Issue |
E3S Web Conf.
Volume 192, 2020
VIII International Scientific Conference “Problems of Complex Development of Georesources” (PCDG 2020)
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Article Number | 04021 | |
Number of page(s) | 9 | |
Section | Innovative Technologies and Methods for Monitoring Natural and Mining Systems | |
DOI | https://doi.org/10.1051/e3sconf/202019204021 | |
Published online | 30 September 2020 |
Land use land cover change detection in the mining areas of V. D. Yalevsky coal mine-Russia
1
Natioanl University of Science and Technology MISIS, Москва, 117997, Россия
2
Tashkent State Technical University, Tashkent, Uzbekistan
* Corresponding author: hoshmendg@gmail.com
The dynamics of land use/land cover (LULC) changes, the effect of coal mining on the LULC changes, and the regional environmental impact are discussed in this study. The different land use classes mainly Forest, Water Bodies, Road, Mining Area, Agriculture and Grass in the study area of V. D. Yalevsky coal field area in Prokorvisk city in Kamerovo region of Russia are identified. On the other hand the impact of V. D. Yalevsky coal mine activities on LULC change on the environment and teritory are discussed. The LULC changes in the V. D. Yalevsky coal field area were analyzed for a period of 27 years e.g., from the year 1992 to 2019. The changes were detected on a 13-years time interval using Landsat-4 TM, Landsat-8 OLI. Furthermore supervised classification techniques using maximum likelihood method through ENVI (Environment for Visualizing Images) 5.1software was utilized. In addition post classification change detection method through ENVI was used to investigate the changes. The study reveals decrecment in LULC cotogories of forest to 25.35km², water bodies to -0.94km², agriculture to -98.48km², road to -10.80km². However increment in the rate of mining area to 100.72km² and grass cover 34.86 km² during the study period. Meanwhile 90.18% overall accuracy and (0.87) kappa coefitient for 1992 classified image, 93.41% overall accuracy and (0.91) Kappa koefitient for 2006 classified image and 88.69% overall accuracy and (0.85) kappa coefitient for 2019 classified image were obtained.
Key words: Land use/ Land cover / Change detection / V. D. Yalevsky coal mine area / Remote sensing
© The Authors, published by EDP Sciences, 2020
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