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Drones
Special Issues
UAV Design and Applications in Antarctic Research
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UAV Design and Applications in Antarctic Research

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Ecology".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 11633

Special Issue Editors

Dr. Anna Zmarz

E-Mail Website
Guest Editor
Department of Geoinformatics, Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warsaw, Poland
Interests: UAS; remote sensing; GIScience; natural resource monitoring; environmental applications
Dr. Rune Storvold

E-Mail Website
Guest Editor
Energy and Technology Department, NORCE Norwegian Reseach Centre, NO-5838 Bergen, Norway
Interests: UAS; remote sensing; cold climate operations; resource mapping; integrated observing systems; radar sensors; sea-ice; atmospheric properties
Dr. Osama Mustafa

E-Mail Website
Guest Editor
Thuringian Institute of Sustainability and Climate Protection, Hainstr. 1a, 07745 Jena, Germany
Interests: remote sensing; UAS; ecosystem monitoring; wildlife conservation; biodiversity; protected areas

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit original manuscripts to the MDPI Drones Special Issue on “UAV Design and Applications in Antarctic Research”.

Nowadays, different types of unmanned aerial systems (UASs) are widely used for multiple civilian purposes, such as archaeology, hydrology, forestry, precision agriculture, glaciology, or environmental monitoring. In the Antarctic, UAS-based surveys are still mostly in an experimental phase.

Unmanned aerial systems, as an alternative to manned aircraft, are excellent, less invasive, safe tools, which are crucial characteristics, especially in sensitive Antarctic regions. UAS operations are very robust in gathering valuable qualitative and quantitative data necessary for the monitoring of distant and isolated polar environments. The use of UAS operations in polar regions has improved environmental monitoring by extending the study area, increasing safety, reducing human footprints, increasing precision, and saving time. These are features that are essential for repetitive observations of a variety of hard-to-access areas which, nowadays, undergo environmental modifications due to climatic changes.

This Special Issue is inspired by the successful work of many UAS teams in Antarctica. Within this context, we invite authors to submit original manuscripts for this Special Issue on “UAV Design and Applications in Antarctic Research”. Specifically, this Special Issue will address (but is not limited to) the following unique issues and challenges in the Antarctic:

  • Communication;
  • Platform navigation;
  • Platform robustness;
  • Cross platform opportunities;
  • Sensor inter comparison;
  • Collecting data;
  • Risk management.

Dr. Anna Zmarz
Dr. Rune Storvold
Dr. Osama Mustafa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • UAS
  • remote sensing
  • communication
  • Antarctic research
  • risk management
  • mapping

Published Papers (4 papers)

