An assessment of forest area dynamics in the Arctic zone of the Arkhangelsk region was carried out using Sentinel-2 satellite data and the Random Forest algorithm. Special attention was given to the forests, which are characterized by high biodiversity, with spruce and pine stands forming the backbone of this ecosystem. The results revealed an increase in forest area in the study region during the period from 2016 to 2023
forest dynamics, Arctic forests, remote sensing, Random Forest
1. Frelich, L. E. Boreal and Taiga Biome. Reference Module in Earth Systems and Environmental Sciences. – 2019. doihttps://doi.org/10.1016/b978-0-12-409548-9.11926-8.
2. Il'incev, A. S., Shamont'ev, I. G., Tret'yakov, S. V. Sovremennaya dinamika lesopol'zovaniya v boreal'nyh lesah Rossii (na primere Arhangel'skoy oblasti) // Lesotehnicheskiy zhurnal. – 2021. – T. 11, № 3 (43). – S. 45–62.
3. Mohajane, M.; Essahlaoui, A.; Oudija, F.; El Hafyani, M.; Teodoro, A. C. Mapping Forest Species in the Central Middle Atlas of Morocco (Azrou Forest) through Remote Sensing Techniques // ISPRS Int. J. Geo Inf. – 2017. – V. 6. – P. 275. doihttps://doi.org/10.3390/ijgi6090275.
4. Panigrahy, R. K.; Kale, M. P.; Dutta, U.; Mishra, A.; Banerjee, B.; Singh, S. Forest cover change detection of Western Ghats of Maharashtra using satellite remote sensing based visual interpretation technique // Curr. Sci. – 2010. – V. 98. – R. 657–664.
5. Kayiranga, A.; Kurban, A.; Ndayisaba, F.; Nahayo, L.; Karamage, F.; Ablekim, A.; Li, H. W.; Ilniyaz, O. Monitoring Forest Cover Change and Fragmentation Using Remote Sensing and Landscape Metrics in Nyungwe-Kibira Park // J. Geosci. Environ. Prot. – 2016. – V. 4. – P. 13–33. doihttps://doi.org/10.4236/gep.2016.411003.
6. Kalinaki, K. et al. FCD-AttResU-Net: An improved forest change detection in Sentinel-2 satellites images using attention residual U-Net // International Journal of Applied Earth Observation and Geoinformation. – 2023. – V. 122. – P. 103453.
7. Ad-Din, Abdo Vissam. Vvedenie v distancionnoe zondirovanie i ego primenenie. – Dammam : Universitet Dammama, 2014. – 473 s.
8. Lu, D., Weng, Q. A survey of image classification methods and techniques for improving classification performance // Int. J. Remote Sens. – 2007. – V. 28. – P. 823–870.
9. Du, P., Samat, A., Waske, B., Liu, S., Li, Z. Random Forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features // ISPRS J. Photogramm. Remote Sens. – 2015. – V. 105. – P. 38–53.
10. Praticò, S.; Solano, F.; Di Fazio, S.; Modica, G. Machine learning classification of Mediterranean forest habitats in Google Earth Engine based on seasonal Sentinel-2 time-series and input image composition optimization // Remote Sens. – 2021. – V. 13. – P. 586.
11. Wang, Y., Chen, X. The use of random forest to identify climate and human interference on vegetation NDVI changes in Southwest China // EGU General Assembly 2023. – Vienna, Austria, 24–28 Apr 2023. – EGU23-2315. doihttps://doi.org/10.5194/egusphere-egu23-2315.
