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The use of UAVs for landslide disaster risk research and disaster risk management: a literature review

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Abstract

On a global scale, from 2005 to 2019, there were 275 high-magnitude, low-frequency disasters that involved 14,172 fatalities and four million affected people. Similar patterns have taken place during longer periods of time in recent decades. This paper aims to analyse the contribution of the international landslide research community to disaster risk reduction and disaster risk management in reference to the use of Unmanned Aerial Vehicles (UAVs) in a literature review. The first section notes the relevance of disaster risk research contributions for the implementation of initiatives and strategies concerning disaster risk management. The second section highlights background information and current applications of drones in the field of hazards and risk. The methodology, which included a systematic peer review of journals in the ISI Web of Science and SCOPUS, was presented in the third section, where the results include analyses of the considered data. This study concludes that most current scholarly efforts remain rooted in hazards and post-disaster evaluation and response. Future landslide disaster risk research should be transdisciplinary in order to strengthen participation of the various relevant stakeholders in contributing to integrated disaster risk management at local, subnational, national, regional and global levels.

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References

  • Afif HA, Rokhmatuloh R, Hernina R (2019) UAV application for landslide mapping in Kuningan Regency, West Java. E3S Web of Conferences 125:1–4. https://doi.org/10.1051/e3sconf/201912503011

    Article  Google Scholar 

  • Ahmad A, Tahar KN, Udin WS, et al. (2013) Digital aerial imagery of Unmanned Aerial Vehicle for various applications. 2013 IEEE ICCSCE 535–540.

  • Aitsi-Selmi A, Blanchard K, Al-Khudhairy D, et al. (2015) UNISDR STAG 2015 Report: Science is used for disaster risk reduction. http://preventionweb.net/go/42848.

  • Akcay O (2015) Landslide fissure inference assessment by ANFIS and logistic regression using UAS-Based photogrammetry. ISPRS Int J Geo-Inf 4(4):2131–2158. https://doi.org/10.3390/ijgi4042131

    Article  Google Scholar 

  • Alcántara-Ayala I, Altan O, Baker D, et al. (2015) Disaster risks research and assessment to promote risk reduction and management. In: Ismail-Zadeh A and Cutter S (eds.), ICSU-ISSC Ad Hoc Group on Disaster Risk Assessment, Paris: ICSU.

    Google Scholar 

  • Alcántara-Ayala I (2016) On the multi-dimensions of Integrated Research on Landslide Disaster Risk, In: Aversa S, Cascini L, Picarelli L, and Scavia C (eds.) Landslides and Engineered Slopes. Experience, Theory and Practice CRC Press, Balkema, Taylor & Francis Group pp 155–168. Volume 1. ISBN: 978-1-138-02989-7.

  • Alcántara-Ayala I (2021) Integrated landslide disaster risk management (ILDRiM): the challenge to avoid the construction of new disaster risk. Environ Hazards. https://doi.org/10.1080/17477891.2020.1810609

  • Ardi ND, Iryanti M, Asmoro CP, et al. (2018) Mapping Landslide potential area using fault fracture density analysis on Unmanned Aerial Vehicle (UAV) Image. 1ST UPI International Geography Seminar 2017 145:1–5. https://doi.org/10.1088/1755-1315/145/1/012010

    Google Scholar 

  • Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31. https://doi.org/10.1016/j.geomorph.2004.06.010

    Article  Google Scholar 

  • Bai SB, Wang J, Lü GN, et al. (2009) GIS-based and data-driven bivariate landslide-susceptibility mapping in the Three Gorges area, China. Pedosphere 19(1):14–20. https://doi.org/10.1016/S1002-0160(08)60079-X

    Article  Google Scholar 

  • Barlow J, Gilham J, Cofra II (2017) Kinematic analysis of sea cliff stability using UAV photogrammetry. Int J Remote Sens 38(8–10):2464–2479. https://doi.org/10.1080/01431161.2016.1275061

    Article  Google Scholar 

  • Barrile V, Bilotta G, Nunnari A (2017) 3D Modeling with photogrammetry by UAVs and model quality verification. ISPRS Annals 4:129–134. https://doi.org/10.5194/isprs-annals-IV-4-W4-129-2017

    Google Scholar 

  • Berardino P, Constantini M, Franceschetti G, et al. (2003) Use of differential SAR interferometry in monitoring and modelling large slope instability at Maratea (Basilicata, Italy). Eng Geol 68:31–51. https://doi.org/10.1016/S0013-7952(02)00197-7

