Uncrewed Aerial Vehicle‐Based Multispectral Imagery for River Soil Monitoring
ABSTRACT Flood hazards pose a significant threat to communities and ecosystems alike. Triggered by various factors such as heavy rainfall, storm surges, or rapid snowmelt, floods can wreak havoc by inundating low‐lying areas and overwhelming infrastructure systems. Understanding the feedback between...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Journal of Flood Risk Management |
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| Online Access: | https://doi.org/10.1111/jfr3.70027 |
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| _version_ | 1849339552901103616 |
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| author | Michael H. Gardner Nina Stark Kevin Ostfeld Nicola Brilli Anne Lemnitzer |
| author_facet | Michael H. Gardner Nina Stark Kevin Ostfeld Nicola Brilli Anne Lemnitzer |
| author_sort | Michael H. Gardner |
| collection | DOAJ |
| description | ABSTRACT Flood hazards pose a significant threat to communities and ecosystems alike. Triggered by various factors such as heavy rainfall, storm surges, or rapid snowmelt, floods can wreak havoc by inundating low‐lying areas and overwhelming infrastructure systems. Understanding the feedback between local geomorphology and sediment transport dynamics in terms of the extent and evolution of flood‐related damage is necessary to build a system‐level description of flood hazard. In this research, we present a multispectral imagery‐based approach to broadly map sediment classes and how their spatial extent and relocation can be monitored. The methodology is developed and tested using data collected in the Ahr Valley in Germany during post‐disaster reconnaissance of the July 2021 Western European flooding. Using uncrewed aerial vehicle‐borne multispectral imagery calibrated with laboratory‐based soil characterization, we illustrate how fine and coarse‐grained sediments can be broadly identified and mapped to interpret their transport behavior during flood events and their role regarding flood impacts on infrastructure systems. The methodology is also applied to data from the 2022 flooding of the Yellowstone River, Gardiner, Montana, in the United States to illustrate the transferability of the developed approach across environments. Here, we show how the distribution of soil classes can be mapped remotely and rapidly, and how this facilitates understanding their influence on local flow patterns to induce bridge abutment scour. The limitations and potential expansions to the approach are also discussed. |
| format | Article |
| id | doaj-art-52155dd6ee5f4c0bbe5f5bf31195c304 |
| institution | Kabale University |
| issn | 1753-318X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Flood Risk Management |
| spelling | doaj-art-52155dd6ee5f4c0bbe5f5bf31195c3042025-08-20T03:44:06ZengWileyJournal of Flood Risk Management1753-318X2025-03-01181n/an/a10.1111/jfr3.70027Uncrewed Aerial Vehicle‐Based Multispectral Imagery for River Soil MonitoringMichael H. Gardner0Nina Stark1Kevin Ostfeld2Nicola Brilli3Anne Lemnitzer4Department of Civil and Environmental Engineering University of California Davis California USAEngineering School for Sustainable Infrastructure and Environment University of Florida Gainesville Florida USADepartment of Geological Sciences and Engineering University of Nevada Reno Nevada USADepartment of Civil and Coastal Engineering Virginia Tech Blacksburg Virginia USADepartment of Civil and Environmental Engineering University of California Irvine California USAABSTRACT Flood hazards pose a significant threat to communities and ecosystems alike. Triggered by various factors such as heavy rainfall, storm surges, or rapid snowmelt, floods can wreak havoc by inundating low‐lying areas and overwhelming infrastructure systems. Understanding the feedback between local geomorphology and sediment transport dynamics in terms of the extent and evolution of flood‐related damage is necessary to build a system‐level description of flood hazard. In this research, we present a multispectral imagery‐based approach to broadly map sediment classes and how their spatial extent and relocation can be monitored. The methodology is developed and tested using data collected in the Ahr Valley in Germany during post‐disaster reconnaissance of the July 2021 Western European flooding. Using uncrewed aerial vehicle‐borne multispectral imagery calibrated with laboratory‐based soil characterization, we illustrate how fine and coarse‐grained sediments can be broadly identified and mapped to interpret their transport behavior during flood events and their role regarding flood impacts on infrastructure systems. The methodology is also applied to data from the 2022 flooding of the Yellowstone River, Gardiner, Montana, in the United States to illustrate the transferability of the developed approach across environments. Here, we show how the distribution of soil classes can be mapped remotely and rapidly, and how this facilitates understanding their influence on local flow patterns to induce bridge abutment scour. The limitations and potential expansions to the approach are also discussed.https://doi.org/10.1111/jfr3.70027multispectral imageryremote sensingriver geomorphologysediment transportUAV |
| spellingShingle | Michael H. Gardner Nina Stark Kevin Ostfeld Nicola Brilli Anne Lemnitzer Uncrewed Aerial Vehicle‐Based Multispectral Imagery for River Soil Monitoring Journal of Flood Risk Management multispectral imagery remote sensing river geomorphology sediment transport UAV |
| title | Uncrewed Aerial Vehicle‐Based Multispectral Imagery for River Soil Monitoring |
| title_full | Uncrewed Aerial Vehicle‐Based Multispectral Imagery for River Soil Monitoring |
| title_fullStr | Uncrewed Aerial Vehicle‐Based Multispectral Imagery for River Soil Monitoring |
| title_full_unstemmed | Uncrewed Aerial Vehicle‐Based Multispectral Imagery for River Soil Monitoring |
| title_short | Uncrewed Aerial Vehicle‐Based Multispectral Imagery for River Soil Monitoring |
| title_sort | uncrewed aerial vehicle based multispectral imagery for river soil monitoring |
| topic | multispectral imagery remote sensing river geomorphology sediment transport UAV |
| url | https://doi.org/10.1111/jfr3.70027 |
| work_keys_str_mv | AT michaelhgardner uncrewedaerialvehiclebasedmultispectralimageryforriversoilmonitoring AT ninastark uncrewedaerialvehiclebasedmultispectralimageryforriversoilmonitoring AT kevinostfeld uncrewedaerialvehiclebasedmultispectralimageryforriversoilmonitoring AT nicolabrilli uncrewedaerialvehiclebasedmultispectralimageryforriversoilmonitoring AT annelemnitzer uncrewedaerialvehiclebasedmultispectralimageryforriversoilmonitoring |