Abstract
The paper presents the results of using UAV, small unmanned aerial vehicles, for the purposes of environmental monitoring of coastal ecosystems of large continental bodies of water. Large water bodies of the Lake Baikal system in the south of Siberia were used as test sites. The survey was carried out using small unmanned aerial vehicles—the Geoscan 101 aircraft type and DJI multi-rotor UAV—during the warm season. From June to September, more than 6,000 high-resolution aerial photographs were obtained, which were used to compile orthophotomaps. As a result of the analysis of the aerial photographs, a classification of objects for environmental monitoring of coastal ecosystems of a specific area was developed, as well as a technical algorithm for the stages of shooting. The results of the study demonstrate the advantages of using small unmanned aerial vehicles as an additional control tool in monitoring the state of environmental objects, which significantly expands the range of factors of potential and real impact on the state of ecosystems, helps to identify the location of impact sources and to obtain quantitative estimates of a wide spatial coverage.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Elvidge CD, Ziskin D, Baugh KE et al (2009) A fifteen year record of global natural gas flaring derived from satellite data. Energies 2(3):595–622. https://doi.org/10.3390/en20300595
Mulac B, Storvold R, Weatherhead EC (2011) Remote Sensing in the Arctic with Unmanned Aircraft: Helping Scientists to Achieve Their Goals. Int Sym Rem Sen Env, Sydney, Australia, (ICRSE), April 10–15. https://www.isprs.org/proceedings/2011/isrse-34/211104015Final00863.pdf
Romankevich AP, Kachanovskaya D, Chernyakov G (2017) Primenenie bespilotnyh letatel'nyh apparatov s cel'yu krupnomasshtabnogo kartografirovaniya i sozdaniya cifrovoj osnovy dlya monitoringa rastitel'nosti (The use of unmanned aerial vehicles for the purpose of mapping and creating a digital basis for monitoring vegetation). Land Bel 3:46−48. http://elib.bsu.by/handle/123456789/209976 (In Russian)
Skudneva OV (2016) Bespilotnye letatel’nye apparaty v sisteme lesnogo hozyajstva Rossii (Unmanned aerial vehicles in the forestry system of Russia). News high educ For J 6(342):150–154 (In Russian)
Frater T, Juzsakova T, Lauer J (2015) Unmanned aerial vehicles in environmental monitoring—An efficient way for remote sensing. J Env Sci Eng A4:85–91. https://doi.org/10.17265/2162-5298/2015.02.004
Alvear O, Zema NR, Natalizio E et al (2017) Using UAV-based systems to monitor air pollution in areas with poor accessibility. J Adv Transp 2017:1–14. https://doi.org/10.1155/2017/8204353
d’Oleire-Oltmanns S, Marzolff I, Peter KD et al (2012) Unmanned aerial vehicle (UAV) for monitoring soil erosion in Morocco. Rem Sen 4:3390–3416. https://doi.org/10.3390/rs4113390
Leifer I, William J, Lehr WJ et al (2012) State of the art satellite and airborne marine oil spill remote sensing: Application to the BP deepwater horizon oil spill. Rem Sen Env 124:185–209. https://doi.org/10.1016/j.rse.2012.03.024
Fu Y, Yang G, Song X et al (2021) Using multiscale textures extracted from UAV-based digital images and hyperspectral feature analysis. Rem Sen 13(4):581. https://doi.org/10.3390/rs13040581
Guo A, Huang W, Dong Y (2021) Wheat yellow rust detection using UAV-based hyperspectral technology. Rem Sen 13(1):123. https://doi.org/10.3390/rs13010123
Machefer M, Lemarchand F, Bonnefond V et al (2020) Mask R-CNN refitting strategy for plant counting and sizing in UAV imagery. Rem Sen 12(18):3015. https://doi.org/10.3390/rs12183015
Chemeris EV, Kutuzov AV, Efimov DY et al (2020) Changes in the vegetation cover of the lake Pleshcheyevo (Yaroslavl region) from 1899 to 2017. Proc IBIW 90(98):33–52. https://doi.org/10.24411/0320-3557-2020-10011
Bhatnagar S, Gill L, Ghosh B (2020) Drone image segmentation using machine and deep learning for mapping raised bog vegetation communities. Rem Sen 12(16):2602. https://doi.org/10.3390/rs12162602
Sivakumar ANV, Li J, Scott S (2020) Comparison of object detection and patch-based classification deep learning models on mid- to late-season weed detection in UAV imagery. Rem Sen 12(13):2136. https://doi.org/10.3390/rs12132136
Ashapure A, Jung J, Chang A (2019) A comparative study of RGB and multispectral sensor-based cotton canopy cover modelling using multi-temporal UAS data. Rem Sen 11(23):2757. https://doi.org/10.3390/rs11232757
Cendero A (1989) Land-use problems planning and management in the coastal zone. Ocean and Shor Manag 12(5/6):367–381. https://doi.org/10.1016/0951-8312(89)90019-2
Hays GC, Richardson AJ, Robinson C (2005) Climate change and marine plankton. Tre Eco Evo 20:337–344. https://doi.org/10.1016/j.tree.2005.03.004
Afanasyeva EL, Shimaraev MN (2006) Long-term zooplankton variations in the pelagial of Lake Baikal under global warming. Aquatic ecology at the dawn of the XXI century. KMK, Moscow, pp 253–265
Hampton SE, Izmest’eva LR, Moore MY et al (2008) Sixty years of environmental change in the world’s largest freshwater lake—Lake Baikal, Siberia. Glo Ch Bio 14(8):1947–1958. https://doi.org/10.1111/j.1365-2486.2008.01616.x
Timoshkin OA, Suturin AN, Bondarenko NA (2011) Biology of the coastal zone of Lake Baikal. 1. Overview of the current knowledge on the splash zone, first results of interdisciplinary investigations, monitoring as a basic tool in ecological research. Irk St Univ Bul 4(4):75–110
Acknowledgements
The work was supported by the grant competition of environmental projects En+ Group, project № БПП/ГК-En-ЦCП-Д-21-350. The work of D. Yu. Efimov was also held within the state assignment of IBIW RAS (theme 121051100099-5).
Author information
Authors and Affiliations
Contributions
DE, AS conceived the idea, analyzed the data, wrote and edited the manuscript; ES, DE, AS translated the text into English.
Declaration of Competing Interest
The authors declare that they have no conflict of interest.
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Efimov, D., Shablov, A., Shavalieva, E. (2023). Environmental Monitoring in the “Land–Water” Contact Zone of Water Bodies with the Help of Small Unmanned Aerial Vehicles. In: Gorbachev, O.A., Gao, X., Li, B. (eds) Proceedings of 10th International Conference on Recent Advances in Civil Aviation. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-19-3788-0_36
Download citation
DOI: https://doi.org/10.1007/978-981-19-3788-0_36
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-3787-3
Online ISBN: 978-981-19-3788-0
eBook Packages: EngineeringEngineering (R0)