Abstract
Monitoring efforts for remote high-latitude and high-altitude glacierized regions heavily rely on remote sensing. Rapid ongoing changes in polar and cryospheric environments owing to contemporary climate change have attracted more attention towards these regions than ever before. Satellite remote sensing has its own limitations related to low sun angles in high latitudes, high acquisition costs for high-resolution images, and persistent cloud cover over ice-dominated land and ocean surfaces. As such, over the past several years, small unoccupied aerial systems (sUAS) have become a viable data collection tool to address the challenges related to spaceborne or expensive airborne remote sensing for monitoring the cryosphere and polar regions. This chapter discusses sUAS adaptations for collecting data on snow, glaciers, permafrost, polar biology, the ocean, and atmosphere; the challenges of conducting sUAS operations in polar latitudes; and the advantages and disadvantages of the technology. It also provides resources that can be used to guide future efforts in applying sUAS to polar and cryospheric research.
Keywords
- sUAS
- UAV
- Polar
- Glaciology
- Cryosphere
- Remote sensing
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Alfredsen K, Haas C, Tuhtan JA, Zinke P (2018) Brief communication: mapping river ice using drones and structure from motion. Cryosphere 12(2):627–633. https://doi.org/10.5194/tc-12-627-2018
Alonzo M, Dial RJ, Schulz BK, Andersen HE, Lewis-Clark E, Cook BD, Morton DC (2020) Mapping tall shrub biomass in Alaska at landscape scale using structure-from- motion photogrammetry and lidar. Remote Sens Environ 245(April):111841. https://doi.org/10.1016/j.rse.2020.111841
Assmann JJ, Kerby JT, Cunliffe AM, Myers-Smith IH (2019) Vegetation monitoring using multispectral sensors – best practices and lessons learned from high latitudes. J Unmanned Veh Syst 7(1):54–75. https://doi.org/10.1139/juvs-2018-0018
Avanzi F, Bianchi A, Cina A, De Michele C, Maschio P, Pagliari D, Passoni D, Pinto L, Piras M, Rossi L (2018) Centimetric accuracy in snow depth using unmanned aerial system photogrammetry and a multistation. Remote Sens 10(5):1–17. https://doi.org/10.3390/rs10050765
Bash EA, Moorman BJ (2020) Surface melt and the importance of water flow – an analysis based on high-resolution unmanned aerial vehicle (UAV) data for an Arctic glacier. Croysphere 14:549–563. https://doi.org/10.5194/tc-14-549-2020
Bash EA, Moorman BJ, Gunther A (2018) Detecting short-term surface melt on an Arctic glacier using UAV surveys. Remote Sens 10:1547. https://doi.org/10.3390/rs10101547
Beamish A, Raynolds MK, Epstein H, Frost GV, Macander MJ, Bergstedt H, Bartsch A, Kruse S, Miles V, Tanis CM, Heim B (2020) Recent trends and remaining challenges for optical remote sensing of Arctic tundra vegetation: a review and outlook. Remote Sens Environ 246(March):111872. https://doi.org/10.1016/j.rse.2020.111872
Benoit L, Gourdon A, Vallat R, Irarrazaval I, Gravey M, Lehmann B, Prasicek G, Gräff D, Herman F, Mariethoz G (2019) A high-resolution image time series of the Gorner Glacier – Swiss Alps – derived from repeated unmanned aerial vehicle surveys. Earth Syst Sci Data 11(2):579–588. https://doi.org/10.5194/essd-11-579-2019
Bhardwaj A, Sam L, Akanksha, Martín-Torres FJ, Kumar R (2016a) UAVs as remote sensing platform in glaciology: present applications and future prospects. Remote Sens Environ 175:196–204. https://doi.org/10.1016/j.rse.2015.12.029
