Le Qui Don Technical University, Hanoi, Vietnam:

Le Hung Trinh, Associate Professor, Candidate of Engineering Sciences

Thi Thu Nga Nguyen, Magister, Lecturer

Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia:

I. V. Zenkov, Professor, Doctor of Engineering Sciences, zenkoviv@mail.ru
Yu. P. Yuronen, Associate Professor, Candidate of Engineering Sciences

The article presents the estimation results on the change in vegetation mantle in the region of Sin Quyen Mine (Bat Xat District, Lao Cai Province, Northern Vietnam) in the area of 19012 hectares. The normalized difference vegetation index (NDVI) and the vegetation mantle density change were calculated using satellite images Landsat 5 TM and Landsat 8 obtained between 1990 and 2020. In the period of copper mining for 30 years in the test area, the plant ecosystem has suffered the essential changes in the structure. From the remote sensing data and analytical results in 2020, the plant community is structured as follows. In the phase of high-rate mining (2000–2020), the sites with NDVI in the range of 0.4–0.6 prevailed (60.74%). The area of sites having NIDVI > 0.6 reduced by 3330 times—to 0.2%. The area of sites with NDVI in the range of 0.2–0.4 grew to 33.78%. The areas of sites with NDVI less the 0 and from 0 to 0.2 made 1.42 and 4.04%, respectively. Such structural changes are explained by drilling-and-blasting and by operation of drill rigs, shovels, dump trucks and dozers. The dusting objects are pit walls, working and nonworking sites, and overburden dumps. At the same time, an increasing trend is revealed in the areas of sites with low NDVI (by 1.5–6 times). The analytical estimation produced an inverse dependence of decrease in the area of sites with dense vegetation cover with increasing areas of mining and dumping. This dependence is explained by the progressive degradation of ecological situation in the region of open pit mining of the deposit composed of sulfide-bearing rocks. The studies were carried out in the framework of international cooperation in the sphere of wider application of remote sensing.

1. Hung Chin Le, Zenkov I. V., Anishchenko Yu. A., Ragozina M. A., Fedorov V. A. Remote Sensing Techniques for Soil Moisture Monitoring Using Landsat Data in Thach Ha District with Open Mining Operation in Vietnam. Ekologiya i promyshlennost Rossii. 2017. Vol. 21, No. 4. pp. 42–47.
2. Safronova O. S., Lamanova T. G., Sheremet N. V. The results of the study of natural regeneration of vegetation cover on overburden dumps in the Republic of Khakassia, which emerged in the 90-years of the Twentieth Century. Ugol. 2018. No. 7. pp. 68–77.
3. Kharionovsky A. A., Frank E. Ya. Validation of the technology of mine technical reclamation for the purpose of reforestation in the Krutokachinskiy ballast quarry. Ugol. 2018. No. 4. pp. 75–77.
4. Titkova T. B. Change in climatic conditions of winter runoff formation in the upper don basin revealed by satellite and ground data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2019. Vol. 16, No. 1. pp. 147–157.
5. Elsakov V. V., Shchanov V. M. Current changes in vegetation cover of Timan tundra reindeer pastures from analysis of satellite data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2019. Vol. 16, No. 2. pp. 128–142.
6. Terekhin E. A., Posternak T. S. Reforestation on abandoned arable lands in the south of Western Siberia and its analysis using remote sensing data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2019. Vol. 16, No. 4. pp. 161–172.
7. Zhdanova E. Yu., Chubarova N. E. Spatial variability of aerosol optical thickness on the territory of Moscow and Moscow Region by satellite and ground based data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2018. Vol. 15, No. 7. pp. 236–248.
8. Islam K., Jasimuddin M., Nath B., Nath T. K. Quantitative Assessment of Land Cover Change Using Landsat Time Series Data: Case of Chunati Wildlife Sanctuary (CWS), Bangladesh. International Journal of Environment and Geoinformatics. 2016. Vol. 3, Iss. 2. pp. 45–55.
9. Rouse J. W., Hass R. H., Schell J. A., Deering D. W. Monitoring Vegetation Systems in the Great Plains with ERTS. Proceedings of 3^rd Earth Resources Technology Satellite-1 Symposium. Washington, 1973. Vol. 1. pp. 309–317.
10. Fernandes K., van der Heyde M., Bunce M., Dixon K., Harris R. J. et al. DNA metabarcoding—a new approach to fauna monitoring in mine site restoration. Restoration Ecology. 2018. Vol. 26, Iss. 6. pp. 1098–1107.
11. Lanterman J., Goodell K. Bumble bee colony growth and reproduction on reclaimed surface coal mines. Restoration Ecology. 2018. Vol. 26, Iss. 1. pp. 183–194.
12. Curran M. F., Cox S. E., Robinson T. J., Robertson B. L., Rogers K. J. et al. Spatially balanced sampling and ground-level imagery for vegetation monitoring on reclaimed well pads. Restoration Ecology. 2019. Vol. 27, Iss. 5. pp. 974–980.
13. Amalesh Dhar, Comeau P. G., Vassov R. Effects of cover soil stockpiling on plant community development following reclamation of oil sands sites in Alberta. Restoration Ecology. 2019. Vol. 27, Iss. 2. pp. 352–360.
14. Lefebvre‐Ruel S., Jutras S., Campbell D., Rochefort L. Ecohydrological gradients and their restoration on the periphery of extracted peatlands. Restoration Ecology. 2019. Vol. 27, Iss. 4. pp. 782–792.
15. Belmonte A., Sankey T., Biederman J. A., Bradford J., Goetz S. J. et al. UAV-derived estimates of forest structure to inform ponderosa pine forest restoration. Remote Sensing in Ecology and Conservation. 2020. Vol. 6, Iss. 2. pp. 181–197.
16. Ruff Z. J., Lesmeister D. B., Duchac L. S., Bharath K. Padmaraju, Sullivan C. M. Automated identification of avian vocalizations with deep convolutional neural networks. Remote Sensing in Ecology and Conservation. 2020. Vol. 6, Iss. 1. pp. 79–92.
17. Mouget A., Goulon C., Axenrot T., Balk H., Lebourges-Dhaussy A. et al. Including 38 kHz in the standardization protocol for hydroacoustic fish surveys in temperate lakes. Remote Sensing in Ecology and Conservation. 2019. Vol. 5, Iss. 4. pp. 332–345.
18. Gauthreaux Jr. S. A., Shapiro A.-M., Mayer D., Clark B. L., Herricks E. E. Detecting bird movements with L-band avian radar and S-band dual-polarization Doppler weather radar. Remote Sensing in Ecology and Conservation. 2019. Vol. 5, Iss. 3. pp. 237–246.
19. Wedding L. M., Jorgensen S., Lepczyk C. A., Friedlander A. M. Remote sensing of three-dimensional coral reef structure enhances predictive modeling of fish assemblages. Remote Sensing in Ecology and Conservation. 2019. Vol. 5, Iss. 2. pp. 150–159.
20. Chilson C., Avery K., McGovern A., Bridge E., Sheldon D. et al. Automated detection of bird roosts using NEXRAD radar data and Convolutional Neural Networks. Remote Sensing in Ecology and Conservation. 2019. Vol. 5, Iss. 1. pp. 20–32.
21. Stepanian P. M., Mirkovic D., Chilson P. B. A polarimetric Doppler radar time-series simulator for biological applications. Remote Sensing in Ecology and Conservation. 2018. Vol. 4, Iss. 4. pp. 285–302.