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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.

Supporting Antarctic scientific investigation is the job of the national Antarctic programmes, the government entities charged with delivering their countries’ Antarctic research strategies. This requires sustained investment in people, innovative technologies, Antarctic infrastructures, and vessels with icebreaking capabilities. The recent endorsement of the International Maritime Organization (IMO) Polar Code (2015) means that countries must address challenges related to an ageing icebreaking vessel fleet. Many countries have recently invested in and begun, or completed, builds on new icebreaking Polar research vessels. These vessels incorporate innovative technologies to increase fuel efficiency, to reduce noise output, and to address ways to protect the Antarctic environment in their design. This paper is a result of a Council of Managers of National Antarctic Programs (COMNAP) project on new vessel builds which began in 2018. It considers the recent vessel builds of Australia’s RSV Nuyina, China’s MV Xue Long 2, France’s L’Astrolabe, Norway’s RV Kronprins Haakon, Peru’s BAP Carrasco, and the United Kingdom’s RRS Sir David Attenborough. The paper provides examples of purposeful consideration of science support requirements and environmental sustainability in vessel designs and operations.

In this study, we analyze a large dataset of seismic signals, recorded by station TROLL in Dronning Maud Land, Antarctica. The signals, recorded in April–December 2012, came from sources near the edge of the ice shelves, at distances of 230–500 km from TROLL. The sources, which moved westward with time, could be associated with four large, tabular icebergs, drifting between 15° E and 8° W. Combining the seismological data with information from satellite remote sensing, we find that one-third of the signals can be attributed to individual icebergs. The trajectories of three of the associated icebergs are known through iceberg-tracking databases, whereas the fourth, a fragment of one of the other three, is untracked, and only scarce information is available from satellite imagery. The observed seismic signals exhibit a wide variety of frequency characteristics, from unstructured episodes to occurrences of iceberg harmonic tremor. Although we are not able to determine the exact cause of the signals, we classify them into five classes on a phenomenological basis. This study demonstrates the potential of regional seismic networks for iceberg monitoring as supplementary resources to information obtained with remote-sensing technologies.

The snow surface roughness at centimetre and millimetre scales is an important parameter related to wind transport, snowdrifts, snowfall, snowmelt and snow grain size. Knowledge of the snow surface roughness is also of high interest for analyzing the signal from radar sensors such as SAR, altimeters and scatterometers. Unfortunately, this parameter has seldom been measured over snow surfaces. The techniques used to measure the roughness of other surfaces, such as agricultural or sand soils, are difficult to implement in polar regions because of the harsh climatic conditions. In this paper we develop a device based on a laser profiler coupled with a GPS receiver on board a snowmobile. This instrumentation was tested successfully in midre Lovénbreen, Svalbard, in April 2006. It allowed us to generate profiles of 3 km sections of the snow-covered glacier surface. Because of the motion of the snowmobile, the roughness signal is mixed with the snowmobile signal. We use a distance/frequency analysis (the empirical mode decomposition) to filter the signal. This method allows us to recover the snow surface structures of wavelengths between 4 and 50 cm with amplitudes of >1 mm. Finally, the roughness parameters of snow surfaces are retrieved. The snow surface roughness is found to be dependent on the scales of the observations. The retrieved RMS of the height distribution is found to vary between 0.5 and 9.2 mm, and the correlation length is found to be between 0.6 and 46 cm. This range of measurements is particularly well adapted to the analysis of GHz radar response on snow surfaces.

We have mapped Antarctic blue-ice areas using the U.S. National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) Antarctica cloud-free image mosaic established by the United States Geological Survey. The mosaic consists of 38 scenes acquired from 1980 to 1994. Our results show that approximately 60 000 km2 of blue ice exist for each of the two main types of blue ice: “melt-induced” and “wind-induced”. Normally, the former type is located on slopes in coastal areas where climate conditions (i.e. persistent winds and temperature), together with favourable surface orientation, sustain conditions for surface and near surface melt. The latter blue-ice category occurs near mountains or on outlet glaciers, often at higher elevations, where persistent winds erode snow away year-round, and combined with sublimation creates areas of net ablation. Furthermore, we have identified an additional area of 121 000 km2 as having potential for blue ice. However, in these areas features such as mixed pixels, glazed snow surfaces, crevasses and/or shadows make interpretation more uncertain. In conclusion, a conservative estimate of Antarctic blue-ice area coverage by this method is found to be 120 000 km2 (∼0.8% of the Antarctic continent), with a potential maximum of 241 000 km2 (∼1.6% of the Antarctic continent).