The article examines the methods of autonomous mapping of forest vegetation using the grouping of unmanned aerial vehicles (UAVs) and the introduction of artificial intelligence (AI). The article describes in detail the materials and methods used to collect and analyze data on forest ecosystems, as well as describes possible practical applications of this technology. Much attention is paid to calculating the efficiency of the UAV group and comparing it with the efficiency of a separate device. The influence of the use of artificial intelligence on the charge and duration of the UAV flight is also considered. An experiment was conducted to perform the construction of a flight task for a group of specialists in the AnyLogic environment on the territory of the 197 block in the Lisinsky educational and experimental forestry.

Marsel R. Vagizov
Doctor of Technical Sciences
Associate Professor, Vice-Rector for Information Technology and Digital Transformation
Russian State Hydrometeorological University
79, Voronezhskaya Str., St. Petersburg, 192007, Russia
e-mail: bars-tatarin@yandex.ru
ORCID ID: 0000-0003-4848-1619
SCOPUS ID: 57194441570
ResearcherID: I-7074-2016
SPIN-code: 4811-8943,
AuthorID: 861495

Pavel I. Kuzin
Candidate of Technical Sciences
Associate Professor, Department of Information Systems and Technologies
Institute of Forest and Nature Management,
St. Petersburg State Forest Technical University
named after S.M. Kirov
5, Institutsky Lane, St. Petersburg, 194021, Russia
e-mail: kuzik78@mail.ru
ORCID ID: 0000-0003-0880-6204
SCOPUS ID: 57216808130
ResearcherID: JCD-5209-2023
SPIN-code: 7682-9125
AuthorID: 785054

Nikolay S. Zhovnerov
Master’s student, Department of Information Systems
and Technologies
Institute of Forest and Nature Management,
St. Petersburg State Forest Technical University
named after S.M. Kirov
5, Institutsky Lane, St. Petersburg, 194021, Russia
e-mail: n.zhovnerov@yandex.ru

Grigory S. Ivanov
Master’s student, Department of Information Systems
and Technologies
Institute of Forest and Nature Management,
St. Petersburg State Forest Technical University
named after S.M. Kirov
5, Institutsky Lane, St. Petersburg, 194021, Russia
e-mail: g550001@yandex.ru

Yaroslav A. Petrov
Candidate of Technical Sciences, Associate Professor
Associate Professor of the Department of Applied Informatics
Institute of Information Systems and Geotechnology,
Russian State Hydrometeorological University
79, Voronezhskaya Str., St. Petersburg, 192007, Russia
e-mail: yaroslav.petrov025@gmail.com
ORCID ID: 0000-0002-9185-441X
SCOPUS ID: 57211202033
ResearcherID: AAL-6209-2021
SPIN-code: 4170-3003
AuthorID: 899415

1. Buzmakov S.A., Andreev D.N., Sannikov P.Yu. Applying of unmanned aerial vehicle in the study of forest conditions. Geology, geography and global energy. 2015;(4):60–69.
2. Alekseev A.S., Nikiforov A.A., Mikhailova A.A., Vagizov M.R. New method for tree stands parameters determination using high resolution images done by unmanned aerial vehicle. Izvestia Sankt-Peterburgskoj Lesotehniceskoj Akademii. 2016;215:6–18. DOI: 10.21266/2079-4304.2016.215.6-18.
3. Demidov V.E. Lidar 3D laser scanning of the Prioksko-Terrasny nature reserve territory for the purpose of terrain mapping, searching for areas of anthropogenic impact, vegetation structure studies. Proceedings of the Mordovia State Nature Reserve. 2021;28:74–82.
4. Denisov S.A., Domrachev A.A., Elsukov A.S. Quadrocopter practical application for forest regeneration monitoring. Vestnik of Volga State University of Technology. Series: Forest. Ecology. Nature Management. 2016;(4):34–46. DOI: 10.15350/2306-2827.2016.4.34.
5. Kutakhov V.P., Mescheryakov R.V. Group control of unmanned aerial vehicles: a generalized problem statement of applying artificial intelligence technologies. Control Sciences. 2022;(1):55–60. DOI: 10.25728/cs.2022.1.5.
6. Cheng Z., Qi L., Cheng Y., Wu Y., Zhang H. Interlacing Orchard Canopy Separation and Assessment using UAV Images. Remote Sensing. 2020;12(5);767. DOI: 10.3390/rs12050767.
7. Beregovoi D.V. Sozdanie topograficheskikh planov na osnove dannykh s”emki s bespilotnogo letatel’nogo apparata i avtomatizatsii protsessa deshifrovaniya [Creation of topographic plans based on survey data from an unmanned aerial vehicle and automation of the deciphering process] : dissertation of Candidate of Technical Sciences. St. Petersburg., 2018. 136 p.
8. Ganbold U., Dash O. Data integration in large-scale topographic mapping // Engineering and Construction Bulletin of the Caspian Region. 2020;(2):91–95.
9. Chibidin A.S. Analysis of the use of web-mapping service for monthly monitoring forest. In: The Current Issues in Forestry: Conference of Young Scientists (St. Petersburg, November 14-15, 2018). St. Petersburg: Poligraf ehkspress; 2018. pp. 141–145.
10. Chibidin A.S. Methods of application of web-cartographic service for time data analysis. In: The Current Issues in Forestry: Conference of Young Scientists (St. Petersburg, November 29-30, 2017). St. Petersburg: SpBGLTU; 2017. pp. 107–112.
11. Khabarov S.P. Bee colony algorithm as a representative of swarm intelligence. In: Science and Technology: Yesterday, Today, Tomorrow: Collection of scientific articles. Krasnodar: Novatsiya; 2024. pp. 27–32.
12. Khabarov S.P. Ant approach to designing ecological trails. In: Technical and Natural Sciences: Yesterday, Today, Tomorrow: Collection of scientific articles. Volgograd: Sfera; 2024. pp. 16–21.
13. Yakovlev K.S., Khitkov V.V., Loginov M.I., Petrov A.V. Navigation system based on markers for UAV group. Robotics and Technical Cybernetics. 2014;(4):44–48.
14. DroneDeploy. Available at: https://www.dronedeploy.com (accessed 07.04.2024).
15. Hamraz H., Jacobs N.B., Contreras M.A., Clark Ch.H. Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees. ISPRS Journal of Photogrammetry and Remote Sensing. 2019;158:219–230. DOI: 10.1016/j.isprsjprs.2019.10.011.