    Article  Google Scholar 

  • Bilasco S, Rosca S, Petrea D, et al. (2019) 3D reconstruction of landslides for the acquisition of digital databases and monitoring spatiotemporal dynamics of landslides based on GIS spatial analysis and UAV techniques. In: Hamid Reza Pourghasemi and Candan Gokceoglu (eds), Spatial Modeling in GIS and R for Earth and Environmental Sciences. pp 451–465. https://doi.org/10.1016/B978-0-12-815226-3.00020-X

  • Boccali C, Biolchi S, Zavagno E, et al. (2017) Rock fall characterization in climbing spots: The case study of the “Napoleonica” tourist route (Trieste, NE Italy). Advancing Culture of Living with Landslides, Vol 2: Advances in Landslide Science 107–115. https://doi.org/10.1007/978-3-319-53498-5_13

  • Borrelli L, Conforti M, Mercuri M (2019) LiDAR and UAV system data to analyse recent morphological changes of a small drainage basin. ISPRS Int J Geo-Inf 8(536) https://doi.org/10.3390/ijgi8120536

    Google Scholar 

  • Bouali EH, Oommen T, Vitton S, et al. (2017) Rockfall Hazard Rating System: benefits of utilizing remote sensing. Environ Eng Geosci 23(3):165–177. https://doi.org/10.2113/gseegeosci.23.3.165

    Article  Google Scholar 

  • Briceño S (2015) Looking back and beyond Sendai: 25 years of international policy experience on disaster risk reduction. Int J Disaster Risk Sci 6(1):1–7. https://doi.org/10.1007/s13753-015-0040-y

    Article  Google Scholar 

  • Buill F, Nunez-Andres MA, Lantada N, et al. (2016) Comparison of photogrammetric techniques for rockfalls monitoring. World Multidisciplinary Earth Sciences Symposium (WMESS 2016) 44:1–4. https://doi.org/10.1088/1755-1315/44/4/042023

    Google Scholar 

  • Busa J, Rusnak M, Kusnirak D, et al. (2019) Urban landslide monitoring by combined use of multiple methodologies-a case study on Sv. Anton town, Slovakia. Phys Geogr 41(2):169–194. https://doi.org/10.1080/02723646.2019.1630232

    Article  Google Scholar 

  • Canuti P, Casagli N, Ermini L, et al. (2004) Landslide activity as a geoindicator in Italy: significance and new perspectives from remote sensing. Environ Geol 45:907–919. https://doi.org/10.1007/s00254-003-0952-5

    Article  Google Scholar 

  • Cardenal J, Fernandez T, Perez-Garcia JL, et al. (2019) Measurement of road surface deformation using images captured from UAVs. Remote Sens 11(12). https://doi.org/10.3390/rs11121507

    Google Scholar 

  • Carvajal F, Aguera F, Perez M (2011) Surveying a landslide in a road embankment using Unmanned Aerial Vehicle photogrammetry. International Conference on Unmanned Aerial Vehicle in Geomatics (UAV-G) 38(1, C22): 201–206.

    Google Scholar 

  • Catane SG, Veracruz NAS, Flora JRR, et al. (2019) Mechanism of a low-angle translational block slide: evidence from the September 2018 Naga landslide, Philippines. Landslides 16(9):1709–1719. https://doi.org/10.1007/s10346-019-01212-9

    Article  Google Scholar 

  • Cescutti F, Cefalo R, Coren F (2018) Application of digital photogrammetry from UAV integrated by terrestrial laser scanning to disaster management Brcko flooding case study (Bosnia Herzegovina). In: Cefalo R, Zielinski J, Barbarella M (Eds) New Advanced GNSS and 3D Spatial Techniques. Springer International. pp 245–260. https://doi.org/10.1007/978-3-319-56218-6_20

  • Chang KJ, Chan YC, Chen RF, et al. (2018) Geomorphological evolution of landslides near an active normal fault in northern Taiwan, as revealed by lidar and unmanned aircraft system data. Nat Hazards Earth Syst Sci 18(3):709–727. https://doi.org/10.5194/nhess-18-709-2018

    Article  Google Scholar 

  • Chen ML, Lv PF, Zhang SL, et al. (2018) Time evolution and spatial accumulation of progressive failure for Xinhua slope in the Dagangshan reservoir, Southwest China. Landslides 15(3):565–580. https://doi.org/10.1007/s10346-018-0946-8

    Article  Google Scholar 

  • Cheng KS, Wei C, Chang SC (2004) Locating landslides using multitemporal satellite images. Adv Space Res 33:296–301. https://doi.org/10.1016/S0273-1177(03)00471-X

    Article  Google Scholar 

  • Chou TY, Yeh ML, Chen YC, et al. (2010) Disaster monitoring and management by the Unmanned Aerial Vehicle technology. 100 Years ISPRS Advancing Remote Sensing Science, PT 2, 38:137–142.

