Unmanned Aerial Vehicles for Real-Time Vegetation Monitoring in Antarctica: A Review
The unique challenges of polar ecosystems, coupled with the necessity for high-precision data, make Unmanned Aerial Vehicles (UAVs) an ideal tool for vegetation monitoring and conservation studies in Antarctica. This review draws on existing studies on Antarctic UAV vegetation mapping, focusing on t...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/304 |
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| author | Kaelan Lockhart Juan Sandino Narmilan Amarasingam Richard Hann Barbara Bollard Felipe Gonzalez |
| author_facet | Kaelan Lockhart Juan Sandino Narmilan Amarasingam Richard Hann Barbara Bollard Felipe Gonzalez |
| author_sort | Kaelan Lockhart |
| collection | DOAJ |
| description | The unique challenges of polar ecosystems, coupled with the necessity for high-precision data, make Unmanned Aerial Vehicles (UAVs) an ideal tool for vegetation monitoring and conservation studies in Antarctica. This review draws on existing studies on Antarctic UAV vegetation mapping, focusing on their methodologies, including surveyed locations, flight guidelines, UAV specifications, sensor technologies, data processing techniques, and the use of vegetation indices. Despite the potential of established Machine-Learning (ML) classifiers such as Random Forest, K Nearest Neighbour, and Support Vector Machine, and gradient boosting in the semantic segmentation of UAV-captured images, there is a notable scarcity of research employing Deep Learning (DL) models in these extreme environments. While initial studies suggest that DL models could match or surpass the performance of established classifiers, even on small datasets, the integration of these advanced models into real-time navigation systems on UAVs remains underexplored. This paper evaluates the feasibility of deploying UAVs equipped with adaptive path-planning and real-time semantic segmentation capabilities, which could significantly enhance the efficiency and safety of mapping missions in Antarctica. This review discusses the technological and logistical constraints observed in previous studies and proposes directions for future research to optimise autonomous drone operations in harsh polar conditions. |
| format | Article |
| id | doaj-art-f6494c79237d4245bb240d2485b08197 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-f6494c79237d4245bb240d2485b081972025-01-24T13:48:04ZengMDPI AGRemote Sensing2072-42922025-01-0117230410.3390/rs17020304Unmanned Aerial Vehicles for Real-Time Vegetation Monitoring in Antarctica: A ReviewKaelan Lockhart0Juan Sandino1Narmilan Amarasingam2Richard Hann3Barbara Bollard4Felipe Gonzalez5Securing Antarctica’s Environmental Future, Queensland University of Technology, 2 George St, Brisbane City, QLD 4000, AustraliaSecuring Antarctica’s Environmental Future, Queensland University of Technology, 2 George St, Brisbane City, QLD 4000, AustraliaSecuring Antarctica’s Environmental Future, University of Wollongong, Northfields Ave, Wollongong, NSW 2522, AustraliaDepartment of Engineering Cybernetics, Norwegian University of Science and Technology, O.S. Bragstads Plass 2D, NO7491 Trondheim, NorwaySecuring Antarctica’s Environmental Future, University of Wollongong, Northfields Ave, Wollongong, NSW 2522, AustraliaSecuring Antarctica’s Environmental Future, Queensland University of Technology, 2 George St, Brisbane City, QLD 4000, AustraliaThe unique challenges of polar ecosystems, coupled with the necessity for high-precision data, make Unmanned Aerial Vehicles (UAVs) an ideal tool for vegetation monitoring and conservation studies in Antarctica. This review draws on existing studies on Antarctic UAV vegetation mapping, focusing on their methodologies, including surveyed locations, flight guidelines, UAV specifications, sensor technologies, data processing techniques, and the use of vegetation indices. Despite the potential of established Machine-Learning (ML) classifiers such as Random Forest, K Nearest Neighbour, and Support Vector Machine, and gradient boosting in the semantic segmentation of UAV-captured images, there is a notable scarcity of research employing Deep Learning (DL) models in these extreme environments. While initial studies suggest that DL models could match or surpass the performance of established classifiers, even on small datasets, the integration of these advanced models into real-time navigation systems on UAVs remains underexplored. This paper evaluates the feasibility of deploying UAVs equipped with adaptive path-planning and real-time semantic segmentation capabilities, which could significantly enhance the efficiency and safety of mapping missions in Antarctica. This review discusses the technological and logistical constraints observed in previous studies and proposes directions for future research to optimise autonomous drone operations in harsh polar conditions.https://www.mdpi.com/2072-4292/17/2/304droneunmanned aircraft system (UAS)remotely piloted aircraft system (RPAS)semantic segmentationpolaradaptive path-planning |
| spellingShingle | Kaelan Lockhart Juan Sandino Narmilan Amarasingam Richard Hann Barbara Bollard Felipe Gonzalez Unmanned Aerial Vehicles for Real-Time Vegetation Monitoring in Antarctica: A Review Remote Sensing drone unmanned aircraft system (UAS) remotely piloted aircraft system (RPAS) semantic segmentation polar adaptive path-planning |
| title | Unmanned Aerial Vehicles for Real-Time Vegetation Monitoring in Antarctica: A Review |
| title_full | Unmanned Aerial Vehicles for Real-Time Vegetation Monitoring in Antarctica: A Review |
| title_fullStr | Unmanned Aerial Vehicles for Real-Time Vegetation Monitoring in Antarctica: A Review |
| title_full_unstemmed | Unmanned Aerial Vehicles for Real-Time Vegetation Monitoring in Antarctica: A Review |
| title_short | Unmanned Aerial Vehicles for Real-Time Vegetation Monitoring in Antarctica: A Review |
| title_sort | unmanned aerial vehicles for real time vegetation monitoring in antarctica a review |
| topic | drone unmanned aircraft system (UAS) remotely piloted aircraft system (RPAS) semantic segmentation polar adaptive path-planning |
| url | https://www.mdpi.com/2072-4292/17/2/304 |
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