Inferring Surface Flow Velocities in Sediment-Laden Alaskan Rivers from Optical Image Sequences Acquired from a Helicopter
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 lar...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/8/1282 |
| _version_ | 1797570330313621504 |
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| author | Carl J. Legleiter Paul J. Kinzel |
| author_facet | Carl J. Legleiter Paul J. Kinzel |
| author_sort | Carl J. Legleiter |
| collection | DOAJ |
| description | 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 (<inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> <mo>></mo> <mn>0.9</mn> </mrow> </semantics> </math> </inline-formula>) 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. |
| first_indexed | 2024-03-10T20:23:26Z |
| format | Article |
| id | doaj.art-a1735f08fcd648729ab51389e676d575 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:23:26Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a1735f08fcd648729ab51389e676d5752023-11-19T22:00:38ZengMDPI AGRemote Sensing2072-42922020-04-01128128210.3390/rs12081282Inferring Surface Flow Velocities in Sediment-Laden Alaskan Rivers from Optical Image Sequences Acquired from a HelicopterCarl J. Legleiter0Paul J. Kinzel1U.S. Geological Survey, Integrated Modeling and Prediction Division, Golden, CO, 80403, USAU.S. Geological Survey, Integrated Modeling and Prediction Division, Golden, CO, 80403, USAThe 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 (<inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> <mo>></mo> <mn>0.9</mn> </mrow> </semantics> </math> </inline-formula>) 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.https://www.mdpi.com/2072-4292/12/8/1282flow velocityAlaskariverremote sensingparticle image velocimetryPIV |
| spellingShingle | Carl J. Legleiter Paul J. Kinzel Inferring Surface Flow Velocities in Sediment-Laden Alaskan Rivers from Optical Image Sequences Acquired from a Helicopter Remote Sensing flow velocity Alaska river remote sensing particle image velocimetry PIV |
| title | Inferring Surface Flow Velocities in Sediment-Laden Alaskan Rivers from Optical Image Sequences Acquired from a Helicopter |
| title_full | Inferring Surface Flow Velocities in Sediment-Laden Alaskan Rivers from Optical Image Sequences Acquired from a Helicopter |
| title_fullStr | Inferring Surface Flow Velocities in Sediment-Laden Alaskan Rivers from Optical Image Sequences Acquired from a Helicopter |
| title_full_unstemmed | Inferring Surface Flow Velocities in Sediment-Laden Alaskan Rivers from Optical Image Sequences Acquired from a Helicopter |
| title_short | Inferring Surface Flow Velocities in Sediment-Laden Alaskan Rivers from Optical Image Sequences Acquired from a Helicopter |
| title_sort | inferring surface flow velocities in sediment laden alaskan rivers from optical image sequences acquired from a helicopter |
| topic | flow velocity Alaska river remote sensing particle image velocimetry PIV |
| url | https://www.mdpi.com/2072-4292/12/8/1282 |
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