Tracking a Surrogate Hazardous Agent (Rhodamine Dye) in a Coastal Ocean Environment Using In Situ Measurements and Concentration Estimates Derived from Drone Images
New tools and technology are needed to track hazardous agents such as oil and red tides in our oceans. Rhodamine dye (a surrogate hazardous agent) was released into the Atlantic ocean in August 2018, and experiments were conducted to track the movement of the dye near the water surface within three...
| Main Authors: | , , , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2022
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| Online Access: | https://hdl.handle.net/1721.1/138115.2 |
| _version_ | 1811088978769608704 |
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| author | Filippi, Margaux Hanlon, Regina Rypina, Irina I. Hodges, Benjamin A. Peacock, Thomas Schmale, David G. |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Filippi, Margaux Hanlon, Regina Rypina, Irina I. Hodges, Benjamin A. Peacock, Thomas Schmale, David G. |
| author_sort | Filippi, Margaux |
| collection | MIT |
| description | New tools and technology are needed to track hazardous agents such as oil and red tides in our oceans. Rhodamine dye (a surrogate hazardous agent) was released into the Atlantic ocean in August 2018, and experiments were conducted to track the movement of the dye near the water surface within three hours following the release. A DrOne Water Sampling SystEm (DOWSE), consisting of a 3D-printed sampling device tethered to a drone, was used to collect 26 water samples at different locations around the dye plume. Rhodamine concentrations were measured from the drone water samples using a fluorometer and ranged from 1 to 93 ppb. Dye images were taken during the drone-sampling of surface water containing dye and at about 10 m above the sampling point. These images were post-processed to estimate dye concentrations across the sampling domain. A comparison of calibrated heat maps showed that the altitude images yielded dye distributions that were qualitatively similar to those from images taken near the ocean surface. Moreover, the association between red ratios and dye concentrations yielded trendlines explaining up to 67% of the variation. Drones may be used to detect, track and assist in mitigating hazardous agents in the future. |
| first_indexed | 2024-09-23T14:11:19Z |
| format | Article |
| id | mit-1721.1/138115.2 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:11:19Z |
| publishDate | 2022 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/138115.22024-06-07T19:10:32Z Tracking a Surrogate Hazardous Agent (Rhodamine Dye) in a Coastal Ocean Environment Using In Situ Measurements and Concentration Estimates Derived from Drone Images Filippi, Margaux Hanlon, Regina Rypina, Irina I. Hodges, Benjamin A. Peacock, Thomas Schmale, David G. Massachusetts Institute of Technology. Department of Mechanical Engineering Woods Hole Oceanographic Institution New tools and technology are needed to track hazardous agents such as oil and red tides in our oceans. Rhodamine dye (a surrogate hazardous agent) was released into the Atlantic ocean in August 2018, and experiments were conducted to track the movement of the dye near the water surface within three hours following the release. A DrOne Water Sampling SystEm (DOWSE), consisting of a 3D-printed sampling device tethered to a drone, was used to collect 26 water samples at different locations around the dye plume. Rhodamine concentrations were measured from the drone water samples using a fluorometer and ranged from 1 to 93 ppb. Dye images were taken during the drone-sampling of surface water containing dye and at about 10 m above the sampling point. These images were post-processed to estimate dye concentrations across the sampling domain. A comparison of calibrated heat maps showed that the altitude images yielded dye distributions that were qualitatively similar to those from images taken near the ocean surface. Moreover, the association between red ratios and dye concentrations yielded trendlines explaining up to 67% of the variation. Drones may be used to detect, track and assist in mitigating hazardous agents in the future. 2022-02-15T16:16:58Z 2021-11-12T13:20:01Z 2022-02-15T16:16:58Z 2021-11 2021-10 2021-11-11T14:57:59Z Article http://purl.org/eprint/type/JournalArticle 2072-4292 https://hdl.handle.net/1721.1/138115.2 Remote Sensing 13 (21): 4415 (2021) http://dx.doi.org/10.3390/rs13214415 Remote Sensing Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/octet-stream Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | Filippi, Margaux Hanlon, Regina Rypina, Irina I. Hodges, Benjamin A. Peacock, Thomas Schmale, David G. Tracking a Surrogate Hazardous Agent (Rhodamine Dye) in a Coastal Ocean Environment Using In Situ Measurements and Concentration Estimates Derived from Drone Images |
| title | Tracking a Surrogate Hazardous Agent (Rhodamine Dye) in a Coastal Ocean Environment Using In Situ Measurements and Concentration Estimates Derived from Drone Images |
| title_full | Tracking a Surrogate Hazardous Agent (Rhodamine Dye) in a Coastal Ocean Environment Using In Situ Measurements and Concentration Estimates Derived from Drone Images |
| title_fullStr | Tracking a Surrogate Hazardous Agent (Rhodamine Dye) in a Coastal Ocean Environment Using In Situ Measurements and Concentration Estimates Derived from Drone Images |
| title_full_unstemmed | Tracking a Surrogate Hazardous Agent (Rhodamine Dye) in a Coastal Ocean Environment Using In Situ Measurements and Concentration Estimates Derived from Drone Images |
| title_short | Tracking a Surrogate Hazardous Agent (Rhodamine Dye) in a Coastal Ocean Environment Using In Situ Measurements and Concentration Estimates Derived from Drone Images |
| title_sort | tracking a surrogate hazardous agent rhodamine dye in a coastal ocean environment using in situ measurements and concentration estimates derived from drone images |
| url | https://hdl.handle.net/1721.1/138115.2 |
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