Mapping landfast sea ice at a fine spatial scale is not only meaningful for geophysical study, but is also of benefit for providing information about human activities upon it. The combination of unmanned aerial systems (UAS) with structure from motion (SfM) methods have already revolutionized the current close-range Earth observation paradigm. To test their feasibility in characterizing the properties and dynamics of fast ice, three flights were carried out in the 2016–2017 austral summer during the 33rd Chinese National Antarctic Expedition (CHINARE), focusing on the area of the Prydz Bay in East Antarctica. Three-dimensional models and orthomosaics from three sorties were constructed from a total of 205 photos using Agisoft PhotoScan software. Logistical challenges presented by the terrain precluded the deployment of a dedicated ground control network; however, it was still possible to indirectly assess the performance of the photogrammetric products through an analysis of the statistics of the matching network, bundle adjustment, and Monte-Carlo simulation. Our results show that the matching networks are quite strong, given a sufficient number of feature points (mostly > 20,000) or valid matches (mostly > 1000). The largest contribution to the total error using our direct georeferencing approach is attributed to inaccuracies in the onboard position and orientation system (POS) records, especially in the vehicle height and yaw angle. On one hand, the 3D precision map reveals that planimetric precision is usually about one-third of the vertical estimate (typically 20 cm in the network centre). On the other hand, shape-only errors account for less than 5% for the X and Y dimensions and 20% for the Z dimension. To further illustrate the UAS’s capability, six representative surface features are selected and interpreted by sea ice experts. Finally, we offer pragmatic suggestions and guidelines for planning future UAS-SfM surveys without the use of ground control. The work represents a pioneering attempt to comprehensively assess UAS-SfM survey capability in fast ice environments, and could serve as a reference for future improvements.

Ice doline is a particular kind of ice morphology, usually scattered on ice streams which are mostly far from the existing research bases. For this reason, glaciologists rarely have opportunities to document its developments in detail. Satellite observations are too coarse to capture such fine features, whereas Unmanned Aerial Vehicle (UAV)-based Structure-from-Motion (SfM) and Light Detection and Ranging (LiDAR) technologies have revolutionized geosciences research, especially in less accessed polar regions. We developed two bespoke UAV systems for glaciological investigation and carried out four campaigns during two consecutive Chinese Antarctic expeditions in 2017 and 2018. Founded on manual co-registration and accuracy assessment, a successful application to characterize a doline's spatio-temporal evolution is presented in this paper. The overlying weight of surface melting directly triggered the collapse event on 30 Jan 2017 near the Dalk Glacier, then the newborn doline grew for another 8135.6 m 2 in area and 280303.38 m 3 in volume by early 2018. The UAV-based results revealed the doline's changes during a year, showing a maximum horizontal extension of 50 m and vertical subsidence of more than 10 m. Furthermore, we evaluate the photogrammetry and LiDAR systems and find the former is cost-effective and time-efficient on a large-scale survey while the latter enjoys a better capability to characterize ice morphological details. Based on systematic comparisons, other pros and cons of the two techniques are discussed. To achieve the best performance for relevant applications in similar scenarios, we recommend adopting an integrated approach, in which the LiDAR restores the fine features on the basis of extensive SfM coverage.

The freshwater flux from icebergs into the Southern Ocean plays an important role in the global climate through its impact on the deep-water formation. Large uncertainties exist in the ice volume transported by Southern Ocean icebergs due to the sparse spatial and temporal coverage of observations, especially observations of ice thickness. The iceberg freeboard is a critical geometric parameter for measuring the thickness of an iceberg and then estimating its volume. This study developed a new, highly efficient shadow-height method to precisely measure the freeboard of various icebergs surrounded by sea ice using Landsat-8 Operational Land Imager 15-m bi-temporal panchromatic image shadows at low-solar-elevation angles. We evaluated and validated shadow length precision according to bi-temporal measurements and comparison with the measurements from the unmanned aerial vehicle. We determined freeboard precision according to shadow length precision and solar elevation angle. In our case study area, 4832 available freeboard measuring points with shadow length precision better than 2 pixels covered 376 icebergs with sizes ranging from 0.002 to 0.7 km² and with freeboard ranging from 2.3 to 83.4 m. At the solar elevation angles of 5.2°, the freeboard precision of 64.1% data could reach 1 m and 86.9% could reach 2 m. Our proposed method effectively filled in the data gap of existing freeboard measurement methods.