  • Chudy F, Slamova M, Tomastik J, et al. (2019) Identification of micro-scale landforms of landslides using precise Digital Elevation Models. Geosciences 9(3). https://doi.org/10.3390/geosciences9030117

    Google Scholar 

  • Cigna F (2018) Observing Geohazards from Space. Geosciences 8(2). https://doi.org/10.3390/geosciences8020059

    Google Scholar 

  • Coe JA, Baum RL, Allstadt KE, et al. (2016) Rock-avalanche dynamics revealed by large-scale field mapping and seismic signals at a highly mobile avalanche in the West Salt Creek valley, western Colorado. Geosphere 12(2):607–631. https://doi.org/10.1130/GES01265.1

    Article  Google Scholar 

  • Colomina I, Molina P (2014) Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J Photogramm Remote Sens 92:79–97. https://doi.org/10.1016/j.isprsjprs.2014.02.013

    Article  Google Scholar 

  • Comert R, Avdan U, Gorum T (2018) Rapid mapping of forested landslide from ultra-high resolution unmanned aerial vehicle data. ISPRS Archives 42(3–4):171–176. https://doi.org/10.5194/isprs-archives-XLII-3-W4-171-2018

    Google Scholar 

  • Cutter SL, Ismail-Zadeh A, Alcántara-Ayala I, et al. (2015) Global risks: Pool knowledge to stem losses from disasters. Nature 522 (7556): 277–9. https://doi.org/10.1038/522277a

    Article  Google Scholar 

  • Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228. https://doi.org/10.1016/S0169-555X(01)00087-3

    Article  Google Scholar 

  • Dalamagkidis K (2015) Definitions and terminology. In: Valavanis KP and Vachtsevanos GJ (eds.), Handbook of Unmanned Aerial Vehicles. Springer, ISBN 978-90-481-9706-4, p 3022.

  • Dall’Asta E, Forlani G, Roncella R et al. (2016) Unmanned Aerial Systems and DSM matching for rock glacier monitoring. J Photogramm Remote Sens 127:102–114. https://doi.org/10.1016/j.isprsjprs.2016.10.003

    Article  Google Scholar 

  • Dang K, Sassa K, Fukuoka H, et al. (2016) Mechanism of two rapid and long-runout landslides in the 16 April 2016 Kumamoto earthquake using a ring-shear apparatus and computer simulation (LS-RAPID). Landslides 13(6):1525–1534. https://doi.org/10.1007/s10346-016-0748-9

    Article  Google Scholar 

  • Danzi M, Di Crescenzo G, Ramondini M, et al. (2013) Use of unmanned aerial vehicles (UAVs) for photogrammetric surveys in rockfall instability studies. Rendiconti Online Soc Geol Ital 24:82–85.

    Google Scholar 

  • Darmawan H, Walter TR, Brotopuspito KS, et al. (2018) Morphological and structural changes at the Merapi lava dome monitored in 2012–15 using unmanned aerial vehicles (UAVs). J Volcanol Geotherm Res 349:256–267. https://doi.org/10.1016/j.jvolgeores.2017.11.006

    Article  Google Scholar 

  • De Beni E, Cantanero M, Messina A (2019) UAVs for volcano monitoring: A new approach applied on an active lava flow on Mt. Etna (Italy), during the 27 February-02 March 2017 eruption. J. Volcanol. Geotherm Res 369:250–262. https://doi.org/10.1016/j.jvolgeores.2018.12.001

    Article  Google Scholar 

  • De Cubber G, Balta H, Doroftei D, et al. (2014) UAS deployment and data processing during the Balkans flooding. 12th IEEE International Symposium on Safety, Security and Rescue Robotics, SSRR 2014-Symposium Proceedings. https://doi.org/10.1109/SSRR.2014.7017670

  • Dominici D, Alicandro M, Massimi V (2016) UAV photogrammetry in the post-earthquake scenario: case studies in L’Aquila. Geomatics, Nat Hazards Risk 8(1):87–103. https://doi.org/10.1080/19475705.2016.1176605

    Article  Google Scholar 

  • Eisenbeiss H (2009) UAV Photogrammetry. PhD Thesis. ETH Zurich. Germany. p 236.

