Hz) yielded strong agreement ( R 2 > 0.9 ) between remotely sensed velocities and field measurements. Similarly, analysis of the sensitivity of PIV accuracy to image sequence duration showed that dwell times as short as 16 s would be sufficient at a frame rate of 1 Hz and could be cut in half if the frame rate were doubled. The results of this investigation indicate that helicopter-based remote sensing of velocities in sediment-laden rivers could contribute to noncontact streamgaging programs and enable reach-scale mapping of flow fields.

scholarly journals Surface Flow Velocities From Space: Particle Image Velocimetry of Satellite Video of a Large, Sediment-Laden River

2021 ◽  
Vol 3 ◽  
Author(s):  
Carl J. Legleiter ◽  
Paul J. Kinzel
Keyword(s):  
Remote Sensing ◽  
Particle Image Velocimetry ◽  
River Discharge ◽  
Water Surface ◽  
Particle Image ◽  
Field Measurements ◽  
Surface Flow ◽  
Cross Sectional ◽  
Image Velocimetry ◽  
Flow Velocities

Conventional, field-based streamflow monitoring in remote, inaccessible locations such as Alaska poses logistical challenges. Safety concerns, financial considerations, and a desire to expand water-observing networks make remote sensing an appealing alternative means of collecting hydrologic data. In an ongoing effort to develop non-contact methods for measuring river discharge, we evaluated the potential to estimate surface flow velocities from satellite video of a large, sediment-laden river in Alaska via particle image velocimetry (PIV). In this setting, naturally occurring sediment boil vortices produced distinct water surface features that could be tracked from frame to frame as they were advected by the flow, obviating the need to introduce artificial tracer particles. In this study, we refined an end-to-end workflow that involved stabilization and geo-referencing, image preprocessing, PIV analysis with an ensemble correlation algorithm, and post-processing of PIV output to filter outliers and scale and geo-reference velocity vectors. Applying these procedures to image sequences extracted from satellite video allowed us to produce high resolution surface velocity fields; field measurements of depth-averaged flow velocity were used to assess accuracy. Our results confirmed the importance of preprocessing images to enhance contrast and indicated that lower frame rates (e.g., 0.25 Hz) lead to more reliable velocity estimates because longer capture intervals allow more time for water surface features to translate several pixels between frames, given the relatively coarse spatial resolution of the satellite data. Although agreement between PIV-derived velocity estimates and field measurements was weak (R2 = 0.39) on a point-by-point basis, correspondence improved when the PIV output was aggregated to the cross-sectional scale. For example, the correspondence between cross-sectional maximum velocities inferred via remote sensing and measured in the field was much stronger (R2 = 0.76), suggesting that satellite video could play a role in measuring river discharge. Examining correlation matrices produced as an intermediate output of the PIV algorithm yielded insight on the interactions between image frame rate and sensor spatial resolution, which must be considered in tandem. Although further research and technological development are needed, measuring surface flow velocities from satellite video could become a viable tool for streamflow monitoring in certain fluvial environments.

scholarly journals USING THERMAL AND RGB UAV IMAGERY TO MEASURE SURFACE FLOW VELOCITIES OF RIVERS

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 717-722
Author(s):  
A. Eltner ◽  
D. Mader ◽  
N. Szopos ◽  
B. Nagy ◽  
J. Grundmann ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Water Area ◽  
Image Sequences ◽  
Surface Flow ◽  
Frame Rate ◽  
Regulated River ◽  
Flying Height ◽  
Average Deviation ◽  
Target Field ◽  
Flow Velocities

Abstract. This study assesses the suitability to use RGB and thermal infrared imagery acquired from an UAV to measure surface flow velocities of rivers. The reach of a medium-scale river in Hungary is investigated. Image sequences with a frame rate of 2 Hz were captured with two sensors, a RGB and an uncooled thermal camera, at a flying height that ensures the visibility of both shores. The interior geometry of both cameras were calibrated with an in-house designed target field. The image sequences were automatically co-registered to account for UAV movements during the image acquisition. The TIR data was processed to keep loss-free image information solely in the water area and to enhance the signal to noise ratio. Image velocimetry with PIV applied to the TIR data and PTV applied to the RGB data was utilised to retrieve surface flow velocities. Comparison between RGB and TIR data reveal an average deviation of about 0.01 m/s. Future studies are needed to evaluate the transferability to other non-regulated river reaches.

FlowVeloTool: Measuring flow velocities in terrestrial and UAV image sequences automatically with PIV and PTV

2020 ◽  
Author(s):  
Anette Eltner ◽  
Jens Grundmann
Keyword(s):  
Flow Direction ◽  
Control Point ◽  
Region Of Interest ◽  
Software Tool ◽  
Image Sequences ◽  
Surface Flow ◽  
Area Of Interest ◽  
Image Velocimetry ◽  
Uav Image ◽  
Flow Velocities

