Monitoring of vegetation conditions in the Kedrovsky coal mine area based on satellite data

DOI: https://doi.org/10.30686/1609-9192-2023-2-70-74
Читать на русскоя языкеA.A. Kamaev, P.P. Manevich, M.S. Satubalova
National University of Science and Technology “MISIS”, Moscow, Russian Federation
Russian Mining Industry №1 / 2023 р. 70-74

Abstract: This paper demonstrates an approach to monitoring vegetation in areas of coal mines using space-derived data from remote sensing of the Earth for the Kedrovsky coal deposit. Space-derived data consists of Landsat-8 snapshots. Visualization and processing of the data was implemented in geospatial data analysis systems such as QGIS and Google Earth Engine. The main classes of land use are identified, based on their distinctive deciphering features that include 11 microclasses of land cover objects. Several machine learning methods were applied to assess the performance of various classification algorithms for their potential for quality and accurate recognition of the highlighted set of macroclasses. The proposed analysis for the temporal dynamics of land use objects, land degradation and restoration dynamics in the area of the Kedrovsky coal mine is based on the chosen classification algorithm. The determination of functional correlation of dynamics for land use object areas was carried out by linear regression equation compiled for each class. Condition of lands of the region at issue was assessed considering the factors influencing degradation of vegetation areas which are developed on anthropogenic activities such as mining, expansion of urban infrastructures and croplands.

Keywords: remote sensing of the Earth, land use, coal mine, vegetation monitoring, machine learning, disturbed lands, supervised classification

For citation: Kamaev A.A., Manevich P.P., Satubalova M.S. Monitoring of vegetation conditions in the Kedrovsky coal mine area based on satellite data. Russian Mining Industry. 2023;(2):70–74. https://doi.org/10.30686/1609-9192-2023-2-70-74

Article info

Received: 24.02.2023

Revised: 27.03.2023

Accepted: 03.04.2023

Information about the authors

Artem A. Kamaev – Student, Department of Geology and Surveying, Mining Institute, National University of Science and Technology “MISIS”, Moscow, Russian Federation; e-mail: artemkakamaev@ gmail.com

Polina P. Manevich – Postgraduate Student, Department of Safety and Ecology of Mining Production, Mining Institute, National University of Science and Technology “MISIS”, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Madina S. Satubalova – Student, Department of Geology and Surveying, Mining Institute, National University of Science and Technology “MISIS”, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

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