A methodological approach to assessing the impact of abiotic factors on restoration of natural ecosystems disturbed by georesource development based on the use of remote sensing technologies

Ostapenko S.P.¹, Mesyats S.P.^1
1  Mining Institute Kola Science Centre of the Russian Academy of Sciences, Apatity, Russian Federation
Russian Mining Industry №6/ 2025 p. 120-125

Abstract: Mining and processing of mineral resources produce negative effects on the state of the natural ecosystems, which justifies the need of their monitoring for efficient environmental restoration. A methodology has been developed that combines abiotic factors, including the slope gradient, land surface temperature, water stress, and atmospheric pollution, with estimates of primary carbon production, utilizing satellite observations to assess the ecosystem recovery in order to further develop the methods of remote sensing data interpretation. A correlation between net primary productivity (NPP) of vegetation and the distance from the pollution source has been established. Six key factors that affect the restoration have been identified, i.e. the plant plants moisture stress index, temperature during the peak vegetative period, vegetation type, elevation, slope aspect, and tropospheric nitrogen oxide concentration. The cause-and-effect relationships among these factors have been identified through designing a probabilistic graph model, as exemplified by analysis of the developed baddeleyite-apatite-magnetite ore deposit located within the Barents Euro-Arctic region of Russia. It was demonstrated that atmospheric nitrogen oxide pollution constitutes a limiting factor governing the boundaries of the vegetation recovery. To enhance adaptive landscape management practices in the areas disturbed by georesource development, a methodological framework is proposed for automated identification of constraints on the restoration of natural ecosystems at the regional scale without the need for extensive field observations.

Keywords: Arctic region, georesource development, restoration of ecosystems, vegetation cover, net primary productivity, satellite data, plants moisture stress index, land surface temperature, nitrogen oxides, probabilistic graph model

Acknowledgements: The work was carried out as part of State Assignment No. FMEZ-2025-0052, “Development of scientific and methodological foundations for multi-scale monitoring of mining facilities in the Barents Euro-Arctic region.” The satellite observation data were obtained from the US Geological Survey (USGS) server and processed using SNAP, QGIS, R, and GNUPlot systems.

For citation: Ostapenko S.P., Mesyats S.P. A methodological approach to assessing the impact of abiotic factors on restoration of natural ecosystems disturbed by georesource development based on the use of remote sensing technologies. Russian Mining Industry. 2025;(6):120–125. (In Russ.) https://doi.org/10.30686/1609-9192-2025-6-120-125

Article info

Received: 15.09.2025

Revised: 10.11.2025

Accepted: 18.11.2025

Information about the authors

Sergey P. Ostapenko – Cand. Sci. (Eng.), Leading Researcher, Mining Institute of the Kola Science Centre of the Russian Academy of Science, Apatity, Russian Federation; https://orcid.org/0000-0002-1513-4250; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Svetlana P. Mesyats – Leading Researcher, Head of Laboratory, Mining Institute of the Kola Science Centre of the Russian Academy of Science, Apatity, Russian Federation; https://orcid.org/0000-0002-9929-8067; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

References

1. M Bouaziz., Benndorf J. Remote monitoring of land degradation in the mining context: an updated review focusing on potentials and challenges. Euro-Mediterranean Journal for Environmental Integration. 2024;9(2):689–703. https://doi.org/10.1007/s41207-024-00479-2

2. Rosa J.C.S., Morrison-Saunders A., Hughes M., Sánchez L.E. Planning mine restoration through ecosystem services to enhance community engagement and deliver social benefits. Restoration Ecology. 2020;28(4):937–946. https://doi.org/10.1111/rec.13162

3. Sparrow B.D., Edwards W., Munroe S.E.M., Wardle G.M., Guerin G.R., Bastin J.-F. et al. Effective ecosystem monitoring requires a multi-scaled approach. Biological Reviews. 2020;95(6):1706–1719. https://doi.org/10.1111/brv.12636

4. Masek J.G., Hayes D.J., Hughes M.J., Healey S.P., Turner D.P. The role of remote sensing in process-scaling studies of managed forest ecosystems. Forest Ecology and Management. 2015;355:109–123. https://doi.org/10.1016/j.foreco.2015.05.032

