Russian Federation
UDK 004 Информационные технологии. Компьютерные технологии. Теория вычислительных машин и систем
UDK 581.9 География растений. Фитогеография. Флора. Географическое распространение растений
Vegetation management in the vicinity of power lines is critical to the safety and efficiency of power transmission systems. Uncontrolled vegetation growth can lead to power outages, increased fire hazards and the need for costly maintenance. Traditional ground-based monitoring methods, while effective, are often labour-intensive and time-consuming. This study assesses the viability of advanced remote sensing technologies - LiDAR, multispectral and hyperspectral imagery - as more effective alternatives.
LiDAR, multispectral imaging, hyperspectral imaging, vegetation management, power line safety, environmental monitoring, power lines, remote sensing, forest technology
1. Mongus D. et al. A Complete Environmental Intelligence System for LiDAR-Based Vegetation Management in Power-Line Corridors // Remote Sensing. – 2021. – T. 13. – №. 24. – S. 5159.
2. Candiago S. et al. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images // Remote sensing. – 2015. – T. 7. – №. 4. – S. 4026-4047.
3. López-Granados F. et al. Monitoring vineyard canopy management operations using UAV-acquired photogrammetric point clouds // Remote Sensing. – 2020. – T. 12. – №. 14. – S. 2331.
4. Platonov A. A. Kompleksnoe upravlenie lesnoy rastitel'nost'yu: etapy i perspektivy razvitiya //Lesotehnicheskiy zhurnal. – 2023. – T. 13. – №. 2 (50). – S. 142.
5. Novikov A.I i dr. Frontirnyy metod sozdaniya zaschitnyh lesnyh nasazhdeniy vokrug pitomnikov na neeffektivnyh uchastkah: tehnologicheskie osnovy // Lesotehnicheskiy zhurnal. – 2022. – T. 12. – №. 2. – S. 115-125.
6. Ecke S. et al. UAV-based forest health monitoring: A systematic review // Remote Sensing. – 2022. – T. 14. – №. 13. – S. 3205.
7. Demidov D. N. Issledovanie algoritma ocenki parametrov predpoletnoy orientacii sredstv upravleniya bespilotnogo letatel'nogo apparata pri monitoringe molodyh lesnyh nasazhdeniy // Lesotehnicheskiy zhurnal. – 2021. – T. 11. – №. 4 (44). – S. 100-111.
8. Lausch A. et al. Understanding forest health with remote sensing, part III: requirements for a scalable multi-source forest health monitoring network based on data science approaches // Remote sensing. – 2018. – T. 10. – №. 7. – S. 1120.
9. Awad M. M. Forest mapping: a comparison between hyperspectral and multispectral images and technologies // Journal of Forestry Research. – 2018. – T. 29. – №. 5. – S. 1395-1405.
10. Jurado J. M. et al. Remote sensing image fusion on 3D scenarios: A review of applications for agriculture and forestry // International journal of applied earth observation and geoinformation. – 2022. – T. 112. – S. 102856.
11. Platonov A. A., Ternovskaya O. V. Osobennosti formirovaniya kapital'nyh vlozheniy dlya sozdaniya sistem mashin udaleniya nezhelatel'noy rastitel'nosti // Lesotehnicheskiy zhurnal. – 2020. – T. 10. – № 3 (39). – S. 164-174.