Bhardwaj A, Sam L, Bhardwaj A, Martín-Torres FJ (2016b) LiDAR remote sensing of the cryosphere: present applications and future prospects. Remote Sens Environ 177:125–143. https://doi.org/10.1016/j.rse.2016.02.031
Bhardwaj A, Sam L, Martín-Torres FJ, Zorzano M-P, Ramírez Luque JA (2019) UAV imaging of a Martian brine analogue environment in a fluvio-aeolian setting. Remote Sens 11:2104
Bollard-Breen B, Brooks JD, Jones MRL, Robertson J, Betschart S, Kung O, Cary SC, Lee CK, Pointing SB (2015) Application of an unmanned aerial vehicle in spatial mapping of terrestrial biology and human disturbance in the McMurdo Dry Valleys, East Antarctica. Polar Biol 38(4):573–578. https://doi.org/10.1007/s00300-014-1586-7
Brun F, Buri P, Miles ES, Wagnon P, Steiner J, Berthier E, Ragettli S, Kraaijenbrink P, Immerzeel WW, Pellicciotti F (2016) Quantifying volume loss from ice cliffs on debris-covered glaciers using high-resolution terrestrial and aerial photogrammetry. J Glaciol 62(234):684–695. https://doi.org/10.1017/jog.2016.54
Bühler Y, Adams MS, Bosch R, Stoffel A (2016) Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations. Cryosphere 10(3):1075–1088. https://doi.org/10.5194/tc-10-1075-2016
Carter J (2019) 8 tips for flying in cold weather. The Drone Girl, November 17. https://www.thedronegirl.com/2018/01/02/drone-cold-weather-tips/
Chandler BMP, Evans DJA, Chandler SJP, Ewertowski MW, Lovell H, Roberts DH, Schaefer M, Tomczyk AM (2020) The glacial landsystem of Fjallsjökull, Iceland: spatial and temporal evolution of process-form regimes at an active temperate glacier. Geomorphology 361:107192. https://doi.org/10.1016/j.geomorph.2020.107192
Che Y, Wang S, Yi S, Wei Y, Cai Y (2020) Summer mass balance and surface velocity derived by unmanned aerial vehicle on debris-covered region of baishui river glacier no. 1, yulong snow mountain. Remote Sens 12(20):1–15. https://doi.org/10.3390/rs12203280
Chudley TR, Christoffersen P, Doyle SH, Abellan A, Snooke N (2019a) High- accuracy UAV photogrammetry of ice sheet dynamics with no ground control. Cryosphere 13:955–968. https://doi.org/10.5194/tc-13-955-2019
Chudley TR, Christoffersen P, Doyle SH, Bougamont M, Schoonman CM, Hubbard B, James MR (2019b) Supraglacial lake drainage at a fast-flowing Greenlandic outlet glacier. Proc Natl Acad Sci U S A 116(51):25468–25477. https://doi.org/10.1073/pnas.1913685116
Cimoli E, Marcer M, Vandecrux B, Bøggild CE, Williams G, Simonsen SB (2017) Application of low-cost UASs and digital photogrammetry for high-resolution snow depth mapping in the Arctic. Remote Sens 9:1144. https://doi.org/10.3390/rs9111144
Cook JM, Tedstone AJ, Williamson C, Mccutcheon J, Hodson AJ, Dayal A, Skiles M, Hofer S, Bryant R, McAree O, Mcgonigle A (2020) Glacier algae accelerate melt rates on the South-Western Greenland Ice Sheet. Cryosphere 14:309–330. https://doi.org/10.5194/tc-14-309-2020
Council of Managers of National Antarctic Programs (CONMAP) (2016) Antarctic Unmanned Aerial Systems (UAS) operator’s handbook (ATCM XXXIX No. 31)
Crowe W, Davis KD, la Cour-Harbo A, Vihma T, Lesenkov S, Eppi R, Weatherhead EC, Liu P, Raustein M, Abrahamsson M, Johansen KS (2012) Enabling science use of unmanned aircraft systems for Arctic environmental monitoring, vol 6, Oslo. Retrieved from http://www.amap.no/documents/download/938
Curry JA, Maslanik J, Holland G, Pinto J (2004) Applications of Aerosondes in the Arctic. Bull Am Meteorol Soc 85(12):1855–1861. https://doi.org/10.1175/BAMS-85-12-1855
