Unmanned aerial vehicle (UAV) survey of the Antarctic shag (Leucocarbo bransfieldensis) breeding colony at Harmony Point, Nelson Island, South Shetland Islands

Waterborne metal levels in four freshwater lakes from Harmony Point, Nelson Island, Antarctica

The aim of this study is to analyze the waterborne metal levels in four lakes (one endorheic and three exorheic) of Harmony Point, Nelson Island, Antarctica. Water samples were analyzed by using a quadrupole type inductively coupled plasma mass spectrometer and an inductively coupled plasma optical emission spectrometer. The levels of As, Cu, Mn, Mo, and V were significantly lower and those of Cr, Mg, Na, and Sr were significantly higher in the endorheic lake than in the other lakes. Most water samples presented levels of Ag, Be, Cd, Pb, Se, Tl, and U below the limit of quantification, while for Ba, Co, and Ni around half of the samples were below this limit. The waterborne metal levels were not significantly different between the exorheic lakes. Waterborne metal levels in the freshwater lakes from Harmony Point did not show any clear relationship with their levels in the soil of the region or with bird guano, and overall, their levels indicate an environment without anthropogenic influence. Apparently, the Na levels are influenced by salt spray from the ocean, as they are related to the distance of the lakes from the ocean.

Remote sensing of salmonid spawning sites in freshwater ecosystems: The potential of low-cost UAV data

Salmonids are especially vulnerable during their embryonic development, but monitoring of their spawning grounds is rare and often relies on manual counting of their nests (redds). This method, however, is prone to sampling errors resulting in over- or underestimations of redd counts. Salmonid spawning habitat in shallow water areas can be distinguished by their visible reflection which makes the use of standard unmanned aerial vehicles (UAV) a viable option for their mapping. Here, we aimed to develop a standardised approach to detect salmonid spawning habitat that is easy and low-cost. We used a semi-automated approach by applying supervised classification techniques to UAV derived RGB imagery from two contrasting lakes in Iceland. For both lakes six endmember classes were obtained with high accuracies. Most importantly, producer's and user's accuracy for classifying spawning redds was >90% after applying post-classification improvements for both study areas. What we are proposing here is an entirely new approach for monitoring spawning habitats which will address some the major shortcomings of the widely used redd count method e.g. collecting and analysing large amounts of data cost and time efficiently, limiting observer bias, and allowing for precise quantification over different temporal and spatial scales.

UAVs for Science in Antarctica

Remote sensing is a very powerful tool that has been used to identify, map and monitor Antarctic features and processes for nearly one century. Satellite remote sensing plays the main role for about the last five decades, as it is the only way to provide multitemporal views at continental scale. But the emergence of small consumer-grade unoccupied aerial vehicles (UAVs) over the past two decades has paved the way for data in unprecedented detail. This has been also verified by an increasing noticeable interest in Antarctica by the incorporation of UAVs in the field activities in diversified research topics. This paper presents a comprehensive review about the use of UAVs in scientific activities in Antarctica. It is based on the analysis of 190 scientific publications published in peer-reviewed journals and proceedings of conferences which are organised into six main application topics: Terrestrial, Ice and Snow, Fauna, Technology, Atmosphere and Others. The analysis encompasses a detailed overview of the activities, identifying advantages and difficulties, also evaluating future possibilities and challenges for expanding the use of UAV in the field activities. The relevance of using UAVs to support numerous and diverse scientific activities in Antarctica becomes very clear after analysing this set of scientific publications, as it is revolutionising the remote acquisition of new data with much higher detail, from inaccessible or difficult to access regions, in faster and cheaper ways. Many of the advances can be seen in the terrestrial areas (detailed 3D mapping; vegetation mapping, discrimination and health assessment; periglacial forms characterisation), ice and snow (more detailed topography, depth and features of ice-sheets, glaciers and sea-ice), fauna (counting penguins, seals and flying birds and detailed morphometrics) and in atmosphere studies (more detailed meteorological measurements and air-surface couplings). This review has also shown that despite the low environmental impact of UAV-based surveys, the increasing number of applications and use, may lead to impacts in the most sensitive Antarctic ecosystems. Hence, we call for an internationally coordinated effort to for planning and sharing UAV data in Antarctica, which would reduce environmental impacts, while extending research outcomes.

