Enhancing Coastal Risk Recognition: Assessing UAVs for Monitoring Accuracy and Implementation in a Digital Twin Framework
This paper addresses the intricate challenges of coastal management, particularly in rapidly forming tidal flats, emphasizing the need for innovative monitoring strategies. The dynamic coastal topography, exemplified by a newly formed tidal flat in Shanghai, underscores the urgency of advancements i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/7/2879 |
| _version_ | 1827286996030062592 |
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| author | Rui Yuan Hezhenjia Zhang Ruiyang Xu Liyuan Zhang |
| author_facet | Rui Yuan Hezhenjia Zhang Ruiyang Xu Liyuan Zhang |
| author_sort | Rui Yuan |
| collection | DOAJ |
| description | This paper addresses the intricate challenges of coastal management, particularly in rapidly forming tidal flats, emphasizing the need for innovative monitoring strategies. The dynamic coastal topography, exemplified by a newly formed tidal flat in Shanghai, underscores the urgency of advancements in coastal risk recognition. By utilizing a digital twin framework integrated with state-of-the-art unmanned aerial vehicles (UAVs), we systematically evaluate three configurations and identify the optimal setup incorporating real-time kinematics (RTK) and light detection and ranging (LiDAR). This UAV configuration excels in efficiently mapping the 3D coastal terrain. It has an error of less than 0.1 m when mapping mudflats at an altitude of 100 m. The integration of UAV data with a precise numerical ocean model forms the foundation of our dynamic risk assessment framework. The results showcase the transformative potential of the digital twin framework, providing unparalleled accuracy and efficiency in coastal risk recognition. Visualization through Unity Engine or Unreal Engine enhances accessibility, fostering community engagement and awareness. By predicting and simulating potential risks in real-time, this study offers a forward-thinking strategy for mitigating coastal dangers. This research not only contributes a comprehensive strategy for coastal risk management but also sets a precedent for the integration of cutting-edge technologies in safeguarding coastal ecosystems. The findings are significant in paving the way for a more resilient and sustainable approach to coastal management, addressing the evolving environmental pressures on our coastlines. |
| first_indexed | 2024-04-24T10:50:30Z |
| format | Article |
| id | doaj.art-f0094eceb76942d384475c084a45a8c2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T10:50:30Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f0094eceb76942d384475c084a45a8c22024-04-12T13:15:05ZengMDPI AGApplied Sciences2076-34172024-03-01147287910.3390/app14072879Enhancing Coastal Risk Recognition: Assessing UAVs for Monitoring Accuracy and Implementation in a Digital Twin FrameworkRui Yuan0Hezhenjia Zhang1Ruiyang Xu2Liyuan Zhang3School of Ocean Science and Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai 201306, ChinaSchool of Ocean Science and Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai 201306, ChinaSchool of Ocean Science and Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai 201306, ChinaSchool of Ocean Science and Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai 201306, ChinaThis paper addresses the intricate challenges of coastal management, particularly in rapidly forming tidal flats, emphasizing the need for innovative monitoring strategies. The dynamic coastal topography, exemplified by a newly formed tidal flat in Shanghai, underscores the urgency of advancements in coastal risk recognition. By utilizing a digital twin framework integrated with state-of-the-art unmanned aerial vehicles (UAVs), we systematically evaluate three configurations and identify the optimal setup incorporating real-time kinematics (RTK) and light detection and ranging (LiDAR). This UAV configuration excels in efficiently mapping the 3D coastal terrain. It has an error of less than 0.1 m when mapping mudflats at an altitude of 100 m. The integration of UAV data with a precise numerical ocean model forms the foundation of our dynamic risk assessment framework. The results showcase the transformative potential of the digital twin framework, providing unparalleled accuracy and efficiency in coastal risk recognition. Visualization through Unity Engine or Unreal Engine enhances accessibility, fostering community engagement and awareness. By predicting and simulating potential risks in real-time, this study offers a forward-thinking strategy for mitigating coastal dangers. This research not only contributes a comprehensive strategy for coastal risk management but also sets a precedent for the integration of cutting-edge technologies in safeguarding coastal ecosystems. The findings are significant in paving the way for a more resilient and sustainable approach to coastal management, addressing the evolving environmental pressures on our coastlines.https://www.mdpi.com/2076-3417/14/7/2879UAVscoastal monitoringdigital twincoastal risk |
| spellingShingle | Rui Yuan Hezhenjia Zhang Ruiyang Xu Liyuan Zhang Enhancing Coastal Risk Recognition: Assessing UAVs for Monitoring Accuracy and Implementation in a Digital Twin Framework Applied Sciences UAVs coastal monitoring digital twin coastal risk |
| title | Enhancing Coastal Risk Recognition: Assessing UAVs for Monitoring Accuracy and Implementation in a Digital Twin Framework |
| title_full | Enhancing Coastal Risk Recognition: Assessing UAVs for Monitoring Accuracy and Implementation in a Digital Twin Framework |
| title_fullStr | Enhancing Coastal Risk Recognition: Assessing UAVs for Monitoring Accuracy and Implementation in a Digital Twin Framework |
| title_full_unstemmed | Enhancing Coastal Risk Recognition: Assessing UAVs for Monitoring Accuracy and Implementation in a Digital Twin Framework |
| title_short | Enhancing Coastal Risk Recognition: Assessing UAVs for Monitoring Accuracy and Implementation in a Digital Twin Framework |
| title_sort | enhancing coastal risk recognition assessing uavs for monitoring accuracy and implementation in a digital twin framework |
| topic | UAVs coastal monitoring digital twin coastal risk |
| url | https://www.mdpi.com/2076-3417/14/7/2879 |
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