de Boer G, Ivey M, Schmid B, Lawrence D, Dexheimer D, Mei F, Hubbe J, Bendure A, Hardesty J, Shupe MD, McComiskey A (2018) A bird’s-eye view: development of an operational ARM unmanned aerial capability for atmospheric research in Arctic Alaska. Bull Am Meteorol Soc 99(6):1197–1212. https://doi.org/10.1175/BAMS-D-17-0156.1
Duffy JP, Cunliffe AM, Debell L, Sandbrook C, Wich SA, Shutler JD, Myers-Smith IH, Varela MR, Anderson K (2018) Location, location, location: considerations when using lightweight drones in challenging environments. Remote Sens Ecol Conserv 4(1):7–19. https://doi.org/10.1002/rse2.58
Eltner A, Kaiser A, Castillo C, Rock G, Neugirg F, Abellán A (2016) Image-based surface reconstruction in geomorphometry-merits, limits and developments. Earth Surf Dyn 4(2):359–389. https://doi.org/10.5194/esurf-4-359-2016
Fernandes R, Prevost C, Canisius F, Leblanc SG, Maloley M, Oakes S, Holman K, Knudby A (2018) Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos. Croysphere 12:3535–3550. https://doi.org/10.5194/tc-12-3535-2018
Florinsky IV, Bliakharskii DP (2019) Detection of crevasses by geomorphometric treatment of data from unmanned aerial surveys. Remote Sens Lett 10(4):323–332. https://doi.org/10.1080/2150704X.2018.1552809
Fraser RH, Olthof I, Lantz TC, Schmitt C (2016) UAV photogrammetry for mapping vegetation in the low-Arctic. Arctic Sci 2(3):79–102. https://doi.org/10.1139/as-2016-0008
Freitas P, Vieira G, Canário J, Folhas D, Vincent WF (2019) Identification of a threshold minimum area for reflectance retrieval from thermokarst lakes and ponds using full-pixel data from Sentinel-2. Remote Sens 11(6). https://doi.org/10.3390/rs11060657
Fugazza D, Scaioni M, Corti M, D’Agata C, Azzoni RS, Cernuschi M, Smiraglia C, Dialaiuti GA (2018) Combination of UAV and terrestrial photogrammetry to assess rapid glacier evolution and conditions of glacier hazards. Nat Hazards Earth Syst Sci 18:1055–1071. https://doi.org/10.5194/nhess-18-1055-2018
Gaffey C, Bhardwaj A (2020) Applications of unmanned aerial vehicles in cryosphere: latest advances and prospects. Remote Sens 12(6):948. https://doi.org/10.3390/rs12060948
Giordan D, Adams MS, Aicardi I, Alicandro M, Allasia P, Baldo M et al (2020) The use of unmanned aerial vehicles (UAVs) for engineering geology applications. Bull Eng Geol Environ 79(7):3437–3481. https://doi.org/10.1007/s10064-020-01766-2
Goebel ME, Perryman WL, Hinke JT, Krause DJ, Hann NA, Gardner S, LeRoi DJ (2015) A small unmanned aerial system for estimating abundance and size of Antarctic predators. Polar Biol 38(5):619–630. https://doi.org/10.1007/s00300-014-1625-4
Goetz J, Brenning A (2019) Quantifying uncertainties in snow depth mapping from structure from motion photogrammetry in an alpine area. Water Resour Res 55(9):7772–7783. https://doi.org/10.1029/2019WR025251
Goetz J, Fieguth P, Kasiri K, Bodin X, Marcer M, Brenning A (2019) Accounting for permafrost creep in high-resolution snow depth mapping by modelling sub-snow ground deformation. Remote Sens Environ 231:111275. https://doi.org/10.1016/j.rse.2019.111275
Graham DE, Wallenstein MD, Vishnivetskaya TA, Waldrop MP, Phelps TJ, Pfiffner SM, Onstott TC, Whyte LG, Rivkina EM, Gilichinsky DA, Elias DA (2012) Microbes in thawing permafrost: the unknown variable in the climate change equation. ISME J 6(4):709–712. https://doi.org/10.1038/ismej.2011.163
Grepow (2020) What is the best lithium battery for cold weather? Grepow Blog, August 5. https://www.grepow.com/blog/the-best-lithium-battery-for-cold-weather/
Harder P, Pomeroy JW, Helgason WD, Helgason WD (2020) Improving sub-canopy snow depth mapping with unmanned aerial vehicles: Lidar versus structure-from-motion techniques. Cryosphere 14(6):1919–1935. https://doi.org/10.5194/tc-14-1919-2020
