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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.

Polar areas are among the regions where climate change occurs faster than on most of the other areas on Earth. To study the effects of climate change on vegetation, there is a need for knowledge on its current status and properties. Both classic field observation methods and remote sensing methods based on manned aircraft or satellite image analysis have limitations. These include high logistic operation costs, limited research areas, high safety risks, direct human impact, and insufficient resolution of satellite images. Fixed-wing unmanned aerial vehicle beyond the visual line of sight (UAV BVLOS) missions can bridge the scale gap between field-based observations and full-scale airborne or satellite surveys. In this study the two operations of the UAV BVLOS, at an altitude of 350 m ASL, have been successfully performed in Antarctic conditions. Maps of the vegetation of the western shore of Admiralty Bay (King George Island, South Shetlands, Western Antarctic) that included the Antarctic Specially Protected Area No. 128 (ASPA 128) were designed. The vegetation in the 7.5 km2 area was mapped in ultra-high resolution (<5 cm and DEM of 0.25 m GSD), and from the Normalized Difference Vegetation Index (NDVI), four broad vegetation units were extracted: “dense moss carpets” (covering 0.14 km2, 0.8% of ASPA 128), “Sanionia uncinata moss bed” (0.31 km2, 1.7% of ASPA 128), “Deschampsia antarctica grass meadow” (0.24 km2, 1.3% of ASPA 128), and “Deschampsia antarctica–Usnea antarctica heath” (1.66 km2, 9.4% of ASPA 128). Our results demonstrate that the presented UAV BVLOS–based surveys are time-effective (single flight lasting 2.5 h on a distance of 300 km) and cost-effective when compared to classical field-based observations and are less invasive for the ecosystem. Moreover, unmanned airborne vehicles significantly improve security, which is of particular interest in polar region research. Therefore, their development is highly recommended for monitoring areas in remote and fragile environments.

While the number of surface ocean CO2 partial pressure (pCO2) measurements has soared the recent decades, the Southern Ocean remains undersampled. Williams et al. (2017, https://doi.org/10.1002/2016GB005541) now present pCO2 estimates based on data from pH-sensor equipped Bio-Argo floats, which have been measuring in the Southern Ocean since 2014. The authors demonstrate the utility of these data for understanding the carbon cycle in this region, which has a large influence on the distribution of CO2 between the ocean and atmosphere. Biogeochemical sensors deployed on autonomous platforms hold the potential to shape our view of the ocean carbon cycle in the coming decades.

Heimefrontfjella is a strongly segmented NE–SW trending mountain range some 130 km long with a maximum width of about 30 km. The range takes the form of a prominent escarpment, which rises more than 1000 m above the ice plains to the northwest. The maximum elevation reaches 2700 m above sea level. Since its discovery during the German Antarctic Expedition 1938/39, very few scientists had visited Heimefrontfjella by 1985. During the mid 1960s two British geologists had visited the Heimefrontfjella and provided a geological overview of the area. Thereafter, detailed geological investigations became possible with the establishment of the Georg von Neumayer Station on the Ekström ice shelf in 1981, situated some 450 km north of Heimefrontfjella. Since then, the Georg von Neumayer Station has provided a logistical base for multidisciplinary research programs within the Atlantic sector of East Antarctica.

We present the first detailed maps of fast ice around East Antarctica (75°E–170°E), using an image correlation technique applied to RADARSAT ScanSAR images from November in 1997 and 1999. This method is based upon searching for, and distinguishing, correlated regions of the ice-covered ocean which remain stationary, in contrast to adjacent moving pack ice. Within the overlapping longitudinal range of ∼86°E–150.6°E, the total fast-ice area is 141,450 km2 in 1997 and 152,216 km2 in 1999. Calibrated radar backscatter data are also used to determine the distribution of two fast-ice classes based on their surface roughness characteristics. These are “smooth” fast ice (−25.4 dB to −13.5 dB) and “rough” fast ice (−13.5 dB to −2.5 dB). The former comprises ∼67% of the total area, with rough fast ice making up the remaining ∼33%. An estimate is made of fast-ice volume, on the basis of fast-ice type as a proxy measure of ice thickness and area. Results suggest that although fast ice forms 2–16% of the total November sea ice area for this sector of East Antarctica in 1997 and 1999 (average 8.3% across maps), it may comprise 6–57% of the total ice volume (average ∼28% across maps). Grounded icebergs play a key role in fast-ice distribution in all regions apart from 150°E–170°E. These are “snapshot” estimates only, and more work is required to determine longer-term spatiotemporal variability.

This project was undertaken by the Norwegian Polar Institute for the Norwegian Petroleum Directorate (Oljedirektoratet) as support for planning of marine geophysical surveys offshore from Dronning Maud Land (DML), Antarctica. The project was carried out by scientists Olga Pavlova and Jan-Gunnar Winther at the Norwegian Polar Institute under the direction of Morten Sand at the Norwegian Petroleum Directorate. The main objectives of the project were to: a) calculate 10-day average historical (i.e., 1978-1996) sea ice concentrations off DML for the months of December through April b) identify the minimum and maximum sea ice extent occurring in the period 1978-1996 c) present the above information graphically. Daily microwave satellite data covering the period 1978-1996 with 25 x 25 km spatial resolution were used as basis for this study (see text below for description of data set).

The main purpose of the present paper is to estimate the orthometric height of the Global Positioning System (GPS)-station at Troll, Dronning Maud Land, Antarctica. Located at approximately 1300 m above sea level Troll has been connected to the Geodetic Infrastructure forAntarctica (GIANT)-network, but it’s orthometric height remains undetermined. A local geoid has been determined based on gravity-, tidal- and GPS-observations and available gravity anomalies. Information about the ice-thickness has been included in the terrain model. Based on tidal measurements the mean sea level was determined in Jutulgryta, a rift zone in the Fimbulisen ice shelf. Several gravity- and GPS-stations were measured between the two points (Jutulgryta andTroll), a distance of approximately 100 km.