    Google Scholar 

  • Eker R, Aydin A (2021) Long-term retrospective investigation of a large, deep-seated, and slowmoving landslide using InSAR time series, historical aerial photographs, and UAV data: The case of Devrek landslide (NW Turkey). Catena 196. https://doi.org/10.1016/j.catena.2020.104895

  • EM-DAT database (n.d.) The CRED/OFDA International Disaster Database, available at: https://www.emdat.be/database (Accessed on 7 February 2020)

  • Favalli M, Fornaciai A, Nannipieri L, et al. (2018) UAV-based remote sensing surveys of lava flow fields: a case study from Etna’s 1974 channel-fed lava flows. Bull Volcanol 80:29. https://doi.org/10.1007/s00445-018-1192-6

    Article  Google Scholar 

  • Fiorucci F, Giordan D, Santangelo M, et al. (2018) Criteria for the optimal selection of remote sensing optical images to map event landslides. Nat Hazards Earth Syst Sci 18(1):405–417. https://doi.org/10.5194/nhess-18-405-2018

    Article  Google Scholar 

  • Francioni M, Salvini R, Stead D, et al. (2015) An integrated remote sensing-GIS approach for the analysis of an open pit in the Carrara marble district, Italy: Slope stability assessment through kinematic and numerical methods. Comput Geotech 67:46–63. https://doi.org/10.1016/j.compgeo.2015.02.009

    Article  Google Scholar 

  • Garnica-Peña RJ, Alcantara-Ayala I (2017) Multi-temporal landslide evaluation by using UAV: Some Insights on Disaster Risk in Teziutlan, Puebla Mexico. In: Mikoš M, Tiwari B, Yin Y, Sassa K (eds.), Advancing culture of living with landslides, vol 2: advances in landslide science. pp 209–218. https://doi.org/10.1007/978-3-319-53498-5_24

  • Ghorbanzadeh O, Meena SR, Blaschke T, et al. (2019) UAV-based slope failure detection using Deep-Learning Convolutional Neural Networks. Remote Sens 11(17). https://doi.org/10.3390/rs11172046

    Google Scholar 

  • Giordan D, Hayakawa Y, Nex F, et al. (2018) Review article: the use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management. Nat Hazards Earth Syst Sci 18(4):1079–1096. https://doi.org/10.5194/nhess-18-1079-2018

    Article  Google Scholar 

  • Giordan D, Manconi A, Remondino F, et al. (2017) Use of unmanned aerial vehicles in monitoring application and management of natural hazards. Geomatics, Nat Hazards Risk 8(1):1–4. https://doi.org/10.1080/19475705.2017.1315619

    Article  Google Scholar 

  • Harder P, Schirmer M, Pomeroy J, et al. (2016) Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle. Cryosphere 10:2559–2571. https://doi.org/10.5194/tc-10-2559-2016

    Article  Google Scholar 

  • Hassanalian M, Abdelkefi A (2017) Classifications, applications, and design challenges of drones: A review. Prog Aerosp Sci 91:99–131. https://doi.org/10.1016/j.paerosci.2017.04.003

    Article  Google Scholar 

  • Horacio J, Munoz-Narciso E, Trenhaile AS, et al. (2019) Remote sensing monitoring of a coastal-valley earthflow in northwestern Galicia, Spain. Catena 178:276–287. https://doi.org/10.1016/j.catena.2019.03.028

    Article  Google Scholar 

  • Hsieh YC, Chan YC, Hu JC (2016) Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan. Remote Sens 8(3). https://doi.org/10.3390/rs8030199

    Google Scholar 

  • Izumida A, Uchiyama S, Sugai T (2017) Application of UAV-SfM photogrammetry and aerial lidar to a disastrous flood: repeated topographic measurement of a newly formed crevasse splay of the Kinu River, central Japan. Nat Hazards Earth Syst Sci 17(9):1505–1519. https://doi.org/10.5194/nhess-17-1505-2017

    Article  Google Scholar 

  • Jayaweera M, Gunawardana B, Gunawardana M, et al. (2019) Management of municipal solid waste open dumps immediately after the collapse: An integrated approach from Meethotamulla open dump, Sri Lanka. Waste Manage 95:227–240. https://doi.org/10.1016/j.wasman.2019.06.019

    Article  Google Scholar 

  • Karantanellis E, Marinos V, Vassilakis E (2019) 3D hazard analysis and object-based characterization of landslide motion mechanism using uav imagery. ISPRS Archives 42(2):425–430. https://doi.org/10.5194/isprs-archives-XLII-2-W13-425-2019

    Google Scholar 

  • Kazahaya R, Shinohara H, Ohminato T, et al. (2019) Airborne measurements of volcanic gas composition during unrest at Kuchinoerabujima volcano, Japan. Bull Volcanol 81(7). https://doi.org/10.1007/s00445-018-1262-9

    Google Scholar 

  • Koschitzki R, Schwalbe E, Cardenas C, et al. (2017) Photogrammetric monitoring concept for remote landslide endangered areas using multi-temporal aerial imagery. 2017 First IEEE International Symposium of Geoscience and Remote Sensing (GRSS-CHILE). pp 108–113.