Hendrickx H, Vivero S, De Cock L, De Wit B, De Maeyer P, Lambiel C, Delaloye R, Nyssen J, Frankl A (2019) The reproducibility of SfM algorithms to produce detailed Digital Surface Models: the example of PhotoScan applied to a high-alpine rock glacier. Remote Sens Lett 10(1):11–20. https://doi.org/10.1080/2150704X.2018.1519641
Hodson A, Anesio AM, Ng F, Watson R, Quirk J, Irvine-Fynn T, Dye A, Clark C, McCloy P, Kohler J, Sattler B (2007) A glacier respires: quantifying the distribution and respiration CO2 flux of cryoconite across an entire arctic supraglacial ecosystem. J Geophys Res Biogeosci 112(4):1–9. https://doi.org/10.1029/2007JG000452
Hodson AJ, Nowak A, Redeker KR, Holmlund ES, Christiansen HH, Turchyn AV (2019) Seasonal dynamics of methane and carbon dioxide evasion from an open system pingo: Lagoon pingo, svalbard. Front Earth Sci 7(February):1–12. https://doi.org/10.3389/feart.2019.00030
Jenssen ROR, Jacobsen S (2020) Drone-mounted UWB snow radar: technical improvements and field results. J Electromagn Waves Appl 34(14):1930–1954. https://doi.org/10.1080/09205071.2020.1799871
Jenssen ROR, Eckerstorfer M, Jacobsen S (2020) Drone-mounted ultrawideband radar for retrieval of snowpack properties. IEEE Trans Instrum Meas 69(1):221–230. https://doi.org/10.1109/TIM.2019.2893043
Jonassen MO, Tisler P, Altsta B, Scholtz A, Vihma T, Lampert A, König-Langlo G, Lüpkes C (2015) Application of remotely piloted aircraft systems in observing the atmospheric boundary layer over Antarctic sea ice in winter. Polar Res 34:25651. https://doi.org/10.3402/polar.v34.25651
Jones C, Ryan J, Holt T, Hubbard A (2018) Structural glaciology of Isunguata Sermia, West Greenland. J Maps 14(2):517–527. https://doi.org/10.1080/17445647.2018.1507952
Jouvet G, Weidmann Y, Kneib M, Detert M, Seguinot J, Sakakibara D, 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 Sens Environ 217:389–399. https://doi.org/10.1016/j.rse.2018.08.027
Kartoziia A (2019) Assessment of the ice wedge polygon current state by means of UAV imagery analysis (Samoylov Island, the Lena Delta). Remote Sens 11(13). https://doi.org/10.3390/rs11131627
Korczak-Abshire M, Zmarz A, Rodzewicz M, Kycko M, Karsznia I, Chwedorzewska KJ (2019) Study of fauna population changes on Penguin Island and Turret Point Oasis (King George Island, Antarctica) using an unmanned aerial vehicle. Polar Biol 42(1):217–224. https://doi.org/10.1007/s00300-018-2379-1
Kraaijenbrink PDA, Shea JM, Pellicciotti F, Jong SMD, Immerzeel WW (2016) Object-based analysis of unmanned aerial vehicle imagery to map and characterise surface features on a debris-covered glacier. Remote Sens Environ 186:581–595. https://doi.org/10.1016/j.rse.2016.09.013
Kraaijenbrink PDA, Shea JM, Litt M, Steiner JF, Treichler D, Koch I, Immerzeel WW (2018) Mapping surface temperatures on a debris-covered glacier with an unmanned aerial vehicle. Front Earth Sci 6(May):64. https://doi.org/10.3389/feart.2018.00064
Krause DJ, Hinke JT, Perryman WL, Goebel ME, LeRoi DJ (2017) An accurate and adaptable photogrammetric approach for estimating the mass and body condition of pinnipeds using an unmanned aerial system. PLoS One 12(11):1–21. https://doi.org/10.1371/journal.pone.0187465
Lamsters K, Karušs J, Krievāns M, Ješkins J (2019) Application of unmanned aerial vehicles for glacier research in the Arctic and Antarctic. In: Proceedings of the 12th international scientific and practical conference, vol 1, pp 131–135. https://doi.org/Print ISSN 1691-5402. Online ISSN 2256-070X