<p>We introduce a Python based software tool to measure surface flow velocities and to estimate discharge eventually. Minimum needed input are image sequences, some camera parameters and object space information to scale the image measurements. Reference information can be provided either indirectly via ground control point measurements or directly providing camera pose parameters. To improve the reliability and density of velocity measurements the area of interest has to be masked for image velocimetry. This can either be performed with a binary mask file or considering a 3D point cloud, for instance retrieved with Structure from Motion (SfM) photogrammetry, describing the region of interest. The tracking task can be done with particle image velocimetry (PIV) considering small interrogation regions or using particle tracking velocimetry (PTV) and thus detecting and tracking features at the water surface. To improve the robustness of the tracking results, filtering can be applied that implements statistical information about the flow direction, flow steadiness and average velocities.</p><p>The FlowVeloTool has been tested with two different datasets; one at a gauging station and one at a natural river reach. Thereby, UAV and terrestrial data were acquired and processed. Velocities can be estimated with an accuracy of 0.01 m/s. If information about the river topography and bathymetry are available, as in our demonstration, discharge can be estimated with an error ranging from 5 to 31 % (Eltner et al. 2019). Besides these results we demonstrate further developments of the FlowVeloTool regarding filtering of tracking results, discharge estimation, and processing of time series. Furthermore, we illustrate that thermal data can be used, as well, with our tool to retrieve river surface velocities.</p><p> </p><p>Eltner, A., Sardemann, H., and Grundmann, J.: Flow velocity and discharge measurement in rivers using terrestrial and UAV imagery, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-289, 2019.</p>

PIV field measurements of surface flow on a river by using a helicopter

2001 ◽  
Vol 21 (2Supplement) ◽  
pp. 61-62
Author(s):  
Ichiro FUJITA ◽  
Shiro AYA ◽  
Masahiro TAMAI ◽  
Kohsei TAKEHARA ◽  
Hitoshi MIYAMOTO ◽  
...  
Keyword(s):  
Field Measurements ◽  
Surface Flow

scholarly journals Drone-Based Remote Sensing for Research on Wind Erosion in Drylands: Possible Applications

2021 ◽  
Vol 13 (2) ◽  
pp. 283
Author(s):  
Junzhe Zhang ◽  
Wei Guo ◽  
Bo Zhou ◽  
Gregory S. Okin
Keyword(s):  
Remote Sensing ◽  
Wind Erosion ◽  
Field Measurements ◽  
Spatially Explicit ◽  
Surface Model ◽  
High Resolution Imagery ◽  
Soil Elevation ◽  
3D Photogrammetry ◽  
Using Data ◽  
Sample Set

With rapid innovations in drone, camera, and 3D photogrammetry, drone-based remote sensing can accurately and efficiently provide ultra-high resolution imagery and digital surface model (DSM) at a landscape scale. Several studies have been conducted using drone-based remote sensing to quantitatively assess the impacts of wind erosion on the vegetation communities and landforms in drylands. In this study, first, five difficulties in conducting wind erosion research through data collection from fieldwork are summarized: insufficient samples, spatial displacement with auxiliary datasets, missing volumetric information, a unidirectional view, and spatially inexplicit input. Then, five possible applications—to provide a reliable and valid sample set, to mitigate the spatial offset, to monitor soil elevation change, to evaluate the directional property of land cover, and to make spatially explicit input for ecological models—of drone-based remote sensing products are suggested. To sum up, drone-based remote sensing has become a useful method to research wind erosion in drylands, and can solve the issues caused by using data collected from fieldwork. For wind erosion research in drylands, we suggest that a drone-based remote sensing product should be used as a complement to field measurements.

scholarly journals Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4118
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
José de Jesús Díaz-Torres ◽  
Mónica Basilio Hazas ◽  
Jingshui Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Field Measurements ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Chl A ◽  
Learning Machine ◽  
Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

Monitoring plastic accumulation in water hyacinths using remote sensing

2021 ◽  
Author(s):  
Niels Janssens ◽  
Lauren Biermann ◽  
Louise Schreyers ◽  
Martin Herold ◽  
Tim van Emmerik
Keyword(s):  
Remote Sensing ◽  
Seasonal Dynamics ◽  
Water Hyacinth ◽  
Naive Bayes ◽  
Pearson Correlation ◽  
Field Measurements ◽  
Naïve Bayes ◽  
Plastic Waste ◽  
Water Hyacinths ◽  
Sentinel 2

<p>While efforts to quantify plastic waste accumulation in the marine environment are rapidly increasing, the data on plastic transport in rivers are relatively scarce. Rivers are a major source of plastic waste into the oceans and understanding seasonal dynamics of macroplastic transport is necessary to develop effective mitigation measures. Macroplastic transport in rivers varies significantly throughout the year. Research shows that in the case of the Saigon river, Vietnam, these plastic transport fluxes are mainly correlated to the amount of organic debris (mostly water hyacinths). Since large water hyacinths patches can be monitored from space, this gives the opportunity for large scale monitoring using freely available remote sensing products. Remote sensing products, such as Sentinel-2, can be applied to areas where water hyacinths occur and plastic emissions are estimated to be high. In this study, we present a first method to detect and monitor water hyacinths using optical remote sensing. This was done by developing an algorithm to automatically detect and quantify water hyacinth coverage for a large section of the Saigon river in Vietnam, for the year 2018. Spectral signatures of water,  infrastructure in the river, and water hyacinths were used to classify the water hyacinths coverage and dynamics using a Naive Bayes algorithm. Water hyacinths were promisingly identified with 95% accuracy by the Naive Bayes classifier. The comparison between the seasonal dynamics of classified water hyacinth and seasonal dynamics of the field measurements resulted in an overall Pearson correlation of 0.72. The comparison we attempted between seasonal dynamics of plastics from satellite and field measurements yielded a Pearson correlation of 0.48. With the next field campaign collecting in-situ data matched to satellite overpasses, we aim to improve this. In conclusion, we were able to successfully map seasonal dynamics of water hyacinth in an automated way using Sentinel-2 data. Our study provides the first step in exploring the possibilities of mapping water hyacinth from satellite as a proxy for river plastics.</p>

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