  • Kosolapov AE, Skripka GI, Bespalova LA, et al. (2018) Study of morphological and morphometric characteristics of Tsimlyansk reservoir shores using Unmanned Aerial Vehicles and GIS technologies. Arid Ecosyst 8(3):184–189. https://doi.org/10.1134/S2079096118030034

    Article  Google Scholar 

  • Kraaijenbrink PDA, Shea J, Pellicciotti F, et al. (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

    Article  Google Scholar 

  • Koukouvelas IK, Nikolakopoulos KG, Zygouri V, et al. (2020) Post-seismic monitoring of cliff mass wasting using an unmanned aerial vehicle and field data at Egremni, Lefkada Island, Greece. Geomorphology 367. https://doi.org/10.1016/j.geomorph.2020.107306

  • Lambiel C, Rüttimann S, Meyrat R, et al. (2017) Capturing the crisis of an active rock glacier with UAV survey. In Proceedings of the 19th EGU General Assembly, EGU2017, Vienna, Austria, 23–29 April. p 7014.

  • Langhammer J, Vackova T (2018) Detection and mapping of the geomorphic effects of flooding using UAV photogrammetry. Pure Appl Geophys 175:3223–3245. https://doi.org/10.1007/s00024-018-1874-1

    Article  Google Scholar 

  • Lavell A, Maskrey A (2014) The future of disaster risk management. Environ. Hazards 13(4): 267–280. https://doi.org/10.1080/17477891.2014.935282

    Article  Google Scholar 

  • Lazar A, Begus T, Vulic M (2018) Monitoring of the Belca Rockfall. Acta Geotech Slov 15(2):2–15. https://doi.org/10.18690/actageotechslov.15.2.2-15.2018

    Article  Google Scholar 

  • Leal-Alves DC, Weschenfelder J, da Guia Alburquerque M, et al. (2020) Digital elevation model generation using UAV SfM photogrammetry techniques to map sea level rise scenarios at Cassino Beach, Brazil. SN Appl Sci 2(2181). https://doi.org/10.1007/s42452-020-03936-z

  • Letortu P, Jaud M, Grandjean P, et al. (2018) Examining highresolution survey methods for monitoring cliff erosion at an operational scale. GIsci Remote Sens 55(4):457–476. https://doi.org/10.1080/15481603.2017.1408931

    Article  Google Scholar 

  • Li CC, Zhang GS, Lei TJ, et al. (2011) Quick image-processing method of UAV without control points data in earthquake disaster area. Trans Nonferrous Met Soc China 21(3):523–528. https://doi.org/10.1016/S1003-6326(12)61635-5

    Article  Google Scholar 

  • Li HB, Qi SC, Chen H, et al. (2019) Mass movement and formation process analysis of the two sequential landslide dam events in Jinsha River, Southwest China. Landslides 16(11):2247–2258. https://doi.org/10.1007/s10346-019-01254-z

    Article  Google Scholar 

  • Lin SW, Hsueh TF (2019) Using drone as a map to draw landslide hazard areas in the application of Community Environmental Education. 2019 5TH International Conference on Energy Materials and Environment Engineering 295. https://doi.org/10.1088/1755-1315/295/3/032057

  • Lucieer A, de Jong SM, Turner D (2014) Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography. Prog Phys Geogr 38(1):97–116. https://doi.org/10.1177/0309133313515293

    Article  Google Scholar 

  • Mavroulis S, Andreadakis E, Spyrou N-I, et al. (2019) UAV and GIS based rapid earthquake-induced building damage assessment and methodology for EMS-98 isoseismal map drawing: the June 12, 2017 Mw 6.3 Lesvos (Northeastern Aegean, Greece) earthquake. Int J Disaster Risk Reduct 37:101–169. https://doi.org/10.1016/j.ijdrr.2019.101169

  • Melis MT, Da Pelo S, Erbi I, et al. (2020) Thermal remote sensing from UAVs: A review on methods in coastal cliffs prone to landslides. Remote Sens 12(1971). https://doi.org/10.3390/rs12121971

    Google Scholar 

  • Menegoni N, Giordan D, Perotti C, et al. (2019) Detection and geometric characterization of rock mass discontinuities using a 3D high-resolution digital outcrop model generated from RPAS imagery - Ormea rock slope, Italy. Eng Geol 252:145–163. https://doi.org/10.1016/j.enggeo.2019.02.028

    Article  Google Scholar 

  • Merino L, Caballero F, Martínez-de-Dios JR, et al. (2012) An unmanned aircraft system for automatic forest fire monitoring and measurement. J Intell Robot Syst 65:533–548. https://doi.org/10.1007/s10846-011-9560-x

    Article  Google Scholar 

  • Merino L, Martínez-de Dios J, Ollero A (2015) Cooperative Unmanned Aerial systems for fire detection, monitoring, and extinguishing. In: Valavanis KP and Vachtsevanos GJ (eds.), Handbook of Unmanned Aerial Vehicles. Springer, ISBN 978-90-481-9706-4, p 3022.

