The Use of Remote Sensing in the Study of Open-pit Mining and the Ecology of Disturbed Lands in Deposits of Cement Raw Materials in the Regions of Russia

Based on the results of remote monitoring, the state of mining operations in quarries for the extraction of mineral raw materials consumed by cement plants in Russia was investigated. The fleet of mining and transport vehicles in operation in cement quarries was studied. The production capacity of each quarry in terms of rock mass and the total production potential of quarries in Russia, estimated at 210 million tons per year, have been determined. The environmental indicators of disturbed lands during the development of mineral deposits for use in the cement industry as a whole are presented.

1. Пономаренко М.Р., Кутепов Ю.И., Волков М.А., Гринюк А.П. Космические методы в составе комплексного деформационного мониторинга земной поверхности горного предприятия. Горный информационно-аналитический бюллетень. 2020. № 12. С. 103—113.

2. Крамаров С.О., Митясова О.Ю., Храмов В.В. Спутниковая идентификация объектов земной поверхности с использованием неортогонального описания исходных данных. Горный информационно-аналитический бюллетень. 2021. № 4. С. 154—166.

3. Крутских Н.В. Оценка трансформации природной среды в зоне воздействия горно-добывающих предприятий с использованием данных дистанционного зондирования земли. Горный журнал. 2019. № 3. С. 88-93.

4. Корниенко С.Г. Характеристика антропогенных трансформаций ландшафтов в районе Бованенковского месторождения по данным спутников Landsat. Современные проблемы дистанционного зондирования Земли из космоса. 2022. Т. 19. № 2. С. 106—129.

5. Пашкевич М.А., Петрова Т.А., Рудзиш Э. Оценка потенциальной возможности использования лигнин-шламов для лесохозяйственной рекультивации нарушенных земель. Записки Горного института. 2019. 235. P. 106. https://doi.org/10.31897/pmi.2019.1.106.

6. Samsonov S., Baryakh A. Estimation of Deformation Intensity above a Flooded Potash Mine Near Berezniki (Perm Krai, Russia) with SAR Interferometry. Remote Sens. 2020; 12(19):3215.

7. Madhuanand L., Sadavarte P., Visschedijk A.J.H. et al. Deep convolutional neural net-works for surface coal mines determination from sentinel-2 images. European Journal of Remote Sens. 2021. V. 54. I. 1. Р. 296—309.

8. Sadavarte P., Pandey S., Maasakkers J.D. et al. Methane Emissions from Superemitting Coal Mines in Australia Quantified Using TROPOMI Satellite Observations. Environmental Science and Technology. 2021.

9. Zenkov I.V., Morin A.S., Vokin V.N., Kiryushina E.V. Remote sensing of mining and haul-age equipment arrangement in Russia: A case-study of the coal and iron ore industry. Eurasian mining. 2020. No. 2. Р. 46—49.

10. Nie X., Hu Z., Ruan M., Zhu Q., Sun H. Remote-Sensing Evaluation and Temporal and Spatial Change Detection of Ecological Environment Quality in Coal-Mining Areas. Remote Sens. 2022. 14. 345.

11. Li X., Lei S., Liu Y. et al. Evaluation of Ecological Stability in Semi-Arid Open-Pit Coal Mining Area Based on Structure and Function Coupling during 2002-2017. Remote Sens. 2021;13(24):5040. doi.org/10.3390/rs13245040.

12. Wang W., Liu R., Gan F. et al. Monitoring and Evaluating Restoration Vegetation Status in Mine Region Using Remote Sensing Data: Case Study in Inner Mongolia, China. Remote Sensing. 2021.13(7):1350. doi.org/10.3390/rs13071350.

13. Claudia Priscila Wanzeler da Costa, Douglas da Silva Ferreira, Nikolas Jorge Carneiro. Climate Extremes along the Mining Chain over the Eastern Amazon: Projections to 2050. Atmospheric and Climate Sciences. 2021. V. 11. № 1. P. 125—147.

14. Zhang Y., Shen W., Li M., Lv Y. Integrating Landsat Time Series Observations and Corona Images to Characterize Forest Change Patterns in a Mining Region of Nanjing, Eastern China from 1967 to 2019. Remote Sens. 2020. 12(19):3191. doi.org/10.3390/rs12193191.

15. https://www.google.com.earth.