Demystifying the Differences between Structure-from-MotionSoftware Packages for Pre-Processing Drone Data

With the increased availability of low-cost, off-the-shelf drone platforms, drone data become easy to capture and are now a key component of environmental assessments and monitoring. Once the data are collected, there are many structure-from-motion (SfM) photogrammetry software options available to pre-process the data into digital elevation models (DEMs) and orthomosaics for further environmental analysis. However, not all software packages are created equal, nor are their outputs. Here, we evaluated the workflows and output products of four desktop SfM packages (AgiSoft Metashape, Correlator3D, Pix4Dmapper, WebODM), across five input datasets representing various ecosystems. We considered the processing times, output file characteristics, colour representation of orthomosaics, geographic shift, visual artefacts, and digital surface model (DSM) elevation values. No single software package was determined the “winner” across all metrics, but we hope our results help others demystify the differences between the options, allowing users to make an informed decision about which software and parameters to select for their specific application. Our comparisons highlight some of the challenges that may arise when comparing datasets that have been processed using different parameters and different software packages, thus demonstrating a need to provide metadata associated with processing workflows.

Un-crewed aerial vehicle population survey of three sympatrically breeding seabird species at Signy Island, South Orkney Islands

Surveying seabirds in polar latitudes can be challenging due to sparse human populations, lack of infrastructure and the risk of disturbance to wildlife or damage to habitats. Counting populations using un-crewed aerial vehicles (UAVs) is a promising approach to overcoming these difficulties. However, a careful validation of the approach is needed to ensure comparability with counts collected using conventional methods. Here, we report on surveys of three Antarctic bird species breeding on Signy Island, South Orkney Islands; Chinstrap (Pygoscelis antarctica) and Gentoo (Pygoscelis papua) Penguins, and the South Georgia Shag (Leucocarbo atriceps georgianus). We show that images from low-altitude UAV surveys have sufficient resolution to allow separation of Chinstrap Penguins from contiguously breeding Adélie Penguins (Pygoscelis adéliae), which are very similar in appearance when viewed from overhead. We compare data from ground counts with manual counts of nesting birds on images collected simultaneously by low-altitude aerial photography from multi-rotor UAVs at the same colonies. Results at this long-term monitoring site confirmed a continued population decline for Chinstrap Penguins and increasing Gentoo Penguin population. Although both methods provided breeding pair counts that were generally within ~ 5%, there were significant differences at some locations. We examine these differences in order to highlight potential biases or methodological constraints that should be considered when analysing similar aerial census surveys and comparing them with ground counts.

Automated Bird Counting with Deep Learning for Regional Bird Distribution Mapping

Simple Summary

To detect changes in migrating bird populations that are usually gradual, regular counts of the flocks should be carried out. This is vital for giving more precise management decisions and taking preventive actions when necessary. Traditional counting methods are widely used. However, these methods can be expensive, time-consuming, and highly dependent on the mental and physical status of the observer and environmental factors. Taking these uncertainties into account, we aimed at taking the advantage of the advances in the artificial intelligence (AI) field for a more standardized counting action. The study has been practically initiated 10 years ago by beginning to take photos on a yearly basis in predefined regions of Turkey. After a large collection of bird photos had been gathered, we predicted the bird counts in photo locations from images by making strong use of AI. Finally, we used these counts to produce several bird distribution maps for further analysis. Our results showed the potential of learning computers in support of real-world bird monitoring applications.

Abstract

A challenging problem in the field of avian ecology is deriving information on bird population movement trends. This necessitates the regular counting of birds which is usually not an easily-achievable task. A promising attempt towards solving the bird counting problem in a more consistent and fast way is to predict the number of birds in different regions from their photos. For this purpose, we exploit the ability of computers to learn from past data through deep learning which has been a leading sub-field of AI for image understanding. Our data source is a collection of on-ground photos taken during our long run of birding activity. We employ several state-of-the-art generic object-detection algorithms to learn to detect birds, each being a member of one of the 38 identified species, in natural scenes. The experiments revealed that computer-aided counting outperformed the manual counting with respect to both accuracy and time. As a real-world application of image-based bird counting, we prepared the spatial bird order distribution and species diversity maps of Turkey by utilizing the geographic information system (GIS) technology. Our results suggested that deep learning can assist humans in bird monitoring activities and increase citizen scientists’ participation in large-scale bird surveys.