A chlorophyll halo over maud rise in the southern ocean - Ducrocq, Bertrand; Steiger, Nadine; du Plessis, Marcel; Sallée, Jean-Baptiste; Moreau, Sébastien; Swart, Sebastiaan (Nature communications, 2025)
Phytoplankton blooms above the seamount Maud Rise in the Antarctic Ocean have been reported but their emerging mechanisms and their importance for the wider Southern Ocean are not well known. We use satellite data spanning over the last two decades and in-situ data collected from a ship, an underwater glider and Biogeochemical-Argo profiling floats to understand the processes involved in the formation of Maud Rise phytoplankton blooms. We find that the seamount generates upwelling of warm deep water that transports heat, and likely dissolved iron, to the surface via diapycnal mixing. This creates a recurring annular structure of chlorophyll concentration (or chlorophyll halo) in correspondence with the previously observed warm water and sea ice halo over Maud Rise. The in-situ observations reveal integrated chlorophyll-a concentrations of up to 100 mg·m(-2), which suggests exceptionally high phytoplankton biomass within the Southern Ocean, thus making the seamount a regional phytoplankton hotspot.
Place: England
Keywords: Sørishavet; biogeokjemi; biomasse; fytoplankton; marin biologi; oseanografi

Unmanned Aerial Vehicles for Real-Time Vegetation Monitoring in Antarctica: A Review - Lockhart, Kaelan; Sandino, Juan; Amarasingam, Narmilan; Hann, Richard; Bollard, Barbara; Gonzalez, Felipe (Remote Sensing, 2025)
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.
Keywords: Antarktis; droner; dyp læring; fjernmåling; kartlegging; kunstig intelligens (KI); kybernetikk; maskinlæring; teknologi; vegetasjon