Lee WY, Park M, Hyun CU (2019) Detection of two Arctic birds in Greenland and an endangered bird in Korea using RGB and thermal cameras with an unmanned aerial vehicle (UAV). PLoS One 14(9):1–17. https://doi.org/10.1371/journal.pone.0222088
Li T, Zhang B, Xiao W, Cheng X, Li Z, Zhao J (2020) UAV-based photogrammetry and LiDAR for the characterization of ice morphology evolution. IEEE J Sel Top Appl Earth Obs Remote Sens 13:4188–4199. https://doi.org/10.1109/JSTARS.2020.3010069
Lousada M, Pina P, Vieira G, Bandeira L, Mora C (2018) Evaluation of the use of very high resolution aerial imagery for accurate ice-wedge polygon mapping (Adventdalen, Svalbard). Sci Total Environ 615:1574–1583. https://doi.org/10.1016/j.scitotenv.2017.09.153
Lovitt J, Rahman MM, McDermid GJ (2017) Assessing the value of UAV photogrammetry for characterizing terrain in complex peatlands. Remote Sens 9(7):715. https://doi.org/10.3390/rs9070715
Lucieer A, Turner D, King DH, Robinson SA (2014) Using an Unmanned Aerial Vehicle (UAV) to capture micro-topography of Antarctic moss beds using an Unmanned Aerial Vehicle (UAV) to capture micro-topography. Int J Appl Earth Obs Geoinf 27:53–62. https://doi.org/10.1016/j.jag.2013.05.011
Malenovský Z, Lucieer A, King DH, Turnbull JD, Robinson SA (2017) Unmanned aircraft system advances health mapping of fragile polar vegetation. Methods Ecol Evol 8(12):1842–1857. https://doi.org/10.1111/2041-210X.12833
Mali VK, Kuiry SN (2018) Assessing the accuracy of high-resolution topographic data generated using freely available packages based on SfM-MVS approach. Measurement 124:338–350. https://doi.org/10.1016/j.measurement.2018.04.043
Manfreda S, McCabe MF, Miller PE, Lucas R, Madrigal VP, Mallinis G, Ben Dor E, Helman D, Estes L, Ciraolo G, Müllerová J (2018) On the use of unmanned aerial systems for environmental monitoring. Remote Sens 10(4). https://doi.org/10.3390/rs10040641
Mustafa O, Barbosa A, Krause DJ, Peter H-U, Vieira G, Rümmler M-C (2018) State of knowledge: Antarctic wildlife response to unmanned aerial systems. Polar Biol 41(11):2387–2398. https://doi.org/10.1007/s00300-018-2363-9
Nolan M, Larsen C, Sturm M (2015) Mapping snow depth from manned aircraft on landscape scales at centimeter resolution using structure-from-motion photogrammetry. Cryosphere 9(4):1445–1463. https://doi.org/10.5194/tc-9-1445-2015
Paredes JA, Saito C, Abarca M, Cuellar F (2017) Study of effects of high-altitude environments on multicopter and fixed-wing UAVs’ energy consumption and flight time. In: IEEE international conference on automation science and engineering, 2017–August, pp 1645–1650. https://doi.org/10.1109/COASE.2017.8256340
Park H, Kim Y, Kimball JS (2016) Widespread permafrost vulnerability and soil active layer increases over the high northern latitudes inferred from satellite remote sensing and process model assessments. Remote Sens Environ 175:349–358. https://doi.org/10.1016/j.rse.2015.12.046
Raoult V, Colefax AP, Allan BM, Cagnazzi D, Castelblanco-Martínez N, Ierodiaconou D, Johnston DW, Landeo-Yauri S, Lyons M, Pirotta V, Schofield G, Butcher PA (2020) Operational protocols for the use of drones in marine animal research. In: Drones. (vol 4, Issue 64, pp 1–35. MDPI AG. https://doi.org/10.3390/drones4040064
Räsänen A, Virtanen T (2019) Data and resolution requirements in mapping vegetation in spatially heterogeneous landscapes. Remote Sens Environ 230(May):111207. https://doi.org/10.1016/j.rse.2019.05.026
Reagan J (2020) LiPo batteries: a drone user’s guide. Dronegenuity. https://www.dronegenuity.com/lipo-drone-batteries-users-guide/
