Summary
settingsOrder Article Reprints Open AccessReview Unmanned Aerial Vehicles for Real-Time Vegetation Monitoring in Antarctica: A Review by Kaelan Lockhart 1,2,*ORCID,Juan Sandino 1,2ORCID,Narmilan Amarasingam 3,4ORCID,Richard Hann 5ORCID,Barbara Bollard 3,4ORCID andFelipe Gonzalez 1,2ORCID 1 Securing Antarctica's Environmental Future, Queensland University of Technology, 2 George St, Brisbane City, QLD 4000, Australia 2 QUT Centre for Robotics, Queensland University of Technology, 2 George St, Brisbane City, QLD 4000, Australia 3 Securing Antarctica's Environmental Future, University of Wollongong, Northfields Ave, Wollongong, NSW 2522, Australia 4 Environmental Futures, University of Wollongong, Wollongong, NSW 2522, Australia 5 Department of Engineering Cybernetics, Norwegian University of Science and Technology, O.S. Bragstads Plass 2D, NO7491 Trondheim, Norway * Author to whom correspondence should be addressed. Remote Sens. 2025, 17(2), 304; https://doi.org/10.3390/rs17020304 Submission received: 26 October 2024 / Revised: 9 January 2025 / Accepted: 10 January 2025 / Published: 16 January 2025 (This article belongs to the Special Issue Antarctic Remote Sensing Applications (Second Edition)) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract 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.