N.Yu. Gushan1

1 Financial University under the Government of the Russian Federation (Moscow, Russia)
1 249351@edu.fa.ru

This article explores modern technologies for spatial monitoring of forest ecosystems, taking into account the integration of ecological and engineering approaches. It summarizes various data collection methods, including satellite and aerial imagery, ground-based sensors, geographic information systems (GIS), machine learning, and artificial intelligence technologies. It is emphasized that monitoring data are highly dynamic and may exhibit complex nonlinear relationships, which imposes high demands on the spatial resolution and accuracy of observation systems. The main challenges are analyzed, and potential solutions are proposed: multi-scale data integration, the use of big data processing techniques, and machine learning algorithms to filter noise and capture complex patterns. Examples are given of the application of these technologies in forest restoration, biomass estimation, wildfire monitoring, and forest condition assessment.

Gushan N.Yu. Spatial data in monitoring forest ecosystems. Nonlinear World. 2025. V. 23. № 3. P. 88–95. DOI: https:// doi.org/10.18127/ j20700970-202503-11 (In Russian)

1. Wang R., Sun Y., Zong J. et al. Remote Sensing Application in Ecological Restoration Monitoring: A Systematic Review. Remote Sens. 2024. V. 16. № 12. P. 2204. DOI: 10.3390/rs16122204 (дата обращения: 15.07.2025).
2. Liu P., Ren C., Wang Z. et al. Evaluating the Potential of Sentinel-2 Time Series Imagery and Machine Learning for Tree Species Classification. Remote Sens. 2024. V. 16. № 2. P. 293. DOI: 10.3390/rs16020293 (дата обращения: 15.07.2025).
3. Ecke S., Dempewolf J., Frey J. et al. UAV-Based Forest Health Monitoring: A Systematic Review. Remote Sens. 2022. V. 14.
   № 13. P. 3205. DOI: 10.3390/rs14133205 (дата обращения: 15.07.2025).
4. Lanzolla A., Spadavecchia M. Wireless Sensor Networks for Environmental Monitoring. Sensors. 2021. V. 21. № 4. P. 1172. DOI: 10.3390/s21041172 (дата обращения: 15.07.2025).
5. Savinelli B., Tagliabue G., Vignali L. et al. Integrating Drone-Based LiDAR and Multispectral Data for Tree Monitoring. Drones. 2024. V. 8. № 12. P. 744. DOI: 10.3390/drones8120744 (дата обращения: 15.07.2025).
6. Kotel'nikov R.V., Lupyan E.A., Bartalev S.A., Ershov D.V. Kosmicheskij monitoring lesnyh pozharov: istoriya sozdaniya i razvitiya ISDM-Rosleskhoz. Lesovedenie. 2019. № 5. S. 399–409. DOI: 10.1134/S0024114819050048 (data obrashcheniya: 15.07.2025) (In Russian).
7. Ershov D.V., Bartalev S.A., Lupyan E.A. Kosmicheskij monitoring lesov Rossii: dostizheniya i perspektivy. Sovremennye problemy distancionnogo zondirovaniya Zemli iz kosmosa. 2018. T. 15. № 6. S. 174–190 (In Russian).
8. Wulder M.A., Coops N.C., White J.C. et al. Remote sensing for operational monitoring of forest disturbances: A review. Remote Sens. Environ. 2008. V. 112. P. 1646–1667.
9. Zhou Y., Wang Y., Liu Y. et al. Forest Inventory Using Airborne LiDAR: A Review. Forest Ecosystems. 2021. V. 8. № 1. DOI: 10.1186/s40663-021-00297-3 (data obrashcheniya: 15.07.2025).
10. Pasquarella V.J., Holden C.E., Kaufman L. et al. From imagery to ecology: leveraging time series of Landsat observations to quantify forest disturbance and ecological response. Remote Sens. Environ. 2022. V. 276. 113063.
11. Li W., Guo Q., Jakubowski M.K. et al. A New Method for Segmenting Individual Trees from the Lidar Point Cloud. Photogramm. Eng. Remote Sens. 2012. V. 78. № 1. P. 75–84.
12. Reiche J., Lucas R., Mitchell A.L. et al. Combining satellite data for better forest monitoring. Nature Climate Change. 2016.
    V. 6. P. 120–122.
13. Busygin B.S., Novikov A.V. Ispol'zovanie dannyh Sentinel-1 i Sentinel-2 dlya monitoringa lesnyh pozharov. Izv. RAN. Ser.: Geograficheskaya. 2020. № 3. S. 89–97 (In Russian).
14. Bartalev S.A., Ershov D.V. i dr. Kompleksnaya sistema sputnikovogo monitoringa lesov Rossii. Lesovedenie. 2022. № 6.
    S. 453–466 (In Russian).
