Assessment of the potential for restoration of the environmental state of the natural ecosystems disturbed by development of geo-resources based on satellite data

DOI: https://doi.org/10.30686/1609-9192-2023-5S-80-86

Читать на русскоя языкеS.P. Ostapenko, S.P. Mesyats
Mining Institute Kola Science Centre of the Russian Academy of Sciences, Apatity, Russian Federation
Russian Mining Industry №5S / 2023 р. 80-86

Abstract: The factors of environmental state restoration of the natural ecosystems disturbed as the result of mining mineral deposits were investigated using the case study of the phytocenosis formed on the enclosing dam of the apatite-nepheline ore processing waste storage in the Khibiny group of deposits according to the technology developed in the Mining Institute of the Kola Scientific Center of the Russian Academy of Sciences for the restoration of disturbed lands through creation of a biologically active environment without deposition of a fertile layer. Based on the data of satellite observations of the monitoring sites during the vegetation period, the impact of relief as well as the heat and moisture availability on the state of the emerging vegetation cover was studied using gradient transects of different exposures along the height of the enclosing dam slope. The spatial resolution of the satellite images was harmonized using the enhanced pan-sharpening procedure with the minimum characteristic size of the dam slope elements and the representativeness of the data obtained when assessing the vegetation cover condition using the vegetation index and the moisture stress index was ensured. Generalization was made of the relationship between the parameters characterizing the vegetation cover state and abiotic factors of its formation using a neural network model. Two artificial neural networks were trained to predict the vegetation and the plant moisture stress indices using the data set obtained from processing of visible, infrared and thermal spectral channels of satellite images of the monitoring sites. The neural network model shows that the vegetation index of the emerging vegetation cover is antibate to the plant moisture stress index - the dominant factor in restoration of the environmental state of the investigated site, and to the surface temperature of the enclosing dam of the tailing dump. Dependence of the predicted moisture stress of the phytocenosis formed ontop the enclosing dam slope on the elevation and exposure was used to assess the potential of its restoration to the forest succession stage corresponding to the phytocenosis of the surrounding natural environment. Zoning of the tailing dump according to the potential for restoration of the environmental state was performed in order to support the decision-making process when planning environmental protection measures.

Keywords: Arctic region, development of georesources, tailing dump, restoration of environmental condition of the territory, emerging phytocenosis, satellite data, pan-sharpening, vegetation index, plant moisture stress index, artificial neural network

Acknowledgments: The study was carried out within the framework of the State Assignment No. FMEZ-2022-0006 «Development of a methodology for an eco-investment approach to restoration of natural ecosystems disturbed by the development of georesources».

For citation: 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. https://doi.org/10.30686/1609-9192-2023-5S-80-86

Article info

Received: 13.10.2023

Revised: 09.11.2023

Accepted: 22.11.2023

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; 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; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

References

1. Gornyy V.I., Brovkina O.V., Kiselev A.V., Tronin A.A. Trends in remote sensing methods for geological and environmental applications. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2023;20(2):9–38. (In Russ.) https://doi.org/10.21046/2070-7401-2023-20-2-9-38

2. Werner T.T., Bebbington A., Gregory G. Assessing impacts of mining: Recent contributions from GIS and remote sensing. The Extractive Industries and Society. 2019;6:993–1012. https://doi.org/10.1016/j.exis.2019.06.011

3. McKenna P.B., Lechner A.M., Phinn S., Erskine P.D. Remote sensing of mine site rehabilitation for ecological outcomes: A global systematic review. Remote Sens. 2020;12(21):3535. https://doi.org/10.3390/rs12213535

4. Li T., Wu M., Duan C., Li S., Liu C. The effect of different restoration approaches on vegetation development in metal mines. Science of The Total Environment. 2022;806-2:150626. https://doi.org/10.1016/j.scitotenv.2021.150626

5. Huang S., Tang L., Hupy J.P., Wang Y., Shao G. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research. 2021;32:1–6. https://doi.org/10.1007/s11676-020-01155-1

