Uncrewed Aerial Vehicles (UAVs)

This UAV article was written by Lauren Rawlins, University of York

A researcher flying a drone over a glacier in Austria. Photo credit: Bethan Davies

This article introduces UAVs, or drones, and how we can use them in glaciology and geomorphology. We can stitch together the thousands of images taken from a UAV from vantage point high in the sky to create new high-resolution image and 3D model of the Earth surface. Plus, UAVs really cool to work with!

What is a UAV?

Uncrewed aerial vehicles (UAV), commonly known as drones, are aircraft that are remotely controlled and flown without the presence of an onboard pilot (1). An Uncrewed Aerial System includes the UAV but also a remote electronic controller (such as a computer or tablet) that controls its movement, the equipment it carries known as payload (camera, radar, etc.) and a data system that connects the components together (e.g., wireless connection or GPS) (2).

UAVs come in a range of different shapes and sizes depending on their purpose, from small hand-launched systems that weigh as little as 250g, to much larger crafts that can weigh as much as 150kg and carry heavier payload (3). They can also be flown at varying levels of autonomy, from manual flying via an operator on the ground (a human controlling its movements) to partial- or full-autonomous flying through autopilot commands or pre-programmed instructions uploaded to the craft prior to take-off (4).

UAVs have a history of use in military applications dating back to the Cold War (5). However, in the last two decades with technological advances, the increasingly availability of cost-effective options and the high-resolution of data collected (sub-metre), UAVs have been increasingly used for a range of different purposes, including scientific research. 

Types of UAVs

UAVs come in a range of different shapes and sizes. Typically, UAVs can be classified into four main categories: (1) single rotor; (2) multi-rotor; (3) fixed-wing and; (4) hybrid systems (6,7,8)

Single-rotor UAV

As the name suggests, a single rotor UAV means it only has one main propellor that generates vertical lift, like a helicopter (figure 1). Larger propellors spin more slowly, meaning efficiency of the UAV is increased, helping to preserve battery life and allowing the UAV to fly for longer.

A key advantage of these systems is their fast-forward flight and hovering capabilities, as well as being able to carry heavy payloads, such as a LIDAR (light detection and ranging) scanner. Single rotor UAVs are, however, not as stable as other UAV choices and are generally more expensive, particularly their maintenance and repair due to the mechanical complexity of the rotor. 

Multi-rotor UAV

Multi-rotor UAVs are aerial systems that use two or more rotors and are one of the most commonly known and used systems (figure 2). One of the main advantages of multi-rotor crafts is their manoeuvrability, allowing them to hover, change direction, and adjust speed and altitude quickly. They also generally have more stability when flying. They are also smaller in size, meaning they can be transported easier (for example in a backpack) and quickly set up and flown in the field. These multi-rotor systems can also be further categorised based on the number of rotors present. For example, tricopter (3 rotors), quadcopter (4 rotors) hexacopter (6 rotors) and octocopter (8 rotors). 

Unfortunately, flight time of multi-rotor UAVs is an issue, meaning for longer flights or surveying larger areas, interruptions for landing and changing the batteries are required.

Fixed wing UAV

Fixed-wing UAVs look very similar to a conventional aircraft, with a central body (or fuselage) and two wings (figure 3). For take-off, these systems may require a runway or launching equipment such as a slingshot, and on landing, a net for safe capture if the ground is hard and bumpy. Fixed wing UAVs can cover much greater distances than other systems, using less battery power as energy is only needed to propel it forward. This means they can survey large areas at high resolutions. Unlike the rotor UAVs, fixed wing crafts are unable to hover and change direction as quickly, and can be costly to maintain.

Figure 3 – Image of a fixed wing UAV taking flight on the Greenland Ice Sheet (image credit: Tom Chudley)

Hybrid UAV

A hybrid UAV is the combination of a fixed wing and multi-rotor system that integrates the advantages of both UAV types (figure 4). This includes the ability to vertically take-off and land (without the need for a launcher or runway), hover, cover large distances, and fly for faster and longer periods. They can be used for a much greater range of applications and are a relatively new platform within the field of glaciology (9, 10).

