Geoinformation approach to correction of satellite data for monitoring restoration of natural ecosystems on the example of stockpiled ore dressing waste

Assessment of natural ecosystems by spectral reflectance properties based on data from satellite observations of the Earth's surface is an effective monitoring method for their state. The permanent multispectral satellite survey gives a chance to study the dynamics of restoration of natural ecosystems disturbed during the development of georesources by their spectral indices, for example, a vegetation index for the vegetation cover. In order to increase the reliability of the assessment of the state of the forming forest phytocenosis during ecological restoration, the authors have studied the factors of shading of the underlying surface and proposed an algorithm for adjusting its spectral reflective properties and the calculated spectral indices on the example of the stockpiled apatite ore processing waste from a Khibiny deposit. The authors have developed a methodical approach to correction of brightness in spectral bands of a satellite image of forest phytocenosis formed during ecological restoration, based on accounting for the total brightness of spectral bands and spectral unmixing for the vegetation cover and stockpiled ore processing waste. The correction allows reducing the error of determining the vegetation index of the forming phytocenosis and increasing the information component of the satellite image. By comparing the corrected vegetation index of the emerging phytocenosis with the index of the surrounding natural landscape phytocenosis, the current state of the dynamics of restoration of natural ecosystems has been established. The proposed approach can be used for remote assessment of the ecological state of the territory of mining regions.

Keywords: stockpiled ore processing waste, restoration of natural ecosystems, formation of forest phytocenosis, vegetation index, shading conditions, multispectral satellite images, brightness of spectral band, spectral unmixing, Sentinel-2.
For citation:

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. MIAB. Mining Inf. Anal. Bull. 2022;(12-1):94-105. [In Russ]. DOI: 10.25018/0236_149 3_2022_121_0_94.

Acknowledgements:
Issue number: 12
Year: 2022
Page number: 94-105
ISBN: 0236-1493
UDK: 004.9:528.7:622.882 (985)
DOI: 10.25018/0236_1493_2022_121_0_94
Article receipt date: 25.03.2022
Date of review receipt: 19.09.2022
Date of the editorial board′s decision on the article′s publishing: 10.11.2022
About authors:

S.P. Ostapenko1, Cand. Sci. (Eng.), Leading Researcher, e-mail: s.ostapenko@ksc.ru, ORCID ID: 0000-0002-1513-4250,
S.P. Mesyats1, Leading Researcher, Head of Laboratory, e-mail: s.mesyats@ksc.ru, ORCID ID: 0000-0002-9929-8067,
1 Mining Institute, Kola Scientific Centre of Russian Academy of Sciences, 184209, Apatity, Russia.

 

For contacts:

S.P. Mesyats, e-mail: s.mesyats@ksc.ru.

Bibliography:

1. Kaplunov D. R., Yukov V. A. Principles of a mine transition to sustainable and environmentally sound development. MIAB. Mining Inf. Anal. Bull. 2020, no. 3, pp. 74—86. [In Russ]. DOI: 10.25018/02361493-2020-3-0-74-86.

2. Akhmadiyev A. K., Ekzaryan V. N. Rehabilitation of the natural environment as the structural element of ecological security. MIAB. Mining Inf. Anal. Bull. 2020, no. 2, pp. 112—120. [In Russ]. DOI: 10.25018/0236-1493-2020-2-0-112-120.

3. Rybnikov P. A., Buzina D. A. Aerospace multispectral and hyperspectral imagery in mining area studies. MIAB. Mining Inf. Anal. Bull. 2021, no. 11-1, pp. 55—70. [In Russ]. DOI: 10.25018/0236-1493-2021-111-0-55.

4. Chang C. I. Hyperspectral data processing: algorithm design and analysis. John Wiley & Sons, 2013, 1164 p.

5. Hargreaves P. K., Watmough G. R. Satellite Earth observation to support sustainable rural development. International Journal of Applied Earth Observation and Geoinformation. 2021, vol. 103, article 102466. DOI:10.1016/j.jag.2021.102466.

6. Dubrovskaya S. A., Noreyka S. Yu. Long-term geoinformation monitoring of mining landscapes in the steppe zone of Russia using a spectral index. Belgorod state university scientific bulletin. Natural sciences. 2019, vol. 43, no. 1, pp. 52—62. [In Russ]. DOI: 10.18413/20754671-2019-43-1-52-62.

7. Mesyats S. P., Ostapenko S. P. Recovery dynamics of lands distributed by mining operation due to self-organizing principle of natural system and its forecasting using satelite data. Russian Mining Industry. 2020, no. 6, pp. 137—142. [In Russ]. DOI: 10.30686/1609-91922020-6-137-142.

8. Wu Q., Jin Y., Fan H. Evaluating and comparing performances of topographic correction methods based on multi-source DEMs and Landsat-8 OLI data. International Journal of Remote Sensing. 2016, vol. 37, no.19, pp.4712-4730. DOI: 10.1080/01431161.2016.1222101.

9. Zhang L., Sun X., Wu T., Zhang H. An analysis of shadow effects on spectral vegetation indexes using a ground-based imaging spectrometer. IEEE Geoscience and Remote Sensing Letters. 2015, vol. 12, no. 11, pp. 2188—2192. DOI: 10.1109/LGRS.2015.2450218.

10. Yasser M. A review on various shadow detection and compensation techniques in remote sensing images. Canadian Journal of Remote Sensing. 2017, vol. 43, no. 6, pp. 545—562. DOI: 10.1080/07038992.2017.1384310.

11. Mesyats S., Ostapenko S. A satellite data driven study of the relief impact on the assessment of the vegetable cover created to suppress the wind and water erosion of stored ore processing waste. International Multidisciplinary Scientific GeoConference SGEM. 2020, vol. 5.1, pp. 11—18. DOI: 10.5593/sgem2020/5.1/s20.002.

12. 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, no. 6, pp. 77—83. [In Russ]. DOI: 10.17580/gzh.2019.06.11.

13. Raykunov G. G., Brusnichkina N. A., Turchenko S. I., Shcherbakov V. L. Giperspektral'noe distantsionnoe zondirovanie v geologicheskom kartirovanii [Hyperspectral remote sensing in geological mapping], Moscow, Fizmatlit, 2014, 136 p.

14. Van Cleemput E., Vanierschot L., Fernández-Castilla B., Honnay O., Somers B. The functional characterization of grassand shrubland ecosystems using hyperspectral remote sensing: trends, accuracy and moderating variables. Remote Sensing of Environment. 2018, vol. 209, pp. 747—763. DOI: 10.1016/j.rse.2018.02.030.

15. Schuwirth N., Borgwardt F., Domisch S., Friedrichs M., Kattwinkel M., Kneis D., Vermeiren P. How to make ecological models useful for environmental management. Ecological Modelling. 2019, vol. 411, article 108784. DOI:10.1016/j.ecolmodel.2019.108784.

16. Paradis E. A review of computer tools for prediction of ecosystems and populations: We need more open-source software. Environmental Modelling & Software. 2020, vol. 134, article 104872. DOI: 10.1016/j.envsoft.2020.104872.

17. Yousefi M., Kreuzer O. P., Nykänen V., Hronsky J. M. A. Exploration information systems-a proposal for the future use of GIS in mineral exploration targeting. Ore Geology Reviews. 2019, vol. 111, article 103005. DOI: 10.1016/j.oregeorev.2019.103005.

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