6. 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. Russian Mining Industry. 2018;(6):72–75. (In Russ.) https://doi.org/10.30686/1609-9192-2018-6-142-72-75

7. Filipponi F., Valentini E., Xuan A.N., Guerra C.A., Wolf F., Andrzejak M., Taramelli A. Global MODIS fraction of green vegetation cover for monitoring abrupt and gradual vegetation changes. Remote Sensing. 2018;10(4):653. https://doi.org/10.3390/RS10040653

8. Shinkarenko S.S., Kosheleva O.Y., Gordienko O.A., Omarov R.S., Dubacheva A.A. The relationship between the seasonal dynamics of surface temperature and NDVI in urbanized areas of an arid zone. the case of the Volgograd agglomeration. Izvestiya, Atmospheric and Oceanic Physics. 2021;57(12):1576–1585. https://doi.org/10.1134/S0001433821120197

9. Mesyats S.P., Ostapenko S.P. Recovery dynamics of lands disturbed by mining operations due to self-organizing principle of natural systems and its forecasting using satellite data. Russian Mining Industry. 2020;(6):137–142. (In Russ.) https://doi.org/10.30686/1609-9192-2020-6-137-142

10. Labib S.M., Lindley S., Huck J.J. Scale effects in remotely sensed greenspace metrics and how to mitigate them for environmental health exposure assessment. Computers, Environment and Urban Systems. 2020;82:101501. https://doi.org/10.1016/j.compenvurbsys.2020.101501

11. Elsakov V.V. The influence of aerospace imagery spatial resolution on mapping results of tundra vegetation. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2023;20(1):176–188. (In Russ.) https://doi.org/10.21046/2070-7401-2023-20-1-176-188

12. Ostapenko S.P., Mesyats S.P. Recovery dynamics of mining-altered natural ecosystems by satellite data. Journal of Mining Science. 2022;58(5):839–848. https://doi.org/10.1134/S1062739122050155

13. Martín-Sotoca J.J., Saa-Requejo A., Moratiel R., Dalezios N., Faraslis I., Tarquis A.M. Statistical analysis for satellite-index-based insurance to define damaged pasture thresholds. Natural Hazards and Earth System Sciences. 2019;19(8):1685–1702. https://doi.org/10.5194/nhess-19-1685-2019

14. Cybenko G.V. Approximation by Superpositions of a Sigmoidal function. Mathematics of Control Signals and Systems. 1989;2(4):303–314. https://doi.org/10.1007/BF02551274

15. Ma L., Liu Y., Zhang X., Ye Y., Yin G., Johnson B.A. Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing. 2019;152:166–177. https://doi.org/10.1016/J.ISPRSJPRS.2019.04.015

16. 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.) https://doi.org/10.17580/gzh.2019.06.11

17. 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

18. Nill L., Ullmann T., Kneisel C., Sobiech-Wolf J., Baumhauer R. Assessing spatiotemporal variations of Landsat land surface temperature and multispectral Indices in the Arctic Mackenzie delta region between 1985 and 2018. Remote Sensing. 2019;11(19):2329. https://doi.org/10.3390/rs11192329

19. Jimenez-Munoz J.C., Cristobal J., Sobrino J.A., Soria G., Ninyerola M., Pons X. et al. Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data. IEEE Transactions on Geoscience and Remote Sensing. 2009;47(1):339– 349. https://doi.org/10.1109/TGRS.2008.2007125

20. Sokolova G.G. The influence of terrain altitude, slope exposure and slope degree on plant spatial distiribution. Acta Biologica Sibirica. 2016;2(3):34–45. (In Russ.) https://doi.org/10.14258/abs.v2i3.1453

21. Markov B. Comparison of remote sensing-based indexes for monitoring drought impact on forest ecosystems. In: Annual of Sofia University “st. Kliment Ohridski” Faculty of Geology and Geography. Book 2: Geography. 2018. Vol. 111, pp. 237–246. Available at: https://www.uni-sofia.bg/index.php/bul/content/download/210057/1421013/version/1/file/15_Ann_Tom_111_geography_237-246.pdf