Recovery Dynamics of Lands Disturbed by Mining Operations due to Self-Organizing Principle of Natural Systems and its Forecasting Using Satellite Data

DOI: http://dx.doi.org/10.30686/1609-9192-2020-6-137-142
S.P. Mesyats, S.P. Ostapenko
Mining Institute of the Kola Science Centre of the Russian Academy of Science, Apatity, Russian Federation
Russian Mining Industry №6 / 2020 р. 137-142

Читать на русскоя языкеAbstract: Environmental issues associated with the development of mineral deposits are largely caused by the need to store mining and processing waste which becomes a source of environmental pollution. Large areas of dumped ore processing wastes determine the expediency of applying satellite data to monitor the environmental condition of the disturbed lands in order to make justified decisions on restoring the integrity of natural landscapes, which is crucial for the Arctic regions. The purpose of the research is to use the satellite data as the basis to reveal the dynamics of plant formation on the surrounding natural terrain when implementing the technology developed in the Mining Institute of the Kola Scientific Center of the Russian Academy of Sciences in accordance with the self-organizing principle of natural systems in the framework of the rock-biota system evolution. This is achieved by introducing a gramineous plant community without creating a fertile layer, which creates a biologically active environment. Analysis of the vegetation index obtained from a time series of the satellite data that characterizes the introduced vegetational change of the gramineous plant community on the bund wall slopes at the Khibiny group of apatite-containing ore deposits demonstrates the determinant influence of phytocoenotic factors on the recovery dynamics of natural ecosystems. A geobotanical study of the monitoring site has shown that in transition from the introduced gramineous to the forest stage of vegetational change, we observe a tier structure and large-scale resettlement of species from the adjacent natural areas, which is consistent with an increase in the vegetation index and allows to predict the dynamics of the natural ecosystem recovery.

Keywords: disturbed lands, ecological restoration, stockpiled ore processing waste, protective dam, sown cereal phytocenosis, succession, species composition of vegetation cover, monitoring, satellite data, vegetation index, moisture stress index

Acknowledgements: The study was carried out within the framework of the State Contract No. 0226-2019-0060 "Development of methodology for monitoring natural ecosystems during reclamation of lands disturbed by mining operations in compliance with the concept of natural soil formation through creation of a biologically active environment".

For citation: Kryukov V.A., Yatsenko V.A., Kryukov Ya.V. Rare Earth Industry – How to take advantage of opportunities. Gornaya promyshlennost = Russian Mining Industry. 2020;(5):68–84. (In Russ.) DOI: Mesyats S.P., Ostapenko S.P. The dynamics of restoration of disturbed lands in the mining industry in accordance with the self-organization principle of natural systems and its prediction by satellite data. Gornaya promyshlennost = Russian Mining Industry. 2020;(6):137-142. (In Russ.) DOI: 10.30686/1609-9192-2020-6-137-142.


Article info

Received: 12.11.2020

Revised: 23.11.2020

Accepted: 01.12.2020


Information about the author

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

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


References

1. Gorshkov V.G., Makar’eva A.M., Losev K.S. A strategy for the survival of humanity is on the agenda. Herald of the Russian Academy of Sciences. 2006;76(2):139–143. DOI: 10.1134/S1019331606020055.

2. Kovda V.A. Problems of vegetation cover and planet biosphere protection. Pushchino; 1989. 155 p. (In Russ.)

3. Mel’nikov N.N., Mesyats S.P., Volkova E.Yu. Methodological approach to restoration of ecosystem functions in the industrial lands. Journal of Mining Science. 2016;52(2):410–416. DOI: 10.1134/S1062739116020586

4. Mesyats S.P., Novozhilova M.Yu., Rumyantseva N.S., Volkova E.Yu. Scientific substantiation of the natural ecosystems restoration disturbed during the development of georesources. Gornyi Zhurnal. 2019;(6):77–83. (In Russ.) DOI: 10.17580/gzh.2019.06.11.

5. Bondur V.G., Vorobev V.E. Satellite monitoring of impact Arctic Regions. Izvestiya, Atmospheric and Oceanic Physics. 2015;51(9):949–968. DOI: 10.1134/S0001433815090054.

6. Mesyats S.P., Ostapenko S.P. The prospect of using satellite data to monitor the impact of mining waste on environment. Gornyi Zhurnal. 2019;(6):72–76. (In Russ.) DOI: 10.17580/gzh.2019.06.10.

7. Mesyats S.P., Ostapenko S.P. Assessment of impact from mining sector of Murmansk region on condition of vegetation cover using satellite observations. Gornaya promyshlennost = Russian Mining Industry. 2019;(6):112–116. (In Russ.) DOI: 10.30686/1609-9192-2019-6-148-112-116.

8. Steiniger S., Hay G.J. Free and open source geographic information tools for landscape ecology. Ecological Informatics. 2009;4(4):183–195. DOI: 10.1016/j.ecoinf.2009.07.004.

9. Bai Y.Q., Di L.P. Review of geospatial data systems’ support of global change studies. British Journal of Environment & Climate Change. 2012;2(4):421–436. DOI: 10.9734/BJECC/2012/2726.

10. Lausch A., Schmidt A., Tischendorf L. Data mining and linked open data – New perspectives for data analysis in environmental research. Ecological Modelling. 2014;295:5–17. DOI: 10.1016/j.ecolmodel.2014.09.018.

11. Zhao P., Foerster T., Yue P. The Geoprocessing Web. Computers & Geosciences. 2012;47:3–12. DOI: 10.1016/j.cageo.2012.04.021.

12. Yang C., Raskin R., Goodchild M., Gahegan M. Geospatial Cyberinfrastructure: Past, present, and future. Computers, Environment and Urban Systems. 2010;34(4):264–277. DOI: 10.1016/j.compenvurbsys.2010.04.001.

13. Raikunov G.G. (ed.) Hyperspectral remote sensing in geological mapping. Moscow: Fizmatlit; 2014. 136 p. (In Russ.)

14. Yengoh G.T., Dent D., Olsson L., Tengberg A.E., Tucker III C.J. Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales: Current Status, Future Trends, and Practical Considerations. Springer; 2016. 110 p. DOI: 10.1007/978-3-319-24112-8.

15. Mesyats S.P., Ostapenko S.P. Methodological approach to the monitoring of the restoration of lands disturbed by the mining sector based on satellite data. Gornaya promyshlennost = Russian Mining Industry. 2018;(6):22–25. (In Russ.) DOI: 10.30686/1609-9192-2018-6-142-72-75.

16. Hunt E.R., Rock B.N., Nobel P.S. Measurement of leaf relative water content by infrared reflectance. Remote Sensing of Environment. 1987;22:429–435. Available at: https://hrsl.ba.ars.usda.gov/ERHunt/hunt_rse1987.pdf