Image Mapping Accuracy Evaluation Using UAV with Standalone, Differential (RTK), and PPP GNSS Positioning Techniques in an Abandoned Mine Site.

Global navigation satellite systems (GNSSs) provide a common positioning method that utilizes satellite signals to determine the spatial location of a receiver. However, there are several error factors in standalone GNSS positioning due to instrumental, procedural, and environmental factors that arise during the signal transmission process, and the final positioning error can be up to several meters or greater in length. Thus, real-time kinematic (RTK) correction and post-mission precise point positioning (PPP) processing technologies are proposed to improve accuracy and accomplish precise position measurements. To evaluate the geolocation accuracy of mosaicked UAV images of an abandoned mine site, we compared each orthomosaic image and digital elevation model obtained using standalone GNSS positioning, differential (RTK) GNSS positioning, and post-mission PPP processing techniques. In the three types of error evaluation measure (i.e., relative camera location error, ground control points-based absolute image mapping error, and volumetric difference of mine tailings), we found that the RTK GNSS positioning method obtained the best performance in terms of the relative camera location error and the absolute image mapping error evaluations, and the PPP post-processing correction effectively reduced the error (69.5% of the average total relative camera location error and 59.3% of the average total absolute image mapping error) relative to the standalone GNSS positioning method. Although differential (RTK) GNSS positioning is widely used in positioning applications that require very high accuracy, post-mission PPP processing can also be used in various fields in which it is either not feasible to operate expensive equipment to receive RTK GNSS signals or network RTK services are unavailable.

Contrasting early successional dynamics of bacterial and fungal communities in recently deglaciated soils of the maritime Antarctic.

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.

Remotely Piloted Aircraft System (RPAS)-Based Wildlife Detection: A Review and Case Studies in Maritime Antarctica.

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.

Detection of two Arctic birds in Greenland and an endangered bird in Korea using RGB and thermal cameras with an unmanned aerial vehicle (UAV).

Unmanned aerial vehicles (UAVs), so-called 'drones', have been widely used to monitor wild animals. Here, we tested a UAV with red, green, and blue (RGB) and thermal cameras to detect free-living birds in a high Arctic region in North Greenland and in a restricted area in the Republic of Korea. Small flocks of molting pink-footed geese (Anser brachyrhynchus) near sea ice and incubating common ringed plovers (Charadrius hiaticula) in the Arctic environment were chosen for the RGB and thermal image studies. From the acquired images, we built mosaicked RGB images and coregistered thermal images, and estimated the animal shapes. Our results showed that geese were discriminated in both RGB and thermal images with water and sea ice backgrounds. Incubating plover bodies were not distinguished in RGB images due to their cryptic coloration, but they were detected in thermal images with cold background areas in the Arctic environment. We further conducted a blind survey in a restricted area under military control in Korea near the breeding sites of black-faced spoonbill (Platalea minor), which is an endangered species. From UAV flights with RGB and thermal cameras operated out of the restricted area, we acquired images of white objects in the mudflats and verified that the objects were resting spoonbills by watching the birds. We suggest that thermal cameras and UAVs can be applied to monitor animals in extreme environments and in restricted areas and help researchers find cryptic wader nests.

Mosaicking Opportunistically Acquired Very High-Resolution Helicopter-Borne Images over Drifting Sea Ice Using COTS Sensors.

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.