  • Mori T, Hashimoto T, Terada A, et al. (2016) Volcanic plume measurements using a UAV for the 2014 Mt. Ontake eruption. Earth Planet Sp 68(49). https://doi.org/10.1186/s40623-016-0418-0

    Google Scholar 

  • Murphy R, Dufek J, Sarmiento T, et al. (2016) Two case studies and gaps analysis of flood assessment for emergency management with small unmanned aerial systems. In: 2016 IEEE international symposium on safety, security, and rescue robotics (SSRR). pp 54–61.

  • Nedjati A, Izbirak G, Vizvari B, et al. (2016) Complete coverage path planning for a multi-UAV response system in post-earthquake assessment. Robotics 5:26. https://doi.org/10.3390/robotics5040026

    Article  Google Scholar 

  • Niedzielski T (2018) Applications of Unmanned Aerial Vehicles in geosciences: Introduction. Pure Appl Geophys 175(9):3141–3144. https://doi.org/10.1007/s00024-018-1992-9

    Article  Google Scholar 

  • Niethammer U, James MR, Rothmund S, et al. (2012) UAV-based remote sensing of the Super-Sauze landslide: Evaluation and results. Eng Geol 128:2–11. https://doi.org/10.1016/j.enggeo.2011.03.012

    Article  Google Scholar 

  • Nikolakopoulos KG, Koukouvelas IK (2017) Emergency response to landslide using GNSS measurements and UAV. Earth Resources and Environmental Remote Sensing/GIS Applications VIII, 10428. https://doi.org/10.1117/12.2278728

  • Nikolakopoulos KG, Antonakakis A, Kyriou A, et al. (2018) Comparison of terrestrial laser scanning and structure-from-motion photogrammetry for steep slope mapping. Earth Resources and Environmental Remote Sensing/GIS Applications IX, 10790. https://doi.org/10.1117/12.2326175

  • Nikolakopoulos K, Kyriou A, Koukouvelas I, et al. (2019) Combination of Aerial, Satellite, and UAV Photogrammetry for mapping the diachronic coastline evolution: the case of Lefkada Island. ISPRS Int J Geoinf 8(11). https://doi.org/10.3390/ijgi8110489

    Google Scholar 

  • Nonami K, Kendoul F, Susuki S, et al. (2010). Autonomous Flying Robots: Unmanned Aerial Vehicles and Micro Aerial Vehicles, Springer. p 328.

  • Obanawa H, Hayakawa YS (2018) Variations in volumetric erosion rates of bedrock cliffs on a small inaccessible coastal island determined using measurements by an unmanned aerial vehicle with structure-from-motion and terrestrial laser scanning. Prog Earth Planet Sci 5. https://doi.org/10.1186/s40645-018-0191-8

  • Oliver-Smith A, Alcántara-Ayala I, Burton I, et al. (2016) Forensic Investigations of Disasters (FORIN): a conceptual framework and guide to research (IRDR FORIN Publication No.2). Beijing: Integrated Research on Disaster Risk. p 56.

  • Pfeiffer J, Zieher T, Rutzinger M, et al. (2019) Comparison and time series analysis of landslide displacement mapped by airborne, terrestrial and Unmanned Aerial Vehicle based platforms. ISPRS Annals 4, 2:421–428. https://doi.org/10.5194/isprs-annals-IV-2-W5-421-2019

    Google Scholar 

  • Pradhan B (2010) Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Adv Space Res 45:1244–1256. https://doi.org/10.1016/j.asr.2010.01.006

    Article  Google Scholar 

  • Rau JY, Jhan JP, Rau RJ (2014) Semiautomatic object-oriented landslide recognition scheme from multisensor optical imagery and DEM. IEEE Transactions on Geoscience and Remote Sensing 52, 2:1336–1349. https://doi.org/10.1109/TGRS.2013.2250293

    Article  Google Scholar 

  • Rossi G, Nocentini M, Lombardi L, et al. (2016) Integration of multicopter drone measurements and ground-based data for landslide monitoring. In: Aversa S, Cascini L, Picarelli L, and Scavia C. (eds.) Landslides and Engineered Slopes. Experience, Theory and Practice CRC Press, Balkema, Taylor & Francis Group. pp 1745–1750. ISBN: 978-1-138-02988-0.