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Research

11 pages, 3816 KiB  
Article
Mapping of Glaciers on Horseshoe Island, Antarctic Peninsula, with Deep Learning Based on High-Resolution Orthophoto
by Mahmut Oğuz Selbesoğlu, Tolga Bakirman, Oleg Vassilev and Burcu Ozsoy
Drones 2023, 7(2), 72; https://0-doi-org.brum.beds.ac.uk/10.3390/drones7020072 - 18 Jan 2023
Cited by 7 | Viewed by 2515
Abstract
Antarctica plays a key role in the hydrological cycle of the Earth’s climate system, with an ice sheet that is the largest block of ice that reserves Earth’s 90% of total ice volume and 70% of fresh water. Furthermore, the sustainability of the [...] Read more.
Antarctica plays a key role in the hydrological cycle of the Earth’s climate system, with an ice sheet that is the largest block of ice that reserves Earth’s 90% of total ice volume and 70% of fresh water. Furthermore, the sustainability of the region is an important concern due to the challenges posed by melting glaciers that preserve the Earth’s heat balance by interacting with the Southern Ocean. Therefore, the monitoring of glaciers based on advanced deep learning approaches offers vital outcomes that are of great importance in revealing the effects of global warming. In this study, recent deep learning approaches were investigated in terms of their accuracy for the segmentation of glacier landforms in the Antarctic Peninsula. For this purpose, high-resolution orthophotos were generated based on UAV photogrammetry within the Sixth Turkish Antarctic Expedition in 2022. Segformer, DeepLabv3+ and K-Net deep learning methods were comparatively analyzed in terms of their accuracy. The results showed that K-Net provided efficient results with 99.62% accuracy, 99.58% intersection over union, 99.82% precision, 99.76% recall and 99.79% F1-score. Visual inspections also revealed that K-Net was able to preserve the fine details around the edges of the glaciers. Our proposed deep-learning-based method provides an accurate and sustainable solution for automatic glacier segmentation and monitoring. Full article
(This article belongs to the Special Issue UAV Design and Applications in Antarctic Research)
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24 pages, 16230 KiB  
Article
Drone-Based Atmospheric Soundings Up to an Altitude of 10 km-Technical Approach towards Operations
by Konrad Bärfuss, Ruud Dirksen, Holger Schmithüsen, Lutz Bretschneider, Falk Pätzold, Sven Bollmann, Philippe Panten, Thomas Rausch and Astrid Lampert
Drones 2022, 6(12), 404; https://0-doi-org.brum.beds.ac.uk/10.3390/drones6120404 - 08 Dec 2022
Cited by 3 | Viewed by 2313
Abstract
Currently, the main in situ upper air database for numerical weather prediction relies on radiosonde and aircraft-based information. Typically, radiosondes are launched at specific sites daily, up to four times per day, and data are distributed worldwide via the GTS net. Aircraft observations [...] Read more.
Currently, the main in situ upper air database for numerical weather prediction relies on radiosonde and aircraft-based information. Typically, radiosondes are launched at specific sites daily, up to four times per day, and data are distributed worldwide via the GTS net. Aircraft observations are limited to frequent flight routes, and vertical profiles are provided in the vicinity of large cities. However, there are large areas with few radiosonde launches, in particular above the oceans and in the polar areas. In this article, the development and technical details of the unmanned aerial system LUCA (Lightweight Unmanned high Ceiling Aerial system) are described. LUCA has the potential to complement radiosonde and aircraft-based observations up to 10 km in altitude. The system ascends and descends (by electrical power) in spiral trajectories and returns to the launching site. This article discusses the requirements for obtaining high data availability under mid-European and Antarctic conditions, with highly automated take-offs and landings under high surface winds, the capacity to deal with icing, and the ability to operate under high wind speeds. The article presents technical solutions for the design and construction of the system and demonstrates its potential. Full article
(This article belongs to the Special Issue UAV Design and Applications in Antarctic Research)
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22 pages, 5006 KiB  
Article
Fixed-Wing UAV Flight Operation under Harsh Weather Conditions: A Case Study in Livingston Island Glaciers, Antarctica
by Ana Belén Bello, Francisco Navarro, Javier Raposo, Mónica Miranda, Arturo Zazo and Marina Álvarez
Drones 2022, 6(12), 384; https://0-doi-org.brum.beds.ac.uk/10.3390/drones6120384 - 28 Nov 2022
Cited by 7 | Viewed by 3707
Abstract
How do the weather conditions typical of the polar maritime glaciers in the western Antarctic Peninsula region affect flight operations of fixed-wing drones and how should these be adapted for a successful flight? We tried to answer this research question through a case [...] Read more.
How do the weather conditions typical of the polar maritime glaciers in the western Antarctic Peninsula region affect flight operations of fixed-wing drones and how should these be adapted for a successful flight? We tried to answer this research question through a case study for Johnsons and Hurd glaciers, Livingston Island, using a fixed-wing RPAS, in particular, a Trimble UX5 UAV with electric pusher propeller by brushless 700 W motor, chosen for its ability to fly long distances and reach inaccessible areas. We also evaluated the accuracy of the point clouds and digital surface models (DSM) generated by aerial photogrammetry in our case study. The results were validated against ground control points taken by differential GNSS techniques, showing an accuracy of 0.16 ± 0.12 m in the vertical coordinate. Various hypotheses were proposed and flight-tested, based on variables affecting the flight operation and the data collection, namely, gusty winds, low temperatures, battery life, camera configuration, and snow reflectivity. We aim to provide some practical guidelines that can help other researchers using fixed-wing drones under climatic conditions similar to those of the South Shetland Islands. Performance of the drone under harsh weather conditions, the logistical considerations, and the amount of snow at the time of data collection are factors driving the necessary modifications from those of conventional flight operations. We make suggestions concerning wind speed and temperature limitations, and avoidance of sudden fog banks, aimed to improve the planning of flight operations. Finally, we make some suggestions for further research. Full article
(This article belongs to the Special Issue UAV Design and Applications in Antarctic Research)
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11 pages, 1416 KiB  
Communication
Evaluating Thermal and Color Sensors for Automating Detection of Penguins and Pinnipeds in Images Collected with an Unoccupied Aerial System
by Jefferson T. Hinke, Louise M. Giuseffi, Victoria R. Hermanson, Samuel M. Woodman and Douglas J. Krause
Drones 2022, 6(9), 255; https://0-doi-org.brum.beds.ac.uk/10.3390/drones6090255 - 15 Sep 2022
Cited by 6 | Viewed by 1984
Abstract
Estimating seabird and pinniped abundance is central to wildlife management and ecosystem monitoring in Antarctica. Unoccupied aerial systems (UAS) can collect images to support monitoring, but manual image analysis is often impractical. Automating target detection using deep learning techniques may improve data acquisition, [...] Read more.
Estimating seabird and pinniped abundance is central to wildlife management and ecosystem monitoring in Antarctica. Unoccupied aerial systems (UAS) can collect images to support monitoring, but manual image analysis is often impractical. Automating target detection using deep learning techniques may improve data acquisition, but different image sensors may affect target detectability and model performance. We compared the performance of automated detection models based on infrared (IR) or color (RGB) images and tested whether IR images, or training data that included annotations of non-target features, improved model performance. For this assessment, we collected paired IR and RGB images of nesting penguins (Pygoscelis spp.) and aggregations of Antarctic fur seals (Arctocephalus gazella) with a small UAS at Cape Shirreff, Livingston Island (60.79 °W, 62.46 °S). We trained seven independent classification models using the Video and Image Analytics for Marine Environments (VIAME) software and created an open-access R tool, vvipr, to standardize the assessment of VIAME-based model performance. We found that the IR images and the addition of non-target annotations had no clear benefits for model performance given the available data. Nonetheless, the generally high performance of the penguin models provided encouraging results for further improving automated image analysis from UAS surveys. Full article
(This article belongs to the Special Issue UAV Design and Applications in Antarctic Research)
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