In wildlife biology, it is important to conduct efficient observations and quantitative monitoring of wild animals. Conventional wildlife monitoring mainly relies on direct field observations by the naked eyes or through binoculars, on-site image acquisition at fixed spots, and sampling or capturing under severe areal constraints. Recently, remotely piloted aircraft systems (RPAS), also called drones or unmanned aerial vehicles (UAV), were successfully applied to detect wildlife with imaging sensors, such as RGB and thermal-imaging sensors, with superior detection capabilities to those of human observation. Here, we review studies with RPAS which has been increasingly used in wildlife detection and explain how an RPAS-based high-resolution RGB image can be applied to wild animal studies from the perspective of individual detection and population surveys as well as behavioral studies. The applicability of thermal-imaging sensors was also assessed with further information extractable from image analyses. In addition, RPAS-based case studies of acquisition of high-resolution RGB images for the purpose of detecting southern elephant seals (Mirounga leonina) and shape property extraction using thermal-imaging sensor in King George Island, maritime Antarctica is presented as applications in an extreme environment. The case studies suggest that currently available cost-effective small-sized RPAS, which are capable of flexible operation and mounting miniaturized imaging sensors, and are easily maneuverable even from an inflatable boat, can be an effective and supportive technique for both the visual interpretation and quantitative analysis of wild animals in low-accessible extreme or maritime environments.

Although microorganisms are the very first colonizers of recently deglaciated soils even prior to plant colonization, the drivers and patterns of microbial community succession at early‐successional stages remain poorly understood. The successional dynamics and assembly processes of bacterial and fungal communities were compared on a glacier foreland in the maritime Antarctic across the ~10‐year soil‐age gradient from bare soil to sparsely vegetated area. Bacterial communities shifted more rapidly than fungal communities in response to glacial retreat; species turnover (primarily the transition from glacier‐ to soil‐favouring taxa) contributed greatly to bacterial beta diversity, but this pattern was less clear in fungi. Bacterial communities underwent more predictable (more deterministic) changes along the soil‐age gradient, with compositional changes paralleling the direction of changes in soil physicochemical properties following deglaciation. In contrast, the compositional shift in fungal communities was less associated with changes in deglaciation‐induced changes in soil geochemistry and most fungal taxa displayed mosaic abundance distribution across the landscape, suggesting that the successional dynamics of fungal communities are largely governed by stochastic processes. A co‐occurrence network analysis revealed that biotic interactions between bacteria and fungi are very weak in early succession. Taken together, these results collectively suggest that bacterial and fungal communities in recently deglaciated soils are largely decoupled from each other during succession and exert very divergent trajectories of succession and assembly under different selective forces.

Sea ice motion and deformation have generally been measured using low-resolution passive microwave or mid-resolution radar remote sensing datasets of daily (or few days) intervals to monitor long-term trends over a wide polar area. This feasibility study presents an application of high-resolution optical images from operational satellites, which have become more available in polar regions, for sea ice motion and deformation measurements. The sea ice motion, i.e., Lagrangian vector, is measured by using a maximum cross-correlation (MCC) technique and multi-temporal high-resolution images acquired on 14–15 August 2014 from multiple spaceborne sensors on board Korea Multi-Purpose Satellites (KOMPSATs) with short acquisition time intervals. The sea ice motion extracted from the six image pairs of the spatial resolutions were resampled to 4 m and 15 m yields with vector length measurements of 57.7 m root mean square error (RMSE) and −11.4 m bias and 60.7 m RMSE and −13.5 m bias, respectively, compared with buoy location records. The errors from both resolutions indicate more accurate measurements than from conventional sea ice motion datasets from passive microwave and radar data in ice and water mixed surface conditions. In the results of sea ice deformation caused by interaction of individual ice floes, while free drift patterns of ice floes were delineated from the 4 m spatial resolution images, the deformation was less revealing in the 15 m spatial resolution image pairs due to emphasized discretization uncertainty from coarser pixel sizes. The results demonstrate that using multi-temporal high-resolution optical satellite images enabled precise image block matching in the melting season, thus this approach could be used for expanding sea ice motion and deformation dataset, with an advantage of frequent image acquisition capability in multiple areas by means of many operational satellites.

Observing sea ice by very high-resolution (VHR) images not only improves the quality of lower-resolution remote sensing products (e.g., sea ice concentration, distribution of melt ponds and pressure ridges, sea ice surface roughness, etc.) by providing details on the ground truth of sea ice, but also assists sea ice fieldwork. In this study, two fieldwork-based methods are proposed, one for the practical acquisition of VHR images over drifting Arctic sea ice using low-cost commercial off-the-shelf (COTS) sensors equipped on a helicopter, and the other for quantifying the compensating effect from continuously drifting sea ice that reduces geolocation uncertainty in the image mosaicking procedure. The drifting trajectory of the target ice was yielded from that recorded by an icebreaker that was tightly anchored to the floe and was then used to reversely compensate the locations of acquired VHR images. After applying the compensation, three-dimensional geolocation errors of the VHR images were decreased by 79.3% and 24.2% for two pre-defined image groups, respectively. The enhanced accuracy of the imaging locations was affected by imaging duration causing variable drifting distances of individual images. Further applicability of the mosaicked VHR image was discussed by comparing it with a TerraSAR-X synthetic aperture radar image containing the target ice, suggesting that the proposed methods can be used for precise comparison with satellite remote sensing products.

Satellite remote sensing can be used effectively with a wide coverage and repeatability in large-scale Arctic sea-ice analysis. To produce reliable sea-ice information, satellite remote-sensing methods should be established and validated using accurate field data, but obtaining field data on Arctic sea-ice is very difficult due to limited accessibility. In this situation, digital surface models derived from aerial images can be a good alternative to topographical field data. However, to achieve this, we should discuss an additional issue, i.e., that low-textured surfaces on sea-ice can reduce the matching accuracy of aerial images. The matching performance is dependent on the matching cost and search window size used. Therefore, in order to generate high-quality sea-ice surface models, we first need to examine the influence of matching costs and search window sizes on the matching performance on low-textured sea-ice surfaces. For this reason, in this study, we evaluate the performance of matching costs in relation to changes of the search window size, using acquired aerial images of Arctic sea-ice. The evaluation concerns three factors. The first is the robustness of matching to low-textured surfaces. Matching costs for generating sea-ice surface models should have a high discriminatory power on low-textured surfaces, even with small search windows. To evaluate this, we analyze the accuracy, uncertainty, and optimal window size in terms of template matching. The second is the robustness of positioning to low-textured surfaces. One of the purposes of image matching is to determine the positions of object points that constitute digital surface models. From this point of view, we analyze the accuracy and uncertainty in terms of positioning object points. The last is the processing speed. Since the computation complexity is also an important performance indicator, we analyze the elapsed time for each of the processing steps. The evaluation results showed that the image domain costs were more effective for low-textured surfaces than the frequency domain costs. In terms of matching robustness, the image domain costs showed a better performance, even with smaller search windows. In terms of positioning robustness, the image domain costs also performed better because of the lower uncertainty. Lastly, in terms of processing speed, the PC (phase correlation) of the frequency domain showed the best performance, but the image domain costs, except MI (mutual information), were not far behind. From the evaluation results, we concluded that, among the compared matching costs, ZNCC (zero-mean normalized cross-correlation) is the most effective for sea-ice surface model generation. In addition, we found that it is necessary to adjust search window sizes properly, according to the number of textures required for reliable image matching on sea-ice surfaces, and that various uncertainties due to low-textured surfaces should be considered to determine the positions of object points.