Figure 4- Example of a hybrid UAV, which is an integration of a fixed wing and rotor UAV system (image credit: Unmanned Systems Technology)

The use of UAVs in Glaciology

Within the field of glaciology, the use of UAVs in recent years has provided a wealth of new and exciting opportunities for the study of glaciers and glacial processes at high spatial and temporal detail (figure 5). 

As UAVs are relatively cheap and easy to transport and deploy in the field, it allows them to be flown on-demand with the flexibility of flight windows, repeated surveying over areas of interest and choosing the resolution of data acquired (the lower the flying altitude, the higher the resolution) (11).

UAVs are also highly adaptable with a range of possible light-weight sensors, including RGB cameras, multispectral, hyperspectral, thermal, LiDAR, atmospheric, and gravimetric sensors. This means UAVs are a powerful tool in examining glacial processes up-close, with data having metre to centimetre-scale resolution and high temporal (hourly) repeated surveying if desired (12)

They also provide a unique vantage point for glaciologists to view and safely survey difficult-to-access or potentially dangerous locations, such as crevasse zones, moulins and supraglacial rivers/lakes. 

These capabilities also provide a great opportunity for conducting ‘ground truthing’ and validation, helping to bridge the spatial and temporal gap between ground-based surveys and satellite-based remotely sensed data (12)

What do we use UAVs for?

Some examples of the uses of UAVs in glaciology include:

  • Geomorphological mapping and glacial reconstructions (13, 14, 15) 
  • Monitoring glacial mass balance (16)  and surface melt (17) 
  • Investigating calving dynamics (18) and events (19)
  • Monitoring plume dynamics (20, 21) 
  • Mapping and analysing supraglacial features, such as channels (22), lakes (23), cryoconite (24) and surface roughness (25)
  • Mapping and monitoring glacial hazards (26, 27, 28)
Figure 5 – Map showing the global distribution of UAV study sites from glaciological publications between 2014 and 2019. Figure taken from Gaffey and Bhardwaj (2020).

Problems for UAVs in glaciated environments 

Of course, using UAVs in the often-harsh climates experienced in glaciated environments can be tricky! Weather conditions such as snow, rain, strong winds (e.g., katabatic winds) and persistent cold temperatures can impact the stability of a flying UAV and significantly reduce its battery life, causing them to lose power much faster (29). This means extra batteries are often required and flights interrupted whilst batteries are changed. 

When used in locations near the North and South poles, such as in Greenland or Antarctica, the natural deviation of the Earth’s magnetic field can cause problems for UAV magnetometers, which help maintain a UAVs altitude and position (30). Also, within high mountainous regions such as the Himalaya, Alps and the Andes, low air pressure systems and poor GPS signals can prove challenging (11).

These are all important considerations when planning UAV fieldwork in glaciated regions. 

Structure-from-Motion (SfM) Photogrammetry 

The most common application that uses aerial photographs taken from UAVs is a technique called Structure-from-Motion (SfM) photogrammetry (31). SfM is a powerful tool that uses computer algorithms to identify matching features in a collection of overlapping images. It then stitches these overlapping images together to create a 3D model, taking into account internal camera geometry, camera positions and orientation (32, 33). The alignment and pairing of overlapping images via triangulation creates a 3D point cloud, which are a set of thousands, or even millions, of data points that represent X, Y and Z coordinates of the photographed scene.  

The short animation below (figure 6) demonstrates how images are gathered over a scene and the thousands of data points that are generated forming a point cloud. 

Figure 6 – A short animation of a point cloud showing the position of the images taken (hovering blue tiles) and the matching feature data points that represent the X, Y and Z coordinates of features in the photographed scene – 60m2 surface of Russell Glacier, West Greenland (Aug 2019). Video credit: Lauren Rawlins 

These point clouds form the backbone for accurately reconstructing high-resolution 3D scenes (sub-metre), as well as the creation of other outputs such as orthomosaics and digital elevation models (DEMs). The short animation below (figure 7) shows a 3D photogrammetric model of a 60m2 section of the surface of Russell Glacier, West Greenland (Aug 2019), created from the point cloud above.