Riihimäki H, Luoto M, Heiskanen J (2019) Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data. Remote Sens Environ 224(January):119–132. https://doi.org/10.1016/j.rse.2019.01.030
Rohner C, Small D, Beutel J, Henke D, Lüthi MP, Vieli A (2019) Multisensor validation of tidewater glacier flow fields derived from synthetic aperture radar (SAR) intensity tracking. Cryosphere 13:2953–2975. https://doi.org/10.5194/tc-13-2953-2019
Rossini M, Di Mauro B, Garzonio R, Baccolo G, Cavallini G, Mattavelli M, De Amicis M, Colombo R (2018) Rapid melting dynamics of an alpine glacier with repeated UAV photogrammetry. Geomorphology 304:159–172. https://doi.org/10.1016/j.geomorph.2017.12.039
Rümmler MC, Mustafa O, Maercker J, Peter HU, Esefeld J (2018) Sensitivity of Adélie and Gentoo penguins to various flight activities of a micro UAV. Polar Biol 41:2481–2493. https://doi.org/10.1007/s00300-018-2385-3
Ryan JC, Hubbard AL, Box JE, Todd J, Christoffersen P, Carr JR, Holt TO, Snooke N (2015) UAV photogrammetry and structure from motion to assess calving dynamics at Store Glacier, a large outlet draining the Greenland ice sheet. Cryosphere 9(1):1–11. https://doi.org/10.5194/tc-9-1-2015
Sam L, Bhardwaj A, Singh S, Martin-Torres FJ (2020a) UAV imaging of small caves in Icelandic lava field as possible Mars analogues. In: Proceedings of the 3rd international planetary caves conference, San Antonio, TX, USA
Sam L, Bhardwaj A, Singh S, Martin-Torres FJ, Zorzano MP, Ramírez Luque JA (2020b) Small lava caves as possible exploratory targets on Mars: analogies drawn from UAV imaging of an Icelandic lava field. Remote Sens 12:1970
Sanz-ablanedo E, Chandler JH, Rodríguez-Pérez JR, Ordóñez C (2018) Accuracy of Unmanned Aerial Vehicle (UAV) and SfM photogrammetry survey as a function of the number and location of ground control points used. Remote Sens 10:1606. https://doi.org/10.3390/rs10101606
Sheridan I (2020) Drones and global navigation satellite systems: current evidence from polar scientists. R Soc Open Sci 7(3). https://doi.org/10.1098/rsos.191494
Sotille ME, Bremer UF, Vieira G, Velho LF, Petsch C, Simões JC (2020) Evaluation of UAV and satellite-derived NDVI to map maritime Antarctic vegetation. Appl Geogr 125(September). https://doi.org/10.1016/j.apgeog.2020.102322
St Germain SL, Moorman BJ (2019) Long-term observations of supraglacial streams on an arctic glacier. J Glaciol 65(254):900–911. https://doi.org/10.1017/jog.2019.60
Storvold R, Sweatte C, Ruel P, Wuennenberg M, Tarr K, Raustein M, Hillesøy T, Lundgren T, Sumich M (2015) Arctic science RPAS operator’s handbook. Retrieved from https://oaarchive.arctic-council.org/handle/11374/719
Stoven-Dubois A, Jospin L, Cucci DA (2018) Cooperative navigation for an UAV tandem in GNSS denied environments. In: Proceedings of the 31st international technical meeting of the Satellite Division of The Institute of Navigation. https://doi.org/10.33012/2018.15949
Sturm M, Racine C, Tape K (2001) Increasing shrub abundance in the Arctic. Nature 411:546. Retrieved from. https://doi.org/10.1038/35079180
Tahar KN (2013) An evaluation on different number of ground control points in unmanned aerial vehicle photogrammetric block. In: International archives of the photogrammetry, remote sensing and spatial information sciences, ISPRS 8th 3DGeoInfo conference & WG II/2 workshop, Istanbul, Turkey, vol XL-2/W2, pp 27–29
Tan A, Eccleston K, Platt I, Woodhead I, Rack W, McCulloch J (2017) The design of a UAV mounted snow depth radar: results of measurements on Antarctic Sea ice. In: 2017 IEEE conference on Antenna Measurements & Applications (CAMA), pp 316–319. https://doi.org/10.1109/CAMA.2017.8273437