  • Rothmund S, Walter M, Joswig M (2015) Linking sub-surface slidequakes to superficial fissure growth and displacement analysis: The super-Sauze mudslide field campaign (2010) Engineering Geology for Society and Territory, Vol 2: Landslide Processes. pp 391–394. https://doi.org/10.1007/978-3-319-09057-3-61

  • Saito H, Uchiyama S, Hayakawa YS, et al. (2018) Landslides triggered by an earthquake and heavy rainfalls at Aso volcano, Japan, detected by UAS and SfM-MVS photogrammetry. Prog Earth Planet Sci 5. https://doi.org/10.1186/s40645-018-0169-6

  • Santangelo M, Alvioli M, Baldo M, et al. (2019) Brief communication: Remotely piloted aircraft systems for rapid emergency response: road exposure to rockfall in Villanova di Accumoli (central Italy). Nat Hazards Earth Syst Sci 19(2):325–335. https://doi.org/10.5194/nhess-19-325-2019

    Article  Google Scholar 

  • Satake K, McLean C, Alcántara-Ayala I (2018) Understanding disaster risk: The role of science and technology. J Disaster Res 13(7):1168–1176. https://doi.org/10.20965/jdr.2018.p1168

    Article  Google Scholar 

  • Serban G, Rus I, Vele D, et al. (2016) Flood-prone area delimitation using UAV technology, in the areas hard-to-reach for classic aircrafts: case study in the north-east of Apuseni Mountains, Transylvania. Nat Hazards 82:1817–1832. https://doi.org/10.1007/s11069-016-2266-4

    Article  Google Scholar 

  • Shi BQ, Liu C (2015) UAV for landslide mapping and deformation analysis. In Zhou G, Kang C (eds.) Proc. SPIE 9808, Intern Conf on Intel Earth Obs & Applic 2015, 98080P. 9 December 2015. https://doi.org/10.1117/12.2207411

  • Singh K, Frazier A (2018) A meta-analysis and review of unmanned aircraft system (UAS) imagery for terrestrial applications. Int J Remote Sens 39(15–16): 5078–5098. https://doi.org/10.1080/01431161.2017.1420941

    Article  Google Scholar 

  • Stoll JB (2013) Unmanned Aircraft Systems for rapid near surface geophysical measurements. UAV-G2013, 391–394.

  • Stumpf A, Malet JP, Kerle N, et al. (2013) Image-based mapping of surface fissures for the investigation of landslide dynamics. Geomorphology 186:12–27. https://doi.org/10.1016/j.geomorph.2012.12.010

    Article  Google Scholar 

  • Sun SQ, Li SC, Li LP, et al. (2019) Slope stability analysis and protection measures in bridge and tunnel engineering: a practical case study from Southwestern China. Bull Eng Geol Environ 78(5):3305–3321. https://doi.org/10.1007/s10064-018-1362-y

    Article  Google Scholar 

  • Tanteri L, Rossi G, Tofani V, et al. (2017) Multitemporal UAV survey for mass movement detection and monitoring. In: Mikoš M, Tiwari B, Yin Y, Sassa K (eds.) Advancing Culture of Living with Landslides, Vol 2: Advances in Landslide Science. pp 153–161. https://doi.org/10.1007/978-3-319-53498-5_18

  • Tekin A, Fornale M (2019) The development of a low-cost UAV-based image acquisition system and the procedure for capturing data in precision agriculture. Turk J Agric For 43(3):288–298. https://doi.org/10.3906/tar-1806-1

    Article  Google Scholar 

  • Themistocleous K, Danezis C, Mendonidis E, et al. (2017) Monitoring ground deformation of cultural heritage sites using UAVs and geodetic techniques: the case study of Choirokoitia, JPI PROTHEGO project. Earth Resources and Environmental Remote Sensing/GIS Applications VIII. https://doi.org/10.1117/12.2279478

  • Themistocleous K (2018) Local monitoring techniques for cultural heritage sites affected by geo-hazards. Sixth International Conference on Remote Sensing and Geoinformation of the Environment (RSCY2018) 10773. https://doi.org/10.1117/12.2503914

  • Thiele ST, Varley N, James MR (2017) Thermal photogrammetric imaging: A new technique for monitoring dome eruptions. J Volcanol Geotherm Res 337:140–145. https://doi.org/10.1016/j.jvolgeores.2017.03.022

    Article  Google Scholar 

  • UN (2015) Transforming the world: The 2030 Agenda for sustainable development (No. A/RES/70/1), vol. 05445, UN, New York.

  • UNFCC (2015) The Paris Agreement, United Nations Framework Convention on Climate Change https://unfccc.int/resource/docs/convkp/conveng.pdf.