Figure 7 – A short animation of a 3D scene created from the point cloud shown above. The 3D scene is of a 60m2 area of the surface of Russell Glacier, West Greenland. In the middle, a group of people can be seen for scale. (Video credit: Lauren Rawlins).

Important components of SfM Photogrammetry

An important component of SfM, and the practice of photogrammetry in general, is making sure there is enough overlap in acquired images so there are no holes or data missing when they are stitched together post-processing (33). It is recommended to have 75% vertical (front) and 60% horizontal (side) overlap in images to avoid this (34) (figure 8). Also gathering a range of images with different camera angles also helps to cover the full surveyed scene. This may mean flying two grids, rotated at 90 degrees to each other, for example.

Another important consideration for generating point clouds and realistic models of glacial surfaces is what the surface actually looks like. For example, photographing a highly saturated surface, such as clean snow, is a common problem and often requires capturing aerial images that are underexposed to better interpret glaciological details (35).

Figure 8 – Simple schematic of a UAV survey showing the general flight line (in red) and the horizontal and vertical overlap required for gathering imagery from the surveyed scene in full (with no gaps or loss of information). Graphics by Lauren Rawlins.

Using Ground Control Points in Photogrammetry

The use of ground control points is also an important component within UAV-based photogrammetric studies. Ground control points (GCPs) are large, distinguishable ‘points’ on the ground with known coordinates that can be used to accurately map and measure features in UAV-generated photogrammetric models (36). These GCPs must be easily visible in the aerial imagery. They could be a natural feature already found in the surveyed area, such as a boulder, or an artificially-installed feature, such as a checkerboard (Figure 9). 

Figure 9  – (a) Aerial image taken from a quadcopter UAV of Russell Glacier ice surface, West Greenland (July 2019). Red square shows the placement of a ground control point in the surveyed scene; (b) Image of the same ground control point (A4 size) from the ground.

About the author

Lauren is a glaciology PhD student at the University of York interested in ice sheet hydrology and the impacts of contemporary climate change on glaciated environments. Her PhD focuses on mapping and examining the evolution of supraglacial channels on the Greenland Ice Sheet at a range of spatial and temporal scales, using both remote sensing from satellites and UAV applications.  

Lauren’s interests also lie in teaching and science communication. She is actively involved in outreach education.

Website – https://www.york.ac.uk/environment/our-staff/lauren-rawlins/

Twitter – @Lauren_Rawlins1

Lauren Rawlins

Further reading and videos of UAV applications in the cryosphere:

UAV search and Rescue in extreme environments (https://www.youtube.com/watch?v=GkIJ2NJQHys)

Caged drone explores the depths of Greenland ice caves (https://newatlas.com/drones/flyability-elios-drone-greenland-ice-caves/)

‘After Ice’ documentary (by Kieran Baxter) – documenting Icelandic glacial retreat https://www.vatnajokulsthjodgardur.is/en/areas/melting-glaciers ‘Drones in a cold climate’ by Guy Williams et al., (2016) https://eos.org/science-updates/drones-in-a-cold-climate

References

(1) Uncrewed Aerial Vehicle (online: https://en.wikipedia.org/wiki/Unmanned_aerial_vehicle)

(2) Narayanan, R.G.L. and Ibe, O.C., 2015. Joint network for disaster relief and search and rescue network operations. In Wireless Public Safety Networks 1 (pp. 163-193). Elsevier.

(3) (online article: https://www.caa.co.uk/Consumers/Unmanned-aircraft/Our-role/An-introduction-to-unmanned-aircraft-systems/)

(4) Valavanis K.P., Vachtsevanos G.J. (2015) UAV Autonomy: Introduction. In: Valavanis K., Vachtsevanos G. (eds) Handbook of Unmanned Aerial Vehicles. Springer, Dordrecht. 