The Arctic Research Consortium of the U.S. (ARCUS) (1997) Logistics recommendations for an improved U.S. Arctic Research Capability. Fairbanks
Tonkin TN, Midgley NG (2016) Ground-control networks for image based surface reconstruction: an investigation of optimum survey designs using UAV derived imagery and structure-from-motion photogrammetry. Remote Sens 8(9):16–19. https://doi.org/10.3390/rs8090786
Turner D, Lucieer A, Malenovský Z, King DH, Robinson SA (2014) Spatial co-registration of ultra-high resolution visible, multispectral and thermal images acquired with a micro-UAV over antarctic moss beds. Remote Sens 6(5):4003–2024. https://doi.org/10.3390/rs6054003
University-National Oceanographic Laboratory System (UNOLS) (2019) Unmanned Aerial Systems (UAS) operations from the U.S. Academic Research Fleet: operator’s handbook
US ATCM (2014) Considerations for the use of unmanned aircraft systems (UAS) for research, monitoring, and observation in Antarctica (ATCM XXXVII); Antarctic Treaty Consultative Meeting: Brasilia, Brazil
Van der Sluijs J, Kokelj SV, Fraser RH, Tunnicliffe J, Lacelle D (2018) Permafrost terrain dynamics and infrastructure impacts revealed by UAV photogrammetry and thermal imaging. Remote Sens 10:1734. https://doi.org/10.3390/rs10111734
Verfuss UK, Aniceto AS, Harris DV, Gillespie D, Fielding S, Jiménez G, Johnston P, Sinclair RR, Sivertsen A, Solbø SA, Storvold R (2019) A review of unmanned vehicles for the detection and monitoring of marine fauna. Mar Pollut Bull 140(February 2018):17–29. https://doi.org/10.1016/j.marpolbul.2019.01.009
Vincent C, Wagnon P, Shea JM, Immerzeel WW, Kraaijenbrink P, Shrestha DJM, Immerzeel WW, Kraaijenbrink P, Shrestha D, Soruco A, Arnaud Y, Brun F, Berthier E, Sherpa SF (2016) Reduced melt on debris-covered glaciers: investigations from Changri Nup Glacier, Nepal. Cryosphere 10:1845–1858. https://doi.org/10.5194/tc-10-1845-2016
Weimerskirch H, Pudor A, Schull Q (2018) Flights of drones over sub-Antarctic seabirds show species- and status-specific behavioural and physiological responses. Polar Biol 41:259–266. https://doi.org/10.1007/s00300-017-2187-z
Wigmore O, Mark B (2017) Monitoring tropical debris covered glacier dynamics from high resolution unmanned aerial vehicle photogrammetry, Cordillera Blanca, Peru. Cryosphere 11:2463–2480. https://doi.org/10.5194/tc-11-2463-2017
Zhang W, Liljedahl AK, Kanevskiy M, Epstein HE, Jones BM, Jorgenson MT, Kent K (2020) Transferability of the deep learning mask R-CNN model for automated mapping of ice-wedge polygons in high-resolution satellite and UAV images. Remote Sens 12(7). https://doi.org/10.3390/rs12071085
Zmarz A, Rodzewicz M, Dąbski M, Karsznia I, Korczak-Abshire M, Chwedorzewska KJ (2018) Application of UAV BVLOS remote sensing data for multi-faceted analysis of Antarctic ecosystem. Remote Sens Environ 217:375–388. https://doi.org/10.1016/j.rse.2018.08.031
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Gaffey, C.B., Bhardwaj, A., Frey, K.E., Estes, L. (2022). Polar and Cryospheric Remote Sensing Using sUAS. In: Konsoer, K., Leitner, M., Lewis, Q. (eds) sUAS Applications in Geography . Geotechnologies and the Environment, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-031-01976-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-01976-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-01975-3
Online ISBN: 978-3-031-01976-0
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)