15. Asner G.P., Martin R.E., Knapp D.E. et al. Spectroscopy of canopy chemicals in tropical forests. Proc. Natl. Acad. Sci. USA. 2011. V. 108. № 4. P. 1498–1503.
16. Hmelevskaya I.V., Nenahov Yu.S. Ispol'zovanie bespilotnyh letatel'nyh apparatov v lesnom monitoringe. Inzhenernyj zhurnal: nauka i innovacii. 2022. № 3. DOI: 10.18698/2308-6033-2022-3-2476 (data obrashcheniya: 15.07.2025) (In Russian).
17. Kennedy R.E., Yang Z., Cohen W.B. Detecting trends in forest disturbance and recovery using yearly Landsat time series. Remote Sens. Environ. 2010. V. 114. P. 2897–2910.
18. Breidenbach J., Astrup R., Næsset E. et al. Empirical models for forest biomass estimation using LiDAR data. Forest Ecol. Manag. 2010. V. 260. P. 156–167.
19. Zhou T., Yu Q., Meng Q. Review of deep learning in forest mapping using remote sensing data. Forest Ecol. Manag. 2023.
    V. 528. 120660.
20. Danilova I.A., Borisova N.N. Primenenie GIS dlya ocenki i monitoringa lesnyh resursov. Geoinformatika. 2021. № 1. S. 23–31 (In Russian).
21. Kochkarov R.A., Chirov D.S., Timoshenko A.V., Kazancev A.M. Model' prostranstvenno-raspredelennoj informacionnoj sistemy nepreryvnogo monitoringa s predfraktal'noj dinamicheskoj strukturoj v usloviyah vozdejstviya destabiliziruyushchih faktorov.
    T-Comm: Telekommunikacii i transport. 2025. T. 19. № 1. S. 4–12. DOI: 10.36724/2072-8735-2025-19-1-4-12 (data obrashcheniya: 15.07.2025) (In Russian).
22. Kochkarov R.A., Baldychev M.T., Kazancev A.M. i dr. Algoritm ocenki strukturno-funkcional'noj ustojchivosti i celostnosti geterogennoj seti peredachi dannyh prostranstvenno-raspredelennoj sistemy monitoringa. Trudy MAI. 2024. № 137. EDN: IJZRAI (In Russian).
23. Shevcov V.A., Kazancev A.M., Timoshenko A.V. i dr. Pokazatel' strukturnoj effektivnosti upravleniya informacionnym vzaimodejstviem v geterogennoj seti peredachi dannyh prostranstvenno-raspredelennoj sistemy monitoringa. Vestnik Voronezhskogo gosudarstvennogo tekhnicheskogo universiteta. 2024. T. 20. № 2. S. 124–131. DOI: 10.36622/1729-6501.2024.20.2.019 (data obrashcheniya: 15.07.2025) (In Russian).
24. Shevnina Yu.S., Ryabov P.E., Prokopchina S.V., Kochkarov R.A. Podhody k prognozirovaniyu izmeneniya sostoyaniya obespechivayushchih komponentov informacionno-upravlyayushchej sistemy. Modelirovanie, optimizaciya i informacionnye tekhnologii. 2024. T. 12. № 2(45). DOI: 10.26102/2310-6018/2024.45.2.023 (data obrashcheniya: 15.07.2025) (In Russian).
25. Kochkarov R.A., Kochkarov A.A. Teoretiko-grafovyj algoritm dinamicheskogo naznacheniya sredstv sistemy nepreryvnogo monitoringa. Uspekhi sovremennoj radioelektroniki. 2023. T. 77. № 9. S. 44–50. DOI: 10.18127/j20700784-202309-05 (data obrashcheniya: 15.07.2025) (In Russian).
26. Timoshenko A.V., Kochkarov R.A., Kochkarov A.A. Vydelenie uslovij razreshimosti NP-polnyh zadach dlya klassa predfraktal'nyh grafov. Modelirovanie i analiz informacionnyh sistem. 2021. T. 28. № 2. S. 126–135. DOI: 10.18255/1818-1015-2021-2-126-135 (data obrashcheniya: 15.07.2025) (In Russian).
27. Kazancev A.M., Kochkarov R.A., Timoshenko A.V., Sychugov A.A. Nekotorye podhody k ocenke processa funkcionirovaniya strukturno-dinamicheskih sistem monitoringa v usloviyah vneshnih vozdejstvij. Modelirovanie, optimizaciya i informacionnye tekhnologii. 2021. T. 9. № 4(35). DOI: 10.26102/2310-6018/2021.35.4.005 (data obrashcheniya: 15.07.2025) (In Russian).
28. Kochkarov A.A., Yackin D.V., Kochkarov R.A. Primenenie metodov teorii prosachivaemosti dlya resheniya zadach potokovogo planirovaniya v transportnyh setyah s uchetom ih strukturnoj dinamiki. Teoreticheskaya i prikladnaya ekonomika. 2021. № 1. S. 13–20. DOI: 10.25136/2409-8647.2021.1.34965 (data obrashcheniya: 15.07.2025) (In Russian).
29. Chernoritskii S.S., Gushan N.Y., Khuranova K.M. Assessment of tree biomass using remote sensing techniques: validation at the territory of the 'forest lake' educational and recreational complex. Environmental Studies and Protection Issues – 2024: Proceedings of the International Youth Scientific and Academic Conference. Moscow. April 19. 2024. Moscow: RUDN University, 2024. P. 74–81.