(5) A brief history of drones (online article: https://www.iwm.org.uk/history/a-brief-history-of-drones)

(6) Types of drones (online article: https://www.circuitstoday.com/types-of-drones)

(7) Tahir, A., Böling, J., Haghbayan, M.H., Toivonen, H.T. and Plosila, J., 2019. Swarms of unmanned aerial vehicles—a survey. Journal of Industrial Information Integration16, p.100106.

(8) Andrew Chapman (2016). DRONE TYPES: MULTI-ROTOR VS FIXED-WING VS SINGLE ROTOR VS HYBRID VTOL (auav) (original – article written for the Australian Drone Magazine, issues 3, June 2016) https://www.auav.com.au/articles/drone-types/ 

(9) Saeed, A.S., Younes, A.B., Cai, C. and Cai, G., 2018. A survey of hybrid unmanned aerial vehicles. Progress in Aerospace Sciences98, pp.91-105.

(10) Jouvet, G., Weidmann, Y., Kneib, M., Detert, M., Seguinot, J., Sakakibara, D., et al. (2018). Short-lived ice speed-up and plume water flow captured by a vtol uav give insights into subglacial hydrological system of Bowdoin glacier. Rem. Sens. Environ. 217, 389–399. doi: 10.1016/j.rse.2018.08.027

(11) Gaffey, C. and Bhardwaj, A., 2020. Applications of unmanned aerial vehicles in cryosphere: Latest advances and prospects. Remote Sensing12(6), p.948.

(12) Bhardwaj, A., Sam, L., Martín-Torres, F.J. and Kumar, R., 2016. UAVs as remote sensing platform in glaciology: Present applications and future prospects. Remote sensing of environment175, pp.196-204. 

(13) Śledź, S., Ewertowski, M. and Piekarczyk, J., 2021. Applications of unmanned aerial vehicle (UAV) surveys and Structure from Motion photogrammetry in glacial and periglacial geomorphology. Geomorphology, p.107620.

(14) Ely, J.C., Graham, C., Barr, I.D., Rea, B.R., Spagnolo, M. and Evans, J., 2017. Using UAV acquired photography and structure from motion techniques for studying glacier landforms: application to the glacial flutes at Isfallsglaciären. Earth Surface Processes and Landforms42(6), pp.877-888.

(15) Mather, A.E., Fyfe, R.M., Clason, C.C., Stokes, M., Mills, S. and Barrows, T.T., 2019. Automated mapping of relict patterned ground: An approach to evaluate morphologically subdued landforms using unmanned-aerial-vehicle and structure-from-motion technologies. Progress in Physical Geography: Earth and Environment43(2), pp.174-192.

(16) Cao, B., Guan, W., Li, K., Pan, B. and Sun, X., 2021. High-Resolution Monitoring of Glacier Mass Balance and Dynamics with Unmanned Aerial Vehicles on the Ningchan No. 1 Glacier in the Qilian Mountains, China. Remote Sensing13(14), p.2735

(17) Bash, E.A., Moorman, B.J. and Gunther, A., 2018. Detecting short-term surface melt on an Arctic Glacier using UAV surveys. Remote Sensing10(10), p.1547.

(18) Ryan, J.C., Hubbard, A.L., Box, J.E., Todd, J., Christoffersen, P., Carr, J.R., Holt, T.O. and Snooke, N., 2015. UAV photogrammetry and structure from motion to assess calving dynamics at Store Glacier, a large outlet draining the Greenland ice sheet. The Cryosphere9(1), pp.1-11.

(19) Jouvet, G., Weidmann, Y., Seguinot, J., Funk, M., Abe, T., Sakakibara, D., Seddik, H. and Sugiyama, S., 2017. Initiation of a major calving event on the Bowdoin Glacier captured by UAV photogrammetry. The Cryosphere11(2), pp.911-921.

(20) Jouvet, G., Weidmann, Y., Kneib, M., Detert, M., Seguinot, J., Sakakibara, D. and Sugiyama, S., 2018. Short-lived ice speed-up and plume water flow captured by a VTOL UAV give insights into subglacial hydrological system of Bowdoin Glacier. Remote sensing of environment217, pp.389-399.