5. Gong D., Huang M., Ge Y., Zhu D., Chen J., Chen Y. et al. Revolutionizing ecological security pattern with multi-source data and deep learning: An adaptive generation approach. Ecological Indicators. 2025;173:113315. https://doi.org/10.1016/j.ecolind.2025.113315

6. Ostapenko S.P., Mesyats S.P. Studying the restoration dynamics of natural ecosystems disturbed by the development of geo-resources based on satellite observations. Russian Mining Industry. 2024;(6):52–58. (In Russ.) https://doi.org/10.30686/1609-9192-2024-6-52-58

7. Ostapenko S.P., Mesyats S.P. Assessment of the potential for restoration of the environmental state of the natural ecosystems disturbed by development of geo-resources based on satellite data. Russian Mining Industry. 2023;(5S):80–86. (In Russ.) https://doi.org/10.30686/1609-9192-2023-5S-80-86

8. Mesyats S.P., Ostapenko S.P. Prediction of the environmental impact of mining industry based on satellite observations. Journal of Mining Science. 2018;54(4):690–696. https://doi.org/10.1134/S106273911804472

9. Ostapenko S.P., Mesyats S.P. Geoinformation approach to correction of satellite data for monitoring restoration of natural ecosystems on the example of stockpiled ore dressing waste. Mining Informational and Analytical Bulletin. 2022;(12-1):94–105. (In Russ.) https://doi.org/10.25018/0236_1493_2022_121_0_94

10. Tonion F., Pirotti F. Sentinel-5P NO2 data: Cross-validation and comparison with ground measurements. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B3-2022 XXIV ISPRS Congress (2022 edition), 6–11 June 2022, Nice, France. pp. 749–756. https://doi.org/10.5194/isprs-archivesXLIII-B3-2022-749-2022

11. Ionescu G.L., Ionescu Gh.C. Atmospheric pollutant dispersion modeling methods. Journal of Applied Engineering Sciences. 2023;13(2):297–302. https://doi.org/10.2478/jaes-2023-0038

12. Hersbach H., Bell B., Berrisford P., Hirahara S., Horányi A., Muñoz-Sabater J. et al. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society. 2020;146(730):1999–2049. https://doi.org/10.1002/qj.3803

13. Melikhov M.V., Kalashnik A.I., Ostapenko S.P., Lebedik E.Yu. Integrated approach to remote monitoring of waterworks facilities in the mining industry using space and digital technologies. Fiziko-Texhnicheskiye Problemy Razrabbotki Poleznykh Iskopaemykh. 2025;(1):170–180. (In Russ.) https://doi.org/10.15372/FTPRPI20250116

14. Мэрфи К.П. Вероятностное машинное обучение. Введение. М.: Пресс; 2022. 990 с.

15. Сукачев В.Н. Избранные труды: в 3 т. Т. 1. Основы лесной типологии и биогеоценологии. Л.: Наука; 1972. 418 с.

16. Lintunen A., Paljakka T., Salmon Y., Dewar R., Riikonen A., Hölttä T. The influence of soil temperature and water content on belowground hydraulic conductance and leaf gas exchange in mature trees of three boreal species. Plant, Cell & Environment. 2019;43(3):532–547. https://doi.org/10.1111/pce.13709

17. Báez S., Fadrique B., Feeley K., Homeier J. Changes in tree functional composition across topographic gradients and through time in a tropical montane forest. PLoS ONE. 2022;17(4):e0263508. https://doi.org/10.1371/journal.pone.0263508

18. McNichol B.H., Wang R., Hefner A., Helzer C., McMahon S.M., Russo S.E. Topography-driven microclimate gradients shape forest structure, diversity, and composition in a temperate refugial forest. Plant-Environment Interactions. 2024;5(3):e10153. https://doi.org/10.1002/pei3.10153

19. Erisman J.W., de Vries W. Nitrogen deposition and effects on European forests. Environmental Reviews. 2000;8(2):65–93. https://doi.org/10.1139/er-8-2-65