  • UNISDR (2015) Sendai framework for disaster risk reduction 2015–2030. United Nations International Strategy for Disaster Reduction, Geneva: UNISDR.

    Google Scholar 

  • Valavanis K, Kontitsis M (2007) A Historical Perspective on Unmanned Aerial Vehicles. In Valavanis K (ed.), Advances in Unmanned Aerial Vehicles. Springer Netherlands. Series Volume 33, pp 15–46. https://doi.org/10.1007/978-1-4020-6114-1

  • Valavanis KP, Vachtsevanos GJ (2015) Handbook of Unmanned Aerial Vehicles. Springer p 2077. On-line ISBN 978-90-481-9707-1.

  • Valkaniotis S, Papathanassiou G, Ganas A (2018) Mapping an earthquake-induced landslide based on UAV imagery; case study of the 2015 Okeanos landslide, Lefkada, Greece. Eng Geol 245:141–152. https://doi.org/10.1016/j.enggeo.2018.08.010

    Article  Google Scholar 

  • Van Blyenburgh P (2006) UAV systems: global review. Presented at the Avionics’06 conference, Amsterdam.

  • Van der Sluijs J, Kokelj SV, Fraser RH, et al. (2018) Permafrost terrain dynamics and infrastructure impacts revealed by UAV photogrammetry and thermal imaging. Remote Sens 10(11). https://doi.org/10.3390/rs10111734

    Google Scholar 

  • Vollgger SA, Cruden AR (2016) Mapping folds and fractures in basement and cover rocks using UAV photogrammetry, Cape Liptrap and Cape Paterson, Victoria, Australia. J Struct Geol 85:168–187. https://doi.org/10.1016/j.jsg.2016.02.012

    Article  Google Scholar 

  • Walter T, Salzer J, Varley N, et al. (2018) Localized and distributed erosion triggered by the 2015 Hurricane Patricia investigated by repeated drone surveys and time lapse cameras at Volcán de Colima, Mexico. Geomorphology 319:186–198. https://doi.org/10.1016/j.geomorph.2018.07.020

    Article  Google Scholar 

  • Wen Q, He HX, Wang XF, et al. (2011) UAV remote sensing hazard assessment in Zhouqu debris flow disaster. In Bostater Jr C, Mertikas S, Neyt X, Velez-Reyes M (eds.) Proc. SPIE 8175, Rem Sens of the Ocean, Sea Ice, Coast Wat and Large Wat Reg 2011, 817510. 13 October 2011. https://doi.org/10.1117/12.898019

  • Yalcin E (2018) Generation of high-resolution digital surface models for urban flood modelling using UAV imagery. In: WIT transactions on ecology and the environment, WIT Press. Lightning Source, UK, Great Britain. pp 357–366.

  • Yang ZH, Lan HX, Gao X, et al. (2015) Urgent landslide susceptibility assessment in the 2013 Lushan earthquake-impacted area, Sichuan Province, China. Nat Hazards 75(3):2467–2487. https://doi.org/10.1007/s11069-014-1441-8

    Article  Google Scholar 

  • Yaprak S, Yildirim O, Susam T, et al. (2018) The Role of Unmanned Aerial Vehicles in monitoring rapidly occurring landslides. GEOD LIST 72(2):113–132.

    Google Scholar 

  • Yeh FH, Huang CJ, Han JY, et al. (2018) Modeling slope topography using Unmanned Aerial Vehicle image technique. The Third International Conference on Sustainable Infrastructure and Built Environment (SIBE 2017) 147. https://doi.org/10.1051/matecconf/201814707002

  • Zekkos D, Clark M, Cowell K, et al. (2017) Satellite and UAV-enabled mapping of landslides caused by the November 17th 2015 Mw 6.5 Lefkada earthquake. ICSMGE 2017 - 19th International Conference on Soil Mechanics and Geotechnical Engineering 2017-september. pp 2235–2238.

  • Zmarz A, Rodzewicz M, Dabski M, et al. (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

    Article  Google Scholar 

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Acknowledgments

This work was carried out within the framework of the PAPIIT project IN300818, sponsored by DGAPA-UNAM. Thanks are due to EM-DAT: The CRED/OFDA International Disaster Database. The authors would like to thank the Editor and two anonymous reviewers for providing comments and suggestions that helped to improve the manuscript.

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    Ricardo J. Garnica-Peña & Irasema Alcántara-Ayala

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Correspondence to Irasema Alcántara-Ayala.

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Garnica-Peña, R.J., Alcántara-Ayala, I. The use of UAVs for landslide disaster risk research and disaster risk management: a literature review. J. Mt. Sci. 18, 482–498 (2021). https://doi.org/10.1007/s11629-020-6467-7

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