(21) Wójcik, K.A., Bialik, R.J., Osińska, M. and Figielski, M., 2019. Investigation of Sediment-Rich Glacial Meltwater Plumes Using a High-Resolution Multispectral Sensor Mounted on an Unmanned Aerial Vehicle. Water11(11), p.2405

(22) Rippin, D.M., Pomfret, A. and King, N., 2015. High resolution mapping of supra‐glacial drainage pathways reveals link between micro‐channel drainage density, surface roughness and surface reflectance. Earth Surface Processes and Landforms40(10), pp.1279-1290.

(23) Chudley, T.R., Christoffersen, P., Doyle, S.H., Bougamont, M., Schoonman, C.M., Hubbard, B. and James, M.R., 2019. Supraglacial lake drainage at a fast-flowing Greenlandic outlet glacier. Proceedings of the National Academy of Sciences116(51), pp.25468-25477.

(24) Cook, J.M., Sweet, M., Cavalli, O., Taggart, A. and Edwards, A., 2018. Topographic shading influences cryoconite morphodynamics and carbon exchange. Arctic, Antarctic, and Alpine Research50(1), p.S100014.

(25) Dachauer, A., Hann, R. and Hodson, A.J., 2021. Aerodynamic roughness length of crevassed tidewater glaciers from UAV mapping. The Cryosphere Discussions, pp.1-23.

(26) Fugazza, D., Scaioni, M., Corti, M., D’Agata, C., Azzoni, R.S., Cernuschi, M., Smiraglia, C. and Diolaiuti, G.A., 2018. Combination of UAV and terrestrial photogrammetry to assess rapid glacier evolution and map glacier hazards. Natural Hazards and Earth System Sciences18(4), pp.1055-1071.

(27) Tomczyk, A.M. and Ewertowski, M.W., 2020. UAV-based remote sensing of immediate changes in geomorphology following a glacial lake outburst flood at the Zackenberg river, northeast Greenland. Journal of Maps16(1), pp.86-100.

(28) Scaioni, M.; Barazzetti, L.; Corti, M.; Crippa, J.; Azzoni, R.S.; Fugazza, D.; Cernuschi, M.; Diolaiuti, G.

Integration of Terrestrial And Uav Photogrammetry for The Assessment of Collapse Risk in Alpine Glaciers.

ISPRS Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. 2018, XLII-3/W4, 445–452

(29) Jouvet, G., Weidmann, Y., van Dongen, E., Lüthi, M.P., Vieli, A. and Ryan, J.C., 2019. High-endurance UAV for monitoring calving glaciers: Application to the Inglefield Bredning and Eqip Sermia, Greenland. Frontiers in Earth Science7, p.206.

(30) Drones in a cold climate (online article: https://eos.org/science-updates/drones-in-a-cold-climate)

(31) Hackney, C., and Clayton, A.I., (2015)  Unmanned Aerial Vehicles (UAVs) and their application in geomorphic mapping (online article: https://www.geomorphology.org.uk/sites/default/files/chapters/2.1.7_UAV.pdf

(32) Smith, M.W., Carrivick, J.L. and Quincey, D.J., 2016. Structure from motion photogrammetry in physical geography. Progress in Physical Geography40(2), pp.247-275.

(33) Mitcheletti, N., Chandler, J.H., Lane, S.N., Structure from Motion (SfM) Photogrammetry (online article: https://www.geomorphology.org.uk/sites/default/files/geom_tech_chapters/2.2.2_sfm.pdf)

(34) Pix4D – How to verify that there is enough overlap between the images (online article: https://support.pix4d.com/hc/en-us/articles/203756125-How-to-verify-that-there-is-enough-overlap-between-the-images)

(35) Fox, A.J. and Nuttall, A.M., 1997. Photogrammetry as a research tool for glaciology. The photogrammetric record15(89), pp.725-737.

(36) Drone Deploy – What are ground control points? (Online article: https://www.dronedeploy.com/blog/what-are-ground-control-points-gcps/)

Leave a Reply

Your email address will not be published.

This site uses Akismet to reduce spam. Learn how your comment data is processed.